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Module Title: Dissertation: Does the Total Volume of Patients in Cardiac Rehabilitation Programmes Influence Patient Outcome?

Course: MSc Applied Health Research

Y1469223

The Department of Health Sciences

The University of York

100 Credit Dissertation

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Abstract Background

There is a lack of consensus across the literature of the effect of volume on

outcomes. In acute care the positive association is well established; however, this

is not present in chronic care. Cardiac Rehabilitation (CR) in any population has

yet to be studied in terms of volume-outcome relationship. With increasing

pressure to increase throughput within the UK NHS, knowledge of this

relationship is vital.

Method

Using the UK National Audit of CR data from 2011-12, volume was calculated for

each CR Centre. Volume was total number of patients enrolled at each centre that

had an assessment 1 (baseline measure) and 2 (immediately post rehabilitation).

The clinical outcomes studied were; body mass index, blood pressure,

psychosocial wellbeing, cholesterol, smoking cessation and exercise. Using

multiple linear regression, associations between volume and outcomes were

calculated.

Results

The population within this study was made up of 92,832 patients, with around

25,000 valid cases per outcome measure, which were enrolled into 202 centres.

The average age per centre was 66 years with a 70% male distribution of gender.

The results of the regression analysis were that all β values were <0.005, with no

statically significant p values between volume and outcome. The analysis

accounted for limitations in data quality and missing cases.

Conclusion

There was no significant association between volume and outcome and the null

hypothesis based on the evidence could not be rejected. This was the first large-

scale study looking at the association between volume and outcome in CR.

Additionally, this research acts as a benchmark for future volume-outcome

relationship studies in CR. A direction for this study is to include centre facility

details within the analysis.


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Table of Contents

Abstract Page 2

Acknowledgments Page 8

List of Figures Page 9

List of Tables Page 10

List of Appendices Page 11

Chapter 1 Background Page 12

1.1. Introduction Page 12

1.2. Literature Review Page 16

1.2.1. VOR Studies Within Secondary Care Page 18

1.2.1.1. Quality Page 18

1.2.1.2. Sample Size Page 19

1.2.1.3. Confounding Factors and Covariates Page 20

1.2.1.4. Methods Page 20

1.2.1.5. Results Page 21

1.2.1.6. Limitations Page 22

1.2.2. VOR Studies Within Cardiac Surgery Page 23

1.2.2.1. Quality Page 23


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1.2.2.2. Sample Size Page 24

1.2.2.3. Confounding Factors and Covariates Page 25

1.2.2.4. Methods Page 26

1.2.2.5. Results Page 26

1.2.2.6. Limitations Page 27

1.2.3. Rationale of Outcome Measures and Baseline

Characteristics in CR Page 28

1.2.6. Effectiveness of Cardiac Rehabilitation Page 28

1.2.6. Conclusions of the Review Page 32

1.3. Aim Page 32

1.4. Rationale Page 32

1.5. Hypothesis Page 33

1.6.Ethical Considerations

Page 34

1.6.1. Clinical Relevance

Page 34

Chapter 2 Methods Page 35

2.1. Design

Page 35

2.2. Patients and Data Collection

Page 35


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2.3.Organisation Information Page 35

2.4. Outcomes Page 36

2.4.1. BMI Page 36

2.4.2. Blood Pressure Page 36

2.4.3. Cholesterol Page 36

2.4.4. HADs Scores Page 37

2.4.5. Smoking Cessation and Physical Activity Page 37

2.5. Predictors Page 37

2.6. Statistical Analysis Page 38

2.6.1. Multiple Linear Regression Page 39

2.6.1.1. Background Page 39

2.6.1.2. Assumptions Page 40

2.6..1.3 Sample Size Page 40

Chapter 3 Results Page 42

3.1. Study Population Page 42

3.2. Cardiac Rehabilitation Centres Page 42

3.3. Multiple Linear Regression Page 44


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3.3.1. BMI Page 44



3.3.4. HADs Anxiety Page 49

3.3.5. HADs Depression

Page 50

3.3.6. Smoking Cessation Page 51

3.3.7. Physical Activity Page 53

Chapter 4 Discussion Page 54

4.1. Participating Population Page 54

4.2. Regression Page 56

4.2.1. BMI Page 56



4.2.4. HADs Page 60

4.2.5. Smoking Cessation Page 61

4.2.6. Physical Activity Page 61

4.3. Main Findings Page 62


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4.4. Limitations Page 63

4.4.1. Nature of the Study Type Page 63

4.4.2. Design Page 64

4.4.3. Data Page 64

4.4.4. Outcome Measure Page 65

4.4.5. Comparison of Conclusions with other

Studies in CR

Page 66


Studies in Secondary Care Page 68


Studies in Cardiac Surgery Page 68

4.5. Further Study Page 70

4.6. Main Conclusions Page 71

Appendices

Page 72

Abbreviations Page 101

References

Page 102


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Acknowledgments

I would like to take this opportunity to thank a number of people for their support

and help throughout the duration of this research project; my supervisor Professor

Patrick Doherty for his direction and advice throughout; Veronica Dale and

Corinna Petre at the University of York for their support and assistance with the

statistical analysis and data preparation; June Rawden with the Disability Team at

the University of York for guidance and support while writing the thesis.

Finally to my friends, course mates, my parents, my sister, and relatives in

Scotland, thank you so much for all your help, support and patience.


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List of Figures

Figure 1: Diagram of included literature in the review Page 17

Figure 2: Diagram of included literature in the review Page 17

Figure 3: Diagram of the actual number of patients receiving CR

plotted against total centre volume Page 44

Figure 4: Diagram of the Plot Between BMI Change and Volume Page 45

Figure 5: Diagram of the Assumptions for BMI Page 91

Figure 6: Diagram of the Assumptions for BMI Page 91

Figure 7: Diagram of the Assumptions for Blood Pressure Diastolic Page 92

Figure 8: Diagram of the Assumptions for Blood Pressure Diastolic Page 92

Figure 9: Diagram of the Assumptions for Blood Pressure Systolic Page 93

Figure 10: Diagram of the Assumptions for Blood Pressure Systolic Page 93

Figure 11: Diagram of the Assumptions for Cholesterol Page 94

Figure 12: Diagram of the Assumptions for Cholesterol Page 94

Figure 13: Diagram of the Assumptions for HADs Anxiety Page 95

Figure 14: Diagram of the Assumptions for HADs Anxiety Page 95

Figure 15: Diagram of the Assumptions for HADs Depression Page 96

Figure 16: Diagram of the Assumptions for HADs Depression Page 96


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Figure 17: Diagram of the Assumptions for Smoking Cessation Page 97

Figure 18: Diagram of the Assumptions for Smoking Cessation Page 97

Figure 19: Diagram of the Assumptions for Physical Activity Page 98

Figure 20: Diagram of the Assumptions for Physical Activity Page 98

List of Tables

Table 1: The PICO criteria of the literature review Page 17

Table 2: The demographics of the study population Page 42

Table 3: The characteristics of the population at baseline and post

rehabilitation Page 43

Table 4: The Regression BMI Page 46

Table 5: The Regression Blood Pressure Diastolic Page 47

Table 6: The Regression Blood Pressure Systolic Page 48

Table 7: The Regression Cholesterol Page 49

Table 8: The Regression HADs Anxiety Page 50

Table 9: The Regression HADs Depression Page 51

Table 10: The Regression Smoking Cessation

Page 52

Table 11: The Regression Physical Exercise Page 53


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Table 12: The inclusion-exclusion criteria for the literature review Page 88

Table 13: The Characteristics of the Included studies in Secondary

Care Page 89

Table 14: The Characteristics of the Included studies in Cardiac

Surgery Page 90

Table 15: The Regression for HADs Anxiety after sensitivity Page 101

Table 16: The Regression for Blood Pressure Systolic after

sensitivity

Page 101

Table 17: The Regression for HADs Anxiety after sensitivity Page 102

List of Appendices

Appendix 1: Research Governance Committee submission form Page 72

Appendix 2: The search strategy for the literature review Page 86

Appendix 3: The inclusion/exclusion criteria for the literature

review Page 88

Appendix 4: The characteristics and details of included studies in

the literature review Page 89

Appendix 5: The assumptions for the regressions

Page 91

Appendix 6: The results of regression analysis after sensitivity was

performed Page 101


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Chapter 1 Background

1.1. Introduction

Over the last 50 years, the UK incidence of Cardiovascular Disease (CVD) has

decreased, with an overall fall in attributable deaths by ~50% (Scarborough 2011).

Despite this reduction, cardiovascular diseases still remain the single biggest killer

of the 21st Century. An estimated 80,000 deaths in Great Britain alone are

attributed to cardiovascular disease, with 45,000 under the age of 75 years

(Townsend et al 2013). A considerable 32% of deaths in the UK are due to CVD

(Scarborough 2011).

In conjunction with primary prevention, which aims to reduce risk factors before

the event, there is a growing need to improve the secondary prevention and

rehabilitation programmes across the world (Clark et al 2005). Current Cardiac

Rehabilitation (CR) programmes have been shown to drastically reduce the risk of

both cardiac mortality (26-36%) and total mortality (13-26%) (Jones et al 2012,

Goel et al 2013, Junger et al 2010). It was set up in the 1960s and defined as a

programme to help patients regain as near normal a place in society as possible

following a cardiac event (WHO 1969). CR is a cost effective therapy with an

estimated price of £1957/Life Year Gained (Fiddan et al 2007).

CR is a long-standing and extremely well established secondary prevention

programme for patients with a range of cardiovascular diseases (Lewinter et al

2012, O’Connor 1989). The programmes are run in over 340 centres across the

UK. The aim of the intervention is to improve health (mental and physical) and

reduce risk of further cardiovascular incidents through education, information and

physical activity. Jones et al (2012) in the ‘BACPR Cardiovascular Disease

Prevention and Rehabilitation 2012’ defined CR as:

“The coordinated sum of activities* required to influence favourably the

underlying cause of cardiovascular disease, as well as to provide the best possible

physical, mental and social conditions, so that the patients may, by their own

efforts, preserve or resume optimal functioning in their community and through

improved health behaviour, slow or reverse progression of disease”.


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The coordinated sum of activities, within the definition, refers to the seven core

components of performing CR. These components are; Long-term management,

Lifestyle (and lifestyle risk factor) management, Psychosocial health, Education,

Cardio-protective therapies, Medical (medical risk factor) management and

Audit/Evaluation (Jones et al 2012, Piepoli et al 2012). These seven components

make up the modern CR. With an increasing throughput within these programmes

each year, these components need to be constantly improved to maintain high

standards of care (Doherty 2013a).

One of most important of all the components is the Audit/Evaluation section. This

allows poor methods, bad practices and improvements to be identified. There has

been many trials, studies and reviews all reassessing the quality application and

running of CR programmes over the years (Aamot et al 2013, Jones et al 2013,

Chatzlefstratlou et al 2013, Dobson et al 2011).

CR programmes and its related patient outcomes are routinely recorded in the

National Audit of Cardiac Rehabilitation (NACR). This audit has been carried out

over 8 years, and includes data on demographic, clinical, Quality of Life (QoL)

and psychosocial wellbeing. The audit has shown an incremental increase in

uptake over the years, with an increase in 2,847 patients from 2010 to 2013 report

(Doherty 2013). Additionally, patient outcomes have shown a consistent

improvement and reduction of symptoms including smoking cessation, physical

activity, BMI, anxiety and depression (Doherty 2013).

The evidence supporting the positive effects of rehabilitation is strong with over

50 randomised control trials, and many subsequent systematic reviews (Heren et al

2011, Davies et al 2011, Taylor et al 2014, Halm et al 2002). The majority of these

trials have shown positive improvements of those patients participating in

rehabilitation, with reductions in mortality and morbidity across the board. Even

with this wealth of evidence, in the study of CR, there are still many aspects that

could be further investigated. These are linked to potential limitations of the

programmes and include the volume and the effectiveness in a real world setting

(Doherty 2012).


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There is a lack of consensus in the literature of how patient volume affects

outcomes. This association, as referred to by Clement et al. (2013) as the volume-

outcome relationship (VOR), has been studied in a range of healthcare settings,

such as Surgery, Neurotrauma, Geriatric Rehabilitation and Care Units (Kanhere et

al 2011, Kulkarni et al 2013, Holstege et al 2013 and Nguyen et al 2005). The

work by Clement et al shows this relationship as positive, in that there are

improved outcomes in higher volume institutions. This conclusion is also

supported in Kanhere et al’s work in adult intensive care units (2011) and Nguyen

et al’s work in Bariatric Surgery (2005).

The work by Holstege et al, set in the acute care of a range of geriatric conditions,

was based on the effects of volume associated with the outcomes of Geriatric

rehabilitation (2013). The study included 2,269 patients enrolled into the

programmes, and showed that with increasing volume there was a variety of

strength for a positive VOR. Some injuries such as total joint replacement showed

association between concentration and outcomes (Short Length of stay and

Discharge, OR 5.7 95% 1.3-24.3 p= 0.020). Other conditions such as stroke had a

limited association between volume and outcome (Short Length of stay and

Discharge, OR 0.82 95% 0.41-1.64 p= 0.568). This single study identifies the lack

of consensus within the literature of the effects of volume on outcome.

Kulkarni et al worked on the VOR within post-surgery of Radical Cystectomy

(2013). Their results showed a positive relationship between both measures of

volume; higher surgeon and overall patient volume, and the outcome of overall

survival of patients. The authors go on to theorise potential causes for this

relationship including; improved organisational capabilities, higher quality follow

up and better treatment of comorbidities. However the study also discusses

potential detrimental effects of high volume as also reported in Fairey et al’s work

(2009).

There have also been some systematic reviews looking at the effect of volume-

patient outcome. In general the consensus is that both hospital and programme

patient volume can affect outcomes, and thus, there is considerable evidence for

the VOR. The work by Halm et al showed that across a wide range of procedures,

high volume was associated with better outcomes (Halm et al 2002). However, this


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has not been investigated within the application of CR. This relationship within

CR programmes is very important, as current uptake is still around 40% of eligible

patients (Scarborough 2011, Dohery2013). The government has set targets for an

increased throughput, thus understanding VOR will be vital in forecasting long-

term effects of increased patient participation. The current ambition for CR is to

have an uptake of 65% from the current ~40% (Hunt et al 2013). As well as this

increase in throughput, there is also a drive for a decrease in expenditure and

staffing within the new austerity focused NHS.

There has been little research on the effectiveness of CR in real world setting or

routine practice. As previously stated, there is a wealth of evidence showing

efficacy of the programmes, but these are all within a trial setting which is both

ideal and well-funded. One study, the RAMIT trial, aimed to assess the efficacy of

CR; however, with low recruitment and some methodological problems the trial

was then conducted in 14 centres in the real world. This allowed for the first time

in the UK an effectiveness study to be performed or real patients going through

real CR centres (West et al 2011). Researchers are starting to use annually

collected data in audits to assess this programme within an observational study

(Doherty 2013a). An understanding of how the rehabilitation centres perform will

improve reporting, and thus lead to improvements within the system.

Finally due to previous examples of reporting being largely trial based, the

reporting at the audit level has been overlooked. What is proposed is a more

sensitive analysis, for the audit data, whereby within the analysis baseline

characteristics will be included; age, gender, co-morbidity and preliminary

outcome measure. This is also related to effectiveness and real world reporting,

and the NHS Choices scheme could illuminate potentially low performing centres.

The scheme aims to increase the choices of patients when going through the NHS,

there has already been publications of surgeons’ success; this practice is to be

adjusted across all sectors (Bridgewater et al 2005, Bridgewater et al 2013).

Because of these three major issues this study aims to analyse the effect of

volume-outcome relationship of patients at the centre level. Additionally, it will

aim to statistically provide the best method for reporting to address the unknown

extent of VOR.


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1.2. Literature Review

This section will, using a comprehensive literature search, provide an in-depth and

detailed review of key topics, trials and studies in the context of VOR. Initial

searching showed that no trials had directly researched the influence of VOR

within CR, and consequently this literature review is investigating two key sectors

in which VOR has been studied. When conducting this literature review, a PICO

table was used to combine multiple populations, interventions and outcomes

(Table 1). This PICO table displays a range of populations from; participants in

rehabilitation, secondary care, acute cardiac care and cardiac surgery were

included. These populations were chosen, as although they are not exactly the

same as this study’s population, many characteristics or demographics were

similar. The Intervention section primarily related to whether the intervention

prescribed had a reported difference in volume. Due to the broad range of

interventions, outcomes were not limited, although special note was taken of any

study reporting one of this study’s outcomes.

The Literature review was performed using BOLEAN logic, a selection of

databases that included; MEDLINE, EMBASE, CINAHL and Cochrane library.

The search strategy can be found in the appendices (Appendix 2) and was

completed in July 2014. There was no language restrictions or type of study

exclusions.

After the initial search two clear interventions were identified that were similar to

CR and included volume; they were either; chronic or secondary care centres

similar to the intervention, or some form of acute care cardiac setting with a

similar population, i.e. had some form of cardiac incident such as Myocardial

Infarction (MI) or percutaneous Coronary Intervention (PCI).

In total 1,687 studies were identified by the search strategies (shown in Appendix

2), of which a total of 83 met the secondary care inclusion criteria and 44 met the

acute care/surgery criteria. The table in Appendix 3 shows the table of

inclusion/exclusion criteria. A breakdown of where the studies came from for each

section is shown in Figure 1 and 2.


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Table 1 Showing the PICO format used within the literature review, due to the novelty of this

precise study a broad range of populations/interventions were included focusing on all studies

reporting Volume. PICO Table

Category of PICO Terms

P. Population Participants in cardiac rehabilitation, other rehabilitation,

secondary care, cardiac acute care, cardiac surgery and long-

term nursing care homes.

I. Intervention Any intervention that participants received where volume of

participants was recorded.

C. Control N/A

O. Outcomes BMI, Blood Pressure, HADs score, Cholesterol, Smoking

Cessation and Physical Exercise.

.Before each article was reviewed, it was ranked using the CASP ranking tool. The

CASP tool assesses the methodological rigour of the studies and can be used on

cohort, reviews and RCT’s (CASP 2006). The tool is used to rank studies using a

checklist of either 10 or 12 key points (10 for reviews, 12 for cohorts). This

ranking of the literature enabled a more in depth understanding of the literature

that has been included, but will also increase the detail to which these articles are

reviewed. This is because no two articles are the same in their; quality of

reporting, assessment of bias and methodological rigour. By ranking these articles

it will allow a more detailed assessment of what is reported.

Figure 1 and 2 showing from which database the studies were found, reviewed and then excluded. The figure on the left is where the studies from the secondary care came from, which had a total of 1,494 hits with the search, this was narrowed down to 83 using abstract and title search. Further exclusion of 74 papers were due to duplication, differing intervention or not relevant such as no volume-outcome relationship. The figure on the right is the cardiac surgery search, which had 193 hits, which was reduced to 44 then leaving 10 after

more detailed review.


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1.2.1. Volume-Outcome Studies Within Secondary Care

A total of 83 studies met the initial title/abstract criteria, of which 74 were

removed (34 duplicates, 21 not relevant, 19 incorrect interventions such as primary

health care or surgery). This left a total of eight articles, which will now be

reviewed for their quality, sample size, confounding factors, methods, results and

limitations.

The studies that qualified for this section of the review were a range of cohort or

systematic reviews. Characteristics of the studies can be seen in Appendix 3.

There is an extremely broad range of clinical conditions and procedures. This

ranges from psychiatric care, rehabilitation for fractures, burn care, perinatal

dialysis, neonatal care and HIV/AIDS care. Although these reflect extremely

varied clinical conditions, the use of classification volume is similar either being

tertile or quintile categorisation or a linear variable.

1.2.1.1. Quality

Of the eight included studies, two were reviews. The work by Hanford and Neogi

et al revealed 7 and 6 out of 10 in the CASP ranking (Hanford et al 2006, Neogi et

al 2012). The quality of these reviews were moderate; however, a lack of

assessment of the quality, of the included studies, and a poor overall reporting are

the main reasons these reviews did not get higher scores.

The other included studies were all cohort studies, with a variety of scores 9-11.

All of these studies were of moderate to high quality. The highest of these studies

were; Evans, Graham Lee and Li et al’s second study (Evans et al 2013, Graham et

al 2013, et al Lee 2007, Li et al 2012). The work by Lee et al and Li et al both

received 11-12, only being let down by a lack of assessment of confounders. Lee

et al found a negative effect of VOR in psychiatric care, which is similar to this

current study in that, it was the first of it’s kind (2007). Because of this there was

no evidence to back up the result; to date in that field and the wider VOR this still

remains one of the only studies to show a consistently negative VOR.


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The other studies with slightly lesser scores were Evans, Graham, Hranjec and Li.

Relatively speaking these studies all had moderate to high CASP scores, which

means they had a clear and concise research question, the cohort was collected

appropriately and the exposure was well defined. The sections within these studies

that reduced their quality were; firstly the confounding factors that were

sometimes overlooked such as age, gender, and facility details such as staffing

profiles (Evans et al 2013, Graham et al 2013, Hranjec et al 2012). Secondly the

results that were reported, were sometimes not very precise (Graham et al 2013

and Hranjec et al 2012). Finally in Evans et al’s work a large proportion of the

eligible population were either never collected in the primary data (15%) or

removed by the authors (13.2%) (Evans et al 2013).

1.2.1.2. Sample size

The studies included in Hanford and Neogi et al’s reviews were all observational

studies conducted retrospectively, similar to the other studies included in this

review. Because of this, the sample sizes are all relatively large, varying between

11,000 in Evans, 482,000 in Graham and an enormous 1,023,000 in Li et al (Evans

et al 2013, Graham et al 2013, Li et al 2012). These large studies provided

extremely well powered statistical analysis, because of this, it is possible to study

very small effect sizes in outcomes (Field 2013).

The combined number of participants in Hanford et al’s review was 39,000

patients. This study was aimed at investigating the effect of volume, in the centres

treating HIV/AIDS, on the outcome of patients. Due to high heterogeneity, the

individual studies could not be combined into a meta-analysis; however, the results

provided a strong result, that higher volume levels within these centres cause a

reduction in patient mortality.

The size of the population in each of these studies varied considerably, either due

to prevalence of population or care setting, or size of population where the study

was based. All the studies varying from ~11,000 in Evans et al to ~1,000,000 in Li

et al had sufficient size to detect results and significance (Evans et al 2013, Li eat l

2012).


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1.2.1.3. Confounding factors and covariates

In the CASP ranking checklist, for the cohort studies, the inclusion of confounding

factors made up two marks. This is also important for the on-going study, as the

methodology proposed in Multiple Linear Regression (MLR). This takes into

account multiple covariates. Almost all of the 5 independent studies took into

account at least one or more covariates or factors (Evans et al 2013, Graham et al

2013, Lee et al 2007, Li et al 2012), apart from Li et al which did not mention

these in the methods section (Li 2010).

Most of the covariates adjusted for were; age, gender, a calculation of risk, if

appropriate Length of Stay (LoS) and referral details. These covariates are

important, as they have each been shown to have an interaction with the outcome

measure be it functional decline, readmission rates or mortality. By including these

into the analysis the association is accounted for and the VOR is more accurate.

Some of the studies, such as Lee and Li et al actually included some facility details

such as, funding type, staff profiles and training (2007, 2012). The inclusion of the

facility details, allows potentially, a more precise estimate of the VOR, as much of

the variation caused by the facility details is then accounted for. However, in a

preliminary piece of research such as this current study, which is the first of its

kind, it is initially important to see if a VOR actually exists before including too

many covariates.

1.2.1.4. Methods

The calculation of volume for each study was very similar. The studies included

all patients who were entered into their respective databases for the period defined

(3 months, 1 year or 3 years). This total number was then in some studies grouped

either into tertile groups, quintiles or left as a continuous scale for linear analysis.

Many of the studies used MLR as the main analysis, which would allow an

association between the volume and outcome to be investigated, taking into

account the covariates previously discussed.


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One study, Graham et al, used a hierarchical approach (2012). This method, allows

a more in depth analysis of the association between volume and outcome. This is

because the method takes into account more variables and ranks them according to

the variation. For example in their study there were three different types of

condition, Stroke, Lower Extremity Fracture (LEF) and Lower Extremity Joint

Replacement (LEJR). They used these three conditions to assign a hierarchy to

patients to increase accuracy of the VOR. This method is very beneficial for

increasing the understanding of the association between volume and outcome;

however, all the elements affecting the setting such as covariates need to be

proven, estimated and well established within the literature.

1.2.1.5. Results

The results for this section of the literature review showed some variation. Of the

eight-included studies, six showed a positive VOR, while one showed no

association (Graham et al 2013) and another showed a detrimental effect (Lee et al

2007).

The detrimental effect was seen in the study by Lee et al, which looked at the

association between volume and outcome in Psychiatric care in Thailand; the

outcome was readmission to hospital within 30 days (2007).

The work by Graham et al investigated the association between volume and the

outcome of three different fractures in rehabilitation (2012). The study’s results

were that very little difference in outcomes is seen with increasing volume. Some

decrease in functional decline was observed in the higher quintile of fracture

patients; although, this was not of clinical significance. The results do not match

the rest of the section within the review, but due to the extremely heterogeneous

nature of the studies it is not of substantial importance. The studies are

heterogeneous, as they include a variety of populations, interventions, and

conditions. The reason why this study remains important is because the CASP

ranking was moderate, and the study was well conducted so even though the

results don’t conform they are still useful.


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The rest of the studies and reviews all showed a positive VOR between the number

of people entered into each centre and their studies outcomes. The two reviews by

Hanford and Neogi et al investigated the VOR in the HIV/AIDS setting and the

neonatal care setting (2006, 2012). The review by Hanford, although unable to

perform meta-analysis, showed that the included studies all showed a significant

association between the increase in volume and a reduction in mortality. The

results of the review by Neogi et al found six studies that looked at Volume of the

unit, in comparison to the Neonatal Mortality Rate (NMR). The studies could not

be combined due to heterogeneity; however, the independent results across the

different countries consistently showed a strong VOR, with higher volume leading

to reduced NMR.

The last four studies, which included, Evans, Hranjec and both studies by Li et al

all found a strong positive VOR (2013, 2012, 2010, 2012). The settings ranged

from perinatal dialysis, burn centres and nursing homes.

1.2.1.6. Limitations

The limitations identified across these studies vary from; the reviews had highly

heterogeneous studies and low quality reporting in some results. This resulted in

no pooling of results, and low quality of results (Hanford 2006).

Within the studies, the limitations ranged from a lack of detail included in the

methodology such as covariates in Li et al, a singular outcome measure in Lee et al

and a considerable loss of participants in Evans et al (2010, 2007, 2013). One

limitation that is identified is the lack of including facility level details in the

analysis. Although not vital for the analysis, this is an important covariate that has

been proven to impact VOR in areas such as surgery, where surgeon volume

impacts outcomes (Seperhripour 2012). This is a useful covariate to include in the

analysis; however, many of these studies were preliminary studies looking at VOR

in their respective settings for the first time. It is important in those instances to

assess the VOR in a simple analysis, as too many covariates can impact precision

just as much as too few (Field 2013).


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The reason for doing the review is twofold: (1) to establish if VOR exists in

secondary care (2) to help inform the planned analyses for this study. The use of

MLR to assess the relationship will be something that will be included in this

research along with covariate analysis of age, gender and baseline characteristics.

Additionally in the context of VOR in secondary care, the analysis will be looking

for a two directional relationship as identified by Lee et al’s and Graham et al’s

work. The review concludes that the highest quality studies are Li’s work, Lee,

Evans and Graham, with lesser quality in others. The use of CASP was vital in this

section as most researchers assume reviews carry more ‘rigour’, which in this

instance is not the case.

1.2.2. Volume-Outcome Studies Within Cardiac Surgery

A total of 44 studies met the initial title/abstract criteria, of which 34 were

removed (12 duplicates, 8 not relevant, 14 wrong intervention), which left 10

articles for review. The studies that qualified for this section of the review were a

range of retrospective cohort or systematic reviews. Characteristics of the studies

can be seen in Appendix 3. Although all studies related to some form of cardiac

surgery; the method, populations and baseline characteristics of each study varied

greatly. Similar to secondary care there were two different study designs that

qualified for the review, which were three reviews and seven studies. These

articles will now be reviewed for their quality, sample size, confounding factors,

methods, results and limitations.

1.2.2.1. Quality

There were ten included articles within this section of the review, of which, three

were reviews. These were the works completed by Markar, Seperhripour and

Zevin (Markar et al 2012, Seperhripour et al 2012, Zevin et al 2014). These

reviews received a variety of scores on the CASP ranking.

The work by Seperhripour was the lowest score for any article with a 5/10. This

review received such a low score mainly because of a lack of pooling and a low

level of detailed reporting, such as no p-values. The other two reviews received

much higher scores with 9/10 for Markar and 10/10 for Zevin et al. Each of these


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reviews were well conducted, a well ordered search strategy was used along with a

high quality level of reporting. The only limitation in Markar et al’s work was that

the quality of included studies was not reported. This is a limitation as it means the

conclusions made within the review are not weighted by the individual studies

quality.

The seven included studies in this review varied between qualities; Allareddy,

Miyata and Nguyen et al who all received 12/12 in the ranking (Allareddy et al

2007, Miyata et al 2009, Nguyen et al 2004). These studies were all conducted in a

high quality way, taking into account covariates, confounders and presenting the

results in a detailed manor.

The lower quality studies, which all had moderate quality, included the work by

Birkmeyer, Gammie, Landon and Urbach (Birkmeyer et al 1999, Gammie et al

2007, Landon et al 2010, Urbach et al 2004). This work ranged between 7/12 for

Birkmeyer, 10/12 for Landon and Urbach, and 11/12 for Gammie. These studies

were all well conducted using high quality methods, and the results were reported

to a good standard. The only limitations of these studies were the lack of some

covariate analysis and detailed reporting or results. For Birkmeyer who received

the lowest rank within all the studies of this review, the problems were; no

confounders included at all, poor detail of results and overall detail of the study.

1.2.2.2. Sample size

The sample sizes within this study varied considerably, depending on the setting of

the study and the type of surgery. Some studies were very small containing fewer

than 5,000 such as Miyata, which had 2,875 participants. (2009). The largest study

was conducted by Allareddy et al which contained 877,131 patients; this study

contained three years of data across the whole of the USA across 5 different

procedures (~200,000 per procedure). The result of this study was that procedure

specific volume significantly affects the odds of mortality, with lower total volume

increasing the mortality of patients.

The sample sizes in this kind of study vary greatly, depending on; the available

data, the commonality of the initiating event and the relative risk of the outcome


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measure. In a high risk procedure the risk of the outcome of mortality increases,

and thus a smaller population is needed; as seen in Miyata which was looking at

operative and general mortality (2009). However, in a study like Li et al in the

Secondary Care section in order to detect a difference in the outcome of hospital

transfer rate, the study population had to be higher with over 1,000,000

participants.

1.2.2.3. Confounding factors and covariates

As seen in the secondary care section, a large number of the studies used MLR to

perform the analysis between volume and outcome. Using this technique, it was

possible to account for many covariates in the regression. These were similar

covariates to the previous section including age, gender and baseline risk. In some

of the studies such as Allareddy the list was very extensive; age, sex, admission

type (elective vs. non-elective), comorbid severity, primary diagnosis, extent/type

of primary procedure, year of procedure, hospital teaching status and hospital bed

size (Allareddy et al 2007). The inclusion of this many covariates improves the

quality of the analysis, as the association between volume and outcome becomes

more precise. Additionally, in many of the studies the facility level details were

included, such as Allareddy, Miyata, Nguyen and Landon, which included surgeon

volume and propensity scores (Allareddy et al 2007, Miyata et al 2009, Nguyen et

al 2004, Landon et al 2010).

However, some of the studies did not mention any covariates, such as the work by

Birkmeyer. This is poor methodologically as much of the association between

volume and outcome is actually being affected by age, gender and other relevant

factors. This does limit somewhat the applicability of these studies results.

It is important to include covariates, which are proven to impact the outcome;

however, the inclusion of too many or some unproven factors, which are not

appropriate to the outcome, can impact the results as random association is

removed (Field 2013).


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1.2.2.4. Methods

The volume predictor variable was generated the same way, as with the Secondary

Care value, for all of the included studies. This involved the total number of

participants enrolled into the hospital in the time period, either in one year or three.

This volume figure was common to most studies as was using MLR analysis,

including the covariates already discussed. Many of the studies also looked at the

impact of surgeon experience, as related to the ‘practice makes perfect’ theory

discussed in Lee et al’s work (2007).

If regression was not used, an odds ratio for high and low volumes were

calculated, this gave a risk of the outcome depending on the level of volume

(Birkmeyer 1999). This is a sound method although does only show a correlation

between two variables and is less detailed than the MLR work. The work by

Birkmeyer, was designed to demonstrate, by using the calculated odds ratio as an

example, how many lives could be save with increasing the volume at specific

hospitals performing the 10 procedures.

1.2.2.5. Results

Although the ten included articles vary in their populations, surgeries, outcomes

and method, all ten showed a strong positive VOR in that the increase in volume

improves patient outcomes. The majority of the studies looked at mortality as

either the main or one of the outcomes.

Marker looked at both mortality and LoS as outcomes in relation to volume

(Markar et al 2012). The results of that review were that both risk of morbidity and

mortality were reduced with increasing volume; however, there was insufficient

evidence for a significant reduction in LoS.

It is a consistent result throughout all the included studies, that there was an

inverse relationship between volume and the mortality in the specific outcome

(Zevin et al 2014, Miyata et al 2009, Nguyen et al 2004, Birkmeyer et al 1999).

Gammie et al’s work builds on these results, in that they not only found the inverse


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relationship, but also that in higher volume hospitals there is also higher frequency

of procedures (Gammie et al 2007).

Some studies were also looking at how total volume interacts with specific

operative success (Allareddy et al 2007, Urbach et al 2004). There is a lack of

consistency in the results of this, as Allareddy et al’s results show that when

volume of different procedures is used to look at outcomes, there was no impact.

This is different from the results by Urbach, which show that there is an

association between total volume at the hospital and individual operative outcome.

Although Allareddy et al has a higher CASP ranking which provides it with a

higher methodological weighting, these two studies are not comparable. They

contain different populations, different interventions and conditions. What can be

taken away is that the lack of consensus gives a rationale for further study into this

area.

The final result of the studies is that there is a strong inverse correlation between

mortality and volume; however, the relationship is relatively complex

(Seperhripour 2012, Gammie 2007). There are other factors that make the

interaction multivariate; the complex learning curve, differing patients’ risks and

differing facility factors could influence the relationship. It is important to include,

if appropriate, the facility level data in an already established VOR affected

setting, this allows precision and reliability of results. However, as with secondary

care, it is important not to complicate the analysis in preliminary studies as it can

convolute the analysis, by having too many covariates (Field 2013).

1.2.2.6. Limitations

There were a variety of limitations identified within the articles included in this

section. They varied from: only having a single outcome measure of mortality

(Allareddy 2007), a lack of detail during reporting of the results (Landon 2010)

and in the review by Markar et al, the quality of the studies were not assessed

(2012).

This section of the literature review has shown, with considerable certainty, that

there is a clear VOR within most if not all cardiac surgery. This is supported


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across all included studies, with significant results and a consistent moderate-high

quality of research. What this section has additionally shown, is that volume

affecting outcome in this demographic is supported; however, this ongoing study

is in a different setting. The Secondary Care setting is different to surgery, in that

care is not independent and staffing profile may play a more integral role.

1.2.3. Rationale of Outcome Measures and Baseline Characteristics in CR

This project is investigating the association of the VOR of patients partaking in

CR. The NACR annual review reports a range of outcomes from; BMI, mental

health, Smoking Cessation (SC) and many other health related measures. It is

proposed, within this project, to study the clinical outcomes routinely reported as

part of the NACR these include; BMI, blood pressure, cholesterol, HADs scores,

SC and Physical Activity (PA). As part of this review, the aim was to identify

where any included studies used these outcomes, and to see how in other

populations they were affected by volume. However, there was no reporting within

these studies of any of these outcomes. Because of this lack of research, it

strengthens the rationale for this project in that there has not been any research into

the effect of volume on these clinical outcome measures. The national association

for NACR routinely reports these 6 measures as key indicators for CR quality

(Doherty 2013a, Jones 2012, Das 2007). With the severe drought of research into

these outcomes and the consistency to which the NACR report these outcomes,

there is a strong rationale for using them within this project.

1.2.4. Effectiveness of Cardiac Rehabilitation

Although there is no work directly analysing the association between volume and

CR, there is extensive literature looking at the efficacy and optimal running of the

programmes. The running and application of CR is constantly being evaluated and

reformed, under the principle of Audit/Evaluation of the seven core components of

BACPR. Because of this there have been many studies investigating the efficacy

of CR, of which 2 systematic reviews and the RAMIT trial (which aimed to look at

efficacy but studied effectiveness) will now be discussed. The reviews investigated

the influence of CR on outcomes, while the RAMIT looked at the effectiveness of

CR in the trial setting.


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The first of the included systematic reviews is the work by Heran et al. (2011).

This study is a combination of previously performed studies and the work by

Taylor et al (2010). Taylor et al’s work only included patients with Heart Failure

(HF), whereas Hearn included all patients that can be enrolled in CR. This review

was a follow up review of previous work into the ‘Exercise-Based Cardiac

Rehabilitation for Coronary Heart Disease’. The rationale for doing this review

was to build upon the study’s results, using more up to date data from studies post

2001. The results of the original study were that Exercise-Based (EB)

rehabilitation led to reduced total mortality (odds ratio: 0.73, 95% CI: 0.54-0.98),

cardiac death, non-fatal MI and lipid profile.

The main limitations identified within the original review were that: the study

population was narrow (primarily white, middle-aged low risk); the wide spread

and varied use of drugs meant methodological heterogeneity; the true extent of a

causal relationship was unclear and there was overall limited understanding of

QoL, costs and costs-effectiveness. As a consequence of all these limitations, the

current review was undertaken 10 years later to reassess the field of research and

to build on these limitations (Joliffe 2001, Heran 2011).

Heran et al’s work aimed to follow the same question as in 2001, but by making

alterations and having a wider literature selection, to further build on the results.

The study included only RCTs of patients who were allocated to receive EB

cardiac rehabilitation or usual care. In total 47 studies meet the inclusion criteria,

which randomised 10,794 patients to EB or usual care. The 47 studies were made

from 30 that met both Joliffe and Heran et al’s criteria plus 17 that were post 2001

or met the new criteria.

The results from this review show a similar reduction in total mortality (RR: 0.74,

95% CI: 0.63-0.87), cardiovascular mortality (RR 0.87, 95% CI: 0.75-0.99) and

hospital admissions. Again this study has considerable limitations, both in the

study populations, the methodological practice and reporting of the RCT’s. This

review suggests that a more realistic study population, demographics that really

enter cardiac rehabilitation, would provide a more useful relationship between EB

rehabilitation and usual care. Additionally, larger trials or studies that conform to

one methodological approach would allow assessment of effect from EB and QoL.


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These two main limitations can be used across the board of research in cardiac

rehabilitation, which is why this study will be observational (using real population

data) and completed on a large enough scale to be stand-alone.

The second study is a systematic review by Davies et al. on ‘Promoting patient

uptake and adherence in cardiac rehabilitation’ (2010). This trial similar to Heran

et al’s work, was an update on previous work by Beswick et al. (2005). The

original study was focused on identifying potential interventions that could

maximise adherence of participants to rehabilitation programmes. The results of

the original study were that there was no evidence reported on improving uptake

and adherence; however, there were potential limitations were identified to be

improvement in further study. Because of this limited result in 2005, Davies et al.

decided to update the review in 2010.

The systematic review included RCT’s with patients with cardiovascular incidents

(MI, CABG, Heart Failure or PCI), who were randomised to receive interventions

to increase uptake or adherence; only trials reporting adherence were included.

The relevance of the results from this study are vital for all cardiac rehabilitation

nationwide. Because of the low uptake and adherence, resulting in only 40% of

eligible patients completing rehabilitation programmes, a more in-depth

understanding on uptake and adherence is needed. The results of this review were

that due to considerable heterogeneity no meta-analysis could be used; however,

three interventions were identified as having positive effects on the uptake and

adherence. Due to the identified heterogeneity, the authors suggest a further uptake

would be needed.

Two major limitations of this review were that the only outcomes measured were

the uptake and adherence. The interventions may have had a positive impact on the

number of patients completing the programmes, however, it was suggested that

they might have had a detrimental result in other outcomes such as physical

activity. This is because a lack of exercise training may the increase proportion of

patients completing the programme. In the short term exercise training has been

shown to increase physical activity and more importantly in the longer term;

decrease cardiovascular mortality and overall mortality. This shows the

complexity within cardiac rehabilitation. This result raises the question of whether

it is throughput or long-term changes that is the main goal (Heran 2011). The


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second limitation, similar to Taylor et al’s work was that the target population was

primarily male, middle-aged and white. This again supports the conclusion that an

observational study of the true population participating is requires to identify both

effectiveness and other research questions.

The final study looking at effectiveness was the RAMIT trial. The RAMIT trial

was a multi-centre randomised study, focused primarily with exploration that

‘modern era rehabilitation’ was not as effective as the literature suggested (West

2011). The study discusses how the majority of literature originated from the

1960s-1970s where mortality was higher, and thus the results were no longer

relevant with modern medicine. The trials premise was to use a RCT approach

within the context of modern medical practice (in situ). This essentially would

have provided an up-to-date understanding of how modern cardiac rehabilitation

programmes affected a range of outcomes.

The results of this study identify no significant difference between treatment arms;

additionally, overall cardiac rehabilitation does not seem to positively affect

outcome measures (West 2011). However, due to early closure by the NHS and

low recruitment, the results shown by the study are not widely accepted. The

RAMIT trial has often been misunderstood in the literature, as a trial suggesting

cardiac rehabilitation does not work. However the authors after issues with

accessibility ended up investigating whether cardiac rehabilitation, in the UK is

effective in the modern era (Doherty 2012, Doherty 2013b).

The rationale of this current study is extremely valid, in the sense that many

studies and subsequent systematic reviews have extremely varied methodologies.

It is possible to see that often the meta-analysis’s comes under scrutiny due to high

heterogeneity. Additionally almost all studies have shown that the ‘efficacy’ of the

CR programmes in a trial setting, but not assessing how effective they are in real

life.

In conclusion after reviewing Heran, Davies and West’s studies, in order to answer

the modern research questions, a need has been identified for large observational

studies in the real world.


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1.2.5 Conclusions of the Review

The reason for doing the review was to; assess the literature reporting the VOR in

a variety of clinical sectors, help to clarify the research question and methods, and

to finally help to identify the analytical methods used for the analysis.

The review has shown that a variety VORs are present within the included

settings; positive, negative and no relationship. It has also helped to provide a

methodological template of how this study was conducted, using total enrolled

patients as volume, MLR as the analysis and using key covariates well established

across the majority of the articles; age, gender and baseline risk/initiating event.

1.3. Aim

• The aim of this study is to investigate whether there is an association

between the volume of patients receiving CR at a centre and the clinical

outcome measures.

1.4. Rationale

Because of the initiatives to increase throughput within the NHS, through NICE’s

guidelines which aims to offer CR to 85% of the eligible population (NICE 2013),

it is important to better understand the VOR specifically within the setting of CR.

In a range of acute and chronic health care settings, including many cardiac

interventions, the VOR has shown to determine the extent of a range of different

clinical outcomes. However, in a select few studies, such as Graham and Hranjec

(2013, 2012), some rehabilitation sectors have shown a deviation from this trend,

with these studies showing no effect of the VOR or even detrimental results. This

inconsistency between the literature, along with a severe lack of evidence

regarding CR, gives strong rationale to perform a large-scale observational study

of volume-outcome with CR. The literature review identified three possible

directions to which volume interacts with outcome; positive improving outcome

with increasing volume, negative with detrimental effects of larger volume and no

effect (Allareddy et al 2007, Graham et al 2012, Lee 2007). Because of this result,

the study will use linear regression that will be able to detect if any of these

interactions exist (Field 2013, Miles and Shevlin 2001).


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It is also vital to find a better method of reporting the outcomes. This is of

increasing importance, especially with the introduction of the NHS choices

scheme, which will illuminate the potential bad practices and make way for

improvements. Current reporting of CR at the overall level has been shown to

improve outcomes and long-term mortality. However, a more in depth study may

highlight centres, which on the surface appear to be poor performing but may just

be affected by volume.

1.5. Hypothesis

The literature has shown that in acute care sectors, especially cardiac

interventions, increased volume improves clinical outcomes. However, in some

secondary or chronic care settings there is no apparent effect. This discrepancy

between the VOR within acute and chronic care is of considerable importance with

recent drives to increase the CR throughput (NICE 2013). If certain literature is

correct then a two-tailed relationship exists within many VORs, in that too little

volume or too high volume can affect outcome measures (Evans 2013, Hranjec

2012).

Taking into account this evidence in other fields, this project aims to establish

whether there is an association between volume and outcome, using regression to

predict whether there is a linear association either positively or negatively

influencing outcomes.

• The primary hypothesis of this study is that volume will determine the outcomes that are routinely reported in the NACR for:

o BMI

o HADS score

o Blood Pressure (Diastolic and Systolic)

o Cholesterol count

o Smoking Cessation (SC)

o Physical Activity (PA)


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• The Null hypothesis is that volume will not influence any of the included

outcomes.

1.6. Ethical Considerations

This study has been approved by The University of York, Department of Health

Sciences Research Governance Committee. The fully completed submission form

can be found in Appendix 1.

The data is an anonymised version of the NACR audit from the years 2011-2012.

All patient level details were removed before being accessed by the NACR team at

The University of York. The audit is a routinely collected clinical programme and

is hosted by the HSCIC (Health and Social Care Information Centre). The data has

had all personal details removed, meaning that the patients cannot be contacted.

The project does not plan to contact patients at any point. The data is clinically

exempt from ‘consent’ under the 251-exemption agreement with the HSCIC. All

access to the data will be through password-protected servers within the University

IT system, or held on encrypted memory drives.

1.6.1. Clinical Relevance

The aim of the study is to inform the field of factors influencing outcome and to

improve on methods of reporting. The results of this study may impact the

participants through improvements to data and subsequently services at the local

level. The impact of the result regarding VOR in CR programmes may affect the

way in which CR programmes are run and administered ongoing.


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Chapter 2 Methods

2.1. Design This project is a retrospective, cross-sectional study in centres providing CR

included in the NACR data. The aim was to assess the relationship of volume-

outcome in UK based CR centres; therefore all patients entered into the NACR

2011-2012 audit were included.

2.2. Patients and Data Collection

The Dataset that was used is the NACR audit, collected annually since 2006. The

period of collected data used was taken from 1 April 2011 to 31 March 2012. This

dataset was chosen because it was the most up to date and complete dataset

available.

This data was anonymised by the Health & Social Care Information Centre, based

in Leeds, as stated in the ethical considerations section, and cleaned by the NACR

team (only valid cases and participants that were enrolled and had any baseline

measures recorded). The data was further cleaned by removing outliers and data

errors, using the NACR data dictionary (Doherty 2014). The dataset contained a

large selection of variables including event type, treatment, age and current

medication. A selection of key, literature-enforced covariates had been selected

and kept in the dataset for subgroup analysis.

2.3. Organisation Information

Of the total 340 CR centres within the UK, only around 70% are included in the

NACR data. Each patient that is entered into one of the included 276 CR centres is

assigned the centre code; an example is York District Hospital (YDH). Patients

can be referred to another centre; however, in this project patients will be assigned

to their original centre. Some centres still enter their data manually, this is because

it is not mandatory to submit digital data to the audit and thus some are not on the

NACR audit. The final number of centres in this study after cleaning (only centres

with at least one patient who had completed assessment 1 and 2) was 203. This

equates to 75% coverage of centres although this percentage does not equate to

participating patients as each centre has different capacities.


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2.4. Outcomes

Within this study six key outcome measures had been identified, studied and

deemed as important for reducing risk of further cardiac events. These outcome

measures were BMI, blood pressure, psychosocial health (HADs score),

cholesterol, Smoking Cessation (SC) and Physical Activity (PA). As stated in the

literature review, these outcomes were chosen as the NACR uses theses to report

successful CR in the audit. The outcomes were average measure at assessment 2

(assessment immediately post CR completion) per centre.

2.4.1. BMI

BMI is strongly associated with CVD; and a reduction in BMI leads to a large

reduction in risk of CVD secondary events (Pack 2013a). BMI was selected as

optimal between 18.5-25kg/m2; however, any reduction in BMI was seen as

positive in relation to volume. Although BMI readings were filtered according to

the NACR data dictionary (between 5-215), the data was also filtered to remove

improbable changes in BMI. These limits were set to ±30 change in BMI over an

8-12 week period. Analysing the audit, this limit was chosen by the top 5± change

outliers and assessing possible and improbable changes over the 8-12 week period.

2.4.2. Blood Pressure

Blood pressure included both systolic and diastolic readings. Similar to BMI the

inputted data conformed to the NACR data dictionary of systolic >50 & <250 and

diastolic >30 & <200 (mm/hg). However, this allowed chance error when

inputting assessment 1 and 2 readings, thus limits were set upon the pre and post

change as systolic ±70 and diastolic ±40. This change limit was set again by

looking at the distribution of values for blood pressure. The top 5 ± change

outliers were analysed and possible changes were theorised for blood pressure in

the period.

2.4.3.Cholesterol

Cholesterol within the dataset had many sub categories. The most complete of all

the categories was the overall cholesterol variable; this had a range based on the


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dictionary as between 1-20 mmol/L. Because of this small range no limits were

imposed on the change of cholesterol outcome.

2.4.4. HADs Scores

HADs score is a test used independently for both anxiety and depression. The test

has a range of scores from 1-21; between 0-8 is considered normal mental health

(Crawford 2001). Within this study we are considering any reduction in this score

as positive, and are also not imposing any limits on change in either anxiety or

depression.

2.4.4. Smoking Cessation and Physical Activity

For SC and PA both outcomes were dichotomous values, either smoking or not

and meeting or not meeting the PA task. For this reason it was merely an exclusion

of non-entered values during the cleaning of these outcomes.

2.5. Predictors

The main predictor in relation to the outcomes measures was ‘volume’. This value

was gained from the total number of participants enrolled in each centre that had

completed either phase 1 or 2. This value gave the volume at each centre;

however, many of these patients dropped out; a completion measure was also

calculated. This value was the number of people per centre who had a completed

assessment 2 (immediately post rehabilitation period) and provided outcome data.

Additionally, a selection of covariates was included in the analysis, which

included average age, gender proportion, and event type/treatment proportion.

Using analysis of covariance, with appropriate covariates improves the statistical

power and hence increases the precision of the estimate of the relationship

between volume and outcome. These covariates have been selected due to multiple

studies stating their effects on outcomes of CR (Li et al 2014, Graham et al 2013

and Hranjec et al 2012). Age and gender have both shown to impact the outcome

of CR. The initiating event was included as this gives a representation of a major

group of patients who undergo CR, and have associated risks.


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It is standard protocol in audits such as the NACR to use aggregated data. This is

because the data set is large enough to be averaged and this allows minimal

cleaning of the data, as any outliers have much reduced statistical power.

Additionally, the current understanding of many confounders within CR is limited,

such as staffing number effects and the theoretical volume. Staffing number which

is the Multi-Disciplinary Team (MDT) database, recorded parallel to the NACR,

could not be imported and analysed within the time frame of this research project.

Because of this limited understanding, it would be unwise to assign a hierarchical

design to the data. (Field 2013)

2.6. Statistical analysis

For both the study population and the centres, descriptive statistics were generated.

This allowed preliminary analysis of the populations comparing them to previous

studies, for validity and transparency.

Due to the nature of the study, comparing centre-level details of volume to patient-

level clinical outcomes, for the analysis the data was aggregated for each centre.

This was completed using the centre codes. Then each pre, post and change

(change between assessment 1 and 2) measurement for each included outcome

measure were aggregated by the centre; along with the covariates to be

incorporated in the analysis. This type of analysis allows large scale, high

statistical power results to be generated with minimal data cleaning and

processing.

The main analysis used in this project is Weighted MLR modelling. This enabled

analysis of the relationship between volume and outcome measure, taking into

account covariates such as age, gender, event type and treatment while weighting

the model for the actual number of responders in each centre. Weighting takes into

account for the fact that different centres included different amounts of data and

when you aggregate, each centre’s differing amount of data is pooled into one

singular value (the mean). When using this pooling method, it is vital to weight as

it allows a level of ranking based on the amount of original data provided. Failure

to weight a centre that has 100 patients would be presumed to be equal to one

having 10 patients.


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2.6.1. Multiple Linear Regression

2.6.1.1. Background

MLR is a technique used to assess the association of one variable against another,

while taking into account additional variables (covariates). The regression allows

the prediction of a value of one variable based on another. The analysis allows the

potential prediction of a two directional relationship, high volume or low volume

influenced outcomes.

MLR was chosen over normal linear regression as it allows the inclusion of

covariates such as age and gender. These are important to include as the literature

has shown that the proportion of age and the gender can affect the outcomes per

centre of CR. If these covariates were not included, then much of the variation

accounted for by these covariates may be misinterpreted as the association

between volume and outcome (Field 2013).

In this analysis, a weighted regression model was used, with the outcome measure

as one of six reported outcomes. Also included in the model were age, gender,

initiating event (% of MI patients) and the model was adjusted for the average

baseline audit score for each centre. The analysis was weighted for the actual total

number of patients in each centre who were present for a reading at measure 2

(post rehabilitation).

Continuous data was used for four of the variables, i.e. cholesterol 1-20, HADs 1-

21 and BMI. These were easy to aggregate, calculating a mean for each centre.

Two variables, SC and PA were dichotomous, and because of this the aggregated

proportions were analysed differently. This required generating the odds of being

in both reading 1 and 2, which is shown below.

Odds of Failing Exercise Task = Number Failed Exercise Task / Number Passed

Exercise Task


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The odds were calculated independently for each centre, and then logged for the

analysis. This allowed the dichotomous data to be analysed in the same way as the

other outcomes such as BMI and cholesterol

2.6.1.2. Assumptions

To perform this test four assumptions had to be met: Linearity, Independence,

Uniform Variance of residuals and Normality of residuals. These assumptions

were met or adhered to as far as possible for the results to be statistically sound.

Within this study the assumptions were tested in the following ways.

Linearity was measured using a scatter plot, where a line of best fit was plotted.

Uniform variance also used a scatter plot, to examine whether the plots vary

uniformly from 0. Normality of residuals was plotted on Q-Q plots to determine

the level of deviation from the normality line. Independence was a theoretical

assumption based on whether the measures and readings are independent of each

other.

All assumptions will be calculated and stated in the regression; if there is some

deviation from the assumption, sensitivity analysis will be used to check if they are

influential in the analysis. The sensitivity analysis will be used to check if there is

a different result from the regression when the assumptions are actually met. If

there is no significant change the original regression will be used.

2.6.1.3. Sample Size

The sample included everyone who was entered into the audit between 2011-2012.

By using the other studies of similar type either cardiac surgery or secondary care

with volume, we can see a large variation between ~10,000 to ~1,000,000

participants. It is predicted that using their methods a minimum sample size of

~50,000 would be sufficient to calculate a statistically significant association of a

VOR within this population. This is because of a range of articles included within

the literature review, who all found statistically significant results with <30,000

participants (Evans et al 2013, Hanford et al 2006, Lee et al 2007, Gammie et al

2007, Nguyen et al 2009, Miyata et al 2004).


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In 2010 there was an increase of participants of 5,357 from the 2009 throughput. If

the same increase were to be observed between 2010 and 2011, then the estimated

number of patients taking part in this studies section of data should be ~60,000 up

from 2010’s 56,589 (Lewin 2011).

The analysis planned for this study, is to use weighted MLR, according to the

work by Field et al (2013), the calculation for sample size in regression is:

R=k/(N-1)

R is the estimate of the regression, of which a smaller size is preferable, as it

reduces the power of random error. k is the number of predictors, which in this

case will be; assessment 1, volume, age, gender and proportion of MI. Thus for

this analysis there will be 5 predictors.

Considering the use of aggregated data, the number of cases represents the number

of centres the cases were grouped into. The number of cases will be >200;

however, even if there are only 200 the equation will be:

R=5/(200-1)

R= 0.025

This value of 0.025 is well within the value Field (2013), Miles and Shevlin (2001)

and Cohen et al (1998) state is acceptable to find a medium-large effect size with

>80% power. The advised sample size for regression varies considerably between

sources, either 10 cases per predictor or 15 cases. Overall with a minimum of 200

valid cases the statistical power generated within the regressions will suffice.


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Chapter 3 Results

3.1. Study Population

The study population’s demographics are summarised in Table 2. This shows a

total of 92,832 valid cases that completed at least one stage of the

rehabilitation/assessment. The population was 70% male (65683), with an average

age of 66 years old (S.D. 3.2). The study population included was anyone who

entered one of the centres in the NACR audit within the months of April 2011-

March 2012.

Table 2 This shows the demographic characteristics of the participating population within the study and the distribution of the population across the centres. This includes the gender proportion, age, volume and event/intervention type all measured at Assessment 1

The Baseline Characteristics of the Population

Baseline Characteristics (%) Participating population

Gender Male 65683 (70.0)

Gender Female 27374 (29.2)

Missing 775 (0.8)

Total 92832

Age 66

Centre Number 202 with valid cases

Centre Volume average 462

Range in Centre volume 1-2883

Average Age per centre range 52-93

Type of

event/Intervention

MI Only 24363 (26.0)

MI and PCI 25739 (27.4)

PCI 13496 (14.4)

CABG 11372 (12.1)

Other 11862 (20.1)

3.2. Cardiac Rehabilitation Centres

The centres potentially included in this research were any of the 276 centres in the

overall programme that entered their data digitally, and had patients conforming to


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the inclusion criteria. A total of 202 centres matched the criteria, shown in Table

1, with an average volume of 462 patients in each centre (ranging from 1-2883

patients). The centres actual volume of patients completing a post rehabilitation

assessment (assessment 2) was calculated and plotted to give a weighted volume

estimate. This percentage of assessment 2, per centre completions, was plotted

against centre volume, and can be seen in Figure 3.

This figure shows that as volume increases, percentage decreases. This means that

although the NACR is thought to be reporting high-volume centres as standard; in

truth there is huge level of under reporting and the high volume centres are being

over weighted. Additionally the graph shows that the average assessment 2

completions were only 28% of participants across the rehabilitation programmes.

The estimated number of people who should be completing CR programmes in

2011 is ~60,000 (Lewin 2011). Even if there was no increase from the previous

year there should be 56,589 participants completing the programmes. This dataset

shows that only 25,993 patients had an assessment 2, thus less than 50% of

participants had immediate post rehabilitation measures recorded.

Table 3 Showing the baseline and post rehabilitation readings for the included outcome measures of the patients participating in CR

The Baseline and Post Rehabilitation Measures

Baseline outcome

measure

Average Baseline Reading of

outcome Measures (Valid)

Average Post assessment

Reading of outcome

Measures (Valid)

BMI 28.11 (32886) 27.96 (13963)

BP Diastolic 73.51 (32930) 73.86 (12330)

BP Systolic 128.77 (34204) 128.81 (12523)

HADs Anxiety 6.41 (25701) 5.67 (15799)

HADs Depression 5.03 (2566) 4.21 (15419)

Cholesterol 4.69 (20229) 4.01 (7405)

Smoking 16.9% smoked

(total population 38568)

6.7%

(total population 18266)

Physical exercise 8.9% (passed the task of a total

30020)

30.8% (passed the task of a

total 16748)


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Figure 3 showing the relationship between CR Centre Volume and Assessment 2 completion percentage per centre. As the volume increases the completion

rate decreases. The average completion rate per centre is 28%

3.3. Multiple Linear Regression

As stated in the methods section, when performing the MLR the assumptions were

all tested and met, these are shown in the Appendix 5. These include the scatter

plots for the Linearity and Uniform Variance, and the Q-Q plot for normality. For

the assumption of independence, all measures included in the regression are

independent of each other and thus all meet this assumption.

3.3.1. BMI

The first analysis was the effect of volume on the BMI measure at assessment 2,

post rehabilitation. Table 3 shows the average reading for BMI at baseline was

28.11, which is positioned in the overweight category; after rehabilitation the

average was 27.96. This shows that on average there is small decrease of 0.17

BMI; however, this does not change the category, the population is still in the


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overweight group. The range in centre average data was 23.50-38.00. This varies

between normal weight and obese class II.

Figure 4 shows the centre volume plotted against the change between BMI

assessment 1 and 2. At the lower centre volumes there is higher variation, this is

because the measure is mean change and the variation increases within the centres

with less patients. Although, what can be seen across the entire volume scale, is

that there is tight grouping around 0 change in BMI, which means there is on

average, very little change. This small level of change is supported as the pre and

post average difference is only 0.17. Also there is no association in higher or lower

change with volume.

The regression analysis in Table 4 shows that volume has no effect on BMI

assessment 2 nor is it statistically significant (β=0.000, p =0.194). However, what

Figure 4 showing the centre volume plotted against the change between BMI assessment 1 and 2, there is higher variation in smaller centres as the measure is mean; there is no

association in higher or lower change with differing volume levels per centre.


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is shown, which is predictable, is that there is a significant relationship between

BMI reading 1 and the reading at point 2 (β=0.811, p < 0.001). This is expected, as

you would assume a strong relationship between pre and post measures.

Within this regression, when testing the assumptions, especially in the normality

test, there were a few outlying variables. These can be seen in Appendix 5. A

sensitivity analysis was performed through eliminating the outlying points and re-

running the regression. As shown in Appendix 6, this had very little effect on the

regression and the results remained very similar with no significant association

between volume and BMI.

Table 4 Showing the MLR analysis of the BMI reading 2 outcome, the results show that volume had no effect and the significance was >0.05 (β=0.000, p 0.194)

Regression Analysis for BMI

Unstandardised Coefficients

95% Confidence intervals

B (Mean

Difference)

Std. Error

Significance Lower Bound

Upper Bound

BMI Reading 1 .811 4.052 <.001 .572 1.049

Volume number

.000 .121 .194 .000 .001

Gender (% female)

-.022 .000 .284 -.061 .018

Average Age -.126 .020 .816 -1.192 .940

Proportion MI only

.009 .540 .170 -.004 .023


The second analysis was of the two measures of blood pressure, both diastolic and

systolic at reading 2. Firstly shown in Table 2 the diastolic measure had at

baseline an average of 73.51, this actually fits within the normal blood pressure

category. The systolic average was 128.77, this is still a normal value; however, is

slightly high. Thus on average the population’s blood pressure is normal. The

average post rehabilitation measures for diastolic and systolic were; 73.59 and

128.17. However, the ranges for these two measures between the centres fluctuate

considerably. Diastolic has a range of 52-94 and systolic 102-185. The change was

a 0.08 increase for diastolic and a drop of 0.6 in systolic. This shows how little on

average, the blood pressures change between pre and post.


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The regression analysis in Table 5 for the diastolic shows no effect or statistical

significance of volume on the blood pressure reading (β=0.001, p =0.849).

However, similar to the BMI there is a strong association between reading 1 and 2

(β=0.615, p <0.001).

The assumptions for the diastolic blood pressure analysis were met and conformed

to the rules, and thus there was no need to perform further sensitivity.

Table 5 Showing the MLR analysis of the diastolic blood pressure reading 2 outcome, the results show that volume had no effect and the significance was above 0.05 (β=0.001, p 0.849)

Regression Analysis for Diastolic BP

Unstandardised Coefficients


B (Mean

Difference)

Std. Error

Significance Lower Bound

Upper Bound

BP Diastolic Reading 1

.615 .082 <.001 .453 .777

Volume number

.000 .001 .849 -.002 .002

Gender (% female)

.092 .070 .189 -.046 .230

Average Age 2.183 1.860 .243 -1.494 5.860

Proportion MI only

-.029 .023 .208 -.075 .016

The regression analysis in Table 6, for the systolic pressure, shows that there was

no effect with β=0 and no significant association between volume and blood

pressure at reading 2 (β=0.000, p =0.943). An important result within this analysis

is that there appears to be no significant association between the pre and post

measures of systolic pressure (β=0.112, p =0.110).

The analysis for the systolic blood pressure met the majority of the assumptions;

however, on the Q-Q plot, there was one deviation from normality, centre code

FAZ. Sensitivity analysis was performed and there was no significant change to

the regression, this can be seen in Appendix 6.


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Table 6 Showing the MLR analysis of the Systolic Blood pressure reading 2 outcome, the results show that volume had no effect and the significance was >0.05 (β=0.000, p 0.943)

Regression Analysis for Systolic BP

Unstandardised

Coefficients

95% Confidence

intervals

B

(Mean

Difference)

Std.

Error

Significance Lower

Bound

Upper

Bound

BP Systolic

Reading 1

.112 .069 .110 -.026 .249

Volume number .000 .002 .943 -.005 .004

Gender (%

female)

.518 .285 .074 -.051 1.088

Average Age -5.272 6.162 .396 -17.612 7.067

Proportion MI

only

-.107 .065 .106 -.238 .023

3.3.3. Cholesterol Table 2 shows that the average cholesterol score was 4.9 at baseline and 4.1 post

rehabilitation. This is a change of 0.8mmol/L which is a beneficial decrease in

total cholesterol. Both readings are within the lowest quarter of the range, and

count as normal. The range of centre averages in total cholesterol of the baseline

population is 2.2-5.9.

The regression analysis in Table 7 for the cholesterol measure revealed no

significant association between volume (β<0.001, p =0.114), any covariates and

the reading at point 1 (β<0.030, p =0.806).

The assumptions of this regression were all met and adhered to thus no sensitivity

analysis was performed.


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Table 7 Showing the MLR analysis of the Cholesterol reading 2 outcome, the results show that volume had no effect and the significance was >0.05 (β=0.000, p 0.114)

Regression Analysis for Cholesterol

Unstandardised

Coefficients

95% Confidence

intervals

B

(Mean

Difference)

Std.

Error

Significance Lower

Bound

Upper

Bound

Cholesterol

Reading 1

.030 .123 .806 -.214 .274

Volume

number

.000 .000 .114 .000 <0.001

Gender (%

female)

.004 .011 .740 -.018 .026

Average Age -.164 .288 .570 -.734 .406

Proportion MI

only

-.005 .004 .160 -.012 .002

3.3.4. HADs Anxiety

The average HADs score for anxiety at baseline was 6.4, the post rehabilitation

score was 5.67. The change between pre and post did not change the category as

both readings are with the definition of normal HADs score, although there was an

overall decrease of 0.73. The classification for normal level is between 0-8. The

range of HADs anxiety per centre average scores across the population is 2-13.

Table 8 shows the regression analysis of the post rehabilitation measure of anxiety

with volume as the predictor variable. Within this regression there is no statistical

significance for volume predicting outcome (β=0.001, p =0.710), however there is

significance for both the pre rehabilitation reading (β=0.285, p <0.001) and

proportion of MI patients at the centre for predicting outcome (β=0.018, p <0.001).

The assumptions for this regression were for the generally met; however, as seen

with BMI and systolic blood pressure there was a level of deviation within the Q-


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Q plot as seen in Appendix 5. Sensitivity analysis was performed on the

regression shown in Appendix 6, which had no effect on the volume significance,

however, this did also make the proportion of age at the centres become significant

(β=-0.107, p =0.05).

Table 8 Showing the MLR analysis of the HADs Anxiety reading 2 outcome, the results show that volume had no effect and the significance was >0.05 (β=0.000, p 0.710)

Regression Analysis of HADs Anxiety

Unstandardised

Coefficients

95% Confidence

intervals

B

(Mean

Difference)

Std.

Error

Significance Lower

Bound

Upper

Bound

HADs Anxiety

score

Reading 1

.285 .056 <.001 .175 .395

Volume number .000 .000 .710 .000 .000

Gender (%

female)

-.018 .014 .200 -.047 .010

Average Age -.667 .388 .088 -1.433 .100

Proportion MI

only

.018 .005 <.001 .008 .028

3.3.5. HADs Depression

As shown in Table 2 the average HADs score for depression at baseline was 5, the

post rehabilitation was 4.2 which indicates a 0.8 decrease in HADs score. The

range of centre averages for depression in the population was actually smaller than

the anxiety with a variation of 8 points, between 2-10.

Table 9 shows the regression analysis for the HADs depression score post rehab

with regards to the volume per centre. The volume seems to have no significant

effect on the outcome of the HADs score (β<0.001, p =0.234). The pre


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rehabilitation score does, similarly to BMI and Diastolic, predict the measure at

point 2 (β<0.200, p <0.001).

The assumptions for this regression were met; there was minor deviation from the

normal distribution Q-Q plot, however it was relatively evenly distributed and no

sensitivity was performed.

Table 9 Showing the MLR analysis of the HADs Depression reading 2 outcome, the

results show that volume had no effect and the significance was >0.05 (β=0.000, p 0.234)

Regression Analysis of HADs Depression

Unstandardised

Coefficients

95%

Confidence

intervals

B

(Mean

Difference)

Std.

Error

Significance Lower

Bound

Upper

Bound

HADs Depression

Reading 1

.200 .042 <.001 .117 .284

Volume number .000 .000 .234 .000 .001

Gender (%

female)

-.028 .013 .031 -.053 -.003

Average Age .013 .347 .971 -.674 .699

Proportion MI

only

.013 .004 .004 .004 .021

3.3.6. Smoking Cessation

The measure for SC was the level of population that continued smoking at post

rehabilitation reading 2. The average percentage of patients still smoking was

16.9%, the range between the centres was 100, as some centres had no smokers

and others had all smokers at baseline. Because of the nature of the outcome

measure, dichotomous yes/no, and the huge range in centre volume, the range in

average smoking is unreliable as there is large fluctuation between full smoking

and complete lack of smoking at baseline.


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Table 10 shows the regression analysis for the level of smokers at reading 2 with

the predictor as the volume per centre. The results show that there is no statistical

significant association between volume and smoking at reading 2 (β<0.000, p

=0.260), nor is there any association between any covariates. There is a strong

association between pre and post smoking percentage, as would be expected

because the second reading should be lower due to the intervention (β=0.034, p

<0.001).

The assumptions for this regression were all met as detailed in Appendix 5. There

was no requirement to perform sensitivity analysis on this regression.

Table 10 Showing the MLR analysis of the SC reading 2 outcome, the results show that

volume had no effect and the significance was above 0.05 (β=0.000, p 0.260)

Regression Analysis of Smoking Cessation

Unstandardised

Coefficients

95%

Confidence

intervals

B

(Mean

Difference)

Std.

Error

Significance Lower

Bound

Upper

Bound

Percentage

Smoking Reading

1

.034 .009 <.001 .017 .052

Volume number .000 .000 .260 .000 .001

Gender (%

female)

-.015 .018 .420 -.051 .021

Average Age .092 .035 .009 .023 .161

Proportion MI

only

.005 .005 .373 -.006 .015


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3.3.7. Physical Activity

As seen in the smoking cessation, the measure of effectiveness was the percentage

of participants either completing or not completing a task (dichotomous). The

percentage of patients who passed the physical task at baseline was 8.9%. The

range between centres was 100, as identified in smoking this is because some

centres had no completion and some had 100%. This is similar to the smoking

analysis that, due to the dichotomous nature of the measure the range can have a

large variation.

The regression analysis for the PA at reading 2 is shown in Table 11. The results

show there is no effect or significant association between volume (β=0.000, P

=0.090), gender and proportion of MI patients. There is an association between the

percentage who completed at baseline (β=0.13, p <0.001) and the average age of

patients per centre (β=-0.135, p <0.001). The assumptions for this regression were

all met and there was no requirement to undertake any sensitivity analysis.

Table 11 Showing the MLR analysis of the PA reading 2 outcome, the results show that

volume had no effect and the significance was >0.05 (β=0.000, p 0.090)

Regression Analysis of Physical Activity

Unstandardised

Coefficients

95% Confidence

intervals

B

(Mean

Difference)

Std.

Error

Significance Lower

Bound

Upper

Bound

Percentage

Exercising

Reading 1

.013 .004 <.001 .006 .020

Volume number .000 .000 .090 -.001 <0.001

Gender (%

female)

.022 .014 .114 -.005 .050

Average Age -.135 .032 <.001 -.198 -.071

Proportion MI

only

.006 .005 .242 -.004 .015


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Chapter 4 Discussion

4.1. Participating Population

When analysing this data, the initial issue that was identified was the considerable

attrition rate between people that are entered into the NACR database and those

that completed the programme. The details of 93,832 patients were recorded into

the database from the period of 1st April 2011 to 31 March 2012. However, when

the dataset was investigated, no outcome measure had above 40,000 completed

values at baseline. In some measures such as cholesterol and the HADs scores the

population only had ~25,000 cases. The post rehabilitation, assessment 2 was even

less, with only 7,405 valid cases being recorded for cholesterol. This is

considerably lower than the reported 56,589 patients from the year before within

the NACR audit (Lewin 2011). Although still of sufficient size to generate

statistically significant results, it does raise significant concerns about the quality

and delivery of the CR programmes. This concern are valid as this study proves

that some centres are operating outside of the agreed minimum standards of

BACPR (standard 4) (Jones et al 2013)

The population statistics show the demographics participating in CR, or at least

those who completed the pre and post rehabilitation measures. This shows that

there is still a distortion between the numbers of males in the study. The

participating population was majority male (70%) the averages age in the study

was 66 years old. The work by Evans et al, who looked at how CR has changed

over the years 1993-2006 found that the population partaking changed over the

years with an increase of 17% to 23% women, which remains the same in this

study, and that the average age was 64 suggesting that there is an increase in

average age by 2 years (2011). Overall the comparison between the two studies

shows that the characteristics of the population in the two studies have not

changed.

This section clearly identifies a problem with analysing the audit data; out of the

93,832 patients who were entered into the database, 775 patients did not have their

gender noted and 155 had no age reported. Due to the fact that the data is inputted

by clinicians and admin staff, and collected under the 251 exemption there is no

way to check or fill in missing data points. This is why the majority of audits such


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as the NACR tend to use aggregated data, which allows maximisation of available

data, while reducing the impact of outliers and miscellaneous data points. The

problem with just excluding centres because of missing or outlying data is that

there is potential to exclude valuable data or extreme but still real data.

As stated in the methods section the data was aggregated by the Centre Code. The

total number of centres with valid data was 202, where the total volume of patients

varied between 1-2883. Unlike some of the included studies in the literature

review, the centres were not spilt into groups and thus allowed for a linear

association to be assessed (Evans 2013, Graham 2013, Landon 2010).

A variable was created for use in the analysis as a weighting tool. This allowed the

centres to be weighted based on actual numbers of patients who completed

assessment 2. The actual number of patients completing assessment 2 (Actual

Number) was plotted against total volume on the scatter plot labelled Figure 2.

This graph demonstrated that, only 28% of people who were enrolled into CR in

the given period had an assessment 2 (assessment immediately post rehabilitation),

and that as total volume increases the actual completion rate % decreases.

Although the distribution shown on the graph may have other causes, such as

smaller centres only entering data of completed patients and larger centres having

larger referral without completion, it does signify the challenges with using audit

data for analysis. This raises problems for this study, but also raises quality

assurance issues for the audit on going. This is because a considerable number of

patients and thus centres are not meeting the minimum standards of the BACPR

(Standard 4) of assessment and reassessment (Jones et al 2013). Highlighting such

issues, is part of the benchmarking nature of this study. As important as the result

of VOR within CR is, this study is also providing a methodological framework for

further research to be conducted in the national audit.

It is expected, that a large amount of people are enrolled into the programmes due

to the fact they are run in house at hospitals. Thus are enrolled before even

attending, this and other reasons account for the high attrition rate. For these

reasons we know that many of the population that make up the 93,832 patients are

unlikely to complete the programme. However, by using the 2010-2011 data,


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which had 56,589 completed cases, and use that as an actual number of theoretical

completions; we can see that in our data set <50% of those cases had an

assessment 2. This is a huge problem as it means that half of the patients

undergoing the programme are not followed up and the audit/evaluation section of

the programme does not represent the full picture.

4.2. Regression

The analysis of this study took the form of MLR, which would allow the analysis

of the impact of volume on the chosen outcomes, but also allow the inclusion of

covariates such as age. This is the same as the work by Graham, Hranjec and Lee,

who all used MLR to assess the relationship between Volume and their respective

outcomes (Graham et al 2013, Hranjec et al 2012 and Lee et al 2007).

4.2.1. BMI

The scatter plot shown in Figure 4, provides a visual representation of volume

plotted against the outcome measures. The majority of centres are located between

1-1000 on total volume x-axis. Because of this higher proportion in the BMI

change there is much higher variation. This is because this measure was the mean

difference between assessment 1 and 2, and this value is highly affected by

changes in the number of cases. The overall trend was a close proximity to 0 with

increasing volume and there was no association present between higher volume

and improved or decreased outcome measure. This scatter plot is similar for most

of the outcome measures, and has no analytical use; just a visual representation of

the data. (Evans et al 2013, Graham et al 2013, Li et al 2012, Hranjec et al 2012).

In the work by Graham et al it shows the complex nature of the association; in

Joint Replacement, there is flat line between volume and decline in FIM; however,

there is a larger association in Fracture volume and this is shown in the plot by a

higher gradient (2013). It is also a useful tool in the work by Hranjec, their study

shows how the volume impacts the odds ratio of mortality; their statistical analysis

shows an inverse relationship i.e. as volume increases the mortality decreases;

however at the tail end there is deviation as the mortality appears to increase with

extremely high volume, supporting a two-tailed relationship such as Fairey et al’s

work (2009).


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The first regression performed was the BMI at reading 2 shown in Table 3. The

result was that volume was not significantly associated with the BMI score. The

effect size was β<0.000, this means that the effect of increasing volume by 1 leads

to no effect in BMI assessment 2 score (Field 2013)

Out of all the outcome measures reported by the NACR audit, BMI is the one that

changes the least (Doherty 2013, Lewin 2011). Due to the fact that in the short

time frame of 8-12 weeks, BMI is unlikely to change a great deal. The assessment

2 was chosen as the outcome measure because it was the most complete dataset

post rehabilitation, which would maintain a high statistical power, and also reduce

attrition bias. However, for this specific outcome, perhaps a longer follow up

might show a larger effect, which may be associated with volume.

Within this regression it was identified that the pre and post score was

significantly associated with a p value of <0.001. This is to be expected, as the

average BMI score per centre should have an association between pre and post

rehabilitation.

With this result the null hypothesis must be accepted, in that there is no association

between volume and BMI, and thus no evidence for or against the recent initiatives

of the regionalisation of CR (NICE 2103). This is the same result as the work

conducted by Graham et al, in which no VOR was found (2013). This counters

much of the secondary care literature included in the review. In all but one study

reviewed there was a strong association seen in at least one outcome measure,

whether is was positive or negative (Evans et al 2013, Lee et al 2007, Li et al

2010, Li et al 2012, Hranjec et al 2013).

The association between volume and outcome is even stronger in the surgery

settings, with all included studies showing not only a strong association but the

same direction (Allareddy et al 2007, Urbach et al 2004, Seperhripour 2012,

Gammie 2007, Landon 2010, Zevin et al 2014, Miyata et al 2009, Nguyen et al

2004, Birkmeyer et al 1999). The BMI was the least expected outcome measure to

show an effect as the average change is so little (Doherty 2013); however, the

complete lack of any effect (β<0.000) suggests that there may be no association in

any outcome measure.


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The second and third regressions looked at the effect of volume on the two

recorded measures of blood pressure including both diastolic and systolic; The

regressions are shown in Table 4 and 5. In both regressions the effect size was β

<0.000, with very close confidence intervals (95% CI 0.002/-0.002 and 0.004/-

0.005). There was no significant association between the volume and the outcomes

measured at assessment 2. With this result there is no evidence that volume is

associated with the change in either blood pressures readings within CR. The

blood pressure outcomes, as with the BMI outcome, dictates that the null

hypothesis must be accepted, and that the volume has no association to outcome.

This again is only supported by one piece of literature (Graham et al 2013).

Although this is not a review, as in the work by Hanford and Neogi, which looked

at secondary care and volume; the CASP ranking for Graham et al’s work was

moderate to high which means it is reasonable to use this as supporting literature

to this study (2006, 2012). The lack of similarity in result to the rest of the

literature is not significant; this is due to a different population and a different

intervention. This importance of comparing it to other literature is to make sure

that methodological approaches which were adopted (such as calculation of

volume, MLR analysis and reporting mechanisms) and adhered to (Hranjec et al

2013, Li et al 2012. Seperhripour et al 2012).

In the Diastolic measure it was again noticed, as with BMI, that the pre and post

measurements were significantly associated. This was expected as the blood

pressures can only vary within limited parameters and if the CR is consistent

within a centre then the average should show a similar pattern in pre and post.

However, this significant association is not found in systolic blood pressure with a

p value of 0.110. This may be due to many factors: differing drugs influencing

readings, differing patient types such as MI or CABG or other baseline

characteristics not accounted for. Diastolic blood pressure (i.e the pressure in the

system when the heart muscle is filling) is less likely to vary compared to systolic

blood pressure (i.e pressure when the heart muscle is contracting) which is the

target for many of the cardiac medications (Jones et al 2012, Smulyan et al 1997).

Perhaps medication dose levels and type should be considered in future research,

especially when looking at the change in systolic blood pressure and volume.


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When performing the assumptions on the Systolic pressure, there was an outlying

point on the Q-Q plot, reordered for the FAZ centre. This centre had no real

outstanding reason to be an outlier although it did have an average increase of

Systolic pressure from 140 to 149. This is also observed in a few centres, which

implies that over the 8-12 weeks the blood pressure increased. This is centre

(FAZ) is an example of how audit data is often difficult to use. Although this

change is possible, it is improbable and normally would be checked against

original records. However with this type of data it is impossible to check

previously collected data.

However, even after sensitivity analysis the result did not change for either pre or

post significance or the main result of volume. Because of this the null hypothesis

must be accepted, that volume has no association with either of the two blood

pressure measures.

4.2.3. Cholesterol The regression performed for the Cholesterol is shown in Table 6, which showed

that there was no effect of volume on assessment 2 (β<0.000, 95% CI 0.000/-

0.001), this is very tight CI which means the result is very precise (Field 2013).

There was no association between volume and the total cholesterol score.

Interestingly as with the Systolic blood pressure readings, again the pre and post

scores were not associated with a p value of 0.806. This is most likely due to the

large range of drugs such as Statins, which reduce cholesterol (Jones et al 2012).

Possible reasons for fluctuations in cholesterol from baseline include drugs,

changes in diet and exercise. With these results the null hypothesis has to be

accepted, in that there is no association between volume and the cholesterol. This

result means that three of the six outcome measures have absolutely no association

in the reading at assessment 2 and the volume per centre. This counters the

majority of the included studies in the literature review (Hranjec et al 2013, Li et al

2012. Seperhripour et al 2012).

The methodology utilised was well performed, with all assumptions met, and if

they were not the sensitivity analysis was performed Additionally if there was a

medium-high effect the sample size would allow detection. The complete lack of

any association identified within all the analysis (all β =~0.000) means that the


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null hypothesis cannot without any doubt be rejected, and the VOR based on this

evidence does not exist in CR (Miles et al 2001, Field 2013).

4.2.4. HADs The measure for mental health, within the CR programmes, is the HADs score,

which is performed for both anxiety and depression. The regressions analysing the

two HADs scores are shown in Table 7-8. The results showed that there was no

effect of volume on HADs score β<0, Also no significant association between the

volume per centre and the HADs score at assessment 2. This means that the null

hypothesis must be accepted and there is no link between volume and outcome in

mental health. This again counters the evidence in the included articles within the

literature review. For example the review conducted by Zevin, had the highest of

the CASP scores for the review, it contained ~458,000 patients (Zevin et al 2014).

This review concluded that, of the 24 studies included, 17 reported hospital

volumes-outcomes and 14 showed a positive association between volumes. This is

important as the majority of studies showed an association but two included

studies did not. This demonstrates how studies vary on this subject, and often each

population or intervention is not comparable with another. Although this study

does not conform to others in the same field of VOR, the result is significant to

this settings, as populations, interventions and outcomes were different.

A significant observation within the analysis of HADs score was that in both

anxiety and depression, the pre and post measures were significantly associated.

Interestingly, for the Anxiety score, there was also an association between the

assessment 2 score and the proportion of MI patients per centre. This association is

expected as it is supported by many other studies, in that the comorbidity affects

the outcome of rehabilitation. After the sensitivity analysis which removed a

centre similar to FAZ in the Systolic, another association was significant which

was Age and the Anxiety score. This is also supported in the literature in that age

affects the outcome of rehabilitation (Evans 2013, Graham 2013).

In the depression regression, another association was significant which was the

link between gender proportion per centre and the HADs score. This is expected,

as, often in the literature, gender is defined as a covariate that affects outcome of


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the rehabilitation often males are more susceptible to the intervention than women

(Hanford 2006, Graham 2013).

In CR gender plays an important role, as although any patient with one of the

included initiating events can partake, there is a distortion in the genders taking

part. Many trials and reviews have commented on this such as Heran et al and

Davies et al saying how ~70% are male and females are underrepresented in trials.

However, what this study showed was that numbers of participating females are

lower in the ‘true’ participating population (Heran et al 2010, Davies et al 2011)

4.2.5. Smoking Cessation

The smoking cessation regression is shown in Table 9, which shows that there is

no significant association between the total volume per centre and the level of

people still smoking post rehabilitation at assessment 2. This result is the same as

all other outcome measures reported so far, in that there is no effect or significant

association between volume and outcome. This counters much of the literature in

secondary care and cardiac surgery on the VOR. The reason why smoking is

expected to have the most significant association is because the data of smoking in

assessment 1 and 2 is the fullest in SC and PA. Additionally, smoking shows

largest change on average pre and post rehabilitation, thus if there was an

association in any outcome measure it would be shown in SC.

As expected there is a strong association between the level of smoking at

assessment 1 and 2, p value <0.001. This association is expected as there is a

obvious link between people who smoked at assessment 1 and are likely to

continue to be smoking through to assessment 2.

4.2.6. Physical Activity

The regression for the PA at assessment 2 and the volume is show in Table 10,

which shows that there was no association between volume and PA. There was an

association between assessment 1 and the average age per centre.

The lack of significance in the volume is the same as all other outcomes within

this study, which means that, for the effect of volume on outcome of CR the null

hypothesis, must be accepted. This counters most of research within secondary


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care (Evans 2013, Hanford 2006, Hranjec 2012, Lee 2007, Li 2010, Li 2012).

Although, it does conform to the conclusions made by Graham et al, which

showed no significant relationship of volume affecting outcome of fractures

(2013).

The regression did show an association between pre and post PA assessment,

which is expected. This is because the ability to complete a task will be similar

across the 8-12 week period, regardless of the intervention imposed. The

association between average age per centre is also expected; this is because the

higher the average age of the population located at the centre, the less chance of

completing the exercise task, which is why the regression showed a significant

association of <0.05.

4.3. Main Findings The purpose of this study was to investigate whether centre volume in CR has an

effect on the outcomes commonly reported by the NACR report. The aim of the

analysis was to test the hypothesis as to whether there is a linear relationship

between volume and the post rehabilitation measures annually reported by the

NACR, taking into account baseline characteristics.

The main finding from this study is that there was, in all outcomes measured, no

effect on outcome that was attributable to volume (β<0) and all p values were not

statistically significant (p value >0.05). Because of this result, there is no evidence

to support the rejection of the null hypothesis and based on the current evidence

there is no support for the VOR to be present within CR (Field 2013). The only

included study in the literature review, which supports this result, is the work by

Graham et al who looked at the effect of volume on outcomes in 3 fracture groups.

This study found that for the outcome measures of functional decline and home

discharge there was a small-no effect of volume (2013). They concluded in their

work that their chosen intervention could be run at local levels, as larger centres do

not impact on patient outcome. However, this is the opposite to what this current

study suggests, which is that large volume does not benefit nor hinder the

outcome, and thus regionalisation is supported. Research on economics of

developing large centres may show a way of saving money, which means that the

regionalisation proposed by the NHS is supported (NICE 2013).


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All other included studies showed strong or at a least significant VOR in their

respective interventions. The lack of consensus between this study in CR, and

others in settings such as nursing homes, psychiatric care and surgery does not

detract from this result (Lee et al 2007, Li et al 2012, Zevin et al 2014, Miyata et al

2009). Not all centre volume affects outcome, and each population/intervention is

different.

This study also generated other conclusions, which were that there needs to be

considerable improvements to both the inputting of data into the audit by

clinicians, and the rigour to which patient data is recorded

4.4. Limitations

Although the literature review performed highlighted some key analysis; it was

beyond the confines of the MSc thesis, due to time constraints, limited access to

data and knowledge of key concepts to undertake them. It is important to note that

this study was the first of its kind and that although it may have some weaknesses,

it has opened the doorway for further research to be performed. This is a

framework for other studies to build upon that can be adopted by a wider team

with more time, funding and access to data.

4.4.1. Nature of the Study Type

Although the study aimed to specifically look at the impact of centre volume on

outcomes, a range of possible extensions for further research were identified.

Many studies identified both in the review and wider literature looking at volume,

also included staffing volume or surgical volume. This added depth to their study

as the facility level data was included in the volume analysis. The staffing profiles

for each centre included in the NCAR audit are available; however, it would have

been beyond the expectations and the confines of the MSc thesis to gain access

and combine with the audit data. Because the research had to be conducted within

a timeframe of 9 months and had no funding, the research and depth of analysis

were conducted to fit within the availability of data and time. If this study were to

be continued, such as by the NACR research team, the inclusion of this data may

affect the current result of volume having no impact, or could identify a different

cause for the differences in outcomes between centres. The inclusion of this data


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would increase the precision of the results, by allowing some of the variation

between centres to be accounted for by the variation of the staffing profiles in the

MDT (Multi Disciplinary Team) data.

4.4.2. Design

The study was a retrospective cross sectional study that used aggregated data, per

centre to look at the VOR in CR. As seen in other studies, such as Graham et al,

there is a potential to use a hierarchical approach. This design would have allowed

a more detailed approach that would have included much of the facility level data.

This was a limitation identified commonly within most the included studies in the

literature review such as the work by Markar who suggested a hierarchical

approach to take into account patient preferences into the main analysis (2012).

This is the same in this study, where apart from average baseline characteristics of

patients, the facilities were assumed to be the same apart from the one predictor,

which is volume. This is known not to be true as there is a vast amount of

literature identifying how facility level differences can impact outcome (Allareddy

2007, Lee 2012, Miyata 2009, Nguyen 2004). This study was limited by having no

facility data, although it was not available applicable to include within the confines

of the MSc thesis. There was a possibility to include details such as the MDT

staffing or intervention type; however, this would not have been possible within

the timeframe.

A hierarchical design would be used to assign a hierarchy to certain variables such

as the different centres. This would take into account details such as staffing

number, type of patient entering the centre and potential maximum volume. This

would allow an in-depth analysis, which would include the patient level data of all

patients individually. This was used by Graham et al and investigated the different

baseline injuries, such as stroke (2013). This was appropriate for their study as the

multilevel variation, i.e. centre and injury could be calculated.

4.4.3. Data

The most influential limitation was the source data used for the study. This is

primarily due to the quality of data recorded within the NACR audit. At first

inspection the expected number of participants was ~100,000 cases. This is about

70% of the total participants of CR in the UK. It was estimated that around 50% of


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these patients would drop out; however, after looking at patients who had

completed assessment 2 this figure dropped to 22,385. This is the number of

people who were registered to have an assessment 2, which is post rehabilitation.

Based on this figure, and the 50% attrition rate of participants not completing

rehabilitation, of the 50,000 successful participants only 44% are having their post

rehabilitation outcomes recorded.

This is a very large number, which means that clinicians and admin staff are not

reporting over half of patients going through CR. This is the first study using the

NACR in this way, and this discovery, of the lack of recoding data is detrimental

to the programmes running, evaluation and development. If patients post

rehabilitation are not having a second assessment then there is no way of assessing

how well a programme has worked. The impact of missing data at assessment 2

should be assessed and reviewed within the running of the CR programmes.

The 6 month follow rate is even lower. This is detrimental to both this individual

research with an increase in potential attrition bias, but also signifies considerable

problems within the CR programmes.

Using the NACR data is the only realistic way of performing this large-scale study

of the effect of volume on outcome. However, if this type of study is to be

replicated or continued with different emphasis or direction, considerable

improvements are required. This includes the way in which data is imputed along

with the level of follow up on patients that actually complete the programme.

4.4.4. Outcome measure The outcome measures chosen for this study were defined due to the annually

reported measures in the NACR audit. This meant that it was possible to use the

NACR data dictionary to eliminate outliers and these variables are the most

complete within the audit.

The first problem is that with a few of the outcome measures i.e. systolic blood

pressure and cholesterol, the pre and post rehabilitation readings are not

associated. This is because the effect of drugs, specialised intervention and

personal changes create a very complex relationship. For example if a patient is on


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a high cholesterol diet, the effect of a change in diet, implementation of statins and

exercise, is going to be greater than with a patient who just gives up smoking. A

possible solution to this issue with complex outcome measures is to take into

account things like drugs, more specific inclusion of commodity and risk and

changes in diet per patient as covariates. This would require patient level data,

which would be a hierarchical approach, which has its own merits.

The final problem raised by using these outcome measures is that some of the

measures such as BMI may need a longer follow up. The reason for not including

assessment 3, which is 6 months post rehabilitation within this study, was because

of the high drop out rate. This increased the risk of attrition bias, in that the people

who did come back were of a certain type, and the sample size would not be

sufficient for aggregated data. However, if this study were to be improved, perhaps

the BMI measure may show more of a variation and thus association with volume

if this 3rd assessment were to be included.

4.4.5. Comparison of Conclusion with other Studies in CR

This study was one of the first of its type to look at the real population of

participants within the CR programme. This type of study would allow highly

statistical powerful conclusions to be made in the real application of CR. This

differs from most other most research that has been previously conducted in CR,

which consisted of mostly small-medium sized trials with often many limitations.

Examples of these are the reviews conducted by Heran and Davies et al, who

found that although the included trials found statistically significant results in their

respective research, the population included was narrow and potentially biased

(2014, 2010). They note that the populations were predominantly male, middle age

and low risk.

However, the demographic within this study, of the population currently

undergoing CR, is 70% male, an average age of 66 and the proportion of MI or MI

and PCI was 50% of the population. This means that the assumption of bias within

these trials is incorrect, as the participating population in practice had the same

demographics. The issue with this conclusion is that with an attrition rate of

assessment 2 as high as 28% the demographic may become biased, losing types of

participants.


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Another limitation identified in Heran et al’s work was the high heterogeneity

between trials, due to the range of drug use. This was identified within this study,

in outcomes such as cholesterol and systolic blood pressure, where the differing

implementation of drugs between patients led to no association between pre and

post assessment which would be expected to be present in a normal simple

intervention.

The issue with reporting a selection of CR outcome measures in any study is that

the selection chosen is a narrow snapshot of the complex relationship. This

complex relationship was discussed in Davies et al’s work as their study aimed to

look at the adherence and uptake of programmes (2010). This measure is vital for

the running of effective programmes as the higher uptake and adherence means

more participants. However, their study showed that the alterations to increase

uptake, may be the reduced PA, which in turn may reduce changes in PA, BMI

and on-going risk of heart problems. The extremely complex nature of CR, means

that it is not appropriate to ‘cherry pick’ a small amount of outcomes, as others

may be associated or influenced by the intervention. This is applicable to this

study as well, as although the outcome measures selected are annually reported in

the audit, others such as mortality and QoL may actually have an association with

volume. The mortality has been shown in many studies to have a relationship with

volume including all surgical literature in the review (Allreddy 2007, Gammie

2010, Landon 2007). Also the QoL was included in the Davies et al study, which

may also have an association to volume, which was shown in VOR looking at

Nursing Care Homes (2010).

Finally, it is assumed that the CR has equal distribution of funding per participant,

with a rough cost of £427 per treatment episode. However, with such a range in

volume per centre, staffing profiles and treatment types, there may be an

association between volume and outcome once this cost per average patient per

centre is included in the analysis. It may be that in this study volume does not

affect outcome, when facility level data such as the cost, or staffing profile is

imported. Lee et al’s work showed that where centre level data was included the

relationship identified was different (2007).


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4.4.6. Comparison of Conclusion with other Studies in Secondary Care

There were seven included studies in the review about the Secondary Care. Five of

these studies showed a positive VOR, in that increased volume has an association

with improved outcome. The other two studies were Graham et al and Lee et al.

Graham et al’s work showed no significant association which was the same result

as this study, and Lee at al showed a negative association.

This section of the review had an extremely broad set of populations included,

from burn victims, psychiatric care and fracture patients. The methods used by

almost all the studies, including this study, were the same using total patients in

the centres as volume. Then in the analysis, the studies either performed linear

regression, or association in volume groups. It is the conclusion of this study, that

there was no VOR; however, this is not a contradiction of the other studies that do

show an association, as their care settings were different. It may be that the type of

injury or the type of care differs in such a way that volume has no effect. Another

reason why CR does not have a proven VOR is that the rehabilitation and care is

patient led. The care is tailored with the aim of reducing key risk factors that are

most prominent in the patient. Because of this style of rehabilitation, the ‘practice

makes perfect’ argument is not as applicable to CR as it is in the SNF

rehabilitation in Li et al (2012). That study theorised that the increased throughput

and experience gained from the high volume of patients, led to improved care and

thus outcomes. This theory was also shown in the work by Hlatky et al, in which

they discuss how volume increases the level of practice and maintains proficiency

of the staff (2013). However, because each patient’s care in CR is different, this

gained experience by the staff may not have an effect.

4.4.7. Comparison of Conclusion with other Studies in Cardiac Surgery

Of the ten included studies within this section of the literature review, three were

reviews and the rest were cohort studies. Unlike the secondary care section, all the

included studies found a statistically significant association between volume and

the study’s outcomes. Because most of these studies were looking at the impact of

volume on surgery, the main outcome was mortality. This outcome may be

possible to test in the setting of CR in future; however, due to explained confines it


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was not possible within this study to gain access to data to perform analysis of the

association.

It may be that mortality is associated to volume in this setting but the association is

yet undiscovered. The reason why the association could exist in CR, is because CR

is patient tailored, aimed at reducing key specific outcomes such as SC or BMI

according to the patient’s needs. These individual outcome measure changes of the

individual, when included into the larger populations data may be diluted but

everyone who takes part reduces or improves their own outcome measures.

Overall, all patients who have successful CR should have reduced mortality in the

longer term. By including the mortality in the analysis, there may be an apparent

association identified between high volume centres providing ‘more practised’

programmes and the reduced mortality.

The baseline characteristics within this study and the data when aggregated

support this theory. This is because as a whole the intervention does not massively

impact individual outcomes. However, if the annual report of CR is correct then

there is a considerable decrease in mortality (cardiac mortality 26-36%, total

mortality 13-26%) as a result of partaking in CR. An updated version of this study

could be completed, including the mortality rates after a few years, or performed

on a previous year’s data with the follow up time for mortality already having been

completed.

The other argument as to why in this study CR does not share the same result as all

the surgical research is that the intervention is less complex in the surgical setting.

This intervention does allow the surgical procedure to improve technique through

repetition and practice, and the organisational structure improves with volume.

However, because of the complex nature of the CR intervention, there is not one

goal, but many varying goals each dependant on many factors and the patients’

needs. Thus volume does not allow practice, as a good programme should make its

care tailored.

Each of these two theories can be tested either by further examination of the VOR

taking into account facility data, or including mortality in the analysis.


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4.5. Further study

An area for future research would be to use mortality rates in parallel to patient

clinical outcomes. The mortality rates data was not available within this project;

this is because, the mortality data required purchasing and this thesis did not have

any funding. Many VOR studies have shown that mortality rates are affected by

volume and this could be the same in CR. There is potential to connect the

mortality data and the NACR audit in such a way to connect the mortality rates per

centre. It may be that on average current outcome measures differ so little between

assessment 1 and 2 that there was no potential to see a relationship between

volume and outcome. However, due to the patient led CR programmes the

successful and effective programmes will improve mortality more than the

individual outcome measures which vary between patient to patient. By using the

mortality rates per centre, there may be an identifiable association.

This study, similar to the audit, has the potential to be replicated in years to come.

This is because it may be possible on the micro scale to see how individual

centres, which have had an increase or decrease in volume change in the outcomes

of the patients. This allows a more detailed look at the VOR in CR as all other

factors should remain the same at that particular centre. The reason why this may

be more effective than overall volume and outcome is because the centres differ in

demographics, staffing type, type of interventions provided, training and expertise.

By controlling factors, it may show a different relationship between volume and

outcome.

Additionally it could be replicated in the larger sense that, the whole programme

may alter in 3-4 years. This study using the 2011-2012 data may be the last year

before major austerity measures come in to action affecting the NHS. This may

count as a reference year for the austerity changes in the NHS, with decreasing

budgets, increasing throughput per centre and decreasing staff. The audit is

completed each year to review the extent of clinical outcomes of the CR

programme. This study, with some alterations, may be used as another review of

the CR system looking at how outcomes, and the VOR changes over time.


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If this study were to be done again, or improved upon there are four possible ways

that this could be done:

1. By including more facility level data, this could be more facility details

(MDT, large hospitals and small hospitals), more details of baseline

characteristics, or potentially combine multiple years’ data and look at

change in volume over time.

2. By using mortality as an outcome measure for attenders and non-attenders

of CR with propensity matching on CVD risk and severity

3. By taking into account specific complexity of outcomes measures such as

cholesterol and the drugs patients are taking

4. By using a data source that is less prone to errors and attrition, such as an

updated and improved NACR with more than 50% assessment 2

completion.

4.6. Main Conclusions

This study set out with one primary aim, which was to investigate whether there is

an association between the volume of patients receiving CR at a centre and the

outcome measure. This was achieved through an audit based retrospective study,

using national data, with multiple linear regression. The results, based on the

routinely collected clinical data, support the null hypothesis, which was there is no

VOR within CR as delivered in the UK. Although the literature review of other

cardiovascular and secondary care services showed, in the majority of studies, a

positive VOR, there is insufficient evidence, from this study, to support any

relationship within CR at this time.

This research identified many challenges for audit-based research, most of which

involved the quality of data and the extent of missing data. This thesis and analysis

as clarified the extent of the problem, while still providing a methodology and a

benchmark from which future research can be conducted.

By removing the limitations identified within this study, future research could

build upon the methodology and results demonstrated here leading to the results

being upheld, or a change in the VOR becoming apparent.


Appendix Pages 72 of 109

Appendices

Appendix 1

RESEARCH GOVERNANCE COMMITTEE

SUBMISSION FORM

Please refer to the Guidance Notes at the end before filling in this form.

Please complete the following check-list before submitting the completed form:

I have completed all relevant sections of the Submission Form having read

the ‘Guidance Notes’.

I have signed my Submission Form.

My Supervisor(s) have read and signed my Submission Form [Student

submissions only]

I have attached all supporting documents (information sheet, consent form,

etc.) to my Submission Form.

I agree to inform the HSRGC of any major changes to my research.

X

X

X

X

X



1. Please provide the following details about the chief investigator.

Name Y1469223

Post Postgraduate Student

Organisation The University of York

Address of Organisation Heslington, York, YO10 5DD, UK

Email *****************

Telephone ***********

2. If the research is being undertaken as part of an educational course, please provide the

following details.

Name and level of course/degree MSc Applied Health Research

Name and address of educational

establishment

The University of York, Heslington, YO10 5DD, UK

Name and email address of

supervisor

Director of NACR

3. Funding and restrictions on publication

Name of funding body N/A

Duration of the grant N/A

Please describe any restrictions

on publication of findings

imposed by a funding body or

other institution

N/A

4. If the research has been or will be reviewed by an ethics committee external to the

university, please provide the following details.

Name of external ethics review N/A



committee

Date of submission for ethics

review

N/A

Outcome of ethics review N/A

5. Please state the full title of the research.

Does the volume of patient activity within CR programmes affect patient outcomes as

measured in the National Audit of CR (NACR)?

6. Please explain the principal research question addressed by the research.

As national audits move to reporting at a local service level the impact of patient volume

on clinical outcomes following CR is something that will need to be addressed. This study

will use national audit data to determine if the volume of patient activity within CR

programmes affect patient outcome?

7. Please explain secondary research questions and objectives addressed by the research.

Which factors interact with volume in explaining the extent of outcome?

8. Please briefly explain the scientific justification for the research, including relevant

background, explaining why it is an area of importance.

The efficacy of CR has been studied in detail, with most RCT and systematic reviews

showing positive results. Further research is needed of how volume affects outcome using

more appropriate methods accounting for propensity of patients to change in respect of the

measured variables.

9. If the research has been done before, please explain why it should be repeated.

N/A



10. Please briefly describe the specific expertise, including experience and training,

researcher(s) will bring to the study.

I have 2.1 BSc degree in biology, where I developed an understanding of diseases such

as CVD. I have also performed a 20-credit research project in Oesophageal cancer, this

involved writing a full dissertation, and performing a substantial level of statistics.

I am currently enrolled on the MSc Applied Health research course, where I have

completed courses such as RCT’s, Health and Social Statistics and Research Methods,

which have given me a detailed understanding of both review and performing research to

a high standard.

11. Please show how all existing relevant evidence, especially systematic reviews, have

been fully considered, for example by giving details of any search strategies that have

been undertaken.

There have been multiple systematic reviews, showing how well CR works in both trial

setting and real settings. (Heran et al 2011) There is a lack of knowledge of how patient

volume affects outcome. Doherty et al (2013) stated a need for more observational studies

to be conducted using large sample audits. These will be able to show, in real practice

effectiveness of practices and potentially improve centre level reporting.

There has not been much research into propensity and the potential for participants to

change. From this we can conclude that this topic is relatively novel and a new analysis

type would be innovative and useful to the field of CR generally and the NACR

specifically.

Heran BS, Chen JMH, Ebrahim S, Moxham T, Oldridge N, Rees K, Thompson DR,

Taylor RS. Exercise-based CR for coronary heart disease. Cochrane Database of

Systematic Reviews 2011, Issue 7. Art. No:CD001800. DOI:

10.1002/14651858.CD001800.pub2.

Doherty. P., Rauch. G., (2013) CR mortality trends: how far from a true picture are we?,

Heart, 0:0:1-3

12. Please provide a brief summary of the method(s) of the research making clear what

will happen to research participants, how many times and in what order.

The study will use the anonymised NACR (National Audit CR) data. This data will be



structured within SPSS and apply normative tests prior to linear regression to determine

which factors determine outcome.

13. Please describe your statistical (or equivalent) methods employed to analyse your

results, including details of the randomisation process to be used, if applicable.

The data will not be randomised; the data used was collected during rehabilitation and

consists of the years 2010-2012. The data will be analysed using regression, of selected

outcomes, based on before and after measures. These will then be analysed against what is

considered low-medium-high volume at centre level.

14. Please state the primary outcome measure for the study.

The primary measure is change in BMI, blood pressure and depression/anxiety measured

on the HADs score.

15. Please state any secondary outcome measures for the study.

This will consist of the before and after rehab outcomes measures when configured to

account for propensity to change in participants.

16. If the size of the study has been informed by a formal statistical power calculation,

please indicate the basis on which this was done, giving sufficient information to allow

replication of the calculation.

The sample size is around 200,000 valid cases which are seen as sufficient for the planned

analysis. Previous studies in the US and Germany have used sample sizes around 70,000.

(Hammill et al 2010, Junger et al 2010, Suaya et al 2009, Goel et al 2011 and Rauch et al

2012) These studies had substantial limitations and could not be generalised to the UK.

Hammill BG, Curtis LH, Schulman KA, et al. Relationship between CR and

longterm risks of death and myocardial infarction among elderly medicare beneficiaries.

Circulation

2010;121:63e70.

Junger C, Rauch B, Schneider S, et al. Effect of early short-term CR after acute

ST-elevation and non-ST-elevation myocardial infarction on 1-year mortality. Curr Med



Res Opin

2010;26:803e11.

Suaya JA, Stason WB, Ades PA, et al. CR and survival in older coronary patients.

J Am Coll Cardiol 2009;54:25e33.

Goel K, Lennon RJ, Tilbury RT, et al. Impact of CR on mortality and

cardiovascular events after percutaneous coronary intervention in the community.

Circulation

2011;123:2344e52.

Rauch G, Kieser M, Ulrich S, et al. Competing time-to-event endpoints in cardiology

trials—a

simulation study to illustrate the importance of an adequate statistical analysis. Eur J

Preventive

Cardiology 2012;0(0): 1–7. doi:10.1177/2047487312460518

17. If you have consulted a statistician, please provide their name, position, and email

address.

Statistician working for the NACR

18. Please describe any ethical problems likely to arise with the proposed study,

including risks to the University of York and/or the Department of Health Sciences, and

explain what steps you will take to address them.

Patient level details are not held at the NACR team, University of York. Additionally the

data that will be accessed is anonymised.

19. Please explain how research participants will be (a) identified (b) approached and (c)

recruited. Note that if your study involves Department of Health Sciences’ staff/students

as participants you must have first contacted the Head of Department to request

permission to access Department of Health Sciences’ staff/students.

The data is collected routinely by clinical programmes and hosted by the HSCIC (Health

social care information centre) and stored in an anonymised format. There is no requirement

to approach participants.



20. If your study involves cases and controls, please give details, including inclusion and

exclusion criteria.

N/A

21. If research participants are to receive any payments for taking part in the research,

please give details, including how much they will receive and the basis on which this was

decided.

N/A

22. If research participants are to receive reimbursement of expenses, or any other

incentives or benefits for taking part in your research, please give details, including what

and how much they will receive and the basis on which this was decided.

N/A

23. Please indicate whether any research participants will be from the following groups;

if so, please explain the justification for their inclusion.

NHS staff N/A

Children under 18 N/A

Those with learning disability N/A

Those who are unconscious, severely ill or have a terminal

illness

N/A

Those in emergency situations N/A

Those with mental illness (particularly if detained under

mental health legislation)

N/A

Those suffering from dementia N/A

Prisoners N/A



Young offenders N/A

Adults who are unable to consent for themselves N/A

University of York students and/or staff N/A

Those who could be considered to have a particularly

dependent relationship with the investigator, e.g. residents of

care homes, administrative staff, students

N/A

Other vulnerable groups N/A

24. During your study, will anyone discuss sensitive, embarrassing or upsetting topics, or

issues likely to disclose information requiring further action, such as evidence of

professional misconduct, neglect, or criminal behaviour? If so, please give details of the

procedures in place to deal with these issues.

N/A

25. If the research involves deception of any kind, please explain and justify the

deception.

N/A

26. Please list and justify potential adverse effects, risks or hazards for participants due

to, for example, giving or withholding medication, medical devices, ionising radiation, or

any other such intervention.

N/A

27. Please explain and justify any pain, discomfort, distress or inconvenience that the

study might cause participants, including details of any procedures in place to deal with

these issues.

N/A

28. Please describe the potential benefits to participants.



Potential improvements to services of which participants would be able to access,

additionally improvements to ability to present data at a local level through the NACR.

29. If the research requires that any intervention or procedure that is normally

considered part of their routine clinical care is to be withheld from participants, please

provide details and a justification.

N/A

30. Will participants, as a result of the research, receive any clinical intervention or

procedures (including taking of biological material) that would not be considered part of

their routine care? If so, please give details, including describing in detail the

intervention or procedure in question.

N/A

31. Will participants, as a result of the research, receive any non-clinical intervention or

procedures? If so, please give details, including describing in detail the intervention or

procedure in question.

N/A

32. Please list and justify potential adverse effects, risks or hazards, pain, discomfort,

distress or inconvenience that the study might cause researchers.

N/A

33. Please explain how voluntary informed consent to participate will be elicited from

participants.

The data is collected routinely by clinical programmes and hosted by the HSCIC (Health

Social Care Information Centre). The NACR, based in Health Sciences, has access to an

anonymised version of this data which is stored on a secure drive with named access. The

audit data inputted clinically is exempt from ‘consent’ under the 251 exemption

agreement with the HSCIC.



34. If you do not envisage obtaining a signed record of consent from participants, please

justify.

Covered under 251 exemption

35. If you do not envisage providing participants with a written information sheet about

your study, please justify.

The study will use only routinely collected audit data that has been anonymised and

therefore will not be approaching participants directly.

36. Please explain what arrangements have been made to explain the research to

participants who do not understand English well.

N/A

37. If the research will involve any of the following activities please indicate so and

provide further details.

Examination of medical records by those outside the NHS, or

within the NHS by those who would not normally have

access

N/A

Electronic transfer of data, e.g. by secure shared file access,

memory stick, 'DropOff service', or equivalent

The anonymised data may

be accessed remotely

through either a password

protected ‘I’ drive or

encrypted memory stick.

Sharing of data with other organisations N/A

Export of data outside the European Union N/A

Use of personal addresses, postcodes, faxes, emails or

telephone numbers

N/A

Publication of direct quotations from respondents N/A



Publication of data that might allow identification of

individuals

N/A

Use of audio/visual recording devices N/A

38. If the research will involve storing personal data, including sensitive data, on any of

the following please indicate so and provide further details.

Manual files (including X-rays) N/A

NHS computers N/A

University computers The data will be password protected and stored

on the university network (via VPN) or


Private company computers N/A

Home or other personal computers The data will be password protected and stored



Laptop computers The data will be password protected and stored



Websites N/A

39. Please explain the measures in place to ensure data confidentiality, including details

of encryption or other methods of anonymisation.

All patient details are removed by the HSCIC before access is given to the NACR team in

York University. Encrypted memory sticks are supplied by IT services in the Department

of Health Sciences.

40. Please detail who will have access to the data generated by the study.

Director of NACR (supervisor), national audit statistician and the MSc research student



41. Please detail who will have control of, and act as custodian for, data generated by the

study.

Director of the national audit and academic supervisor will fulfil this role

42. Please explain where, and by whom, data will be analysed.

Director of NACR (supervisor) national audit statistician and the MSc research student in

the University and by the student on a laptop.

43. Please give details of data storage arrangements, including where data will be stored,

how long for, and in what form.

The NACR is integrated into the Health Sciences IT support and access to the data is

through a password protected remote server for the duration of the study (6 months)

44. If data protection officers are aware of your study, please give details.

The MSc student has been briefed by IT services and will remain in communication with

IT services during the study.

45. Please indicate whether your results will be reported and disseminated in any of the

following ways, giving any relevant details.

Peer reviewed academic journals Planned

Internal report The findings will help inform the

format of the national audit report in

2015

Conference presentation British Association for Cardiovascular

Prevention and Rehabilitation Oct

2014 or 2015

Other publication N/A

Submission for academic assessment Yes, this is a 100 credit dissertation

with the University of York



Submission to regulatory authorities N/A

Other (e.g., Cochrane Review, University

Library)

N/A

46. If results are not to be reported and disseminated in any of the above ways please

explain how they will be reported and disseminated.

The data will be disseminated primarily for an academic dissertation. After which a

revised version may be submitted to peer reviewed journals etc.

47. Please explain how results will be made available to participants and the communities

from which they are drawn.

The benefits of the study will help national reporting which will help quality assure patient

services

48. If the Chief Investigator or any other key investigators or collaborators have any

direct personal involvement in the organisation sponsoring or funding the research that

may give rise to a possible conflict of interest, please supply details.

N/A

49. If individual researchers are to receive any personal payment over and above their

normal salary for taking part in this research, please supply details.

N/A

50. Please explain any arrangements that have been made to provide indemnity and/or

compensation in the event of a claim by, or on behalf of, participants for negligent harm.

N/A



51. Please explain any arrangements that have been made to provide indemnity and/or

compensation in the event of a claim by, or on behalf of, participants for non-negligent

harm.

N/A

Signature of Chief Investigator: ****************

Signature of Supervisor(s): ****************

Date of Completion: 14th April 2014



Appendix 2

Search Strategy MEDLINE, EMBASE and CINAHL

Cardiovascular Surgery and volume

1. volume-outcome.mp.

2. cardi*.mp.

3. aortic.mp.

4. bariatric.mp.

5. surg*.mp.

6. 2 or 3 or 4

7. 5 and 6

8. 1 and 7

Secondary health care and volume

1. volume-outcome.mp.

2. patient volume.mp.

3. hospital volume.mp.

4. center volume.mp.

5. centre volume.mp.

6. health care volume .mp.

7. 1 or 2 or 3 or 4 or 5 or 6

8. secondary care.mp. or exp Secondary Care/

9. chronic care.mp. or Long-Term Care/

10. nursing care.mp. or exp Nursing Care/

11. Rehabilitation Nursing/ or Rehabilitation Centers/ or Rehabilitation/

12. 8 or 9 or 10 or 11

13. 12 and 7

Search strategy for Cochrane

Cardiovascular Surgery and volume

1. Volume-outcome

2. “Hospital volume”

3. “patient volume”

4. “Healthcare volume”

5. “Healthcare volume”



6. “Center volume”

7. “centre volume”

8. #1 or #2 or #3 or #4 or #5 or #6 or #7

Secondary health care and volume

1. Volume-outcome

2. Surg*

3. Cardi*

4. Aortic

5. Bariatric

6. #1 and #2

7. #3 or #4 or #5

8. #6 and #7



Appendix 3

Table 12 showing the inclusion/exclusion criteria of the literature review performed for

the secondary care and acute care studies that are reporting volume-outcome

relationships.

Inclusion Exclusion Criteria of the Literature Review

Inclusion Exclusion

Secondary Care with

volume-outcome

relationship

Any secondary care

setting, of any clinical

condition, that has

some recording or

reporting of volume per

centre that is compared

with the primary

outcome

Any study that takes

place in acute care,

hospital, Emergency

department or ICU.

Additionally any study

that does not report

Volume-outcome

relationship

Acute care/cardiac

surgery with volume-

outcome relationship

Any acute care setting,

where any form of

cardiac surgery is

performed, that has

some recording or

reporting of volume per

centre that is compared

with the primary

outcome

Any study that reports

on a surgery that isn’t

cardiac (Thoracic,

bariatric, aortic etc.).

Additionally any study

that does not directly

look at the relationship

of hospital Volume-

outcome, or only

reports surgeon

volume.



Appendix 4

Secondary Care Literature

Table 13 Showing the characteristics of the 8 included studies within this section of the

literature review

Characteristics of Included Studies in Secondary Care

Author Study Study

design

Population Results CASP

Ranking

Evans

(2013)

The effect of

volume on the

outcomes of

perinatal dialysis

Cohort 11,068 Positive VOR

identified, two

tailed

relationship

10/12

Graham

(2013)

3 common

fractures

Cohort 482,694 Little or no

relationship

identified

9/12

Hanford

(2006)

HIV/AIDS Review 39,776 Higher volume

levels were

associated with

decreased

mortality

7/10

Hranjec

(2012)

Burn centres Cohort 154,574 Positive VOR

identified, two

tailed

relationship

9/12

Lee (2007) Inpatient

psychiatric care

Cohort 31,528 Negative VOR

identified

11/12

Li (2010) Decline of

function in

nursing homes

Cohort 603,433 Positive VOR

identified

10/12

Li (2012) Readmission in

SNF

Cohort 1,023,771 Positive VOR

identified

11/12

Neogi

(2012)

Mortality in

neonatal infants

including

volume

Review N/A Positive VOR

identified

6/10



Acute Cardiac Care Table 14 Showing the characteristics of the 10 included studies within this section of the

literature review

Characteristics of Included Studies in Cardiac Surgery

Author Study Study

design

Population Results CASP

Ranking

Allareddy

(2007)

5 unrelated

operations

Cohort 877,131 Positive

VOR

identified

12/12

Birkmeyer

(1999)

10 high-risk

operations


VOR

identified

7/12

Gammie

(2007)

Heart valve

surgery


VOR

identified

11/12

Landon

(2010)

AAA Surgery Cohort 230,736 Positive

VOR

identified

10/12

Markar

(2012)

Bariatric

surgery

Review 289,732 Positive

VOR

identified

9/10

Miyata (2009) Thoracic

Surgery


VOR

identified

12/12

Nguyen

(2004)

Bariatric

surgery


VOR

identified

12/12

Sephripour

(2012)

CABG Review N/A Positive

VOR

identified

5/10

Urbach

(2004)

4 operations Cohort 31,632 Positive

VOR

identified

10/12

Zevin (2014) Bariatric

surgery

Review 458,032 Positive

VOR

identified

10/10



Appendix 5

Assumptions

BMI

Figure 5 Showing the tests for normality for the residuals of the BMI reading 2 regression model. The graph on the left is the Q-Q plot of the residual, this shows a relatively consistent normal distribution, however, it is skewed significantly due to outliers at the tails. To account for these deviations from the normality curve,

sensitivity analysis was performed, with little impact on the regression model shown in Appendix 6. The graph on the right is a histogram also assessing the normality of the residuals. Although the majority of the plots are consistent to normality curve, there is some deviations from the bell curve, which are the outliers within the Q-Q plot. Again once these plots were removed during the sensitivity analysis there was little effect and we can

assess that the assumption of normality has been met.

Figure 6 Showing the tests for Uniform Variance and Linearity of the residuals in BMI reading 2 regression model. The graph on the left is the scatter plot with of the residual, this shows a relatively strong uniform

variance, however, there is some deviation out of the lines at 2 and -2. This deviation is within reason and not a problem due to the large dataset. This was not significant enough to justify sensitivity analysis. The graph on the

right is a scatter plot of the linearity. This plot shows that the majority of the plots form a linear relationship along the line of best fit. Thus both assumptions, Uniform Variance and Linearity are met.



Blood Pressure Diastolic

Figure 7 Showing the tests for normality for the residuals of the Diastolic Blood Pressure reading 2 regression model. The graph on the left is the Q-Q plot of the residual, this shows a relatively consistent normal distribution,

however, it is skewed slightly at the tails due to some deviation. This was not significant enough to justify sensitivity analysis. The graph on the right is a histogram also assessing the normality of the residuals. Although the majority of the plots are consistent to normality curve, there is some deviations from the bell curve, which are the outliers within

the Q-Q plot.

Figure 8 Showing the tests for Uniform Variance and Linearity of the residuals in Diastolic Blood Pressure reading 2 regression model. The graph on the left is the scatter plot with of the residual, this shows a relatively strong

uniform variance, however, there is some deviation out of the lines at 2 and -2. This deviation is within reason and not a problem due to the large dataset. This was not significant enough to justify sensitivity analysis. The graph on

the right is a scatter plot of the linearity. This plot shows that the majority of the plots form a linear relationship along the line of best fit. Thus both assumptions, Uniform Variance and Linearity are met.



Blood Pressure Systolic

Figure 9 Showing the tests for normality for the residuals of the Systolic Blood pressure reading 2 regression model. The graph on the left is the Q-Q plot of the residual, this shows a relatively consistent normal

distribution, however, it is skewed significantly due to outliers at the tails. To account for these deviations from the normality curve, sensitivity analysis was performed, with little impact on the regression model

shown in Appendix 6. The graph on the right is a histogram also assessing the normality of the residuals. Although the majority of the plots are consistent to normality curve, there is some deviations from the bell

curve, which are the outliers within the Q-Q plot. Again once these plots were removed during the sensitivity analysis there was little effect and we can assess that the assumption of normality has been met.

Figure 10 Showing the tests for Uniform Variance and Linearity of the residuals in Systolic Blood Pressure reading 2 regression model. The graph on the left is the scatter plot with of the residual, this shows a relatively strong uniform variance, however, there is some deviation out of the lines at 2 and -2. This

deviation is within reason and not a problem due to the large dataset. This was not significant enough to justify sensitivity analysis. The graph on the right is a scatter plot of the linearity. This plot shows that the majority of the plots form a linear relationship along the line of best fit. Thus both assumptions, Uniform

Variance and Linearity are met.



Cholesterol

Figure 11 Showing the tests for normality for the residuals of the level of Cholesterol reading 2 regression model. The graph on the left is the Q-Q plot of the residual, this shows a relatively consistent normal

distribution. The graph on the right is a histogram also assessing the normality of the residuals. This shows are very strong and normal distribution of the residuals.

Figure 12 Showing the tests for Uniform Variance and Linearity of the residuals in Cholesterol reading 2 regression model. The graph on the left is the scatter plot with of the residual, this shows a relatively strong

uniform variance, however, there is some deviation out of the lines at 2 and -2. This deviation is within reason and not a problem due to the large dataset. This was not significant enough to justify sensitivity analysis. The graph on the right is a scatter plot of the linearity. This plot shows that the majority of the plots form a linear

relationship along the line of best fit. Thus both assumptions, Uniform Variance and Linearity are met.



HADs Anxiety

Figure 13 Showing the tests for normality for the residuals of the HADs Anxiety reading 2 regression model. The graph on the left is the Q-Q plot of the residual, this shows a relatively consistent normal distribution,

however, it is skewed significantly due to outliers at the tails. To account for these deviations from the normality curve, sensitivity analysis was performed, with little impact on the regression model shown in

Appendix 6. The graph on the right is a histogram also assessing the normality of the residuals. Although the majority of the plots are consistent to normality curve, there is some deviations from the bell curve, which are the outliers within the Q-Q plot. Again once these plots were removed during the sensitivity analysis there was

little effect and we can assess that the assumption of normality has been met.

Figure 14 Showing the tests for Uniform Variance and Linearity of the residuals in HADs Anxiety reading 2 regression model. The graph on the left is the scatter plot with of the residual, this shows a relatively strong

uniform variance, however, there is some deviation out of the lines at 2 and -2. This deviation is within reason and not a problem due to the large dataset. This was not significant enough to justify sensitivity

analysis. The graph on the right is a scatter plot of the linearity. This plot shows that the majority of the plots form a linear relationship along the line of best fit. Thus both assumptions, Uniform Variance and Linearity

are met.



HADs Depression

Figure 15 Showing the tests for normality for the residuals of the HADs Depression reading 2 regression model. The graph on the left is the Q-Q plot of the residual, this shows a relatively consistent normal

distribution, however, it is skewed slightly at the tails due to some deviation. This was not significant enough to justify sensitivity analysis. The graph on the right is a histogram also assessing the normality of the residuals.

Although the majority of the plots are consistent to normality curve, there is some deviations from the bell curve, which are the outliers within the Q-Q plot.

Figure 16 Showing the tests for Uniform Variance and Linearity of the residuals in HADs Depression reading 2 regression model. The graph on the left is the scatter plot with of the residual, this shows a relatively strong

uniform variance, however, there is some deviation out of the lines at 2 and -2. This deviation is within reason and not a problem due to the large dataset. This was not significant enough to justify sensitivity analysis. The graph on the right is a scatter plot of the linearity. This plot shows that the majority of the plots form a linear

relationship along the line of best fit. Thus both assumptions, Uniform Variance and Linearity are met.



Smoking Cessation

Figure 17 Showing the tests for normality for the residuals of the level of Smoking Cessation reading 2 regression model. The graph on the left is the Q-Q plot of the residual, this shows a relatively consistent

normal distribution, however, it is skewed slightly at the tails due to some deviation. This was not significant enough to justify sensitivity analysis. The graph on the right is a histogram also assessing the normality of the residuals. Although the majority of the plots are consistent to normality curve, there is some deviations from

the bell curve, which are the outliers within the Q-Q plot.

Figure 18 Showing the tests for Uniform Variance and Linearity of the residuals in Smoking Cessation reading 2 regression model. The graph on the left is the scatter plot with of the residual, this shows a relatively strong uniform variance, however, there is some deviation out of the lines at 2 and -2. This

deviation is within reason and not a problem due to the large dataset. This was not significant enough to justify sensitivity analysis. The graph on the right is a scatter plot of the linearity. This plot shows that the majority of the plots form a linear relationship along the line of best fit. Thus both assumptions, Uniform

Variance and Linearity are met.



Physical Activity

Figure 19 Showing the tests for normality for the residuals of the level of Physical Exercise reading 2 regression model. The graph on the left is the Q-Q plot of the residual, this shows a relatively consistent

normal distribution. The graph on the right is a histogram also assessing the normality of the residuals. This shows are very strong and normal distribution of the residuals.

Figure 20 Showing the tests for Uniform Variance and Linearity of the residuals in Physical Exercise reading 2 regression model. The graph on the left is the scatter plot with of the residual, this shows a relatively strong

uniform variance, however, there is some deviation out of the lines at 2 and -2. This deviation is within reason and not a problem due to the large dataset. This was not significant enough to justify sensitivity

analysis. The graph on the right is a scatter plot of the linearity. This plot shows that the majority of the plots form a linear relationship along the line of best fit. Thus both assumptions, Uniform Variance and Linearity

are met.



Appendix 6

Results of sensitivity analysis

BMI

Table 15 Showing the MLR analysis of the BMI reading 2 outcome after removal of

outliers

Regression after Sensitivity analysis of BMI

Unstandardized Coefficients 95% Confidence intervals

B (Mean Difference)

Std. Error

Significance

Lower Bound

Upper Bound

BMI Reading 1 .803 .077 .000 .650 .956

Volume number .000 .000 .140 <0.001 .001

Gender (% female)

-.026 .013 .048 -.051 .000

Average Age -.385 .346 .267 -1.069 .299

Proportion MI only

.010 .004 .027 .001 .018

Blood Pressure Systolic

Table 16 Showing the MLR analysis of the Systolic Blood Pressure reading 2 outcome

after removal of outliers

Regression after Sensitivity analysis of Blood Pressure Systolic

Unstandardized Coefficients


B (Mean Difference)

Std. Error

Significance

Lower Bound

Upper Bound

Blood pressure Systolic 1

.811 .121 .000 .572 1.051

Volume number .000 .000 .197 .000 .001

Gender (% female)

-.021 .020 .288 -.061 .018

Average Age -.121 .542 .824 -1.191 .950

Proportion MI only

.009 .007 .166 -.004 .023



HADs Anxiety

Table 17 Showing the MLR analysis of the HADs Anxiety reading 2 outcome after

removal of outliers

Regression after Sensitivity analysis of Anxiety

Unstandardized Coefficients


B (Mean Difference)

Std. Error

Significanc

e

Lower Bound

Upper

Bound

Anxiety Reading 1 .277 .055 .000 .169 .386

Volume number .000 .000 .623 .000 .001

Gender (% female)

-.008 .015 .614 -.037 .022

Average Age -.107 .038 .005 -.182 -.032

Proportion MI only

.018 .005 .000 .008 .027


Abbreviations 101 of 109

Abbreviations

BACPR- British Association for Cardiovascular Prevention and

Rehabilitation

BMI- Body Mass Index

CABG- Coronary Artery Bypass Graft

CASP- Critical Appraisal Skills Programme

CI- Confidence Intervals

CR- Cardiac Rehabilitation

CVD- Cardiovascular Diseases

EB- Exercise-Based

HSCIC- Health & Social Care Information Centre

LEF- Lower Extremity Fracture

LEJR- Lower Extremity Joint Replacement

LoS- Length of Stay

MDT- Multi Disciplinary Team

MI- Myocardial Infarction

MLR- Multivariate Linear Regression

NACR- National Audit for Cardiac Rehabilitation

OR- Odds Ration

PA- Physical Activity

PCI- Percutaneous Coronary Intervention

PICO- Population, Intervention, Control, Outcome

QoL- Quality of Life

RAMIT- Rehabilitation After Myocardial Infarction Trial

RCT- Randomised Control Trials

RR- Relative Risk

SC- Smoking Cessation

VOR- Volume-Outcome Relationship

Exam number: Y1469223 100 Credit Dissertation

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does the total volume of patients in cardiac …...rehabilitation programmes across the world (clark...

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