bond university research repository effectiveness of ... · literature using google and google...

Bond UniversityResearch Repository

Effectiveness of continuous or intermittent vital signs monitoring in preventing adverse eventson general wardsa systematic review and meta-analysis

Cardona-Morrell, M.; Prgomet, M.; Turner, R. M.; Nicholson, M.; Hillman, K.

Published in:International Journal of Clinical Practice

DOI:10.1111/ijcp.12846

Published: 01/10/2016

Document Version:Peer reviewed version

Licence:CC BY-NC-ND

Link to publication in Bond University research repository.

Recommended citation(APA):Cardona-Morrell, M., Prgomet, M., Turner, R. M., Nicholson, M., & Hillman, K. (2016). Effectiveness ofcontinuous or intermittent vital signs monitoring in preventing adverse events on general wards: a systematicreview and meta-analysis. International Journal of Clinical Practice, 70(10), 806-824.https://doi.org/10.1111/ijcp.12846

General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.

For more information, or if you believe that this document breaches copyright, please contact the Bond University research repositorycoordinator.

Download date: 13 Mar 2021

https://doi.org/10.1111/ijcp.12846

https://research.bond.edu.au/en/publications/665078f8-2a83-407d-a6bc-faca3f0b39fe

https://doi.org/10.1111/ijcp.12846

For Peer Review O

nly

Effectiveness of continuous or intermittent vital signs

monitoring in preventing adverse events on general wards: A systematic review and meta-analysis

Journal: International Journal of Clinical Practice

Manuscript ID IJCP-03-16-0103.R1

Wiley - Manuscript type: Systematic Review

Date Submitted by the Author: n/a

Complete List of Authors: Cardona-Morrell, Magnolia; The University of New South Wales, The

Simpson Centre for Health Services Research, South Western Sydney Clinical School Prgomet, Mirela; Macquarie University, Australian Institute of Health Innovation Turner, Robin; The University of New South Wales, School of Public Health and Community Medicine Nicholson, Margaret ; Liverpool Hospital, Intensive Care Unit Hillman, Ken; The University of New South Wales, The Simpson Centre for Health Services Research, South Western Sydney Clinical School

Specialty area:

International Journal of Clinical Practice


For Peer Review O

nly

Effectiveness of continuous or intermittent vital signs monitoring in preventing

adverse events on general wards: A systematic review and meta-analysis

Magnolia Cardona-Morrell, Senior Research Fellow1, Mirela Prgomet, Postdoctoral Research

Fellow2, Robin M Turner, Senior Lecturer

3, Margaret Nicholson, Nurse Practitioner

4, Ken Hillman,

Staff Specialist1,4.

1The Simpson Centre for Health Services Research, The University of NSW, Sydney, Australia

2Australian Institute of Health Innovation, Macquarie University, Sydney, Australia

3School of Public Health and Community Medicine, The University of NSW, Sydney, Australia

4Intensive Care Unit, Liverpool Hospital, Sydney, Australia

Running Title: Effectiveness of vital signs monitoring: A Review

Corresponding author contact details:

Dr Magnolia Cardona-Morrell Email: [email protected]

The Simpson Centre for Health Services Research

The University of New South Wales

PO Box 6087 UNSW Sydney NSW 1466

Australia

Telephone: +61 2 8738 9373 Cell: +61 423 824 373

Keywords: effectiveness, hospital, observations, physiological deterioration, vital signs monitoring

Word count: 4,793 excluding abstract, references and tables

Number of references: 61

Disclosures KH worked as a clinical consultant for a company in the development of a mobile wireless device to monitor

six vital signs. None of the products of that company are listed or referred to in this review. The company has not published effectiveness results and had no involvement in the conduct of this review. RMT received

fees for her work in producing the meta-analyses. None of the other investigators have a perceived or actual

conflict of interest to declare.

Page 1 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 1 of 41

Effectiveness of continuous or intermittent vital signs monitoring in

preventing adverse events on general wards: A systematic review and

meta-analysis

Summary

Background

Vital signs monitoring is an old hospital practice but evaluation of its effectiveness is not widespread.

We aimed to identify intermittent or continuous strategies to improve vital signs monitoring in general

wards; and their effectiveness in preventing adverse events on general hospital wards.

Methods

Publications searched between 1980 and June 2014 in five databases. Main outcome measures were

in-hospital death, cardiac arrest, ICU transfers, length of stay, identification of physiological

deterioration, and activation of rapid response systems.

Results Twenty-two studies assessing the effect of continuous (9) or intermittent monitoring (13) and

reporting outcomes on 203,407 patients in general wards across hospitals in 13 countries were

included in this review. Both monitoring practices led to early identification of patient deterioration,

increased rapid response activations, and improvements in timeliness or completeness of vital signs

documentation. Intermittent monitoring leads to modest reduction in in-hospital mortality. However,

there was no evidence of significant reduction in ICU transfers or other adverse events with either

intermittent or continuous monitoring.

Conclusions

Page 2 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 2 of 41

This review of heterogeneous monitoring approaches found no conclusive confirmation of

improvements in prevention of cardiac arrest, reduction in length of hospital stay, or prevention of

other neurological or cardiovascular adverse events. The evidence found to date is insufficient to

recommend continuous monitoring in general wards as routine practice. Future evaluations of

effectiveness need to be undertaken with more rigorous methods and homogeneous outcome

measurements.

Keywords: hospital, vital signs monitoring, general wards, review

Systematic review registration: PROSPERO registration number CRD42015015751.

Review Criteria

• We searched English-language articles published between January 1980 and June 2014 using

Medline, EMBASE, CINAHL and EBM Reviews databases using the terms ‘monitoring’, ‘vital

signs’, ‘hospital’ and ‘ward’’ among others.

• Two reviewers independently assessed and extracted the data and discrepancies were resolved

by a third.

• A random effects meta-analysis was conducted to obtain a pooled odds ratio, with studies

weighted to account for the standard error and heterogeneity

Message for the clinic

• Both vital signs monitoring practices increase the detection of deterioration and activation of

Rapid Response Systems.

• However, evidence that continuous monitoring prevents serious adverse events on general

wards, cardiac arrests, or reduces ICU transfers is lacking.

• Chart redesign and use of early warning scores for intermittent monitoring has modest impact

on reduction of hospital mortality but does not reduce other serious adverse events.

• There is insufficient evidence of effectiveness of continuous monitoring to recommend its

Page 3 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 3 of 41

routine use in general wards

Page 4 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 4 of 41

Introduction

The recording of vital signs in patients is arguably the most common patient care intervention in acute

hospitals. Yet there is a paucity of research into the effectiveness of vital signs monitoring and the

optimal frequency of measurements,(1) particularly considering that many potentially preventable

deaths and serious adverse events still occur in acute hospitals,(2, 3) i.e. health facilities with

emergency or critical care departments excluding psychiatric hospitals.

Hospitalised patients today increasingly are elderly and frail with multiple chronic conditions and at

higher risk of death, especially if requiring intensive care procedures.(4) (5) Intensive care units

(ICU), high dependency units, operating theatres, coronary care units and recovery wards historically

have monitored patients continuously, with limited evidence of efficacy.(6) It is generally accepted

that ICU patients at high risk of deterioration require continuous monitoring (CM).(7) However, based

on these changing levels of illness and risk of deterioration, there is growing realisation that

monitoring and response in general wards should be approached similarly to ICUs.(8, 9)

Rapid response systems (RRS) exemplify one approach that has been widely implemented in hospitals

with the intention of preventing adverse patient events such as cardiac arrest and unexpected deaths in

general ward patients. RRS relies on the speed of the team’s response, which is crucially dependant

on the timely identification of patient deterioration.

The absence of timely information on vital signs can result in failure to identify deterioration(10) (11)

In addition to sub-optimal identification and management of deterioration on general wards,(12-14) an

important likely reason for the mixed evidence of RRS effectiveness(15, 16),

5 is that activation

criteria are reliant on intermittent and/or incomplete measurement of vital signs.(17, 18) This calls

into question whether routine intermittent monitoring (IM) is sufficient to cater for the changing

profile of patients on general hospital wards today. Early work on different strategies to improve

Page 5 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 5 of 41

monitoring on general wards(19) has occurred, but there is no general consensus on the most effective

monitoring strategy to improving patient safety.(1) Thus there is a need to review the literature on the

effectiveness of monitoring in general wards of acute hospitals in order to establish whether more

intensive monitoring is warranted.

This systematic review aims to answer the following questions:

1. What strategies, intermittent or continuous, are being implemented to improve vital signs

monitoring in general wards?

2. How effective have these strategies been in improving monitoring practices and/or preventing

adverse events on general wards?

Methods

Search Strategy

We searched literature on vital signs monitoring interventions on general hospital wards aimed at

improving monitoring practices (e.g., earlier detection of deterioration), or patient outcomes (e.g.,

mortality, length of stay (LOS), ICU transfers). We searched English-language articles published

between January 1980 and June 2014 using Medline, EMBASE, CINAHL and EBM Reviews

databases. Our search strategy included a combination of keywords and subject headings related to

vital signs and monitoring (Appendix 1). We also manually searched both the peer-reviewed and grey

literature using Google and Google Scholar, and reviewed the reference lists of all full-text articles

that we assessed for potential inclusion. The protocol for this review was guided by the PRISMA

statement(20) and was registered with PROSPERO (registration number CRD42015015751;

Appendix 2).

Page 6 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 6 of 41

Eligibility Criteria

To be eligible for inclusion, studies had to meet the following 'PICOS' criteria:

Population (P): adult inpatients on general wards with any condition(s); Interventions (I): introduction

of technology or changes to practices/strategies/protocols for monitoring at least one vital sign (i.e.,

heart rate/pulse, blood pressure, respiratory rate, oxygen saturation, temperature, electrocardiography,

or level of consciousness); Comparator (C): any contrast with usual care either concurrently or prior

to the intervention defined in (I); Outcomes (O): any quantitative measures of patient events from

minor (e.g., dehydration or LOS) to major (e.g., transfer to ICU, cardiac arrest, or death) or effects on

monitoring practices (e.g., escalation calls (RRS activation), earlier detection of deterioration,

improved vital sign documentation). Setting (S): conducted in general hospital wards.

Data Extraction Process

Appendix 3 illustrates the search and selection process. Identified citations were independently

screened by two reviewers (MCM and MP) to determine whether they met the eligibility criteria.

Discrepancies between the reviewers were resolved by detailed discussion. Data were extracted from

each potentially relevant article using a worksheet developed by one of the authors (MCM; Appendix

4) and classified according to CM or IM.

Bias and Quality Assessment

We undertook critical appraisal of the included studies to score their quality and risk of bias using a

purpose-specific assessment tool, a modified brief version of the Kmet 2004 tool for assessing study

quality(21) as agreed by the authors to cater for the inclusion of both randomised controlled trials

(RCT) and non-RCT study designs. The score was calculated by assigning one point if a criterion

was met and zero if not specified or not met to each of 13 criteria and summing the result (Appendix

4). Levels of evidence were assessed using the national guidelines from the National Health and

Medical Research Council.(22) These were derived in 2007 from extensive consultation, scrutinized

Page 7 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 7 of 41

and testing, and are widely used in Australia for critical appraisal of literature reviews and production

of clinical guidelines.

Data Synthesis

Where there were at least three studies measuring the same outcome using the same units (e.g.,

mortality rates or proportion of ICU transfers), meta-analyses were undertaken to pool the results.

Study-specific odds ratios were calculated comparing the odds of the outcome of interest between the

intervention and control groups. A random effects meta-analysis was conducted to obtain a pooled

odds ratio, with studies weighted to account for the standard error and heterogeneity.(23) Pooled odds

ratios were also estimated for continuous versus intermittent monitoring. Forest plots of the study-

specific and pooled odds ratios were calculated, and ordered by year of study publication.

Heterogeneity was assessed using the I-squared statistic: an intuitive expression of inconsistency of

study results expressed as a percentage of variation across studies due to clinical and methodological

diversity rather than to chance.(24, 25) Meta-regression helps improve generalisability as it

incorporates specific predictive uncertainties in addition to intrinsic uncertainties such as qualitative

factors (health system and patient clinical profiles). Accordingly, meta-regression was used to

investigate study heterogeneity of the log of the odds ratio against the different pre-specified quality

indicators (RCT, multi-centre, higher quality (score>=10), LOS, ICU transfers, escalation calls, other

adverse events, detection of deterioration, high-risk patients vs. all patients, surgical vs. medical

wards, and number of monitored parameters), where these indicators differed across the studies. All

analyses were conducted in Stata version 13.1 (StataCorp. 2013. Stata Statistical Software: Release

13. College Station, TX: StataCorp LP). As not all studies measured the same outcomes,

denominators varied for different parameters.

Role of the Funding Source

Page 8 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 8 of 41

The funding source had no involvement in the development of the research question, study design,

analysis, interpretation, conclusions of the review, the writing of the manuscript, or the decision to

submit it for publication.

Results

We identified 22 studies that met our eligibility criteria: 9 assessing the effect of CM and 13

examining the impact of IM interventions. Overall, the 22 studies report outcomes on a total of

203,407 patients in general wards across hospitals in 13 countries (USA, Scotland, Ireland, Wales,

England, Sweden, Netherlands, Belgium, Switzerland, Italy, Spain, Australia, and New Zealand). Five

were RCTs. The characteristics of the studies, including study design, intervention(s), comparison

groups and outcomes measured are summarised in Table 1. The study quality scores were generally

high, with 17/22 studies scoring an 8 or above.

<TABLE 1 HERE>

CM studies generally evaluated results after 48 to 72 hours of monitoring. Comparisons in CM studies

were with: IM in the same wards before the intervention; IM at various frequencies in other wards at

the time of intervention; or against automated IM strategies. In one case,(26) universal CM was

compared with selective CM of high-risk patients on other wards.(26)

IM studies evaluated a range of interventions, such as changes to monitoring (i.e., increased frequency

of selected vital signs); introduction of early warning scores (EWS); redesign of observation charts;

manual entry of vital signs into electronic devices for assessment of trends, calculation of EWS, or

automated alerts; and/or a combination of these strategies. The control group in the majority of IM

studies was usual ward care with manual charts or existing IM protocols used in similar wards.

Page 9 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 9 of 41

CM findings presented here were from level II/III-2 evidence,(22) from nine single-centre studies:

four RCTs, one pseudo-RCT (with randomisation after stratification), three before-and-after

controlled trials and one cohort study. Six studies investigated multiple vital sign parameter

monitoring, two examined two vital sign parameters, and one study examined temperature

monitoring only. Most IM studies were level III evidence, and the majority of these (11/13) examined

the effect of multiple vital sign parameter surveillance, with two assessing interventions to improve

BP and HR monitoring only. Eight IM studies were single-centre studies and the remaining five were

multi-centre studies.

Findings by study outcome

The diversity of measurements for adverse events and escalation calls (Tables 2 and 3) precluded their

meta-analysis, so interpretation of these selected process results are presented in Appendix 5. Some

denominators were reported per 1,000 admissions, per 1,000 discharged patients, or per 1,000 patient-

bed-days; and LOS comparisons were reported as mean or median number of days. Outcomes most

commonly reported in CM studies included mortality (7/9), LOS (7/9), incidence of other adverse

events such as stroke or re-surgery rates (6/9), and detection of deterioration (5/9) (Table 2). The most

commonly reported outcomes in IM studies included quality of vital sign observations (11/13 studies),

incidence of other adverse events (11/13), mortality (10/13), LOS (9/13), and ICU transfers (8/13)

(Table 3).

<TABLE 2 HERE>

<TABLE 3 HERE>

Mortality

Page 10 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 10 of 41

Of four CM studies of mortality outcomes, only one small RCT(27) found marginally significant

reductions of in-hospital mortality and in mortality up to 3 months post-discharge in stroke patients.

Another small study reporting in-hospital mortality reductions(28) did not provide statistical testing in

their results; the remaining RCTs found no significant difference in mortality at discharge or within

30 days.(29, 30) The largest CM studies only reported numbers of deaths but no statistical

significance of differences between cases and controls.

Intermittent monitoring coupled with early warning systems appears to have a mixed effect on in-

hospital mortality. Six of the ten IM studies reporting mortality did not find significant post-

intervention mortality reductions, despite large patient numbers in three.(31-33) The smallest study43

indicated improvement in mortality rates, but a small sample size may have precluded statistical

significance. Four large studies using chart redesign and early warning scores showed significant

relative reductions of around 50%, although the absolute reductions were small in magnitude (1-

4%).(34-37)

Sufficient studies examining in-hospital mortality using equivalent measures enabled meta-analysis of

this outcome (4 CM and 9 IM studies). When meta-analysed (top part of Figure 1) and compared with

intermittent approaches, CM strategies were not associated with significant in-hospital mortality

reductions (OR=0.87 (95%CI 0.57 to 1.33)). The small heterogeneity between the CM studies was not

significant (I2 = 27%, p=0.25)

<FIGURE 1 HERE>

By contrast, enhanced IM strategies were associated with statistically significant but modest mortality

reductions when compared with usual care (OR=0.78 (95%CI 0.61 to 0.99; lower part of Figure 1)).

Since the largest study, by Schmidt et al. had a large weight in the original meta-analysis, sensitivity

analysis excluding it was conducted but did not change the overall point estimate substantially (OR

with largest study 0.81, 95%CI 0.66-0.99, p=0.004; OR excluding largest study 0.74, 95%CI 0.55-

Page 11 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 11 of 41

0.99, p=0.002) but reduced the power with consequent increased confidence interval due to the loss of

a large number of patients. There was significant moderate heterogeneity among the meta-analysed

IM studies (I2 =67.7%, p=0.002).

The meta-regression of study quality scores and other study characteristics (as outlined in the methods

section) did not explain the study heterogeneity (p>0.2).

ICU transfers

Of the three CM studies reporting unplanned ICU transfers (Table 2), one large before-and-after study

(38) and one medium sized RCT(30) of multi-parameter monitoring, failed to find a statistically

significant change in the proportions of unanticipated transfers to ICU. However, the remaining, and

largest, before-after study(26) of single parameter monitoring found CM of oximetry to be associated

with an almost 50% decrease in unplanned transfers to ICU per 1,000 patient-days.

IM studies also produced mixed results in the prevention of ICU transfers (Table 3). Five of the eight

IM studies measuring unplanned transfers to ICU or critical care reported no significant impact on

transfer rates following intervention, including a very large study where ICU transfers remained

constant in the intervention and control periods.(31) In contrast, three large IM studies found

significant reductions in transfers to ICU of at least 50%.(33, 35, 39)

Seven studies measuring ICU transfers (1 CM and 6 IM studies) used equivalent measures to enable

the meta-analysis of this outcome. Figure 2 shows study-specific and pooled odds ratios for number of

patients transferred to ICU comparing the intervention and control arms. Neither the single CM study

(top part of the graph) nor IM interventions (lower part of Figure 2) produced any significant

association between ICU transfers and the intervention (p=1.00 and p=0.40 respectively). The meta-

analysis showed no significant association between the intervention and reductions in ICU transfers

(OR=0.85; 95%CI 0.58 to 1.25, p=0.42). There was significant moderate heterogeneity between IM

Page 12 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 12 of 41

studies (I2=61.5%, p=0.024) and differences between studies were not associated with the study

quality scores (p>0.4).

<FIGURE 2 HERE>

None of the indicators of quality or other factors that might influence the results were associated with

differences in the odds ratios across studies (p>0.4).

Length of hospital stay

Of the seven CM studies reporting hospital LOS, five (two large(26, 38) and three small(29, 30, 40)

reported no significant difference following introduction of CM; whereas two smaller studies(27, 28)

found that CM led to significant reductions in mean LOS of approximately 7 days.

Most (7/9) IM studies reporting LOS failed to find reductions of overall mean or median hospital LOS

compared with usual care. One study found an increase in LOS.(35) A large before-and-after study

using manual input of vital signs data into bedside electronic devices found no significant differences

in mean LOS when the analysis was adjusted for confounders.(31) The effect sizes reported by the

two before-and-after studies finding significant change in LOS ranged from <1 day(34) to 3 days.(33)

Other adverse events

Regardless of the nature of the strategy used, most (9/11) of the IM studies reporting other serious

adverse events such as cardiac arrests or cerebrovascular events failed to find a significant difference

in complication rates. The remaining two studies reported significant reductions in the incidence of

sepsis(41) and rates of return to surgery within six days.(34) None of the four IM studies examining

cardiac arrest rates found significant reductions associated with IM strategies.

Page 13 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 13 of 41

Three of the selected CM studies reported impact on functional status at the time of discharge, and

only the cohort study(28) showed statistically significant evidence of improvement: 66% in

intervention, 35% in controls (p<0.0001). The other two RCTs indicated a similar finding but did not

achieve statistical significance, likely due to small sample sizes.(27, 40)

Discussion

Numerous strategies using different combinations of manual, semi-automated or fully automated

monitoring technologies, bedside or patient-worn and clinician-portable devices, for IM or CM of

vital signs are being trialled in hospitals today. Our review of 22 studies of the effectiveness of vital

sign monitoring in general wards indicates that introduction of CM strategies or increased frequency

of IM has mixed impacts on patient outcomes. The principal findings are summarised separately.

Continuous Monitoring

Studies of CM were generally of medium duration, with 6 of the 9 reviewed lasting between 6 and 23

months. Bias assessment of the nine CM studies identified potential validity and generalisability

issues including cross-contamination where the same staff cared for intervention and control

subjects;(28) unblinded convenience sampling;(30) selection of controls not directly comparable, for

example from the two decades before the intervention;(42) small sample sizes;(27, 40, 43) or a small

proportion (16%) of patients complying with CM for the anticipated observation period. (30) In some

cases, findings lost statistical significance after adjustment for potential confounders.(38) Other

limitations included biased allocation according to bed availability, with the intervention unit being

filled first and subsequent patients allocated to conventional care.(28) In another study, the control

group was allowed to use different manual, or electronic devices including the intervention device

under investigation for some of the time.(30) The heterogeneity of study designs and target patient

Page 14 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 14 of 41

populations made it inappropriate to attempt sub-group analyses. Several studies were either pilots,

small, or not well designed RCTs. The above makes it difficult to interpret pooled estimates.

However, this review is not only concerned with statistical heterogeneity in the studies (forest plot).

Clinical heterogeneity in this review likely reflects differences in practice across health systems,

disparities in the way studies implemented monitoring approaches, variations in patient populations

under investigation, selection of control groups, and how they defined their outcomes of interest.

Our analysis indicates that the majority of CM studies do not demonstrate effectiveness in prevention

of serious adverse events, cardiac arrests, or reductions in ICU transfers. Effect sizes varied

substantially between studies and some findings may or may not have clinical importance despite

marginally statistical significance. For example, one study reported overall decrease in LOS from 4.0

to 3.6 days after the intervention, with similar values for the control units of 3.8 to 3.6 days.(38) Such

marginal reductions are unlikely to translate into health gains for patients or financial benefit for

hospitals and may not be attributable to the intervention.

A non-significant reduction of 13% in in-hospital mortality (Figure 1) was probably a consequence of

lack of power of the small CM studies. Some of the other benefits of CM identified in this review

were not all necessarily interpretable as life-saving. For instance, early detection of fever by an

automated system does not preclude identification of a temperature spike by a nurse some time later;

and reductions in LOS may have been influenced by early discharge policies or other system factors

which were not discussed in the published studies.

Intermittent monitoring

Most of the IM effectiveness studies had a before-after design. Controls were generally selected from

the same ward before the intervention (7/13), or other wards in the same hospital (4/13), with two

studies selecting controls from other hospitals. Patient assignment was non-random in 6 of the studies,

and the remaining 7 included all consecutive admitted patients (5/13) and only two were randomised

Page 15 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 15 of 41

(2/13). The quality scores of the IM studies were generally high, with 10 scoring 8 and above.

Weaknesses of the IM studies included convenience sampling of the study individuals(44) or

intervention site;(32) or had seasonal differentials in recruitment between the control and intervention

phases.(33, 36)

Study duration was generally short, with 8 lasting 3-6 months and the remaining 5 between 9 and 18

months. The Hawthorne effect may have also played a role in the differences between groups(39);[say

how] hence validity of the observed changes is not certain. Short follow-up times precluded sufficient

numbers of outcome events of interest after the intervention to detect significant differences. Some

monitored only for 24 hours from admission or from the time of a procedure. (39, 44-46) The

likelihood of underestimating adverse event rates is high if LOS was below two days. With three

exceptions,(32, 35, 37) the majority of studies (9/13) did not attempt adjustment for potential

confounders such as age, comorbidities, illness severity, or any hospital workload effects. Adjusting is

necessary if study subjects are not randomized to study arms. Only one of the studies was a RCT.(45)

The long-term practice of IM of vital signs in routine general ward care has shown potential for

identification of patient deterioration, assistance with escalation of care, and increased rapid response

attendances. There is some indication of reduction of nurses’ time for measurement and recording of

vital signs, in particular when vital signs are supplemented with algorithms that calculate patient’s

risk,(33) real-time instability indices(47) or pre-defined prompts for clinical decision.(31) Yet most

IM studies regardless of the type of intervention indicated mixed findings. Most IM studies did not

report the impact on outcomes such as cardiac arrest or severe complications requiring aggressive

treatment. The pooled mortality reduction was estimated to be 19% (Figure 1).

The proportion of ICU readmissions increased after implementation of a new IM vital signs

monitoring strategy, possibly indicating chronic deterioration of the same patients rather than new

deterioration.(44) The lack of change in cardiac arrest rates in many studies was probably due to a

very low baseline rate (<1%), so further improvement may be infeasible.(31, 33, 35) Sample sizes

Page 16 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 16 of 41

were small in some cases, or observations data were only analysed for a sub-sample of charts per

ward.(32, 37, 46)

Diversity in the IM initiatives used is even larger than strategies used for CM. This is likely due to the

lack of consensus on the optimal monitoring frequency, variation in acceptability of the use of EWS,

and difference in availability of resources at various sites. IM study results were also affected by:

changes in hospital policy limiting availability of ICU beds(46); participation rates not reported in

most studies (9/13); changes in patient casemix and age profile during the study period (36); for

mortality, only selected causes reported(36); partial outcome ascertainment limited to a non-random

sample of patient records(32, 37, 46); and low inter-rater agreement between case note reviewers.(37)

Choice of adverse event indicators

While studies report overall in-hospital mortality, this indicator has been criticised as too crude a

measure of quality of care that may be affected by early discharge policies and by the presence of do-

not-resuscitate orders (DNR).(48) Unfortunately, the included studies did not report proportions of

DNR orders and therefore results must be interpreted with this caveat in mind. ICU transfer is also a

contentious indicator, as it could reflect either preventable complications(49) or proactive early

identification and management to prevent critical deterioration.(50) Other measures such as total

length of stay in ICU and rate of unplanned ICU re-admissions have been suggested to better reflect

quality of care,(51) but this issue is beyond the scope of this review, as those indicators were not

generally reported in the eligible studies. This review included any vital sign monitoring studies that

met the eligibility criteria regardless of heterogeneity of design and outcome measurements reflecting

the diversity of real-world practice. Hence the evidence for effectiveness of CM and IM in preventing

other adverse events is not conclusive, as the study designs were heterogeneous and sample sizes

small, and most were non RCTs. Most non-RCT study subjects were non-randomly selected; and

most of these studies failed to adjust for confounders (12 of the non-RCTs); and the impact of

educational activities and clinical response types was not systematically evaluated. Discordance in the

Page 17 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 17 of 41

findings for hospitals using the same device across different countries(31) suggests that there are

system, management, or patient factors contributing to heterogeneous outcomes which could not be

adjusted for in the analyses to arrive at generalisable conclusions.

Increased monitoring frequency is common after an adverse event requiring a RRS call to enhance

identification of further deterioration.(52) There has been a growing trend of introducing CM

technology with alerts,(53) along with clinical decision support(54) in non-critical care areas. It is

assumed that this will minimise delays in activating the RRS(55) and save more lives than current

practice. Perhaps this trend is based on the principle that mortality in ICUs is not unexpected(56) due

to CM technology used in ICUs for decades.(57) However, as demonstrated by our review, evidence

of the effectiveness of CM on general wards is limited(58) and the impact of CM on patient outcomes

has not been extensively studied.

Strengths and limitations of this review

To our knowledge, this is the first systematic review addressing the question of effectiveness of vital

signs monitoring on general wards using either continuous or intermittent monitoring strategies. This

extensive comparison reveals the many ways in which monitoring is implemented and effectiveness is

measured in clinical practice. This is both a strength and weakness of our review. Other strengths of

this study are the rigorous search of over three decades of publications; inclusion of both electronic

devices and manual practices with and without clinical decision support aids; inclusion of studies

evaluating mostly multi-parameter monitoring interventions; inclusion of pilot studies and large scale

interventions; use of sensitivity analysis to examine the impact of the largest IM study; use of a bias

score covering 13 quality criteria; inclusion of medical and surgical wards; inclusion of a variety of

real-life interventions relevant to various clinical settings; assessment of a wide range of outcomes;

and interventions in numerous countries and patients with a potential for wide applicability.

Page 18 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 18 of 41

Limitations of the study are the heterogeneity of outcome measurements and denominator types

precluding a meta-analysis of many outcomes; absence of published details on the description of the

team’s response protocols to deterioration which could have affected the ultimate outcome; inclusion

of several studies with small numbers of patients; absence of information on unpublished studies; and

searches in English language only. The 22 studies in this review were mostly conducted in UK,

Australia and USA, so caution must be exercised on their generalisability in light of the variations in

health systems and hospital profiles. Some may argue that the inclusion of non-RCTs is a weakness

but we believe lower evidence study designs better reflect the current evaluation research feasible in

hospitals today. And this review of them may prompt improved observational study designs where

RCTs are infeasible.

Future research

Several studies were affected by suboptimal design and inappropriate reporting of statistical testing.

We recommend more rigorous evaluation in the future in order to make best use of evidence for

effectiveness of monitoring on patient safety. In particular, random subject assignment; total sample

sizes larger than 150; longer duration of individual inpatient follow-up with the intervention/device

beyond the first 3 days after admission; patient recruitment not affected by seasonality; adjustment for

potential confounders for observational studies (eg, co-morbidities, disease severity, etc.); disclosure

of confidence intervals or standard errors of estimates, significance levels and p values for all figures

presented. Further areas for improvement include: associating efficacy of the process with clinical

outcomes; reporting outcomes with standard measures such as events per patient-days to control for

enhanced likelihood of events among people with longer LOS; exclusion of patients with DNR or

inclusion of 30-day mortality to account for early discharge or transfer policies; determination of total

length of stay in ICU and rate of unplanned ICU readmissions as an alternative to reflect quality of

care; and further investigating accuracy of measurements using comparable methods. In the end,

properly conducted and sufficiently powered RCTs of IM and CM, particularly with regard to adverse

Page 19 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 19 of 41

outcomes, are needed before clear conclusions can be drawn as to the overall efficacy of IM and CM

of vital signs in general wards.

Conclusions

Despite the caveats and study weaknesses, it appears that the introduction of continuous monitoring is

effective in detecting deterioration of general ward patients earlier than usual care, and both

intermittent and continuous monitoring improve the activation of RRS. New approaches to IM appear

to have significantly improved documentation and frequency of vital signs measurement and, in some

cases, this has led to reporting of more adverse events in the intervention group because they were

more frequently identified. IM appears to have modest impact on reduced in-hospital mortality.

Overall, however, early detection through vital signs monitoring did not appear to consistently

translate into reductions in ICU transfers, LOS, or incidence of other in-hospital adverse events.

Based on English-language findings available to mid-2014, we conclude that there is insufficient

evidence of effectiveness to recommend routine use of continuous monitoring in general wards.

Funding

This work was funded by a grant from the National Health and Medical Research Council of Australia

(# 1054146).

Acknowledgments

Our appreciation goes to the Liverpool Hospital librarians for assistance with printing articles and

processing interlibrary loans.

Authors’ Contributions

Page 20 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 20 of 41

MCM, KH and MN participated in the design. MCM led the bias assessment, data analysis,

interpretation and writing and contributed to eligibility assessment. MP led the systematic search and

contributed eligibility assessment. RT led the meta-analysis and contributed to eligibility assessment.

All authors contributed to protocol development, data gathering, consolidation and processing of

findings, and participated in the writing and editing of several versions of this manuscript.

Data sharing statement

MCM, RMT and MP had full access to raw data. MN and KH had access to processed data. No

additional data are available. All information covered in the tables and graphs.

Page 21 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review Only

Page 21 of 41

Table 1. Summary of included studies and bias assessment score

Author,

Date,

Country

Single/

Multi-

centre

Study

Design

Intervention (I) Comparison

(C)

Number

of

Patients

Type of

Patients

Included

Vital

Signs

Assessed

Key Outcomes Measured Mortality (M), LOS, ICU Transfers (TRF),

Escalation Calls (ESC), Other Adverse

Events (AE), Detection of Deterioration (DD);

Quality of VS Observations (OBS)

Sco

re

M LOS TRF ESC AE DD OBS

Continuous Monitoring (CM) Studies (9 studies)

Brown et

al.(38),

2014,

USA

Single B/A

controlled

trial

Motion-sensing

device placed under

patient’s mattress

for CM.

IM in a ‘sister’

control ward

and same ward

pre-intervention.

C: 5,329

I: 2,314

All

patients.

HR, RR.

�� 11

Cavallini et

al.(28), 2003,

Italy

Single Non-RCT Multi-parameter

device connected to

patients for >72

hours CM.

IM in a non-

intensive control

ward

concurrently.

C: 134

I: 134

Stroke

patients.

BP, HR,

SPO2, Tº,

RR, ECG.

�� 6

Kisner et

al.(42),

2009,

Switzerland

Single B/A trial

with

historical

controls

Small sensor device

placed on patient’s

ear lobe for CM.

IM in same

ward pre-

intervention.

C: 238

I: 119

Post-

operative

patients.

HR,

SPO2.

�� 8

Langhorne et

al.(40), 2010,

UK

Single RCT CM for ≤7 days, and

new protocol on

response to VS

abnormalities.

IM concurrently

on randomised

patients in same

ward

C: 16

I: 16

Stroke

patients.

BP, HR,

Tº, SPO2.

�� 10

Sulter et

al.(27),

2003,

Netherlands

Single RCT Multi-parameter

device connected to

patients for >48

hours CM.

IM in a control

ward

concurrently.

C: 27

I: 27

Stroke

patients.

BP, HR,

Tº, SPO2,

ECG.

�� 10

Taenzer et

al.(26), 2010,

USA

Single B/A

controlled

trial

Finger probe for CM

of SPO2, alerts to

nurse via pager, and

IM or selective

CM in two

control wards

C: 7,006

I: 6,392

All

patients.

BP, HR,

SPO2,

RR, LOC.

�� 10

Page 22 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


Page 22 of 41

Author,

Date,

Country

Single/

Multi-

centre

Study

Design


(C)

Number

of

Patients

Type of

Patients

Included

Vital

Signs

Assessed





Sco

re


education. and same ward

pre-intervention.

Tarassenko et

al.(29), 2005,

UK


device connected to

patients for 72 hours

CM.

IM concurrently

on randomised

patients in same

ward

C: 201

I: 201

High-risk

patients.

BP, HR,

SPO2,

skin Tº,

RR.

�� 6

Varela et

al.(43), 2011,

Spain

Single Cohort

study

CM for 24 hours

with ‘Holter’ device.

IM concurrently

on same patients

with tympanic

thermometer.

C & I: 55 Patients

with Tº

>38ºC.

Tº.

�� 8

Watkinson et

al.(30), 2006,

UK


device connected to

patients for 72 hours

CM.

IM with manual

or automated

non-study

monitors

C: 201

I: 201

High-risk

patients.

BP, HR,

SPO2,

skin Tº,

RR.

�� 10

Intermittent Monitoring (IM) Studies (13 studies)

Bellomo et

al.(31), 2012,

USA, Sweden,

Netherlands,

UK, Australia

Multi B/A trial Automated monitor

for electronic

capture of BP, HR,

SPO2, and Tº.

Manual entry of RR

and LOC.

Automated EWS

calculation.

Manual

measurement

and entry of VS

in same wards

pre-intervention.

C: 9,617

I: 8,688

All

patients.

BP, HR,

SPO2, Tº,

RR, LOC.

�� 9

Benning et

al.(32), 2011a,

UK

Multi B/A

controlled

trial

Multi-component

patient safety

intervention

including EWS and

RRS introduction.

Usual care on

control wards

and same wards

pre-intervention.

C: 617

I: 620

Patients

aged 65+

with acute

respiratory

disease.

BP, HR,

SPO2, Tº,

RR, LOC.

�� 8

Page 23 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


Page 23 of 41

Author,

Date,

Country

Single/

Multi-

centre

Study

Design


(C)

Number

of

Patients

Type of

Patients

Included

Vital

Signs

Assessed





Sco

re


Benning et

al.(37),

2011b,

UK

Multi B/A

controlled

trial

Multi-component

patient safety

intervention

including EWS and

RRS introduction.

Usual care on

control wards

and same wards

pre-intervention.

C: 476

I: 226

Patients

aged 65+

with acute

respiratory

disease.

BP, HR,

SPO2, Tº,

RR, LOC.

�� 7

Cahill et

al.(59), 2011,

Australia

Single B/A trial New observation

chart and associated

education program.

Usual care in

same wards pre-

intervention.

C: 2,557

I: 4,685

All

patients.

BP, HR,

RR,

SPO2.

�� 7

De Meester et

al.(34), 2013,

Belgium

Single B/A trial Education on RR

and LOC, and new

observation and

MEWS protocol.

Usual care in

same wards pre-

intervention.

C: 2,359

I: 1,888

Post-

operative

patients.

BP, HR,

SPO2, Tº,

RR, LOC.

�� 11

Fernandez &

Griffiths (45),

2005,

Australia

Single RCT New protocol to

increase frequency

of observation and

education program.

Usual care in

same ward pre-

intervention.

C: 96

I: 93

Post-

operative

patients.

BP, HR,

SPO2, Tº,

RR, LOC.

�� 9

Hammond et

al.(44), 2013,

Australia


chart, introduction

of MEWS, and

education program.

Usual care in

same ward pre-

intervention.

C: 69

I: 70

Post-ICU

patients.

BP, HR,

SPO2, Tº,

RR.

�� 9

Jones et

al.(33), 2011,

UK

Single B/A trial Electronic entry of

manually measured

VS, introduction of

EWS, and

automated alerts to

doctor.

Paper-based VS

charts with no

EWS in same

ward pre-

intervention.

C: 705

I: 776

Patients

with stay

>1 day.

BP, HR,

Tº, RR,

LOC.

�� 9

Ludikhuize et

al.(39), 2014,

Single Quasi-

RCT

New observation

and MEWS

Usual care with

MEWS ‘when

C: 432

I: 372

Patients

with stay

BP, HR,

SPO2, Tº,

�� 9

Page 24 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


Page 24 of 41

Author,

Date,

Country

Single/

Multi-

centre

Study

Design


(C)

Number

of

Patients

Type of

Patients

Included

Vital

Signs

Assessed





Sco

re


Netherlands protocol, and

education.

clinically

indicated’ in

same wards pre-

intervention.

>1 day. RR, LOC.

Mitchell et

al.(35), 2010,

Australia

Multi B/A trial New observation

chart, introduction

of MEWS, and

education program.

Usual care in

same wards pre-

intervention.

C: 1,157

I: 985

All

patients.

BP, HR,

SPO2, Tº,

RR.

�� 11

Robb &

Seddon (46),

2010,

New Zealand


chart, introduction

of EWS, and

education program.

Usual care in

same wards pre-

intervention.

Not

reported.

All

patients.

BP, HR,

Tº, RR,

LOC.

�� 6

Sawyer et

al.(41), 2011,

USA

Single Non-RCT Automated sepsis

score alert to nurses

after entry of VS

into electronic

record.

Sepsis score on

entry of VS into

electronic record

- no automated

alerts.

C: 181

I: 89

Patients

generating

a sepsis

alert.

BP, HR.

�� 11

Schmidt et

al.(36), 2014,

UK

Multi B/A trial Electronic entry of

manually measured

vital signs, and

automated EWS

calculation.

Paper-based

charting and

EWS in same

wards pre-

intervention.

C:

64,861

I: 79,177

All

patients.

BP, HR,

Tº, RR,

LOC.

�� 9

AE=other adverse events (e.g., cardiac arrests); B/A=before and after; BP=blood pressure; C=control; CM=continuous monitoring; DD=detection of deterioration; ECG=electrocardiography;

ESC=escalation calls (e.g., rapid response system activation); EWS=early warning scores; HR=heart rate; I=intervention; ICU=intensive care unit; IM=intermittent monitoring; LOC=level of

consciousness; LOS=length of stay; M=mortality; MEWS=modified early warning scores; OBS=quality of vital sign observations; RCT=randomised controlled trial; RR=respiratory rate;

RRS=rapid response system; SPO2=oxygen saturation; Tº=temperature; TRF=intensive care unit transfers; VS=vital signs

Page 25 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


Page 25 of 41

Table 2. Effect of continuous monitoring studies by key outcomes measured

Outcome Findings from Continuous Monitoring Studies Total

Patients

p value Effect*

Early

Detection of

Deterioration

Number of complications detected per patient – 1·1 in the control and 2·3 in the

intervention.(28)

Complications requiring treatment – 19% in the control and 64% in the intervention.(28)

Good outcome post-complications – 35% in the control and 66% in the intervention group.(28)

268

268

268

<0·0001

<0·0001

<0·0001

�� +

�� +

�� +

Adverse physiological events detected – 5 in the control and 12 in the intervention group.(40)

Detection of abnormal BP – 12·5% in the control and 62·5% in the intervention group.(40)

32

32

<0·001

0·03

�� +

�� +

Detection of hypoxia – 22·2% in the control and 59·3% in the intervention group.(27) 54 0·01 �� +

Detection of fever peaks in 21 patients with intervention not detected by usual monitoring.(43) 55 – �� +

Acute changes in treatment – 49% in the control and 51% in the intervention group.(30) 402 – ��

Escalation

Calls

Rescue events per 1,000 discharges – 3·4 in the control and 1·2 in the intervention.(26) 13,398 0·01 �� +

Cardiac arrest calls per 1,000 patients 6·3 in the control and 0·9 in the intervention.(38) 7,943 <0·01 �� +

Unscheduled visit by medical staff – 53% in the control and 47% in the intervention.(30)

Unscheduled visit by critical care team – 8·5% in the control and 8% in the intervention.(30)

402

402

–

–

��

��

Mortality There were 2 deaths in the control and 1 in the intervention group.(38) 7,643 – ��

Mortality at discharge – 6% in the control and 4% in the intervention group.(28) 268 – ��

There were no deaths in the control and 1 death in the intervention group.(40) 32 – ��

There were 4 deaths in the control and 2 deaths in the intervention group.(26) 13,398 – ��

Patients alive at discharge – 79·9% in the control and 79·2% in the intervention group.(29) 402 – ��

Final hospital mortality – 21% in the control and 20% in the intervention group.(30) 402 – ��

Mortality – 25·9% in the control and 3·7% in the intervention group.(27) 54 0·05 �� +

Page 26 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


Page 26 of 41

Outcome Findings from Continuous Monitoring Studies Total

Patients

p value Effect*

ICU Transfers Transfers per 1,000 patients – 26·52 in the control and 25·93 in the intervention group.(38) 7,643 0·92 ��

Unscheduled ICU admissions – 5% in both the control and intervention.(30) 402 – ��

Transfers per 1,000 patient-days – 5·6 in the control and 2·9 in the intervention group.(26) 13,398 0·02 �� +

LOS Mean hospital LOS – 17·1 days in the control and 9·2 days in the intervention.(28) 268 <0·0001 �� +

Mean ±SD time to discharge – 25±7 days in the control and 16±5 in the intervention.(27) 54 – �� +

Mean ward LOS – 3·61 days in the control and 3·63 days in the intervention.(38) 7,643 0·37 ��

LOS – 3·69 days in the control and 3·68 days in the intervention.(26) 13,398 >0·05 ��

Mean hospital LOS – 19 days in the control and 21 days in the intervention group.(29) 402 – ��

Mean hospital LOS – 21 days in the control and 22 days in the intervention group.(30) 402 – ��

Hospital LOS – 10 days in the control and 11 days in the intervention group.(40) 32 0·27 ��

Other Adverse

Events

Patients with poor outcome – 42% in the control and 15% in the intervention group.(28)

Mean duration of adverse event – 2·4 days in the control and 1·0 days in the intervention.(28)

Stroke progression – 14·9% in the control and 11·2% in the intervention group.(28)

268

268

268

<0·02

<0·02

0·58

�� +

�� +

��

Incidence of post-operative AF – 28% in the control and 18% in the intervention.(42) 357 0·056 ��

Stroke progression – 31·3% in both control and intervention group.(40) 32 1·00 ��

Patients with poor outcome – 48·1% in the control and 25·9% in the intervention.(27) 54 0·15 ��

Emergency surgical procedures – 3% in the control and 2% in the intervention group.(30) 402 – ��

Emergency surgical procedures – 3% in the control and 2% in the intervention group.(30) 402 – ��

Increased incidence of complications due to immobility of CM (OR 1·0 vs 1·7) (40)

NR – �� -

Functional

Status

Achieved walking within 5 days: OR 0·5 in intervention, OR 4·2 in controls(40)

32 >0·05 ��

Independent at 3 months (Rankin score 0-2): OR 3·4 in intervention, 2·3 in controls(40)

32 >0·05 ��

Page 27 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


Page 27 of 41

Poor outcome at discharge: 25·9% in intervention group, 48·1% in controls(27) 54 0·16 ��

Improved discharge outcomes: 66% in intervention, 35% in controls(28) 268 <0.0001 �� +

* Effect direction is indicated by �� if increased or �� if decreased in the intervention group, or lack thereof X) reported as per conclusion of included studies.. The signs + or – denote whether the

resulting indicator is a beneficial health outcome (e.g. reduced length of stay) or a detrimental health outcome (e.g. increased complications)

Page 28 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


Page 28 of 41

Table 3. Effect of intermittent vital signs monitoring interventions by key outcomes measured

Outcome Intervention Findings from Intermittent Monitoring Studies Total

Patients

p value Effect*

Early

Detection of

Deterioration

Changes to monitoring

and/or EWS protocol.

Abnormal VS at transfer – 6% in the control and 7·5% in the intervention.(45)

Abnormal VS at 4 hours – 4·1% in the control and 3·8% in the intervention.(45)

Abnormal VS at 24 hours – 3·6% in the control and 2·7% in the intervention.(45)

189

189

189

0·44

0·77

0·11

��

��

��

Delays in escalation – 44 hours delay in control and 20 hours in the intervention.(39) 804 0·79 ��

Chart redesign ± EWS. Clinical instability reviews – 43·6% in the control and 69.6% in the intervention.(35) 2,142 0·001 �� +

Automated alerts. Intervention ≤12 hours of alert – 55·8% in control and 70·8% in the intervention.(41) 270 0·018 �� +

Escalation

Calls

Changes to monitoring and/or EWS protocol.

84 RRS activations – 22 from the control and 64 from the intervention group.(39)

Activations per 1,000 admissions – 6·5 in the control and 19·6 in the

intervention.(39)

804

804

<0·003

– �� +

�� +

Chart redesign ± EWS. RRS reviews – 2·2% in the control and 3·9% in the intervention.(35) 2,142 0·03 �� +

Median RRS calls per month – 27·5 in the control and 70·5 in the intervention.(46) NR – �� +

Electronic capture of VS, EWS

calculation/alerts.

RRS calls per 1,000 admissions – 21·3 in the control and 24·1 in the

intervention.(31)

RRS calls triggered by RR – 21% in the control and 31% in the intervention.(31)

18,305

18,305

0·21

0·029 ��

�� +

Clinician attendance (EWS 3-5) – 29% in the control and 78% in the

intervention.(33)

Clinician attendance (EWS >5) – 67% in the control and 96% in the intervention.(33)

1,481

1,481

<0·001

<0·001 �� +

�� +

Mortality Changes to monitoring


19 deaths in the control group and 4 deaths in the intervention group.(34) 4,247 0·015 �� +

1% in the control and 2% in the intervention group.(39) 804 – ��

Chart redesign ± EWS. 5·8% in the control and 2·9% in the intervention group.(44) 139 0·44 ��

Page 29 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


Page 29 of 41


Patients

p value Effect*

2·6% in the control and 0·6% in the intervention group.(35) 2,142 <0·001 �� +

Multiple changes,

including EWS and

RRS.

16·5% in the control and 12·9% in the intervention group.(32) 1,237 >0·05 ��

10·3% in the control and 6·1% in the intervention group.(37) 702 0·043 �� +


calculation/alerts.

1·8% in the control and 2·0% in the intervention group.(31) 18,305 0·36 ��

9·5% in the control and 7·6% in the intervention group.(33) 1,481 0·19 ��

7·75% in the control and 6·42% in the intervention.(36)

7·57% in the control and 6·15% in the intervention.(36)

72,677

71,361

<0·0001

<0·0001

�� +

�� +

Automated alerts. 11·6% in the control and 10·1% in the intervention group.(41) 270 0·714 ��

ICU

Transfers



There were no ICU transfers in either the control or intervention groups.(45) 189 – ��

Transfers post RRS activation – 50% in the control and 26% in the intervention.(39) 804 – �� +

Chart redesign ± EWS. ICU admission – 20% in the control and 21% in the intervention group.(44) 139 – ��

Transfers to ICU reported as no significant change.(46)

Unplanned ICU admissions – 1·8% in the control and 0·5% in the intervention.(35)

NR

2,142

–

0·005

��

�� +

Electronic capture of

VS, EWS

calculation/alerts.

Unplanned ICU admissions – 5·4% in both the control and intervention.(31) 18,305 0·95 ��

Critical care bed admissions – 2·0% in the control and 0·6% in the intervention.(33) 1,481 0·04 �� +

Automated alerts. Transfer rate – 23·2% in the control and 25·8% in the intervention group.(41) 270 0·634 ��

LOS Changes to monitoring


Mean – 4·55 days in the control and 4·11 days in the intervention.(34) 4,247 0·004 �� +

Mean ±SD – 1·80±1·36 days in the control and 2±1·33 days in the intervention.(45) 189 – ��

Median – 8 days in the control and 10 days in the intervention.(39) 804 – ��

Chart redesign ± EWS. Mean – 7·4 days in the control and 5·0 days in the intervention.(59) 7,242 – ��

Median – 13 days in the control and 17 days in the intervention.(44) 139 0·06 ��

Page 30 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


Page 30 of 41


Patients

p value Effect*

Median – 4 days in the control and 4·8 days in the intervention.(35) 2,142 0·03 �� –


VS, EWS

calculation/alerts.

Median adjusted– 3·9 days in the control and 3.8 days in the intervention.(31) 18,305 0.26 ��

Median – 9·7 days in the control and 6·9 days in the intervention.(33) 1,481 <0·001 �� +

Automated alerts. Median – 7 days in the control and 9 days in the intervention.(41) 270 0·805 ��

Other

Adverse

Events



Postoperative re-surgery – 141 in the control and 78 in the intervention.(34) 4,247 0·007 �� +

Untoward events – 17 in the control and 21 in the intervention.(45) 189 0·44 ��

Events per 1,000 admissions – 6·5 in the control and 8·5 in the intervention.(39) 804 – ��

Chart redesign ± EWS. Cardiac arrests – 3 in the control and 2 in the intervention group.(44) 139 0·68 ��

Cardiac arrests – 4 in the control and 0 in the intervention group.(35) 2,142 – ��

Cardiac arrest calls stated as no change from 5/month in both groups.(46) NR – ��

Multiple changes, including EWS and

RRS.

Event rate per 100 patients – 6·2 in the control and 3·7 in the intervention.(32) 1,237 0·12 ��

Event rate per 100 patients – 0·9 in the control and 0 in the intervention.(37) 702 >0·5 ��


VS, EWS

calculation/alerts.

Cardiac arrests – 0·4% in the control and 0·3% in the intervention.(31)

Serious adverse events – 11·5% in the control and 10·8% in the intervention.(31)

18,305

18,305

0·3

0·12

��

��

Cardiac arrests – 3 in the control and 0 in the intervention.(33) 1,481 0·21 ��

Automated alerts. Incidence of sepsis – 23·2% in the control and 12·4% in the intervention.(41) 270 0·035 �� +

Quality of

Vital Signs

Observations



Mean frequency per shift – 0·9076 in the control and 0·9940 in the intervention.(34)

Mean number of VS recorded – 1·81 in the control and 2·45 in the intervention.(34)

4,247

4,247

<0·001

<0·001

�� +

�� +

Number of VS sets in 24 hours – 6 in the control and 7 in the intervention.(45) 189 – ��

Calculation of EWS – 2% in the control and 70% in the intervention.(39)

Number of complete VS recorded – 1% in control and 43% in the intervention.(39)

804

804

<0·001

<0·001

�� +

�� +

Page 31 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


Page 31 of 41


Patients

p value Effect*

Chart redesign ± EWS. Number of complete VS recorded – 47·6% in control and 96·4% in intervention.(59) 7,242 <0·001 �� +

Number of complete VS recorded – 210% increase from control to intervention.(44) 139 <0·001 �� +

Mean daily frequency – 3·4 in the control and 4·5 in the intervention.(35) 2,142 0·001 �� +

Completeness of observations – 80% in control and 91% in intervention.(46) NR – �� +

Multiple changes,

including EWS and

RRS.

Completeness of VS recorded on admission was not significant across all VS.(32)

Completeness of RR recording at 6 hours and at 12 hours – 43% and 37%

(respectively) in the control and 82% and 78% in the intervention.(32)

1,237

1,237

1,237

–

0·015

0·008

��

�� +

�� +

Completeness of VS recorded was not significant across all VS.(37) 702 – ��


calculation/alerts.

Minutes required to measure VS – 4·1 in the control and 2·5 in the intervention.(31) 18,305 <0·0001 �� +

Rechecking VS (for EWS 3-5) – 27% in the control and 22% in the intervention.(33) 1,481 0·07 ��

EWS=early warning scores; ICU=intensive care unit; LOS=length of stay; NR=not reported; RR=respiratory rate; RRS=rapid response system; SD=standard deviation; VS=vital signs

* Effect direction is indicated by �� if increased or �� if decreased in the intervention group, or lack thereof (X) reported as per conclusion of included studies. Effect (+) beneficial (e.g. increased

RRS activations), (-) detrimental (e.g. increased length of stay). ��claimed as effective (–) = p value not included where not reported in the included studies.

Page 32 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 32 of 41

Figure 1. Forest plot and pooled estimates of mortality outcomes reported by 4 CM and 9 IM

studies (* denotes RCT).

Page 33 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 33 of 41

Figure 2. Forest plot and pooled estimates of unplanned ICU transfers reported by 1 CM and 6

IM studies (*denotes RCT).

Page 34 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 34 of 41

REFERENCES

1. Lockwood C, Conroy-Hiller T, Page T. Vital Signs. Systematic Review. JBI Reports. 2004;2:207-

30.

2. Hillman KM, Bristow PJ, Chey T, Daffurn K, Jacques T, Norman SL, et al. Antecedents to

hospital deaths. Internal Medicine Journal. 2001;31(6):343-8.

3. Donaldson LJ, Panesar SS, Darzi A. Patient-safety-related hospital deaths in England:

thematic analysis of incidents reported to a national database, 2010-2012. PLoS Med.

2014;11(6):DOI: 10.1371/journal.pmed.1001667.

4. Ryan H, Cadman C, Hann L. Setting standards for assessment of ward patients at risk of

deterioration. British Journal of Nursing. 2004;13(20):1186-90.

5. Le Maguet P, Roquilly A, Lasocki S, Asehnoune K, Carise E, Martin M, et al. Prevalence and

impact of frailty on mortality in elderly ICU patients: a prospective, multicenter, observational study.

Intensive Care Med. 2014 2014/03/21:1-9.

6. Evans D, Hodgkinson B, Berry J. Vital signs in hospital patients: a systematic review.

International Journal of Nursing Studies. 2001;38(6):643-50.

7. Task Force of the American College of Critical Care Medicine. Guidelines for intensive care

unit admission, discharge, and triage. Crit Care Med. 1999;27(3):633-8.

8. Jones D, Mitchell I, Hillman K, Story D. Defining clinical deterioration. Resuscitation.

2013;84(8):1029-34. PubMed PMID: 23376502.

9. Elliott M, Coventry A. Critical care: the eight vital signs of patient monitoring. British journal

of nursing (Mark Allen Publishing). 2012;21(10):621-5.

10. Van Leuvan CH, Mitchell I. Missed opportunities? An observational study of vital sign

measurements. Crit Care Resusc. 2008;10(2):111-15.

11. MERIT Study Investigators. Introduction of the medical emergency team (MET) system: a

cluster-randomised controlled trial. The Lancet. 2005;365(9477):2091-7.

12. Nurmi J, Harjola VP, Nolan J, Castrén M. Observations and warning signs prior to cardiac

arrest. Should a medical emergency team intervene earlier? Acta anaesthesiologica Scandinavica.

2005;49(5):702-6.

13. Chen J, Ou L, Hillman KM, Flabouris A, Bellomo R, Hollis SJ, et al. Cardiopulmonary arrest and

mortality trends, and their association with rapid response system expansion. Med J Aust.

2014;201(3):167-70.

14. Laurens N, Dwyer T. The impact of medical emergency teams on ICU admission rates,

cardiopulmonary arrests and mortality in a regional hospital. Resuscitation. 2011 6//;82(6):707-12.

15. Chan PS, Jain R, Nallmothu BK, Berg RA, Sasson C. Rapid response teams: A systematic

review and meta-analysis. Archives of Internal Medicine. 2010;170(1):18-26.

16. Winters BD, Pham JC, Hunt EA, Guallar E, Berenholtz S, Pronovost PJ. Rapid-response

systems: a systematic review. Crit Care Med. 2007;35(5):1238-43.

17. Curry JP, Jungquist CR. A critical assessment of monitoring practices, patient deterioration,

and alarm fatigue on inpatient wards: a review. Patient safety in surgery. 2014;8:29.

18. Sarani B. Identifying the ICU Recidivist in the Hospital [Editorial]. Critical Care Medicine.

2014;42(7):1725-6.

19. Hourihan F, Bishop G, Hillman KM, Daffurn K, Lee A. The medical emergency team: a new

strategy to identify and intervene in high risk patients. Clin Intensive Care. 1995;6:269-72.

20. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews

and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097.

21. Kmet LM, Lee RC, Cook LS. Standard Quality Assessment Criteria for Evaluating Primary

Research Papers from a Variety of Fields. Edmonton, Alberta, Canada Alberta Heritage Foundation

for Medical Research (AHFMR),, 2004 February. Report No.: Contract No.: ISBN: 1-896956-71-XX

(Online).

Page 35 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 35 of 41

22. National Health and Medical Research Council. NHMRC levels of evidence and grades for

recommendations for guideline developers Canberra2009 [cited 2015 May]. Available from:

https://www.nhmrc.gov.au/files_nhmrc/file/guidelines/developers/nhmrc_levels_grades_evidence_

120423.pdf.

23. DerSimonian R, Laird N. Meta-analysis in clinical trials. Controlled clinical trials.

1986;7(3):177-88.

24. Deeks JJ, Higgins JPT, Altman DG on behalf of the Cochrane Statistical Methods Group. Part

2.General methods for Cochrane reviews. Chapter 9. Identifying and measuring heterogeneity2011

May 2015; 2015. Available from:

http://handbook.cochrane.org/chapter_9/9_5_2_identifying_and_measuring_heterogeneity.htm.

25. Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses.

BMJ. 2003;327(7414):557-60.

26. Taenzer AH, Pyke JB, McGrath SP, Blike GT. Impact of pulse oximetry surveillance on rescue

events and intensive care unit transfers: a before-and-after concurrence study. Anesthesiology.

2010;112(2):282-7.

27. Sulter G, Elting JW, Langedijk M, Maurits NM, De Keyser J. Admitting acute ischemic stroke

patients to a stroke care monitoring unit versus a conventional stroke unit: a randomized pilot study.

Stroke; a journal of cerebral circulation. 2003;34(1):101-4.

28. Cavallini A, Micieli G, Marcheselli S, Quaglini S. Role of monitoring in management of acute

ischemic stroke patients. Stroke; a journal of cerebral circulation. 2003;34(11):2599-603.

29. Tarassenko L, Hann A, Patterson A, Braithwaite E, Davidson K, Barber V, et al. BioSign:

Mulitparameter monitoring for early warning of patient deterioration. 2005.

30. Watkinson PJ, Barber VS, Price JD, Hann A, Tarassenko L, Young JD. A randomised controlled

trial of the effect of continuous electronic physiological monitoring on the adverse event rate in high

risk medical and surgical patients. Anaesthesia. 2006;61(11):1031-9.

31. Bellomo R, Ackerman M, Bailey M, Beale R, Clancy G, Danesh V, et al. A controlled trial of

electronic automated advisory vital signs monitoring in general hospital wards. Crit Care Med.

2012;40(8):2349-61.

32. Benning A, Ghaleb M, Suokas A, Dixon-Woods M, Dawson J, Barber N, et al. Large scale

organisational intervention to improve patient safety in four UK hospitals: mixed method evaluation.

BMJ. 2011;342:d195.

33. Jones S, Mullally M, Ingleby S, Buist M, Bailey M, Eddleston JM. Bedside electronic capture of

clinical observations and automated clinical alerts to improve compliance with an Early Warning

Score protocol. Crit Care Resusc. 2011;13(2):83-8.

34. De Meester K, Haegdorens F, Monsieurs KG, Verpooten GA, Holvoet A, Van Bogaert P. Six-

day postoperative impact of a standardized nurse observation and escalation protocol: a

preintervention and postintervention study. J Crit Care. 2013 Dec;28(6):1068-74.

35. Mitchell IA, McKay H, Van Leuvan C, Berry R, McCutcheon C, Avard B, et al. A prospective

controlled trial of the effect of a multi-faceted intervention on early recognition and intervention in

deteriorating hospital patients. Resuscitation. 2010;81(6):658-66.

36. Schmidt PE, Meredith P, Prytherch DR, Watson D, Watson V, Killen RM, et al. Impact of

introducing an electronic physiological surveillance system on hospital mortality. BMJ Quality &

Safety. 2014;0:1-11. Epub 23 September 2014.

37. Benning A, Dixon-Woods M, Nwulu U, Ghaleb M, Dawson J, Barber N, et al. Multiple

component patient safety intervention in English hospitals: controlled evaluation of second phase.

BMJ. 2011 03/02/2011;342:d199. Epub 03/02/2011.

38. Brown H, Terrence J, Vasquez P, Bates DW, Zimlichman E. Continuous Monitoring in an

Inpatient Medical-Surgical Unit: A Controlled Clinical Trial. The American journal of medicine.

2014;127(3):226-32.

Page 36 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 36 of 41

39. Ludikhuize J, Borgert M, Binnekade J, Subbe C, Dongelmans D, Goossens A. Standardized

measurement of the Modified Early Warning Score results in enhanced implementation of a Rapid

Response System: a quasi-experimental study. Resuscitation. 2014;85(5):676-82.

40. Langhorne P, Stott D, Knight A, Bernhardt J, Barer D, Watkins C. Very early rehabilitation or

intensive telemetry after stroke: a pilot randomised trial. Cerebrovascular diseases (Basel,

Switzerland). 2010;29(4):352-60.

41. Sawyer AM, Deal EN, Labelle AJ, Witt C, Thiel SW, Heard K, et al. Implementation of a real-

time computerized sepsis alert in nonintensive care unit patients. Crit Care Med. 2011;39(3):469-73.

42. Kisner D, Wilhelm MJ, Messerli MS, Zünd G, Genoni M. Reduced incidence of atrial

fibrillation after cardiac surgery by continuous wireless monitoring of oxygen saturation on the

normal ward and resultant oxygen therapy for hypoxia. European Journal of Cardio-Thoracic Surgery.

2009;35(1):111-5.

43. Varela M, Ruiz-Esteban R, Martinez-Nicolas A, Cuervo-Arango JA, Barros C, Delgado EG.

'Catching the spike and tracking the flow': Holter-temperature monitoring in patients admitted in a

general internal medicine ward. International journal of clinical practice. 2011 Dec;65(12):1283-8.

44. Hammond NE, Spooner AJ, Barnett AG, Corley A, Brown P, Fraser JF. The effect of

implementing a modified early warning scoring (MEWS) system on the adequacy of vital sign

documentation. Australian critical care : official journal of the Confederation of Australian Critical

Care Nurses. 2013;26(1):18-22.

45. Fernandez R, Griffiths R. A comparison of an evidence based regime with the standard

protocol for monitoring postoperative observation: a randomised controlled trial. The Australian

journal of advanced nursing : a quarterly publication of the Royal Australian Nursing Federation.

2005;23(1):15-21.

46. Robb G, Seddon M. A multi-faceted approach to the physiologically unstable patient. Quality

and Safety in Health Care. 2010 October 1, 2010;19(5):e47.

47. Hravnak M, Edwards L, Clontz A, Valenta C, DeVita MA, Pinsky MR. Defining the incidence of

cardiorespiratory instability in patients in step-down units using an electronic integrated monitoring

system. Archives of Internal Medicine. 2008;168(12):1300-8.

48. Holloway RG, Benesch CG, Burgin WS, Zentner JB. Prognosis and Decision Making in Severe

Stroke. JAMA. 2005;294:725-33.

49. Marquet K, Claes N, De Troy E, Kox G, Droogmans M, Schrooten W, et al. One fourth of

unplanned transfers to a higher level of care are associated with a highly preventable adverse event:

a patient record review in six Belgian hospitals. Crit Care Med. 2015;43(5):1053-61.

50. Churpek MM, Yuen TC, Park SY, Meltzer DO, Hall JB, Edelson DP. Derivation of a cardiac

arrest prediction model using ward vital signs. Crit Care Med. 2012;40(7):2102-8.

51. Berenholtz SM, Dorman T, Ngo K, Pronovost PJ. Qualitative review of intensive care unit

quality indicators. J Crit Care. 2002;17(1):1-12.

52. Dombrowski W. Acutely ill patients will likely benefit from more monitoring, not less. JAMA

internal medicine. 2014;174(3):475.

53. Zimlichman E, Szyper-Kravitz M, Shinar Z, Klap T, Levkovich S, Unterman A, et al. Early

recognition of acutely deteriorating patients in non-intensive care units: assessment of an innovative

monitoring technology. Journal of hospital medicine : an official publication of the Society of

Hospital Medicine. 2012;7(8):628-33.

54. Hravnak M, Devita MA, Clontz A, Edwards L, Valenta C, Pinsky MR. Cardiorespiratory

instability before and after implementing an integrated monitoring system. Crit Care Med.

2011;39(1):65-72.

55. Tirkkonen J, Yla-Mattila J, Olkkola KT, Huhtala H, Tenhunen J, Hoppu S. Factors associated

with delayed activation of medical emergency team and excess mortality: an Utstein-style analysis.

Resuscitation. 2013;84(2):173-8.

56. McGloin H, Adam SK, M. S. Unexpected deaths and referrals to intensive care of patients on

general wards. Are some cases potentially avoidable? J R Coll Physicians Lond 1999;33(3):255-9.

Page 37 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 37 of 41

57. Goldstein B. Intensive Care Unit ECG Monitoring. Card Electrophysiol Rev. 1997;1(3):308-10.

58. Taenzer AH, Pyke JB, McGrath SP. A review of current and emerging approaches to address

failure-to-rescue. Anesthesiology. 2011;115(2):421-31.

59. Cahill H, Jones A, Herkes R, Cook K, Stirling A, Halbert T, et al. Introduction of a new

observation chart and education programme is associated with higher rates of vital-sign

ascertainment in hospital wards. BMJ Quality & Safety. 2011.

Page 38 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 38 of 41

Appendix 1. Database search strategy

OvidSP Search Strategy:

Medline, EMBASE, and EBM Reviews

EBSCOhost Search Strategy:

CINAHL

1 vital signs.mp. or Vital Signs/ S1 (MH “Vital Signs”) OR “vital signs”

2 vitals.mp. S2 vitals

3 heart rate.mp. or Heart Rate/ S3 (MH “Heart Rate”) OR “heart rate”

4 pulse.mp. or Pulse/ S4 (MH “Pulse”) OR “pulse

5 blood pressure.mp. or Blood Pressure/ S5 (MH “Blood Pressure”) OR “blood pressure”

6 respiratory rate.mp. or Respiratory Rate/ S6 (MH “Respiratory Rate”) OR “respiratory rate”

7 respiration.mp. or Respiration/ S7 (MH “Respiration”) OR “respiration”

8 temperature.mp. or Temperature/ S8 (MH “Temperature”) OR “temperature”

9 skin temperature.mp. or Skin Temperature/ S9 (MH “Skin Temperature”) OR “skin temperature”

10 body temperature.mp. or Body Temperature/ S10 (MH “Body Temperature”) OR “body temperature”

11 oxygen saturation.mp. S11 (MH “Oxygen Saturation”) OR “oxygen saturation”

12 saturation.mp. S12 saturation

13 SpO2.mp. S13 SpO2

14 electrocardiography.mp. or Electrocardiography/ S14 (MH “Electrocardiography”) OR “electrocardiography”

15 ECG.mp. S15 ECG

16 EKG.mp. S16 EKG

17 consciousness.mp. or Consciousness/ S17 (MH “Consciousness”) OR “consciousness”

18 level of consciousness.mp. S18 “level of consciousness”

19 LOC.mp. S19 LOC

20 AVPU.mp. S20 AVPU

21 1 OR 2 OR 3 OR 4 OR …. 20 S21 S1 OR S2 OR S3 OR S4 OR …. S20

22 observation.mp. or Observation/ S22 observation

23 Monitoring, Physiologic/ S23 (MH “Monitoring, Physiologic”)

24 monitoring.mp. S24 monitoring

25 monitor.mp. S25 monitor

26 telemetry.mp. or Telemetry/ S26 (MH “Telemetry”) OR “telemetry”

27 oximetry.mp. or Oximetry/ S27 (MH “Oximetry”) OR “oximetry”

28 sphygmomanometer.mp. or Sphygmomanometers/ S28 (MH “Sphygmomanometers”) OR “sphygmomanometer”

29 22 OR 23 OR 24 OR …. 28 S29 S22 OR S23 OR S24 OR …. S28

30 hospital unit.mp. or Hospital Units/ S30 (MH “Hospital Units”) OR “hospital unit”

31 ward*.mp. S31 ward*

32 patient rooms.mp. or Patients’ Rooms/ S32 (MH “Patients’ Rooms”) OR “patient rooms”

33 30 OR 31 OR 32 S33 S30 OR S31 OR S32

34 21 AND 29 AND 33 S34 S21 AND S29 AND S33

35 limit 34 to (English language and yr=”1980-2014”) S35 Limiters – English language

Page 39 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 39 of 41

Appendix 3. PRISMA diagram of search and eligibility process

Page 40 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 40 of 41

Appendix 4. Data Extraction and Bias Assessment Form for RCTs and non-RCT eligible studies

1. Clear description of objectives 2. Clear description of intervention

3. Sample size >100

4. Clear patient inclusion criteria 5. Proper random selection or complete patient coverage

6. Clear definition of outcomes

7. All intended/measured outcomes reported

8. No bias in exclusion of subjects from analysis

9. Response rate reported and greater than 50%

10. At least 80% follow-up achieved

11. Analysis included adjustment for confounders

12. Conclusions supported by findings

13. Not industry sponsored and no conflict of interest with monitoring device company

QUALITY ASSESSMENT

Clear description of project objectives

1□ Yes 0□ No Describe_____________________________________________

Clear description of intervention

1□ Yes 0□ No

Sample size (numbers in each group or total if not specified by group)

Total _______ intervention group ________ Controls ________

1□ > 100 participants 0□ < 100 participants

Clear patient selection criteria

1□ Yes 0□ No 0□Not specified Describe ____________________________________________________

Random selection of patients (Consecutive, blinded, computer generated)

1□ Yes 0□ No 0□Not specified

Clear definition of outcome measures

1□ Yes 0□ No 0□Mixed

All intended/measured outcomes reported (or selected only)

1□ Yes 0□ No

Excluded important cases from analysis

1 □ No 0 □ Yes 0 □Not specified Which______________________________________________

Response rate (%) ______ % 1□ > 50% 0□ <50% or Not specified

Incomplete data (% lost to follow-up at the time of outcome assessment)

_____ % 1□ > 80% 0 □ more on one group than the other

0 □Unknown Analysis included various potential confounders or effect modifiers

1□ Yes 0□ No

Summary of article’s findings and contributions to knowledge. Conclusions and/or Recommendations supported by findings

1□ Yes 0□ No

Industry sponsored 0□ Yes 1□ No

Page 41 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Peer Review O

nly

Page 41 of 41

Appendix 5. Interpretation of heterogeneous process outcomes

Early detection of deterioration

Of the five CM studies assessing impact on early detection of deterioration (Table 2), four reported

significantly positive improvements, while one study(30) found no change. Benefits were found

through earlier identification of abnormal physiological signs,(28, 40) hypoxia,(27) and fever.(43)

Two of the four IM studies examining early identification of deterioration (Table 3) reported a

significantly positive impact,(35, 41) while two studies found no significant difference in the

timeliness of identification of deterioration.(39, 45)

Escalation calls

RRS activations were significantly reduced by CM of oximetry(26) and cardiac arrest calls were significantly reduced by CM of respiratory and heart rate(38) in the two large before-after studies

assessing RRS activations (Table 2). However, in the smaller RCT of multi-parameter monitoring, no

significant differences were found in the frequency of senior staff consultation once patient instability

was identified.(30)

Of five multi-parameter IM studies examining escalation calls (Table 3), three reported a significant

increase in RRS activations.(33, 35, 39) One study observed an increase, related to respiratory rate

activation criteria only,(31) while another study indicated increases but reported no statistical

testing.(46)

Page 42 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

bond university research repository effectiveness of ... · literature using google and google...

Documents