OUTCOMES OF CARDIAC ARREST PATIENTS

IN METROPOLITAN TORONTO

Sohail A. Waien MD, MSc

A thesis submitted in conformity with the requkements for the degree of Master of Science

Graduate Department of Community Health (Subspecialization in Clinical Epidemioiogy)

University of Toronto

O Copyright by Sohail A. Waien, 1996

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ABSTRACT

Cardiac Arrest (CA) is most often an out-of-hospital event. In the best case scenario. the

victim quickly receives cardiopulmonary resuscitation (CPR) by a trained bystander, who

also activates the emergency medical services (EMS) by dialling 91 1. The arriva1 of

trained ambulance staff provides access to appropriate care and transport to a hospital.

Theoretically, this course maximizes the potential for a successful rescue and full recovery

of the patient.

Examining survival rates among CA victims using an acceptable uniform set of guidelines

(Utstein) is one method currently available for evaluating the cardiac rescue outcome of

an EMS program. The goal of this study was to determine the survival rates of those

patients who suffer an out-of-hospital CA in Metropolitan Toronto. By breaking down the

components of care from CPR to defibrillation and transport of patient to a hospital, it was

possible to examine individually and in combination their impact on survival rates. In order

to accomplish this task, a cohort of CA patients in the Metro Toronto Ambulance (MTA)

database was identified, and subsequently linked to records in two other databases: Vital

Statistics Information System (VSIS) and Canadian lnstitute of Hospital Information (CIHI).

This process was accomplished using probabilistic matching of records.

A total of 7,079 CA patients were identified from the MTA database using Utstein

definitions. A definitive and accurate outcome was established for 6,448 (91 %) of these

patients by linking the MTA records to a corresponding record in either the VSlS or ClHl

database. This was accomplished by using commercially available software to perfom

probabilistic data linkage. Accuracy of the matched records was discerned by examining

names. Major Ciinical Category and International Classification of Diseases. 9th revision

(1 C D-9).

The CA cohort (n=6,448) was subdivided into two groups (A and B) based on MTA data

collection patterns. Group A (n=4,772) represented a group of CA patients records not

influenced by any data collection priority and potentially unbiased data. All univariate

analyses were conducting using this group where as the complete cohort of 6.448 was

utilized in constructing logistic regression models.

The survival rate for Group A, defined as "discharged alive from hospital" for CA patients

in Toronto was 8%. no temporal trends in survival rates were observed over the 6-year

time period. Significant improvement in survival rates was observed among those whose

CA was witnessed, received cardiopulmonary resuscitation (CPR) and received

defibrillation. Logistic regression analysis demonstrates that Age (30-75 year) (odds

ratio= 1 -27; confidence interval=1 .O54 -54) and witnessed CA (odds ratio= 1 .5O; confidence

interval=1.21-1.85) are significant (p=0.01, p<0.0001 respectively) independent predictors

of survival. Absence of complete data for heart rhythm restricts the interpretation of the

positive prog nosis associated with ventricular tachycardia, and fibrillation.

Toronto was observed to have a survival rate of 16.3% among those whose CA was

witnessed and the initial recorded heart rhythm was ventricular fibrillation in cornparison

to the lower rates seen in other metropolitan cities. New York (5.3%) and Chicago (4%).

The 8% survival rate for Toronto as observed in this study suggests that better outcornes

are possible in large urban centres. However, despite a number of initiatives by Toronto's

EMS, this rate has remained static over the 6-year study period (1 988-1 993). Programs

targeting specific areas of weakness may lead to an increase in the survival rate.

ACKNOWLEDGEMENTS

I would like to thank a number of individuals who have assisted throughout the course of

this thesis. First and foremost, Dr. Antoni Basinski, my thesis supervisor, who made the

whole process not only stimulating but also enjoyable. I would also like to thank my

committee members, Dr. Paul Dorian and Dr. David Naylor who provided invaluable

insights and direction throughout the course. I would also like to acknowledge Dr. Jack

Williams and Dr. Stephen Lloyd, my reviewers, for their critical appraisal of the thesis.

I would also like to thank the various organizations: Metro Toronto Ambulance; Office of

the Registrar General; and the Ministry of Health for providing the necessary data to

complete the project. A personal support award from the Institute for Clinical Evaluative

Sciences along with additional resources from both the lnstitute and the Clinical

Epidemiology Unit at Sunnybrook Health Science Centre facilitated the process.

On a personal note, 1 would like thank my friends at ICES. especially Pamela Slaughter;

on day I will get those ": ; ," right! Finally, 1 would like to thank rny parents for their endless

support and encouragement.

This thesis is dedicated to the memory of

G. Akbar YVaien

. . Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements v Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ListofTables ix Listof Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x List of Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

Chapter I INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

STUDY OBJECTIVES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 RATIONALE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

BACKGROUND . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Sudden cardiac death . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Emergency medical services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Data linkage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Chapter Il SETTING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 METROPOLITAN TORONTO EMS PROGRAM . . . . . . . . . . . . . . . . . . . . . . . 11

Personnel and equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 DATABASES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Metropolitan Toronto Ambulance database . . . . . . . . . . . . . . . . . . . . . 13

. . . . . . . . . . . . . . . . Canadian lnstitute of Health Information database 14 Vital Statistics Information Systems database . . . . . . . . . . . . . . . . . . . 15

RESEARCH SITE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Chapter III METHODS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 COHORT DETERMINATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 DATA LINKAGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Linkage 1 (MTA patient records with VSlS records) . . . . . . . . . . . . . . . 23 a . Pro-rated cornparison option . . . . . . . . . . . . . . . . . . . . . . . . . 24 b . Delta percentage cornparison option . . . . . . . . . . . . . . . . . . . 25

Linkage 2 (Unlinked MTA records from linkage 1 with ClHl records) . . 25

. . . Linkage 3 (Unlinked MTA records from linkage 2 with VS records) 26 Clerical review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Accuracy of probabilistic data linkage . . . . . . . . . . . . . . . . . . . . . . . . . . 27

MTA DATA REVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 STATISTICAL ANALYSES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Univariate analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Logistic regression analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

ETHICS AND CONFIDENTIALITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Chapter IV RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 DESCRIPTIVE ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

. . . . . . . . . . . . . . . . . . . . Metro Toronto Ambulance senrices database 35 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cohort determination 35

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data linkage results 39 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Accuracy of data linkage 41

. . . . . . . . . . . . . . . . . . . Metro Toronto Ambulance data re-abstraction 41 SENSITIVITY ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 UNIVARIATEANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . The role of CPR and bystanders 46 Defibrillation rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Survival rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

. . . . . . . . . . . . . . . . . . . . Defibrillation and suwival rates by age group 54 LOGISTIC REGRESSION ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Chapter V DISCUSSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 DATA LINKAGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 CHAIN OF SURVIVAL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

Early access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . EarlyCPR 65

Early defibrillation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 EarlyALS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

SURVIVAL RATES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 PREDICTORS OF SURVIVAL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 UTSTEIN GUIDELINES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 LIMITATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

viii

LIST OF TABLES

Table 1 . Table 2 .

Table 3 . Table 4 .

Table 5 .

Table 6 .

Table 7 .

Table 8 .

Table 9 .

Table I O .

Table 11 .

Table 12 .

Table 13 .

Table 14 .

Table 15 .

Table 16 .

Table 17 .

Patient classification based on ACR forms . MTA database . . . . . . . . . . . . . . 36

Ambulance personnel and sex . MTA database . . . . . . . . . . . . . . . . . . . . . . . 37

. Data collection patterns CA cohort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Comparison of data collection patterns . CA cohort, Group A and Group B . . 45

CPR initiators . CA cohort and Group A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

CPR initiators by sex and witness . Group A . . . . . . . . . . . . . . . . . . . . . . . . . 48

. . . . . . . . . . . . Outcome status by defibrillation and cardiac rhythm Group A 49

Defibrillation rate by subgroups . Group A . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Survival rates . CA cohort. Group A and Group 6 . . . . . . . . . . . . . . . . . . . . . 52

Outcome status by subgroups . Group A . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

. Outcome status by witness and Ambulance crew Group A . . . . . . . . . . . . . 55

. Defibrillation and outcome rates by age groups CA cohort . . . . . . . . . . . . . . 56

Logistic regression analysis . Group 1 Model 1 . . . . . . . . . . . . . . . . . . . . . . . 57

Logistic regression analysis . Group 1 Mode12 . . . . . . . . . . . . . . . . . . . . . . . 58

Logistic regression analysis . Group 2 Model 1 . . . . . . . . . . . . . . . . . . . . . . . 60

Logistic regression analysis . Group 2 Mode12 . . . . . . . . . . . . . . . . . . . . . . . 61

Comparison of survival rates among witnessed CA . North Arnerica . . . . . . . 66

LIST OF FIGURES

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Figure 1 . Overview of methods 20

Figure 2 . Diagrammatic representation of linkage algorithm . . . . . . . . . . . . . . . . . . 22

Figure 3 . Age distribution - MTA patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Figure 4 . Overview of linkage results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Figure 5 . Age distribution - CA cohort with an outcome . . . . . . . . . . . . . . . . . . . . . . 43

Figure 6 . Defibrillation . number of attempts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Figure 7 . Admission and survival rates by hospital . . . . . . . . . . . . . . . . . . . . . . . . . 76

LIST OF APPENDICES

Appendix A

Appendix B.

Appendix C.

Appendix D.

Appendix E.

Appendix F.

Appendix G.

Appendix H.

Appendix 1.

Appendix J.

.Ustein template for reporting EMS data

Metro Toronto Paramedic cardiac arrest protocol

Overview of record linkage & Automatch software

Ambulance cal1 report forrn

Medical death certificate, statement of death

Letters of agreement

Data linkage algorithm

Missing data and re-abstraction results

Accuracy of linkage 2

Recommendations

Chapter I INTRODUCTION

Cardiac disease is the nurnber one cause of death in North America. This translates into

56.000 deaths annually in Canada'. of which 20.000 (74/100,000) are a result of sudden

cardiac death (SCD)2. Comparatively in the United States, the incidence of SCD is

reported to be lZ4I l 00.0003a4. Recent reports suggest that these rates are decreasing;

however, the magnitude of the problem continues to be significant'?

Cardiac arrest (CA), a component of SCD, has been described as the cessation of cardiac

pump activities which although potentially reversible, but in the absence of any

intervention, will lead to deatl~''~. The onset of symptoms heralding a CA may Vary in

duration and specificity8.10 providing, perhaps, one explanation for the differences in

motivation by patients to seek emergency care". Since CA is most often an out-of-hospital

event, the role of bystanders (ie. witness to event or discovered victim) and Ernergency

Medical Services (EMS) is critical. It is the bystander who will most often be responsible

for initiating care, by either telephoning for assistance, and/or directly intemening, by

initiating cardiopulrnonary resuscitation (CPR).

EMS programs have becorne an integral link in the delivery of health care in North

America. These programs provide a broad spectrum of emergency services, of which

assistance provided to CA victims is a srnall portion- only 2 to 3% of the case loadg. This

service to CA patients is of particular importance as access to appropriate and timely care

2

has been demonstrated to drarnatically affect sumival rates among this group of

patients'"? Reported survival rates following CA in North American cities are low (less

than 20%); the exception is King County, Washington with a reported survival rate of

34~ /09 .~3 .~4 .~6 -28 . These survival data generally represent American centres; few data are

currently available from Canadian centres. The available Canadian data illustrate survival

rates at the lower end of the spectrum (2.5% to 1 l%)2.16.23.29-32.

The American Heart Association has coined the term chain of survival to represent the

sequence of events necessary for improved outcomes following CAU.? Individually, this

sequence of events includes: early access; early CPR; early defibrillation; and early ALS

care. As with al1 chains, the overall strength of this chain of sunival is limited by its

weakest Iink. ldentifying the "weak link" is perhaps the first step in strengthening the chain,

and thereby improving outcomes.

From time to time the work of EMS is highlighted by the media. Media reports generally

include sensational stories with positive or negative consequences, but such anecdotal

reports do not provide evidence-based methods of evaluation. Given the current fiscal

restraint imposed by funding agencies, it would be difficult to justify allocation of funds to

new EMSladvanced cardiac life support (ALS) programs, or even to defend existing

programs in the absence of evidence of benefit. This underscores the need to evaluate

individual EMS programs.

STUDY OBJECTIVES

A. Methodology

To examine the feasibility of probabilistic matching to link a cohort of cardiac arrest patients

identified in the Metro Toronto Ambulance (MTA) database to either Vital Statistics

Information System (VSIS) or Canadian lnstitute of Hospital Information (CIHI) databases.

B. Health Services

1. To determine outcomes of patients experiencing an out-of-hospital cardiac arrest

in Metropolitan Toronto using standardized definitions (Utstein template).

2. To examine which factors are significant and independent determinants of survival

following an out-of-hospitai CA.

RATONALE

The variations in reported survival rates can be attributed in part to differences in EMS

programs, population demographics and geographical layout of communities, suggesting

that the task of improving survival rates may not be the same for al1 communities. For

example, in cities where EMS programs have good response times (ie. short elapsed time

from receiving a cal1 to arriva1 at patient's side) may need to focus on programs to

encourage involvement of bystanders in early recognition and initiating CPR. Alternatively,

in other communities, response tirne by EMS may need to be reduced.

4

Among the available interventions, timely access to defibrillators by trained personnel has

been demonstrated to decrease m~rtalit?~.~"~. Its use has not been subjected to rigorous

randomized clinical trials, but nonetheless it has been embraced by many as the

cornerstone of care for pre-hospital CA'0A'Y2. Given this enthusiasm for defibrillation, it is

unlikely that a randomized clinical trial comparing defibrillation to placebo would be

considered ethically justifiable.

Before embarking on initiatives to improve care delivered by a program, current practice

patterns rnust be measured, and a planned response forrnulated to address challenges

highlighted by this information. Simply providing funds for ambulances and staff is not

enough to Save l i ~ e s ~ ~ . Detemining overall survival rates among CA victims provides one

method by which to measure progress of initiatives within an existing EMS program over

tirne and to compare it to those in other ju r isd ic t ion~~~."~~.

Early studies examining CA patients were fraught with variations in the definition of a CA

patient and outcome; thus a comparison between programs was dificult. In an effort to

standardize the reporting of these data, and enable comparison between EMS programs,

a joint task force with representation from the European Resuscitation Council, American

Heart Association, Heart and Stroke Foundation of Canada, and the Australian

Resuscitation Council has developed the Utstein guidelines. This consensus document

defines ternis, methods of collection, and a template for reporting CA (Appendix

A). Its growing use in publications reporting CA data refIects its acceptance by

re~earchers~~m~~.

5

Cardiac arrest victims can be divided into 2 groups: those whose event is witnessed; and

those whose event is not witnessed. This grouping is based on a random event;

furthenore, the degree to which a bystander is involved in initiating care is also a random

event. However, subsequent care-paths available to both groups as provided by EMS

should be equal. While it is important to examine the role of bystanders, especially among

those whose CA is witnessed, it must be stressed that published data from large

metropolitan cities suggest that a majority of CA patients do not receive bystander initiated

CPR24; therefore, EMS care becomes critical.

The presenting cardiac rhythm is critical to the EMS care giver. as it is the guide for further

decision regarding care dispensed to CA patients. For the purpose of discriminating

ventricular fibrillation (VF) from asystole, the Utstein guidelines define asystole as a

deflection of less than 1 mm amplitude on electrocardiogram (calibrated at 10 rnmIrnV); an

amplitude equal to or greater than 1 mm is equivalent to VF4'. This criterion is also used

by automated external defibrillators. Grading of VF into fine, moderate, or coarse is not

recommended by the Utstein guidelines. Patients in ventricular tachycardia (VT) can

clinically be grouped into 2 separate groups: those with pulseless VT (representing

haemodynamically unstable patients); and those with a pulse. Utstein guidelines do not

recommend grouping pulseless VT patients with VF patients, but noted that since this is

potentially a small group of patients, they are therefore frequently combined with VF45. It

is suspected that, this ambiguity has led many researchers to combine VT (regardless of

pulse) and VF patients as one group when reporting results. It would seem prudent to

6

combine pulseless VT with VF since the two groups are clinically managed using similar

"standing orders". EMS personnel in Toronto and other jurisdictions have standing orders

which recommend that pulseless VT patients are managed as VF and defibrillated

(reproduced with permission, Appendix B). VT patients who are not pulseless are

cardioverted only at the discretion of the base hospital physician and for that reason, a

"true VT' may not be defibrillated. Regrouping defibrillated VT patients with VF is a closer

approximation of care and should provide greater understanding into the process of care

and outcorne. Patients observed to be in VF should be defibrillated unless otherwise

contraindicated.

BACKGROUND

Sudden Cardîac Death

Myerburg and Castellanos have defined SCD as "a natural death due to cardiac causes,

heralded by an abrupt loss of consciousness within 1 hour of the onset of acute symptoms,

in a person with or without known preexisting heart disease, but in whom the t h e and

mode of death are une~pected"'~. Coronary artery disease has been implicated as the

underlying cause in at least 80% of SCD; however, other causes. cardiac and non-cardiac,

have also been d o c ~ r n e n t e d ~ ~ ~ ~ ' ~ . The pathophysiology of SCD has not been fully

understood: one model hypothesizes that short- or long-term structural abnormalities

(myocardial infarction; hypertrophy; ventricular myopathy; and structural electrical

abnonnalities) interact with functional changes (transient ischemialre-perfusion, systemic

factors, neurophysiological interactions. toxic effects). The end result is a predisposition

7

to premature ventricular contractions, which initiate anhythmias such as VT or VF. leading

to CA''?

Despite the lack of a definitive pathophysiology, CA is regarded as potentially both a

preventable and reversible event. Ambulatory cardiac monitoring has demonstrated that,

in general, VF is believed to be the initial cardiac rhythm in approximately 75% of al1

cAs 18.55-58 and rnay be preceded by mg . Asystole is estimated to occur in 20% of the

victims, and this frequency is proportional to the tirne elapsed since collapse (ie. the longer

the delay the greater the nurnber of victims found in a~ys to le) '~ .~~. The remaining 5% have

been attributed to electromechanical dissociation (EMD). The significance of the

underlying rhythm lies in the propensity for a better prognosis among patients found initially

in VT and VF22.24-60.

Emergency Medical Services

The fundamental problem afflicting the arresting patient is a lack of blood Row to vital

organs, especially the braina.l0. The brain is able to withstand hypoxia for 4-5 minutes

under normal conditions and therefore, the time interval between the onset of the cardiac

arrest to the restoration of normal blood flow is critical. A number of interventions are

available to help restore circulation, leading to an improved patient outcorne. Potentially

the most widely available of the early critical interventions is cardiopulmonary resuscitation

(CPR), which can be performed anywhere, anytime, and by almost anyone with minimal

training.

8

The beneficial effects of CPR were first dernonstrated by Kouwenhoven and colleagues

in 19606'. They concluded that following CA, CPR provided adequate circulation to

maintain the heart and brain until supportive treatrnent such as defibrillation could be

provided, and that the real value of the procedure lay in the fact that it could be used both

inside and outside of the hospital. Since Kouwenhoven's report sorne 30 years ago.

widespread use of prompt CPR rernains the first line non-invasive and non-medical

management strategy available to victims of CA that almost everyone in the cornmunity

can be trained to provide. Advanced life support must be provided to the victim as early

as possible since the value of CPR alone is short-lived. Patients who developed their CA

outside of the home have been shown on average to receive CPR two minutes sooner

than their counterparts who develop a CA inside the home6*, reflecting the increased

probability of the event being witnessed outside of the home and thus increasing not only

the probability of receiving CPR but also early CPR from a bystander. Notwithstanding

individual experiences, CPR should be initiated within 4-5 minutes, and without ALS the

benefits of CPR are limited at best to 12 minutess5.

The time interval between CA and initiation of treatrnent is an important CO-variable in the

overall process of care. Studies have examined factors such as location of patient,

geographical and other physical barriers which may prolong the delivery of are'^.^^".

Travel times by EMS within large metropolitan cities have been evaluated and on average

they range between 4-5 r n i n ~ t e s ' ~ ~ ~ ~ . ~ ~ . Once at the scene further delays rnay occur in

situations where extra time is required to reach the victim, such as in the case of hig h rise

9

buildings65. Thus, the total time interval between CA to EMS care can easily exceed 5

minutes, hence the need for early bystander CPR. Models developed to examine the

relationship between survival rates and incremental delays to the delivery of various

cornponents of care, bystander CPR. defibrillation and ALS have demonstrated decreasing

survival rates with increasing d e l a y ~ ~ ~ .

Historically, the current ambulance systern has its roots in the American Civil War. Dr.

Jonathan Letteman. who was in charge of the Union Amy of the East (Army of the

Potomac), is credited with establishing an ambulance service for transporting injured

soldiers to field hospitals6'. Sometime latter in Europe (1 967). Pantridge and Geddes

reported the benefits of a "mobile intensive care unit? Today's EMS programs equipped

to provide prirnary rnedical care outside of the hospital to victims of CA are descendant of

the work begun by Drs. Letterman, Pantridge and Geddes.

Data Linkage

Data linkage has evolved dramatically in recent years. The methodology for record linkage

can be summarized under three broad headings: clerical. deterministic. and proba bilistic.

Historically, the clerical method is the oldest, most time consuming (manually performed),

rnost prone to errors and most costly. but remains the gold standard. It is not a feasible

option when dealing with large datasets. In the ideal setting, the second method is the

simplest for linking patient records across databases. Deterministic linkage requires that

individual records within the two data sets in question have a common unique variable (e.g.

10

social insurance number). Records are linked across datasets using this unique variable.

Errors in the unique variable, regardless of how trivial, will result in either an unlinked

record or an inappropriate linkage. Despite these potential constraints, this method may

be suitable in those circumstances when the investigator is confident in the reliability of the

unique identifying variables recorded in the database. The third option is a far more flexible

method of data linkage, which has made it possible to link individual records across a

number of databases without the use of one common unique identif~ef"~.

The objective of probabilistic linkage is to identify and link records from one data set to

corresponding records in a second dataset in a statistically justifiable manner. Unlike the

deterministic method, probabilistic matching utilizes more than one comrnon variable (e.g.

name, age, date of birth) between the two databases. This rnodel for performing record

linkage requires that "tuning" parameters, typically cut-off probabilities or "weightsn, be set

based upon the data in the two datasets being linked. These weights are used in deciding

whether the linked records from the two datasets are deemed to relate to the same

individual. A detailed theoretical summary of probabilistic linkage is provided in Appendix

C. Commercial software programs are available to perform probabilistic linkage and for this

study Automatch@ 2.7 ( MatchWare Technologies, Inc. Silver Spring , Maryland) was

u~ed~~. ' * .

Chapter II SETTING

METROPOLITAN TORONTO EMS PROGRAM

Metropolitan Toronto stretches across some 250 square miles and is divided into five cities

and one borough. The total population fluctuates over the course of 24 hours;

consequently, the daytime population served by the Metro Toronto Ambulance program

is estimated to be three million, while the nighttime population is estimated at 2.2 million,

due to the exit of cornmuting workers to adjacent suburbs.

The Toronto EMS program incorporates a "tiered-response" protocol, consisting of: Fire

Department personnel trained in CPR; ambulance attendants trained in basic airway

management and CPR; and pararnedics trained in providing ALS including defibrillation,

intubation, obtaining IV access, rhythm interpretation and drug administration. A small

proportion of ambulance attendants also receive training in the use of semi-automatic

defibrillators (see personnel and equipment).

Efforts to improve the delivery of care by EMS have resulted in a number of initiatives.

One such move has been the development of a central dispatch services which incorporate

EMS, Fire and Police Departments. All emergency cals in Metropolitan Toronto are

channelled through the "91 1 systern", which is operated by the Police Department, and is

linked to the Department of Ambulance Services (DAS) dispatch centre. All dispatchers

are trained to provide CPR instruction over the telephone and have the capability to locate

12

the caller using technical support even if the caller is unable to communicate for any given

reason. The goal of this service is to dispatch according to protocol the most appropriate

caregiver to the site and provide backup support to the responding team. This backup

support can be in the forrn of additional physical support, help in navigation to the site, or

other needs that may arise.

It is estimated that EMS will respond to over 400,000 emergency calls in Metropolitan

Toronto in 1996. The Ambulances Services Department is Metropolitan Toronto's sole

provider of both emergency and nonemergency ambulance services. In May of 1991 the

EMS program launched a 5-year plan to expand paramedic capabilities, such that every

patient who appears to require critical care would receive paramedic services. Another

recent initiative (1996) has been to equip and train Fire personnel in the use of automatic

defibrillators.

Personnel and Equipment

In 1994 the EMS program was staffed by approximately 1,000 personnel: 685 involved in

providing care, while 315 were involved in an administrative capacity which includes

dispatcher^'^. Of the 685 care-givers, 600 were BLS trained (80 had additional training in

the use of automatic defibrillators), and the remaining 85 had paramedic or ALS training73.

Basic life support (BLS) certified personnel are trained to provide first aid to CA victims.

This includes: airway maintenance; ventilation (with bag-valve-mask systems, positive

pressure devices or pocket masks); and CPR training. Paramedics are trained in

13

accordance with the guidelines set by the Ontario College of Physicians and Surgeons, the

Ministry of Health and the Metropolitan Toronto Paramedic Program Cornmittee. These

trained personnel may perform procedures on direct verbal orders of a Base Hospital

P hysician, or according to pre-desig nated "Standing Orders" (Appendix B). Procedures

associated with CA that a Paramedic rnay perform include the following: peripheral venous

cannulation; venous blood sampling; administration of approved medications and IV fluids;

cardiac rhythm interpretations; defibrillation and synchronized cardioversion, carotid sinus

massage and Valsalva manoeuvre; and endotracheal intubation.

In 1994 the EMS program maintained a fleet of 135 vehicles. During peak times (day

time), 70 ambulances equipped with BLS trained staff and ten with ALS staff were on the

road, this figure dropped to 35 and 5 respectively at off-peak hours (night time).

DATABASES

Cornpiete patient data necessary to conduct this study were not available on one

database. Therefore, three administrative databases were accessed: the Metro Toronto

Ambulance database; the ClHl database; and the VSIS database. The study cohort was

identified from the MTA database; the VSIS and ClHl databases provided confirmation of

outcome for individuals in the cohort.

Metropolitan Toronto Ambulance Database

The Metro Toronto Ambulance service maintains a database which contains data

abstracted frorn Ambulance Call Report (ACR) forms (Appendix D). These forms are

14

generated each time an ambulance responds to an ernergency call. They are cornpleted

by any one of the ambulance staff responding to the call. The forms are routinely collected

from the ambulance station and brought to the MTA head office, where the data from these

forms are directly entered into the database by two dedicated data entry clerks. The

database is maintained using Fox pro@ software (version 2.6; Microsoft Corporation) on

an IBM" compatible personal computer at the MTA head office.

Variables routinely abstracted from the ACR form can be classified into demographic,

clinical and procedural groups. The demographic variables abstracted include: patient

names; age; sex; and accepting hospital. The clinical variables abstracted include

symptoms, and the nature of the emergency (e-g. trauma. medical). All procedures and

interventions, including administration of drugs, are also abstracted. All variables outlined

in the Utstein template (Appendix A) were available in the MTA database with the

exception of: return of spontaneous circulation; admission to hospital; discharge status,

including location of death; and status at one year for those discharged alive.

Canadian lnstitute of Health lnformation Dafabase

The Canadian lnstitute of Health Information is a non-profit, federally chartered Company

which specializes in health care related information processing. The overall objectives of

ClHl are to acquire, develop and promote the use of an independent non-profit health care

information system for the purposes of planning, managing and monitoring the quality,

suitability, effectiveness, and cost of health care delivery. Process of care analysis,

15

resource allocation and utilization, review, education, and research are the main

applications of the ClHl systems.

ClHl receives an abstract for each separation (death, discharge, transfer) from an acute

care hospital in Ontario. The abstract contains information from the patient's chart

regarding the patient (age, sex etc), as well as diagnosis. procedures and length of stay.

Some of the information abstracted for the purpose of this study from ClHl included:

admission category; hospital number; age; sex; admission date; discharge date; and

discharge status.

The report of the Ontario Data Quality Re-abstracting Study found that reliability between

original and re-abstracted data in the ClHl database ranged between 93-100% for non-

medical fields7'. This reliability rate was reported to be nearly 81 % (2,419 records) for the

"Most Responsible Diagnosis code"74. A recent review of studies examining the

completeness of data and agreement of hospital discharge data with re-abstraction studies

concluded, that patient demographic data recorded in ClHl patient files is "complete and

reliable"75. The authors also report variations in the completeness and accuracy for the

most responsible diagnosis, with acute myocardial infarction being rated as "reasonably

relia ble"75.

Vital Statistics Information Systems Database

The Office of the Registrar General (ORG) is a division of The Ministry of Consumer and

Commercial Relations of Ontario. All vital events such as births, deaths, marriage. stillbirth,

16

divorce, parentage, adoption and change of name occumng in the province of Ontario are

recorded and stored at the ORG. Proof of these events is provided in the form of certified

copies and certificates. Information concerning demographic data and the medical

diagnosis of the deceased is obtained from the "Medical Certificate of Death" and

"Statement of Death" (reproduced with permission. Appendix E). The Medical Certificate

of Death is completed by the physician certifying the death; the Statement of Death is

completed at the time of death by the family and the funeral director. These certificates

are optically scanned and stored. Data (e.g. cause of death) from these certificate is

reviewed manually by a Divisional Registrar and ORG staff prior to entry into the VSlS

database. The cause of death variable is reviewed by a trained medical coder7!

Deaths of Ontario residents occurring in another province are recorded by that province

and sent to the office of the Registrar General of Ontario, which then assigns them a

special series of numbers. After receiving approval from the Office of the Registrar

General, access to VSlS data was made available through the Ontario Cancer Registry

office in Toronto.

No published data are available on the reliability of data stored in VSlS database.

However, based on Statistics Canada testing, the ORG was commended on its "cause of

death registration datatt7!

RESEARCH SITE

lnstitute for Clinical Evaluafive Sciences

All data analysis was perfomed at the lnstitute for Clinical Evaluative Sciences in Ontario

(ICES). CES is a non-profit corporation dedicated to health services research, co-

sponsored by the Ontario Medical Association and the Ontario Ministiy of Health. CES

is also linked to Sunnybrook Health Science Centre's Clinical Epidemiology Program, and

to the University of Toronto. The mandate of ICES is to carry out research that will improve

the quality, efficiency and accessibility of physician and allied professional setvices in

Ontario. ICES is physically located on the campus of Sunnybrook Health Science Centre.

Chapter III METHODS

The lack of comprehensive patient information on one database has, to date, impeded

efforts towards conducting an evaluation of Toronto's EMS program. In order to perform

such an evaluation, patient data must be linked across three large administrative

databases: MTA; VSIS; and CIHI. This process is further complicated by the absence of

a common unique patient identifier among any two of the three databases. These

obstacles can be overcome by linking patient records using probabilistic matching.

This retrospective analysis was conducted using al1 patients within the MTA database

deemed to have had a CA upon whom cardiac resuscitation was attempted during the

calendar years 1988-1993. CA victims in whom the primary etiology was not cardiac

(smoke inhalation, trauma. overdose, etc.) were excluded. The selection criteria for CA

patients adopted in this study reflects the criteria outlined in the Utstein guidelines 455.M.52,53.

COHORT DETERMINATION

The cohort of CA patients was selected from the MTA database in accordance with the

Utstein g u i d e l i n e ~ ~ ~ ~ ~ ~ " ~ ~ ~ . The selection of this cohort was a three-step process; the first

step involved selecting patients using the inclusion criteria, as outlined below:

Step 1 Inclusion criteria:

Variable name 1. Problem 2. Procedure code 3. Cardiac rhythm

Accepted response Cardiac Arrest CPR, defibrillation VF, VT, Asystole, EMD

f 9

The MTA database allows the recording of 22 possible procedures for each individual

patient. Patient records with an acceptable response for any one of the 3 variables were

included in the cohort. Step two involved limiting the cohort identified in Step 1 to only

those patients with a presumed underlying cardiac etiology, thus eliminating patients in

whom a non cardiac CO-morbidity may have infiuenced their eventual outcome. This was

accomplished by including only those patients in whom the variable "type of emergency"

was coded as either cardiac or cardiac arrest cause unknown. In the third step, duplicate

patient records, as well as those for whom there existed both a BLS and an ALS record,

were identified. To ensure that no information was lost, duplicate records were only

removed after appending data from the record earmarked to be removed to its

corresponding original record. Similarly, BLS records were appended to their

corresponding ALS record before being removed. These steps ensured that the CA cohort

(Figure 1) included only those CA patients in whom the primary etiology was cardiac and

that no duplicates were included in the analysis.

DATA LINKAGE

In order to ascertain the eventual outcornes for the identified CA cohort, it was necessary

to link the CA cohort to either VSlS or ClHl records. This was accomplished using the

Automatch" software program (Appendix C). In general, the algorithm for each linkage

depends on the datasets being linked. An appropriate algorithm can be developed by

using a small portion of the data and testing the performance of linkages using different

blocking variables, matching variables and assigned weights.

Figure 1 Ovewiew of Methods

1 MTA database (1988-1993) n=115,ûZ2 I

Unking variab(et Link > Narne

> NWlS > Age > Sex > Dote of event/ date of death same day or +1 day >

C A cohort - not linked to Wal stots

n=1,588 (22%) 1 > Hospital ' Aee > Sex > Date of event/

date of admission same day or + 1 day

CA cohort - linked to \Mal sta ts n4.491 08%)

Link 3 unking varicibler > Name > m i l s

Sex > Date of event/

dote of deuth > 1 duy

+. n-609 (8%)

I

M'TA pahmts iinked to ClHl with an MCC code other thon '1' or ' 5 -76) were rematched to ClHl An approprbte iinkoge v a established for 229

CA cohort - linked to ClHl dota

n479 (14%)

I

3Q patients were coded as 'pronounced dead by base

I hospital physiclan'

CA cohort - not Iinked to Mtal sta ts

n415 0%)

4 P

1 Of the remainina 485 patients 1 345 (71%) had mksing- dota for one of the followlng linklng variables:

l I

I I

I

!

4 1 ,

> Rrst name > Last name > Admltting hospital

CA cohort - linked to Wal stats n 4 4 (1%)

CA = Cardloc Anest MTA = Metro Toronto Ambulance NYSStS = New York State lntelllgence IdenMcution System Mt01 stots = Wol StatCMs lnfornwtion Systems ClHl = Canadian Instiîute of Heak Information

Outcome established for 6,4443 (91%) paHents b

Data Linkuge

21

In order to optimize the linkage of records between data sets it was necessary to perform

the following prelirninary manoeuvres. The first was to create a phonetic coding of the

surname using the New York State Intelligence Identification System (NYSIIS)n-79. A

NYSllS code accommodates variations in the spelling of names (e.g. Cathy and Kathy) and

has been used in the past with success by Statistics Canada and The Ontario Cancer

Research Foundation in performing data Iinkage7'-Tg.

Secondly, the ClHl data were limited to only those patients that were: brought to the

hospital by an ambulance; admitted via the emergency department; and the date of

admission was during the calendar years 1988 to 1993. This was carried out using the

variables admitting category; admitted by ambulance; entry code; and admit date in the

ClHl data set. Thereafter, these data were further limited to include only those patient

records from hospitals in Toronto accepting emergency patients transported by MTA.

These manoeuvres resulted in a modified ClHl data set of 253,319 records.

Data linkage of records between more than two datasets requires two processes. The first

is to determine the order in which to Iink datasets; and the second is to develop an

algorithm for each linkage process which includes choosing blocking variables. These

variables are used in the linkage process to partition data sets into mutually exclusive

blocks. Matches between data sets are then limited to pairs within blocks. This process

increases the potential for matched pairs while decreasing the number of pairs to be

compared (Appendix Cl Figure 2).

Figure 2

Dataset A

DlAGRAMATlC REPRESENTATION OF LINKAGE ALGORITHM

Dataset 8

- - 8 PASS

Dataset A Records not linked on Passl

I Dataset B Records not linked

Dataset A Records not linked on Pass 1 &2

Dataset A Records not Iinked on Passl,2&3

Dataset A Records not Iinked on Pass1,2,3&4

on Passl

Records

lin ked

on Pass 1,2

I Dataset B Records not linked on Passl&2

Dataset 6 Records not Iinked on Pass1,2&3

PASS 4 - - I

Dataset B Records not lin ked on Pass1,2,3&4

Records linked

on Pass l , 2 & 3

Records linked on Pass 1,2,3 & 4

In this study the first linkage was conducted between records in the MTA database and

those in the VSlS database. as depicted in Figure 1. This decision was in accordance with

published studies which suggest low survival rate for CA patients in most centres2'. Thus

it was postulated that the majority of MTA records would be linked to a VSlS record.

Linkage 1 (MTA patient records with VSlS records)

This process involved linking the CA cohort of 7,079 patients to records in the VSlS

database (n=467,420) for the 6-year time period. The linkage algorithm adopted involved

a four step process (Appendix G). The two blocking variables assigned for the first pass

were NYSllS and SEX, while the matching variables were SURNAME, YEAR, MONTH.

DAY of event fdeath, and AGE. The 3 blocking variables for the second pass were the first

3 letters of the surname (SURINTI) and first letter of the first name (INITI), while the

matching variables were: SURNAME; first NAME; MONTH and DAY of eventldeath; SEX;

and AGE. In the third pass. the variables YEAR and MONTH were combined as one

variable and thereafter assigned as the blocking variable. The matching variables in this

pass were: SURNAME; first NAME; DAY of evenvdeath; SU: and AGE. For the fourth and

final pass, SURNAME was the blocking variable, and first NAME, YEAR, MONTH, DAY,

SEX, and AGE were the matching variables.

Weights for each pass were assigned after reviewing results of matched and unmatched

records using different sets of weights. Matched records with a composite weight above

the upper threshold are considered as true match, while those below the lower threshold

24

were considered unmatched. Those with an intermediate weights were resewed for

clerical review (Appendix C).

The ability to "tune" the weights reflects the operator's understanding of the data and

experience in performing data linkage. The assigning of a high value to the upper threshold

!owers the chance of a false positive match, and vise versa. Setting too high a value for

the upper threshold can result in eliminating a true match because of one or tow keystroke

errors in the linking variables. Thus the operator must make allowance for minor keystroke

errors without accepting false positive matches by assigning too low a threshold.

Conversely, a high lower threshold will eliminate possible matches with rnany keystroke

errors. The difference in the value between the upper and lower thresholds values

influences the number of records assigned as true matches, possible matches or not

matched. Ultimately, the tradeoffs are between including false positive matches by setting

a low upper threshold; and eliminating possible matches by setting too high a lower

threshold.

a. Prorated comparison option

In order to accommodate the discrepancies in dates recorded, the prorated option was

used. This option allows numeric fields to disagree by a specific, assigned, absolute value.

For example, if the parameter is assigned as 10, and an absolute value of the difference

in the variable values is greater than 10, then the full disagreement weight would be

assignment to the comparison. Similarly, if the difference is O, then full agreement weight

25

will be assigned. Differences between O and 10 will be assigned weights proportionally

equal to the difference. This is equivalent to accepting an agreement for the variable DAY

when the difference is + 10 days. Specifically, it was used in linkages 1 and 2 to accept

a discrepancy of +1 for the variable DAY. This was to accommodate situation where the

CA occurred close to rnidnight. and the patient was pronounced deadhivas admitted shortly

after midnight. and thus a change in the date had occurred.

b. Delta percentage cornparison option

This option allows the cornparison of age in a setting where errors might be prevalent; the

differences between ages recorded in two datasets can be measured as a percent. A

reported differences of two years in an individual who is 89 rnay be far less serious then

a similar error in a five year old. Thus the delta percentage parameter allows the setting

of a maximum tolerable percent difference. This option was used when comparing ages;

a disagreement of 10% in the reported ages was tolerated.

Linkage 2 (Unlinked MTA patient records frorn linkage 1 with ClHl records)

The goal of linkage 2 was to match those MTA records that had not matched to an

appropriate record in the VSlS database in linkage 1 to ClHl records (253,319). A three

step program was used to perform this linkage. The blocking variables assigned for the

first pass were: event YEAR; SEX and HOSPITAL NUMBER; with AGE, event MONTH

and DAY as the linking variables. For the second pass. the blocking variables were: event

YEAR; MONTH; and HOSPITAL NUMBER. The linking variables were: AGE; SEX; and

26

event DAY. Finally, in pass three, the blocking variables were: event DAY; event MONTH;

and SEX along with AG€, event DAY and HOSPITAL NUMBER as rnatching variables.

A tme match in this linkage was one in which al1 variables matched with allowance given

for a difference of plus one day for the date of admission as recorded on the ClHl record

and MTA data.

Linkage 3 (Unlinked MTA records from linkage 2 with VSIS records)

This linkage was perforrned in an attempt to Iink the remaining unmatched patients from

linkage 2 to the remaining records in the VSIS database. The goal of this linkage was to

accommodate the possibility that an unacceptable difference for the DAY variable (+ 1 day)

rnay have occurred in situations where patients were held in the hospital for more than a

day, without being admitted and subsequently died, or because of a transposition error

between day and month at the time of data entry.

The algorithm in this match was identical to that described for linkage 1. incorporating the

same number of passes, blocking variables, linking variables and weights with one

exceptions. lncreased latitude was given to the definition of "agreement" between the date

of event and date of death. A match was acceptable (ie. accurate) when a difference of

greater then one day existed but agreement for the year variable and al1 other variables

matched. This prevented linking patients who may have survived their original CA event

as recorded in the MTA data but who had died later after discharge from another event or

CO-morbidity. Dates of all records matched with this linkage process were reviewed to

confimi transposition of month and day in situations were the differenœ between the dates

were greater than two days. If the disagreement between date of event and date of death

was greater then two days with no obvious transposition of day and month, the records

were assumed to represent two specific events and the records were not considered as

a possible match.

Clerical revie w

As outlined earlier, records that lay within the assigned upper and lower cutoff values

following each pass were reviewed. This clerical review process allowed the investigator

to decide whether these intemediate matches should be considered as either a tme match

or as an unmatched record. This process was standardized by using a preset algorithm

and as a precautionary measure, al1 rnatched records (records with a composite weight

above the upper threshold) were also reviewed in order to eliminate any possible false

positive matches.

Accuracy of probabilistic data lin kage

In linkage 1 and 3, MTA records were linked to VSIS records and accuracy of each

matched record was established by examining patients names. This advantage was lost

with linkage 2, since names were not available on ClHl records and thus could not be

used. Therefore, the accuracy of the records linked was examined using a surrogate

measure, Major Clinical Category (MCC)".

28

ClHl records routinely generate a MCC code for each patient. It was assumed that the CA

patients would be coded as either "1" (diseases and disorders of the nervous system) or

"5" (diseases and disorders of the circulatory system). This assumption was tested using

the results of matched patients records frorn two hospitals. The names for the CHI data

were obtained by using the hospital chart and generating a list of the names associated

with those chart numbers.

AI1 linked records in linkage 2 with an MCC code other than "1" or "5" were considered as

false positive matches. These 375 records were rematched to possible ClHl records using

an alternative method. This involved expanding the cohort of ClHl records to include al1

patient records for the time period 1988 to 1993 regardless of mode of entry to arriva1 to

hospital(n=lO1 1 88,823). MTA and Cl Hl data were then partitioned by hospital. Twenty-two

possible combinations for each of the 375 remaining MTA records were created using the

following variables and acceptable responses: date of admission = equal to date of event

or plus one day; age = + 5 years; sex = no difference was tolerated. Thereafter,

deteministic matches between the 22 possible variations for each of the MTA records and

Cl Hl records was cond ucted b y hospital .

The resulting matches for each record (maximum of 22) were then manually reviewed. A

[rue match was assigned on the basis of the International classification of diseases, 9th

revision (ICD-9)81 record for the first 3 diagnosis variables on the ClHl portion of the

matched record. Acceptable ICD-9 codes consistent with a cardiac arrest, arrhythmias,

29

myocardial infarction, or collapse were considered as a match regardless of the MCC code.

In the situation where more than one possible match existed on the basis of ICD-9 codes,

the mode of entry to hospital and agreement between the date of event and date of

admission were examined. The order of preference for establishing matched records was

entry by ED (mode of entry to hospital) followed by date of admission.

MTA DATA REVIEW

To assess the quality of data abstraction from ACR forms and data entry into the MTA

database, a sample of ACR forms with missing data were manually reviewed (n=455,6.5%

of the CA cohort). These included ACR forms with missing data for any of the following

variables: first name; last name; age; and sex. These variables were chosen since they

were crucial linking variables, and their reliability was essential to perfoning an adequate

match between databases.

STATISTICAL ANALYSES

MTA data were provided as Fox Pro@ compatible files on diskettes while ClHl data and

VSlS data were in ASCll format. MTA data were converted to ASCll format to facilitate

linkage using Automatch', and thereafter al1 data files were converted to SA* (release

6.1 1 ; Cary, North Carolina) and STATAQ (release 3.1 ; Santa Monica. California) compatible

format. Data analyses were perfonned using both SAS" and STATAm statistical software

programs on a UN»( platform.

30

Personal communication with MTA personnel responsible for data collection (Alan Craig)

had identified prioritization of data collection/abstraction for the calendar years (1990 and

1991). This prioritization was a result of shortage of staff assigned to data collection and

abstraction. Given these shortages, prioritization of data abstraction was as follows: CA

related ACR forms generated by ALS; CA forms generated by any crew (ALSIBLS); and

those generated by ALS crews regardless of disease. Routine recording of cardiac

rhythms was abandoned by MTA abstractors after March, 1993. Accordingly, analyses

were constructed to examine not only the overall CA cohort but also sub-cohorts for

potentiai biases.

Sensitivity analysis

As mentioned, during the tirne period 1990 to 1991, MTA data entry was prioritized, and

routine recording of cardiac rhythms by MTA Data clerks was abandoned in April 1993.

To examine the possibility of any inherent biases this may have created, it was necessary

to conduct a sensitivity analysis. Two distinct subcohorts (Group A and Group B) were

constructed based on data volume and by termination of cardiac rhythms data recording

(April 1993).

Univaria te analyses

Analyses were conducted to examine the relationships between patient presentation,

demographics, interventions, care givers and survival. The analyses for comparing the

relationship of individual variables with dichotomous outcome variables were perforrned

3 1

using Chi-square and sirnilar statistical manoeuvres were conducted for the subcohorts

and the complete cohort where relevant.

Logis tic regression analysis

lnterpretations of results from analysis such as Chi-square test nay often be Iimited by its

inability to accommodate possible interrelationships between variables. In such cases

logistic regression is the method of choice for exarnining the independent relationship of

variables with a dichotomous o u t ~ o m e ~ ~ . ~ ~ . Therefore, to determine the independent role

of each relevant variable on outcome and to provide additional understanding into the

relationship between the process of care and outcorne, logistic regression analyses were

perforrned.

Regression analysis was performed using a backward stepwise process. A cut off value

of p=0.2 for the odds ratio (OR) differing from 1 was set for entering a variable into the

model, and a value p=0.4 for rernoving a variable. Simply put, this process generates OR

differing from 1 for al1 the variables initially included and in turn removes one variable at

time those with OR with a value of pz 0.4. This process continues until al1 the remaining

variables have an OR with acceptable p value (5 0.4).

The CO-variables used in constructing the models were: age; initial cardiac rhythm; sex;

witnessed CA; CPR; and ambulance crew. In order to examine the role of CPR initiator,

this variable was grouped in 3 possible ways: CPR provided by anyone; CPR provided by

32

a bystander; and CPR provided by EMS personnel. The reference level for each of these

factors was: patients above 75 years of age; asystole as the initial cardiac rhythm; BLS

managed; female sex; CA not witnessed; and CPR not provided. Patients below 30 years

of age were excluded as it has been postulate that a different etiology may be responsible

for CA in the younga4.

A second model was created in which the reference group was defined as: age above 75;

female; BLS managed; not receiving CPR; and CA not witnessed. This model did not

examine the role of the initial cardiac rhythm.

ETHICS AND CONFlDENTlALlTY

The study protocol was approved by the Research Ethics Board at Sunnybrook Health

Science Centre (Appendix F). Agreement from the Ministry of Consumer and Commercial

Relations of Ontario was obtained to use patient-identified VSlS data (Appendix F). A

sirnilar approval was granted by the Ontario Ministry of Health to use ClHl data (Appendix

F). Metro Toronto Ambulance agreed to provide patient identifying data from the MTA

database, after securing agreement that al1 necessary procedures would be undertaken

to protect confidentiality (Appendix F).

Personal identifiers. such as names, were used initially in the linkage process; thereafter,

al1 analyses incorporated a unique numeric identifer. Confidentiality was further

strengthened by archiving the original data and stonng it under lock and key in a Marlok

33

controlled environment. Apart from physical security, access to the working files on the

SPARC (Sun Microsystems, Inc. Mountainview, California) work station required two

personal access codes. The files were stored in UNiXdirectories with access limited solely

to principal investigator and not accessible by modem.

Chapter IV RESULTS

The results of this study describe the characteristics of CA patients in Metropolitan Toronto

and their outcornes serve as one measure of the care provided by EMS. In the past. a

number of studies have examined the outcome of CA patients in Canadian cities. including

one study which examined the impact of introducing defibrillators3'. However. these

studies were conducted or designed prior to the introduction and utilization of the Utstein

guidelines; therefore. their results may not be comparable. This study includes only CA

patients as defined by the Utstein guidelines.

Survival following CA was defined as "discharged alive" from hospital following admission

for a CA. The remainder, non-survivors, were considered as one group regardless of

whether death occurred out-of-hospital or during hospital stay. Death was deemed to have

occurred on the same day as the CA for patients linked to a VSlS record . It was assumed

that al1 patients surviving the initial event would be admitted to hospital as is the usuai

pattern. In addition, card iac rhythms were regrouped to reflect EMS practice patterns;

pulseless VT were grouped with VF. This regrouping was used throughout the analyses

when referring to the CA cohort unless othewise stated.

For ease of presentation results have been grouped into four sections: Descriptive;

Sensitivity; Univariate; and Logistic Regression analyses.

DESCRIPTIVE ANALYSE

Metro Toronto Ambulance services database

A total of 11 5,022 records were available in the MTA database for the calendar years 1988

through 1993. These individual records represent data abstracted from ACR forms

generated by both ALS and BLS crew (Table 1). Demographic data such as sex and age

distribution for this group are described in Table 2 and Figure 3.

In 1991. the number of ACR forms abstracted for entry into the MTA database was

substantially reduced (Table 1). This reflects reduced MTA staff available for data entry.

Specifically, during this time period, and in general. data entry by MTA was prioritized with

preference given to entry of ACR forms generated for CA followed by ALS calls. Cardiac

patients, as coded by MTA personnel. represent 29% of the population within the MTA

database. This figure has varied over the 6-year time period from a low of 17% in 1992

to a high of 75% in 1988 (Table 1). The low 17% value for 1992 is reflective of the large

number of other records recorded in the database, and is not due to a decline in the

number of cardiac patients recorded (Table 1). In fact the number of CA for 1992 is similar

to 1988 and 1989. A number of emergencies or responses (Obstetrics and Gynaecology.

Medical, and Transfer) did not have a unique entry code prior to 1992 (Table 1).

Cohort determination

The 11 5,022 patient records in the MTA database served as the total population from

which 9,129 patients were identified as those on whom resuscitation was attempted by an

Table 1

Patient classification based on Ambulance Call Report furrns - MTA database

Year

Table 2

Ambulance personnel and patient sex distribution

- MTA database (n = 115,022) - CA cohort (n = 6,448)

ALS Not n VA) recorded

n (%)

Males Not n (%) recorded

n PA)

5,445(56.8) 84(0. 8)

Figure 3

Age distribution - MTA patients (n = 1 15,022)

Age groups (years)

39

EMS personnel. This subset was then further limited to only those with a primary cardiac

etiology (n=7,174). Removal of duplicate records (n=31) and combining those with both

an ALS and BLS record into one record (n=64) resulted in further lirniting the cohort to

7,079 CA patients (Figure 1).

Data linkage results

The first linkage was perfomed between the CA cohort of 7,079 patient records and VSlS

records (n= 467,420). This linkage resulted in successfully identifying a VSlS record for

5,491 CA patient records (Figure 1). The second link between the remaining 1.588 CA

patient records and ClHl records (n=253,319) resulted in identifying a ClHl record for 979

MTA patients. The third and final linkage was perfomed using the remaining 609 MTA

patients and remaining VSlS records. This resulted in identifying a VSlS record for an

additional 94 MTA patient records (Figure 1). The overall result of this linkage exercise

resulted in matching 6,564 (93%) of the 7,079 CA patients identified in the MTA database

to a record in either the VSlS or ClHl database (Figures 1, 4).

Of the remaining 51 5 MTA records not linked, 30 records were coded as pronounced dead

by base hospital physician and thus an outcome was established. On examining the

matching variables among the remaining 485 patients without a definite outcorne, 314

(65%) were missing data for variables (last name, first name, and hospital number)

necessary in performing data linkage (Appendix H). Of these 314 patient records, 100

records had no receiving hospital number and 4 patients had an incorrect hospitai number.

Figure 4

Overview of linkage results

MTA - Cardiac Arrest cohort (1988 - 1993)

tinked to either \/SIS or ClHl records

MTA patients linked to ClHl with an MCC code other than '1' or '5' ( d 7 5 ) were rematched to ClHl records.

An appropriate llnkage was established for 229 patients.

146 MTA patient records were dropped from the cohort

Not linked to WS or ClHl records

I

30 patients were coded as , 'prounced dead by base hospita1 physician'

I CA cohort used in analyses 6,448 (9146)

Of the remaining 485 pcrtients 345 (71%) had missing data for one of the following linking variables:

> first name > Last name > Admiting hospital

number

CA = Cardiac anest MTA = Metro Toronto ambulance WS = Wal Stutktics Info~onSysterns ClHl = canadlan lnstitute of Heaith Informatlor

41

The variable last name was not recorded for 281 patients, and in 276 patients the first

name was not recorded.

Accuracy of data linkage

Accuracy of names as reflected by the MCC code for linkage two is detailed in Appendix

1. Those with MCC code "1" or "5" had a 94% agreement while those with an MCC code

other than "1" or "5" had a 15% agreement. This rnethod of establishing accuracy of Iinked

data demonstrated a sensitivity of 92% and a specificity of 86%. Based on MCC code

(code other than "1" or "5"). 375 patients were identified as false positive matches

(Appendix 1). A rematch of the false positives from linkage 2 resulted in identifying a ClHl

record for 229 of the MTA records. Therefore, an accurate outcome was established for

6,448 (98%) of the MTA 6,594 patients originally linked to a corresponding VSlS or ClHl

record (Figure 1,4) and records coded by MCC as either "1" or "5" increased from 61.1 %

to 93% (Appendix 1).

Metro Toronto Ambulance data re-abstraction

A manual review of 455 (6.4%) ACR forms which were missing an entry for variables

coding patient names, age and sex (Appendix H) from the years 1989 and 1993 was under

taken . The results demonstrate that among 250 patients who had no last name recorded,

no name was found upon re-abstraction. A similar result was seen for those with a missing

f is t name; three names were found during data re-abstraction. However, a greater

proportion of values for age (28%) and sex (32%) were found upon re-abstraction.

Reliability of ClHl and VSlS data was described earlier (see background).

43

SENSlTlVlTY ANALYSE

The cohort of 6.448 CA patients represent 91 % of those originally identified as CA patients

(n=7,079); representing 6.2% of the total MTA database population for the 6-year time

period. Care was provided equally by ALS and BLS ambulance crews, and males

represent 65% the cohort, and a majority of the patients were within the 60 to 75 years age

group (Table 2. Figure 5).

In-order to examine the influence of variations in data collection; volume of data abstraction

by MTA was calculated for each month of the study period (Table 3). Lower rates of data

entry were prevalent for each month throughout the years 1990 and 1991. and cardiac

rhythms were not recorded for the last 9 rnonths (April - December) of 1993. These

differences were used to define two groups: Group A (n=4,772), representing patient

records for the years 1988-1 989, 1992, and first three months (January - March) of 1 993;

the reminder were grouped as Group 8 (n=1,676). As a result, Group A was assumed to

represent "unbiased data" and al1 univariate analyses utilized this su bset unless stated

otherwise. The influence of data entry prioritization is evident from examining the number

of ALS managed patients in the two groups (Table 4).

Figure 5

Age distribution - CA pts. (n = 6,448) CA cohort* (n=6,4 12) Group A (n=4,742) Group B (n= 1,670)

7 - . . .O! 1.800 . , - + m 1.600.

a 1.400 Ccr 0 1,200 - . 1 . a; 1.000

9 aoo

600

z 400

zoo - n -- id- -

Age groups (years)

Group A= (Jan 1988 - Dec 1989) + (Jan 1992 - Mar 1993) = 4,772 Group B= (Jan 1990 - Dec 1991) + Mpr 1993 - Dec 1993) = 1,676

" Age not recorded- CA cohort n=36); Group A (n=30); Group B (n= 6)

Table 3

Data collection Patterns - CA cohort (n = 6,448)

Year 1 Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec 1 Total

Total 1 614 542 690 611 583 583 522 477 457 501 409 459 16,448

Group A= (Jan 1988 -- Dec 1989) t (Jan 1992 -- Mar 1993) = 4,772 Group B= (Jav 1990 -- Dec 1991) + (Apr 1993 -- Dec 1993) = 1,676

UNIVARIATE ANALYSE

The role of CPR and bystanders

A majority of the patients (68%) in Group A were documented to have received CPR (Table

5). Police and Fire personnel were the rnost likely to initiate CPR (35%). whereas

bystanders were infrequent initiators (6%). This low rate by bystanders is influenced by

whether the CA is witnessed (12%) and the sex of the victim (Table 6).

Defibrillation rates

Defibrillation was performed on 24% of the patients in Group A, substantially lower than

the 37% rate for those in Group B (Table 4). The highest rate of defibrillation was noted

among those found in VF (Table 7) and the majority of the patients defibrillated received

only one shock (Figure 6). Sex. CPR initiators, ambulance crew and whether the CA was

witnessed were al1 observed to have a significant effect on defibrillation rates (Table 8).

Survival rates

The survival rate for CA patients (Group A. non prioritized data) in Toronto was observed

to be 8% (Table 9). A significant difference in survival rates was noted within the following

subgroups of patients: witnessed patients; defibdiated patients; and CPR initiators. Sex

and ambulance crew did not contribute to a significant difference in the outcome (Table

1 O).

Table 7

Outcome status by defibrillation and cardîac rhythm - Group A (n = 4,772)

Cardiac Rhythm n

V. Fib 41 6

Asystole 714

EMD 199

Other 162

Not recorded 3,257

Yes 409(98.3) 1 47 (1 1.3) 1

Yes 99 (13.9) 1 3 (3) 1

Yes 56 (28.1) 1 4 (7.1) 1

Yes 52 (32.1) 16 (30.8)

L

Yes 517(15.9) 39 (5)

' Chi-Square (2-tail) V. Tach = Ventricular tachycardia (not defibrillated) V. Fib = Ventricular fibrillation + defibrillated ventricular tachycardia EMD = electromechanical dissociation Other = All other recorded rhythms (eg. heart blocks) Group A= (Jan 1988 - Dec 1989) + (Jan 1992 - Mar 1993) = 4,772

Figure 6

Defibrillation - Num ber of attempts Group A (1,13 3 defibrillated)

Number of attempts

Table 8

Defibrillation rates by subgroups - Group A (n = 4,772)

Sex*

CPR lnitiator

Am bulance crewt

Subgroup n 1 Defibrillation 1 P 1

Male 3,095 1 811 (26.2) 1

Bystander 284 1 113 (39.8) 1

1

Female 1,663 1 320 (19.2) < 0.001

All Otherstt 2,943

Not recorded 1,545

.- - - . . -

Yes 1,471 1 511 (34.7) 1

ALS 2,203

BLS 2,567

BLS, Police and Fire personnel ' not recorded = 30 II Chi-square (2-tail) 'net recorded = 2 %ot recorded = 1,586 Group A= (Jan 1988 - Dec 1989) + (Jan 1992 - Mar 1993) = 4,772

761 (25.9)

259 (16.8)

,o.00l

841 (38.2)

292 (1 1.4) < 0.001

Table 9

Survival rates - CA cohort (n = 6,488); Group A (n = 4,772) and Group B (n = 1,676)

' Chi-square (2-tail) Group A= (Jan 1988 - Dec 1989) + (Jan 1992 - Mar 1993) = 4,772 Group B= (Jan 1990 - Dec 1931) + (Apr 1993 - Dec 1993) = 1,676

Group B n (%) P*

- -

62 (9.9) 0.9

57 (10.7)

-

55 (10.5)

Time period

1988

1989

1990

7 991

1992

1993

CA Cohort n (%) P*

129 (8.6)

114 (7.4)

62 (9.9) 0.05

57 (10.7)

103 (7.7)

92 (10)

Group A n (%) P*

129 (8.6)

114 (7.4)

- 0.46

- 103 (7.7)

37 (9.3)

Table I O

Sex (n=4,758)

. . - . .

CA Witnessed (n=3,186)

Defibrillation Attempted (n=4,772)

CPR Initiator

(n=4,772)

Ambulance crew

(n=4,770)

Outcome status by subgroups - Group A (n = 4,772)

Subgroups n ( Alive n (%)

Male 3,095 1 257 (8.3)

Witnessed 1,471 1 138 (9.4)

Not Witnessed 1,715 1 85 (5)

Yes 1,133 1 109 (9.6)

Citizen 284 1 22 (7.8)

All others * 2,943 1 204 (6.9)

No recorded 1,545 1 157 (1 0.2)

ALS 2,203 1 175 (7.9)

' Chi-square (2-tail) " Fire. Police and Ambulance crew Group A= (Jan 1988 - Dec 1989) + (Jan 1992 - Mar 1993) = 4,772

54

A further breakdown of the variable witnessed CA by CPR initiator demonstrates no

significant difference in the outcorne regardless of defibrillation (Table 1 1 ). As noted

earlier, ambulance crew had no significant effect on survival rates, but defibrillation was

significant factor in the outcome of those rnanaged by ALS (Table 11). The best prognosis

was observed for those patients with VT as their initial cardiac rhythm (83%); those in

asystole were at the other end of the spectrum (2.7%) (Table 7). Unfortunately, a majority

of the cohort did not have a recorded cardiac rhythm.

Defibrillation and survival rates by age group

The number of CA patients increased with advancing age, peaking in the 60-75 year old

age group. Defibrillation rates also increased in a similar manner; however, their rate

peaked in an younger age group, 45-60 year old (Table 12, Figure 5). Overall, the majority

of patients were above 45 year of age; defibrillation, and survival rates were noted to be

proportionally higher among this group (Table 12).

LOGISTIC REGRESSION ANALYSE

Two groups of logistic rnodels were constructed using a backward stepwise logistic

regression. The models constructed in group 1 (Tables 13, 14) exarnined the independent

role the initial cardiac rhythm and other relevant variables had on patient outcome.

Regrouping VT patients based on defibrillation (as described earlier) eliminated the use of

defibrillation is an independent variable in regression analysis.

Table 11

Outcome status &y Witness status and Ambulance crew - Group A (n = 4,772)

CA Witnessed

CA Not Witnessed

Am bulance Crew

CPR initiators n 1 Defibrillation n 1 Alive n (%) 1 p*

All others 1.166 1 YES 390 1 41 (10.5) 1

Bystander 179

Not providedl 1 YES 411 7 (17.1) 1

YES 80

NO 99

Not recorded 126

Not providedl 1 YES 25 1 0 (0) 1

10 (12.5)

8 (8.1)

Bystander 78

Allotherst 1.371

0.33

NO 85

Ambulance Crew 1 Defibrillation 1 Alive n (%) 1 P*

YES 24

NO 54

YES 262

Not recorded 266

ALS <O.OU0 71 (8.2) 1

6 (7.1)

BLS

0.08

1 (4.2)

1 (1.9)

12 (4.6)

1 YES 30 1 1 (3.3) 1

O 5 5

0.30 NO 241

Fire, Police and Ambulance crew Group A= (Jan 1988 - Dec 1989) + (Jan 1992 - Mar 1993) = 4.602

10 (4.2)

Table 13

Logistic Regression Analysis - CA cohort (Group 1, Modell)

Outcome : Survival (discharged alive from hospital)

Reference group: Age >75; Asystolic heart rhythm; BLS crew; Female; no CPR provided; and CA not witnessed

Independent variables

Oddr ( Standard ( 95% Confidence ratio error interval

Male

Age (30-75 year)

V. Fib

CPR provided by bystander

-

CPR by anyone +

CA witnessed

ALS = Advanced Life Support BLS = Basic Life Support CA = Cardiac Arrest CPR = Cardio-Pulmonary Resuscitation EMS = Emergency Medical Services EMD = Electromechanical dissociation V-Tach = Ventricular Tachycardia (not defibrillated) V. Fib = Ventricular Fibrillation + defibrillated ventricular tachycardia

Bystander, Fire, Police and Ambulance crew

Number of obsenrations = 2,081 Chi-square (7) = 206.44 Prob > chi square < 0.0001 Pseudo RS = 0.18 Log Iikelihood = 465.89

Table 14

Logistic Regression Analysis -- CA cohort (Group 1, Model2)

Outcome : S u ~ i v a l (discharged alive from hospital)

Reference group: age ~ 7 5 ; asystolic heart rhythm; BLS crew; Fernale; no CPR provided; and CA not witnessed

Odds Ratio

Standard error

95% Confidence Interval

Independent variables

1 Male

1 Age (30-75 year)

1 V. Fib

1 EMD

CPR provided 1 by EMS

1 CA witnessed

ALS = Advanced life support BLS = Basic life support CA = Cardiac Arrest CPR = Cardiopulmonary resuscitation EMS = Emergency medical services EMD = Electromechanicat dissociation V.Tach = Ventricular tachycardia (not defibrillated) V. Fib = Ventricular fibrillation + defibrillated ventricular tachycardia

Number of observations = 2,081 Chi-square (7) = 206.1 5 Prob > chi square c 0.0001 Pseudo R2 = 0.1 8 Log Iikelihood = 466.04

59

Models in group 1 (Tables 13, 14) suggest that VT (not defibrillated). VF (including

defibrillated VT) and EMD (OR=l15.02; 4.63; 1.51 respectively) are al1 independent factors

associated with a good prognosis. CPR provided by a bystander and witnessed CA

(OR=I .92; 1.68 respectively) are also associated with a favourable outcome. Missing

information regarding cardiac rhythm was responsible for reducing the cohort used for

regression analysis in group 1 (Tables 13, 14).

Elimination of cardiac rhythm variables from the analysis substantially increased the cohort

used to construct group 2 models (Tables 15, 16). The OR associated with the CPR

variables in Tables 15 and 16 suggest that CPR is a predictor of poor outcome regardless

of the initiator. Other differences between group 1 and 2 rnodels was the inclusion of ALS

as positive predictor, thoug h not significant (OR=l .l ; CI=0.92-1.32). However, a witnessed

CA was seen as positive predictor regardless of the group.

The possibility of collinearity or interrelationship between explanatory variables was

examined for each of the models constructed. Such relationships can effect the predictive

fits of the models in question. To assess this, regression models were reconstructed by

introducing the variables sequentially and examining changes to the resulting odds ratios.

The odds ratios previously included in the model did not change appreciatively with the

introduction of each additional variable for each of the models. This finding suggests the

absence of any collinearity or interrelationship between the explanatory variables within

each of the models.

Table 15

Logistic Regression Analysis -- CA cohort (Group 2, Mode1 1)

Outcome : Survival (discharged alive from hospital)

Reference group: Age >75; BLS crew; Fernale; no CPR provided; and CA not witnessed

1 lndependent 1 1 Odds 1 Standard 1 95% Confidence 1 1

ALS = Advanced Life Support BLS = Basic Life Support CA = Cardiac Arrest CPR = Cardiopulmonary resuscitation EMS = Emergency Medical Services

variables

Age (30-75 year)

ALS

CPR provided by anyone

Witnessed CA

Nurnber oobservationsbs = 6,205 Chi-square (4) =52.70 Prob > chi square < 0.007 Pseudo R2 = 0.014 Log likelihood = -1817.6

n

3,950

3,503

3,999

1,813

Ratio

1.27

1.1

0.52

1.51

error

0.1 2

0. 1

0.05

0.16

Ps[4

O .O 1

0.3

< 0.0001

< 0.0001

Interval

1 .O5

0.92

0.43

1 .23

1.54

1.32

0.64

1.87

Table 16

Logistic Regression Analysis - CA cohort (Group 2, Mode1 2)

Outcome : Suwival (discharged alive from hospital)

Reference group: Age >75; BLS crew; Female; no CPR provided; and CA not witnessed

lndependent variables

Odds Standard 95% Confidence 1 n 1 Ratio 1 eiror 1 Interval

Age (30-75 year)

ALS

CPR provided by bystander

ALS = Advanced life support BLS = Basic life support CA = Cardiac Arrest CPR = Cardiopulmonary resuscitation EMS = Ernergency medical services

CPR provided by EMS

CA witnessed

Number of observations = 6,205 Chi-square (5) =52.70 Prob > chi square < 0.001 Pseudo R2 = 0.014 Log Iikelihood = -1 81 7.26

3,950

3,503

339

3,660

1,813

1.27

1.1

0.61

0.52

1.5

0.12

0.1

0.13

0.05

0.16

0.02

0.3

0.02

1 .O5

0.92

0.4

0.42

1.213

1.53

1.32

0.92

0.63

1.85

< 0.0001

< 0.0001

Chapter V DISCUSSION

DATA LINKAGE

Probabilistic matching of records was used to link patient records. as no one variable could

be relied upon between any of datasets. This method facilitated linkage of 6,448 (91%)

MTA records to one of the other two administrative datasets. While it was possible to

examine the accuracy of linkage 1 and 3 by exarnining names and other identifier. this was

not possible with the results of linkage 2.

Linkage 1 and 3 were able to incorporate names in the matching process. thus enabling

a means to detemine the accuracy of the linkage process. AH matched records resulting

from these two linkages had identical names. ensuring accuracy of the exercise. Since first

and last names were not available on the ClHl data provided, M a g e 2 was unable to

verify the accuracy of the linkage process by examining names of the two linked records.

As an alternative. recorded responses for the variable MCC were used to asses the

accuracy of this linkage. Linked records coded as either "1" or "5" for the MCC variable

were considered an accurate link.

The decision to use MCC codes "1" and "5" was supported by the by the accuracy of

names of linked records coded with these two codes for two hospitals (sensitivity 92% and

specificity 86%) (Appendix 1). The expanded match of the false positives (n=375) resulted

in increasing the accuracy of the linkage process to 98% of the MTA records linked to

63

either VSlS or ClHl (Figures 1, 4). In light of the fact that CO-rnorbidity and complications

may influence the MCC, the use of ICD-9 diagnosis codes represents a finer tool for

discriminating matched and unmatched records. The results obtained by using the MCC

cut off ('1' and '5') and the ICD-9 codes reflects MTA and ClHl data at a particular period

of time. It is quite possible that these results may Vary over time and for diagnosis.

It can be postulated that those considered as "inaccurate" matches because of

unacceptable ICD-9 codes may have occurred because of one or more of three reasons:

an error was made in the entry of the ICD-9 code; an error was made by the data

abstractor in deciding the underlying disease process; or, an error occurred during coding

or entry by the MTA data entry clerk. This issue can only be settled by performing an audit

of the MTA database. Currently no protocol exits for perfoming routine audit of MTA data

to ensure validity and reliability.

Re-abstraction of MTA data (Appendix H) suggest that the number of matches between

MTA data and VSlS were less likely to be affected by incomplete data entry as compared

to those between MTA and CIHI. The reason for this lies in the use of names for linking

records in Iinkage 1 and 3. Since the linkage between MTA and ClHl was conducted by

hospital, records missing a value for this variable could not be linked. Age and sex were

two of the three linking variables, rnissing data for any one of these two variables frequently

resulted in a unrnatched pair, even when al1 other variables were well within the " matching"

criteria. These were the major limiting factor in linkage 2, and may have been overcome

64

if ClHl data had included patient names. Despite the unavailabiiity of narnes. the overall

results from data linkage as reported in this study are similar to those reported in a earlier

study designed to examine ambulance response tirnes to highway accidents for the state

of Maine7'. In the Maine study. a 96% match rate was reported using probabilistic

matching; however, accuracy of the match records was not reported.

CHAIN OF SURVIVAL

The chain of survival consisting of early access. early CPR: early defibrillation, and early

ALS conveniently divides the process of CA patient care into four measurable components

33? While each of theses components can be individually measured. their

interrelationships must be scrutinized when evaluating overall care and outcornes. Access

to each of these four components. with the exception of the first depends on the preceding

cornponent. In an ideal setting, each of these components would be initiated with the

minimum of delay.

Early access

This term encompasses a number of events which occur during the time period after

collapse of the patient and until the arrival of any emergency care giver at the scene.

Individually, they include: 1) recognition of patient collapse; 2) rapid notification by

telephone to EMS dispatcher; 3) recognition by the dispatcher of a CA; 4) rapid dispatch

of appropriate EMS crew; 5) Prompt amval by crew at the given location; and 6) arrival at

patient's side. Each of these sub-components are critical, but none more so than

65

recognition and rapid notification. The first is a random event which is not predictable;

however. it has been demonstrated that CAS occurring outside of the home are more likely

to be witnessed (73%) than those occurring at home (54%)62.

It can be postulated that the two components (recognition and rapid notification) may

represent learned behaviours, and those individuals that have received CPR training are

more likely "to know what to do". These "trainedn individuals are therefore more likely to

identify a CA emergency and thus promptly notify the EMS dispatcher. This study did not

attempt to assess the initiation of early access; however. the surrogate measure

"bystander-initiated CPR" and "witnessed CA" do provide some insight into this

phenornenon as discussed in the following section.

Early CPR

The rate of bystander-initiated CPR has been scrutinized in a nurnber of published

studies"*36~55+62~8588 most of which have demonstrated an improved outcome when early

CPR is initiated by a bystander. These studies support the assumption that when CPR is

provided promptly by a private citizen, they will also initiate earlier access to care by

telephoning the EMS dispatcher. This is supported by the reported results of CA victims

in New York Cityz4. ln New York City, 68% of the witnessed CA patients did not receive

bystander-initiated CPR24. This group had a survival rate of 0.8%, whereas a 2.9%

survivor rate was obsewed among those receiving bystander-initiated CPR (Table 17). In

Toronto, 87% of the witnessed CAS did not receive CPR and had a survival rate of 9.3%.

Table 17

Setting

Toronto *

New York **

Chicago

King Countytt

Cornparison of Survival rates among Witnessed CA - North America

Witnessed Survival CA in V. Fib rate n n (%)

202 33 (16.3)

415 22 (5.3)

37 1 15 (4.0)

2074 705 (34.0)

Witnessed CA Survival & no bystandar rate CPR n (%) n (%)

1,626 (87.7) 152 (9.3)

Witnessed CA Survival 8 bystander rate CPR n (%) n (%)

* Group A (Jan 1988 -- Dec 1989) + (Jan 1992 -- Mar 1993) = 4,772; V. Fib = Ventricular fibrillation + defibrillated ventricular tachycardia " Outcome of out -of-hospital cardiac arrest in New York.

JAMA 1994;271:678-683. Outcome of cardiopulmonary resuscitation in a large metropolitan area: Where are the survivors? Ann Emerg Med 1991 ;20:355-361. Numerators, denominators, and survival rates: Reporting survival from out-of-hospital cardiac arrest. Am J Emerg Med 1991 ;9:544-546.

67

This rate rose to 12.2% when witnessed CA patients received bystander initiated CPR

(Table 17). In contrast to the rates reported for New York City and Toronto are those for

King County, Washington5=. Here, among witnessed CA receiving bystander CPR the

reported survival rate is 32%; for those witnessed and receiving delayed CPR (EMS

initiated), 22%. The overall survival rate among witnessed CA patients was 26% for King

County. compared to Toronto's 9% (Table 10). These results suggest that among cities

of comparable size, Toronto has a better suwival rate among witnessed CA patients

regardless of the initiator (Tables 10, 1 1).

The discrepancy between the two cities may reflect the fact that CPR initiated by a

bystander is perhaps a marker for elapsed time rather than a predictor of outcome. The

differences in OR obtained for CPR begun by a bystander, compared to that for CPR by

anyone (OR= 1.92 versus 0.63) is clearly influenced by the large number of patients

receiving CPR by EMS (Tables 13, 14). Patients can be grouped into three groups based

CPR provider: no CPR; CPR by bystander; and CPR by EMS. The first or reference group

represents those who were either unconscious with no apparent pulse or cardiac rhythm

(clinically dead) or alternatively, breathing spontaneously with adequate circulation, and

therefore not in need of any CPR. The second group were judged by a bystander to need

CPR. Whether these individuals were in need of CPR or received CPR without having the

necessary criteria for CPR could not be ascertained from the available data. Accordingly,

the favourable outcome associated with this group may reflect either the prompt delivery

of CPR or a selection of less severe CAS.

68

In contrast to this second group are those who received CPR by EMS and were likely to

experience a poor outcome- those who experienced prolonged delays to receiving CPR

or experienced a severe CA. As a result, Ambulance staff may have withheld CPR to

those who were obviously dead as CPR would have not altered their outcome. Based on

the absence of any observed collinearity between variables within the logistic models, the

possibility that CPR-related variables are prognostic markers for any one of the other

measured variables in this study can be discarded based. Therefore, one can hypothesize

that CPR variables rnay be markers for other unmeasured factors. such as overall severity

or elapsed time. Whether these finding are particular to Toronto's data or a generalizeable

finding requires further evaluation.

The quality of CPR provided by bystanders has been shown to impact on patient

O U ~ C O ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ . It was not possible to ascertain the quality of the CPR performed or

whether the provider had any CPR training. One factor which is sometimes overlooked

when examining bystander-in itiated C PR rates is the impact of patient characteristics. This

may not be a factor when the victim is a family or close relative, but the fear of disease

(such as AIDS) may cause bystanders to be reluctant to render assistance. As the majority

of CAS occur outside of the home, this may be a much more important factor than

previously recognized. In a study by McCormack and colleaguesgO, bystanders were

interviewed, and those patient characteristics responsible for curtailing bystander CPR

were documented. They included: "disagreeable physical characteristics" (which included

odour on patient's breath, especially alcohol or general malodour); visible blood; and

69

vomiting before or within a minute of beginning CPR. Researchers noted that these

characteristics may have no consequence when a close family relation is involved. This

study by McCormack was performed just as AIDS was ernerging as a public health

concem. and AlDS was therefore probably not perceived as a concem by the responders.

It is not possible to deduce from the MTA data whether the decision by a bystander not to

provide CPR to a witnessed CA was dictated by patient presentation or other factors.

Early Defibrillation

Access to early defibrillation has been cited by rnany researchers as the "link" most likely

to jmprove survivallO. 12.16.37.4~2.91.92 . Clinical research has demonstrated that defibrillation is

extremely beneficial in reverting VF, pulseless VT and, in some cases asystole back to

normal sinus rhythm8~'0~16~38~93. In this study, univariate analysis dernonstrated that 24% of

the CA patients were defibrillated (Table 5), and a significant difference in survival rate

was noted between the defibrillated (9.6%) and non-defibrillated group (7.5%). p=0.02

(Table 10). Among the defibrillated group, the rnajority (29.5%) received only one

defibrillation, range 1-1 2 (Figure 6); proportionately the best outcome (1 3% survival rate)

was seen among those receiving 1,2 or 4 shocks. The appropriate number of atternpts

to defibrillate a CA patient is difftcult to calculate; perhaps it is inappropriate to attempt to

define this since clinical presentation plays a major role in management. However, it is

possible to postulate that the large number of patients receiving only 1 defibrillation may

in fact be a reflex response by EMS personnel to "try sornething". Similarly greater than

5 attempts may represents a futile and "heroic atternpt", or patients who continuously

relapsed into an non-sinus rhythm requiring numerous defibrillation (Figure 6).

70

Resuits from New York and Chicago have reported survival rates of 13% and 10%

respectively for patients whose CA was witnessed, initial cardiac rhythm was VF, received

bystander CPR and definitive care (ALS, defibrillati~n)",~~. These results are at one end

of the spectrum when compared to the 49% survival rate for a sirnilar group in King

Countys5. Observed rates for a similar group in Toronto are 20%. These observed

differences could be due in part to the geographical layout of the communities; King

County is an urbanlsuburban community while Toronto, New York and Chicago are dense

urban cities and thus access to definitive care rnay be delayed. In New York and Chicago,

a median of 12.4 and 16 minutes elapsed from occurrence to definitive care; in King

County, paramedics arrived within 7.8 minutes. Response times by ambulance crew in

Toronto have not been reported, but prelirninaiy work suggests that it may be greater than

7 minutes.

Early A LS

The overall distribution of ALS and BLS responders is equally divided arnong the CA

population (Table 4). Defibrillation in the setting of ALS care has a positive role (14.6%

survival verses 8.2%, p= c0.0001). Table 11. The delivery of defibrillation by BLS crew

was recorded for only 225 patients and therefore, it would be premature to draw

conclusions from the observed outcome of these patients. Log istic models constructed

using the larger cohort demonstrates a trend that ALS crew has a positive influence on

outcome. though not statistically significant.

7 1

SURViVAL RATES

Survival rates for CA reported in the literature Vary from 2 to 44%90'3.'4.16-28. The IOW rates

have led some to consider that CA management outside of the hospital may at times be

a futile atternpt to Save l i v e ~ ~ ~ ~ ~ ~ . Other investigators suggest that by adjusting

management strategies as they pertain to their specific community, an improvement in

survival rates can be obtained '3-27~41.43.49~95~96.

Toronto's survival rate of 8% is among the highest reported for large metropolitan ~it ies' ' .~~,

and is better than the recently reported rate of 2.5% by Brison and colleagues for five

smaller communities in Ontario3'. Published Canadian studies were performed in the pre-

Utstein era: therefore. it is difficult to compare Toronto's results, as reported in this study,

objectively with earlier studies. Comparing the results from cities of comparable size, using

Utstein definitions, demonstrates a high survival rate for Toronto (Table 17).

It has been reported that the etiology of cardiac arrest among those below 40 is different

from those over 40 years of age, and that the survival rate arnong the two groups also

differsa4. The majority of CA patients in Toronto were older than 45 years (91%). with a

suwival rate of 8% versus 5% for the younger age group (Table 12). Reported etiologies

for those below age 40 include: cardiac 37%; overdoselpoisoning 26%; respiratory 13%;

undetemined 11 %; exsanguination 6%; neurologic 4%; sepsis; electrocution and

hypotherrnia each represented 1%. The survival rate among this group is reported to be

5%". Since this survival rate includes al1 etiologies, and those in Toronto included only CA

72

with a cardiac etiology. it is not possible to draw any conclusions from the observed

d ifferences.

Following CA, cardiac rhythms change over tirne. One common pattern of change is

described as VT, followed by a change to coarse VF, to fine VF. and finally. A s y ~ t o l e ~ ~ ' ~ .

It is not unreasonable then to postulate that these changes are a function of time elapsed

from the onset of a CA. Therefore, if we use cardiac rhythms as a surrogate measure of

elapsed time, the survival rate is best (83%) among patients in VT (shortest time elapsed

time from onset of CA), followed by 12% for VF patients and 4% for EMD and 3% for

asystolic patients (Table 7). This further supports the necessity for early recognition and

prompt delivery of definitive care.

PREDICTORS OF SURVIVAL

Survival of CA patients is dependent on care, the quality of which has been debated for

some time. A number of factors (chain of survival) are integral components, but their role

may Vary from patient to patient and jurisdiction to jurisdiction. In an attempt to understand

the independent role of these components. regression analysis was performed.

In this study, two groups of logistic rnodels (Tables 13-16) were developed to examine the

role of individual components. The difference between the two groups lay in the size of the

cohort and variables. Despite these differences. younger patients (30-75 years old) and

the presences of a witness were CO-variables consistently found to be positive predictors

73

of survival (Tables 13-16). The group 1 rnodels (Tables 1 3, 14) support the hypofheses

that males (OR=0.7), are at greater risk of death than fernalesg7 and that VT and VF

(OR=111.6, 4.7 respectively) are good prognostic factors.

The positive role of witnessed CA is what one would expect intuitively but it may also

highlight the importance of quick access to care. This assumption is not a large leap of

faith when we see that CPR provided by a bystander is also associated with a good

prognosis (Tables 13, 14). Therefore, as discussed earlier, al1 variables, with exception

of age and sex in the regression models, rnay be markers of severity or of elapsed time

rather than outcome.

The results of regression analysis must be validated with other datasets to access the

consistency of these findings. The results obtained are reflective of the datasets.

Important CO-variables such as those coding CO-rnorbidity could not be used in the analysis.

Adjusting the outcomes for CO-morbidity may have affected both the survival rate and

predictors of survivals.

UTSTEIN GUIDELINES

The Utstein guidelines were developed as consensus document and have helped to

standardize definitions for reporting of CA data, and as such attempt to address necessary

criteria for exarnining outcome studiesg8. However, the template for reporting outcomes

fails to address or provide the means by which to accommodate possible biases effecting

74

survival rates. For example. published data suggest that CAS occur in approxirnately 1%

of the general population; and among this group the majority will have survived an initial

CA. or have other concurrent cardiovascular comorbidity? Survival among this majority

is low. and therefore it is no stretch of imagination to postulate that in communities with a

large percentage of such patients, survival rates will be infiuenced by this bias. Thus. to

effectively compare and plan EMS programs, it behoves researchers and planers to

examine EMS outcornes in light of the population being served. For Canadian centres,

comorbidity data can be obtained from ClHl data.

Utstein definitions for VF and asystole reflect management protocols used by EMS.

However. confusion may arise with defining VT patients. Ventricular tachycardia patients

can be grouped into two groups based on the presence or absence of a pulse. As

discussed earlier, this distinction is important. as standing orders for the two groups differ.

This distinction is important if the goal is to measure outcome as reflective of practice

patterns. Therefore, it would seem prudent to combining pulseless VT patients with VF

patients.

Factors other than those defined by the chain of sunival may have a significant rote in

influencing the outcome of out-of-hospital CA. Dispatch protocols may be one infiuencing

factor responsible for observed differences in survival rate; others include experience of

EMS personnel (level of training and number of years). the volume of patients managed

by individual personnel, and the integrity of the complete EMS program. The outcome of

75

pre-hospital care is probably not independent of hospital care; training of the Emergency

Department (ED) physicians and care provided to victims during their hospital stay should

also be scrutinized, since the relationship of patient volume. physician and outcorne has

been demonstrated in both the cardiovascular and trauma l i t e ra t~ re~~- '~ ' . This partial list

of cofactors is overlooked by Utstein and is perhaps one reason for differences in survival

rates between communities. Al1 possible components of care should be intergrated into the

analysis.

This current study was not designed to examine many of the components listed. However,

a univarite analysis of outcorne of patients by hospital, unadjusted for volume, physician

training or patient demographics suggest that hospital may influence outcornes (Figure 7).

As a minimum, patient comorbibity. community demographics. EMS and hospital volume

should be included in the analysis. The incorperation of covariables and other prognistic

factors. such as those highlighted by logistic regression in this study, can be used to

construct a quality of care index. Such an index would adjust for confounding variables

and thus enable a direct comparison of different programs.

LIMITATIONS

The MTA database was designed and launched prior to the release of the Utstein

guidelines, and data on a number of desirable variables (e.g. return to spontaneous

circulation) are not included. However, this limitation does not hinder measuring survival

rates. Of major concem were whether al1 possible CA patients were entered into the MTA

Figure 7

Admission and Survival rates by Hospital* Patients admitted to hospital (n=833)

Hospitals

Wnadjusted for Hospital volume, Physician training or Patient demographics

77

database, accuracy of data transcribed into the MTA database and reliability of the ACR

data recorded by EMS personnel.

Patient distribution over the 6-year time period suggests that not al1 patient ACR forms

generated were abstracted into the MTA database (Tables 1, 2). This discrepancy was

overcome by perforrning a sensitivity analysis. A window into the accuracy of the data

transcribed was gained by re-abstracting four variables for 7% of the CA cohort (Appendix

H). In order to ascertain the reliability of data inscribed on ACR forms and ultirnately MTA

database, a comprehensive evaluation of adherence by EMS personnel to preset

management protocols and the accuracy with which they record their findings and

interventions is necessary. This in itself is a separate study and was beyond outside the

scope of this thesis.

One of the difficulties encountered with probabilistic matching centred around surnames.

In particular, short Asian surnames, which on average have less than five characters long

and are often shared by many individuals. Such names do not provide the discriminating

power seen with other "larger" sumames. Finally, the linkage results may have improved

substantially if comprehensive patient information was available in the MTA database, such

as: birth date; postal code; health insurance number; social insurance number; and if ClHl

data included patient names.

78

By far the major limitations were missing cardiac rhythm data and lack of constant time

interval recordings. No one clear explanation could be obtained for omitting the recording

of cardiac rhythm from the MTA database prior to 1993. Thereafter, with the elirnination

of the cardiac rhythm codes from ACR forms. data pertaining to these variable were no

longer recorded on ACR forms. and ultimately MTA database. All telephone call received

via "91 1" and notification of EMS by dispatcher are "tirne stamped" by the dispatchers.

Other time intervals are recorded by EMS personnel directly ont0 the ACR forrn.

Recording of values for these variables was limited and inconsistent. As a result, recorded

times were available for the "91 1 call" and time of dispatch but availability of other time

intervals were lirnited. The inclusion of time variables as recorded by EMS would have

further reduced the number of observations available for constructing logistic regression

models. Furthemore. inclusion of only ambulance response times would have overlooked

the role of firefighters who were most the first at the scene (Table 5). Firefighter response

times were not available as they are not recorded in any comprehensive database.

Despite these limitations, the MTA database serves as good starting point for examining

EMS sewices in Toronto and this analysis can seme as a baseline for future evaluation.

CONCLUSIONS

The existence of large health care administrative databases provides a relatively

inexpensive opportunity to conduct research. In general, these databases are not

designed for research, and therefore clinical variables important to the researcher may not

be available. Retrospective and observational studies employing such data can be

79

criticized for having inherent biases in patient selection as well as incomplete data98.'02.

These criticisms may be valid, but should be weighed against labour, tirne, and expense

required to conduct an effective prospective longitudinal or randomized study. In some

cases, a randomized study may not be ethically justifiable; examining the role of

defibrillators is a case in point. lnferences drawn from studies using such data should be

examined in the context of the data used, and the fact that they are non- rand~rn ized~~~ '~~.

While observational studies do not replace randomized clinical trials, they can provide an

invaluable opportunity to examine the role of different management strategies in a "real

world" setting. provided adequate care is taken in formulating and analysing the s t ~ d y ~ ~ ~ ' ~ .

They can also provide important information about the natural history of a disease process,

especially rare events, and for the purpose of hypothesis-generating.

In keeping with limitations associated with administrative databases, some pertinent

variables were not available for this study. For example, EMS response times and time of

first defibrillation were either severely limited or not available at al1 in the MTA database.

or for that matter in any other database. Availability of these variables may have

strengthened sorne of the inferences drawn from results obtained in this study. The

employment of a surrogate measures such cardiac rhythm for EMS response times was

a manoeuvre which overcame one of the shortcomings of the MTA data. Successful data

linkage using probabilistic matching was another; it provided the ability to measure

outcome of individuals within the cohort identified from MTA data. Therefore, despite the

limitations with the available data the goals of this study as outlined in the study objectives

were reached.

80

The present study demonstrate that out-of-hospital CA occurring in Metropolitan Toronto

affects primarily adults above age 45. Despite the initiation of a program in 1991 to

improve care provided by EMS, no improvement in suwival rates among CA patients was

observed between the years prior to its inception and those following (1988=8.6%,

1992=7.7%). While these rates are appreciably higher than those see in similar

jurisdictions, initiatives to increase this rate must be forged by carefully giving due

consideration to al1 components of care.

The high survival rates reported by King County are at one end of the spectrurn while those

reported by other communities are on the other end. King County's results can be

considered as either the beacon towards which al1 programs should strive, or as an outlier.

The truth, as in many other similar situations. probably lies somewhere in between.

Communities may not be able to raise their overall rates above the 20% threshold without

individualized changes in the process of delivering care to these patients. As the process

of care to CA patients involves integration of a number of services and personnel

(bystanders, fire personnel, ambulance crew and hospital staff) with varying levels of

training, it is critical that their individual roles are incorporated in the evaluation process.

Similarly, patient comorbidity and community dernographics shouid be included. This

cannot be accomplished without first collecting cornprehensive MTA data which are the

building blocks for EMS research and planing.

8 1

The initial cost necessary to establish such comprehensive data collection and evaluation

are substantial. However, the availability of comprehensive MTA (pre-hospital) data to

evaluative EMS programs is a powerful tool. The combination of ongoing data collection

and evaluation should help identify specific areas as opposed to general areas in the

continuum of care that may need to be modified from time to tirne. Theoretically. this

should then result in maxirnizing the benefits for expenditures incurred. A number of

recommendations for potentially improving the outcome of CA patients in Toronto are listed

in Appendk J.

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Cummins ROI Eisenberg MS, Hallstrom AP, Litwin P. Survival of out-of-hospital cardiac arrest with early initiation of cardiopulrnonary resuscitation. Am J Emerg Med l985;3:li4-8.

Thompson RG, Cobb LA. Hypokalemia after resuscitation from out-of-hospital ventricular fibrillation. JAMA 1982;248:2860-3.

Weaver WD, Cobb LA, Hallstrom AP, Copass MK, Ray R, Emery M. Fahrenbruch C. Considerations for improving survival frorn out-of-hospital cardiac arrest. Ann Emerg Med 1986;15:Il8l-6.

American Heart Association. Advanced canliac life support. Boston, MA. American Heart Association; 1981.

Bayes de Luna A, Coumel P, LeCleroq JF. Ambulatory sudden cardiac death: Mechanism of production of fatal arrhythmia on the basis of data from 157 cases. Am Heart J 1989;117:151-9.

Myerburg R, Conde C, Sung R, et al. Clinical, electrophysiologic and hemodynamic profile of patients resuscitated from pre-hospital cardiac arrest. Am J Med 1980;68:5678-576.

Kouwenhoven WB, Jude JR, Knickerbocker GG. Closed-chest cardiac massage. JAMA 1969; l73(lO): 1064-7.

Litwin P, Eisenberg MS, Hallstrom AP, Cummins RO. The location of collapse and its effect on survival from cardiac arrest. Ann Emerg Med 1987; 16(7):787-9 1 .

Larsen MP, Eisenberg MS, Cummins RO, Hallstrom AP. Predicting survival from out-of-hospital cardiac arrest: A graphic model. Ann Emerg Med l993;22(ll): 1652-7.

Valenzuela TD, Spaite DW, Meislin DW, Clark LL. Wright AL, Ewy GA. Emergency vehicle intervals versus collapse-to-CPR and collapse-to-defibrillation intervals: Monitoring emergency medical services system performance in sudden cardiac arrest. Ann Emerg Med 1993;2Z(ll):l678-84.

Campbell JP, Gratton MC, Salomone JAI, Watson WA. Ambulance arriva1 to patient contact: The hidden component of pre-hospital response time intervals. Ann Emerg Med l993;22: 1254-7.

Callaham M, Braun O, Valentine W, Clark DM, Zegans C. Pre-hospital cardiac arrest treated by urban first-responders: Profile of patient response and prediction of outcome by ventricular fibrillation wavefon. Ann Emerg Med 1993;22(11):1664-77.

Dammann G. 1996; Chairman of the board, National Museum of Civil War Medicine; Fredrick, Maryland. As it happens. CBC radio; June 14 1996.

Pantridge JF, Geddes JS. A mobile intensive-care unit in the management of myocardial infarction. Lancet 1 967;August 5:271-5.

Jaro MA. Advances in record-linkage methodology as applied to matching the 1985 census of Tampa, Florida. J Am Statist Assoc l989;84(406):414-20.

Jaro MA. Automatch. Generalized record lin kage system. Version 2.9.

Jackson RE. Probabilistic linkage of large public health data files. Stats Med l995;14:491-8.

Fellegi IP, Sunter AB. A theory for record linkage. J Am Statist Assoc 1969;1183-210.

Personal communication: Craig AM. Senior EMS Development Officer MA. The Municipality of Metropolitan Toronto.

Ontario Hospital Association. Ontario Ministry of Health: Hospital Medical Records Institute. Executive sumrnagc Reporf of the Ontario data quality re-abstracting study.

Williams JI, Young W. Goel V, Williams JI, Anderson GM, Fooks C, Naylor CD, Eds. Patterns of Health Care in Ontario. The /CES Practice Atlas. 2nd ed. Toronto: lnstitute for Clinical Evaluative Sciences in Ontario; 1996; Appendix 1, A summary of studies on the quality of health care administrative databases in Canada. p. 339-45.

Personal Communication: Kelly EJ, Deputy Registrar General.

Smith ME, Newcombe HB. Automated follow-up facilities in Canada for monitoring delayed health effects. Am J Public Health I980;7O: 1261 -8.

Schnatter AR, Acquavella JF, Thompson FS, Donaleski D, Theriault G. An analysis of death ascertainment and follow-up through Statistics Canada's rnortality data base system. Can J Publ Health 1990;81:60-5.

Fair ME. Recent advances in matching and record linkage frorn a study of Canadian f a m operators and their faming practices. Proceed ings of the l nternational Conference on Establishment Surveys American Statistical Association 1993;600-5.

CMG 7992 Directory (ICD-9). HMRl CMG directory 1 992;

Manual of the International classification of diseases, injuries, and causes of death. 1 977; Geneva.

Rosner B. Fundarnentals of biostatistics. 3rd ed. Boston: PWS-Kent Publishing Company; 1990.

Hosmer DW, Lemeshow S. Applied logistic regression. New York: John Wiley and Sons; 1989.

Clinton JE, McGill J, lnnrin G, Peterson G, Lilja GP, Ruiz E. Cardiac arrest under age 40: Etiology and prognosis. Ann Emerg Med 1984; l3:lOll-5.

Eisenberg MS, Bergner L, Hallstrom AP. Cardiac resuscitation in the community: Importance of rapid provision and implications for program planing. JAMA 1979;24I:l905-7.

Enns J, Tweed WA, Denon N. Pre-hospital cardiac rhythm deterioration in a systern providing only basic life support. Ann Emerg Med 1983;12:478-81.

Swor RA. Jackson RE, Cynar M. Sadler E, Basse E, Boji BI et al. Bystander CPR, ventricular fibrillation, and survival in witnessed, unmonitored out-of-hospital cardiac arrest. Ann Emerg Med 1995;25(6):780-4.

Wilcox-Gok VL. Suwival from out-of-hospital cardiac arrest. A multivariate analysis. Med Care 1991 ;29:104-14.

Bossaert L, VanHoeyweghen R. Cerebral resuscitation study group. Evaluation of cardiopulmonary resuscitation (CPR) techniques. Resuscitation 1989; 17(Suppl):S99-S109.

McComiack AP, Damon SK, Eisenberg MS. Disagreeable physical characteristics affecting bystander CPR. Ann Emerg Med 1989;18(3):283-5.

Cummins RO, Eisenberg MS, Litwin P. Graves JR, Hearne Tt Hallstrom AP. Automatic external defibrillators used by emergency medical technicians. JAMA 1987;257:1605-1 O.

Eisenberg MS, Hallstrom AP, Copass MK, et al. Treatment of technician defibrillation and paramedic services. JAMA 1 984;ZI : 1 723-6.

Hoekstra JW, Banks JR, Martin DR. Cummins RO, Pepe PE, Stueven HAI Jastremski M. Gonzalez E. Brown CG, The Multicentre High-Dose Epinephrine Study Group. Effect of first-responder automated defibrillation on time to therapeutic interventions during out-of-hospital cardiac arrest. Ann Emerg Med 1 993;22(8): 1247-53.

Bonnin MJ, Pepe PEI Kimball KT, Clark PSJ. Distinct criteria for termination of resuscitation in the out-of-hospital setting. JAMA l993;270:1457-62.

Richless LK, Schrading WA, Polana J, Hess DR, Ogden CS. Early defibrillation program: Problems encountered in a rurallsuburban EMS system. J Emerg Med 1993; 1 1 : 127-34.

Stiell IG. Cardiac arrest in your community: Are there weak links in the chain of suwival? Can Med Assoc J 1993;U9(5):563-5.

Lemire JG, Johnson AL. Is cardiac resuscitation worthwhile? A decade of experience. N EngI J Med l972;286(l8):97O-2.

Naylor CD, Guyatt GH. Users' guides to the medical literature. X. How to use an article reporting variations in the outcomes of health services. JAMA 1996;275(7):554-8.

Grumbach K. Anderson GM, Luft HS, Roos LL, Brook R. Regionalization of cardiac surgery in the United States and Canada. Geographic access, choice, and outcomes. JAMA l995;274(16): 1282-8.

Konvolinka CW, Sacco WJ. Institution and per-surgeon volume versus survival outcome in Pennsylvania's trauma centers. Am J Surg 1995; l70:333-40.

Kimmel SE, Berlin JA, Laskey W. The relationship between coronary angioplasty ~rocedure volume and maior com~lications. JAMA l995:274(14): 1 137-42.

102. Eilenberg JH. Cohort studies selection bias in observational and experirnental studies. Stats Med 1994; 13:557-67.

103. Wen SW, Hernandez R, Naylor CD. Pitfalls in non-randomized outcornes studies. The case of incidental appendectomy with open cholecystectomy. Lancet l995;274(21): 1687-97.

104. Naylor CD. The grey zones of clinical practice: Some lirnits to evidence-based medicine. Lancet 1 995;345:840-2.

APPENDIX A

UTSTEIN TEMPLATE FOR REPORTING EMS DATA

Utstein Template* 1. Population served by

EMS system N=

I 2. Confirmeci cardiac arrests considered for resuscitation N= -

4. Resuscitations attempted

6. Non-Cardiac 5. Cardiac etiology etiology n= n=

7. Arrest witnessed

witnessed n= (bystanders) n= -

12. Initial rhythm I a s y s t o k ~ 10. Initial rhythm 1 1. Initial rhythm

14. Detemine presence of bystander CPR: yes or no for each subset II 16. Never achieve 15. Any Return of Spontaneous

Circulation (ROSC) n=-

1 1 9 Expired in hospital a. total n= 1 b. within 24hrs. n= I)

I 21. Expired within one year of discharge n=

22. Alive at one year

n=

' Adapted frorn: Recornrnended Guidelines for Uniforni Reporting of Data from Outsf-Hospital cardiac Arrest: The Utstein Style. Ann. Emerg. Med. August. 1991 ; ZO:861-874.

APPENDIX B

METRO TORONTO PARAMEDIC CARDIAC ARREST PROTOCOL

Standing Orders

CARDIAC ARREST PROCEDURES ALL PATIENTS

A paramedic who determines a patient to bc vital signs absent may initiate the pfocedures in the algorithm appropriate for that patient prior to contacting a base hospital physician for medical ad delegation.

The paramedic will determine the appropriate algorithm based upn:

1. evïdnice as to the initiatùig cause of the cardiac arrest, i-e., medical versus traumatic,

and

2. the presentîng E.C-G. rhythm

and

3. the age of the patient.

If, prior to patchllig to the base hospaal physician, the E.C.G. rhythm changes nom one pulseless rhythm to aclofher, the paramedic wiii initiate procedures indicated in the algorithm appropriate for the new rhythm.

If unable to establish a patch, continue with the appropriate aigorithm(s) and document on ACR and complete an incident report.

Epinephrine, Atropine and Lidocaine may be admhktered via the endatracheal tube, if Intravenous access is not readily obtained.

The paramedic wiii patch to a base hospM phpician upon.

1. the return of a paipable pulse;

2. the wmpletion of the SeQuence ofprocedurcs specified in the appropriate algorithin, with an unchanged E.C.G. rh*

3. the initiation of procedures in a second algorithm necessitatcd by an E.C.G. rhythm chans;

Issued March 1994 Page 7

Standing Orders

MI3DICAL CARDIAC ARRIEST (Patients 12 years old and older)

Algorithms for: 1. Fuiseless Ventricular Tirchycardia (VT) 2, Ventricuiar Fibrillation

CONFIRM: CARDIAC ARREST NO EVrlDENCE OF TRAUMA

*

CARDIAC MONïïOR *

DEFIBRILLATE 200 JOULES *

DEFIBRILLATE 300 JOULES *

DEFIBRILLATE 360 JOULES

ENDOTRACHEAL INTUBATION *

INTRAVENOUS ACCESS NORMAL SALINE AT TKVO RATE

*

DEFIBRILLATE 360 JOULES *

DEFIBRELATE 360 JOULES *

PATCH

Issued March 1994 Page 8

MEDICAL CARDIAC ARREST (For patients 12 years old or oIder)

Algorithnis for: 3. mie 4. Pulseless Electrical Activity (PEN

CONFIRM: C A R D M ARREST NO EVIDENCE OF TRAUMA

*

CARDLAC MONITOR

I ASYSTOLE

I PULSELESS ELECTRICAL

ACTIVITY

ASYSTOLE IS CONFIRMED (1) ENDOTRACHEAL INTUBATION * *

ENDOTRACKEAL INTUBATION

*

INTRAVENOUS ACCESS N O W SALINE

* INTRAVENOUSACCESS . 1.0 mg EPINEPHRINE (1 : 10,000) IV

NORMAL SALINE @ TKVO RATE or 2.0 mg EP~PHZRE (1 : 1 o,ooo) ETT

* * 1.0 mg EPINEPHRINE (1 :10,000) IV NITiATE INTRAVENOUS BOLUS

or 300 cc NORMAL SALINE 2.0 mg E P I N E P ~ (1: 10,000)ETT

REPEAT IN 3 - 5 l'HDWlW * *

1.0 mg ATROPINE N PATCH or

2.0 mg ATROPINE ETT REPEAT IN 3 - 5 MINUTES

* PATCH

(1) CONFLRM ASYSTOLE M TWO (2) LEADS a

IF RHYTHM UNCLEAR CONSIDER FINE VF

APPENDIX C

OVERVIEW OF RECORD LINKAGE AND AUTOMATCH SOFTWARE

C l

AUTOMATCH

Automatch is a data linage software program which permits matching of files containing

individual records where data may be missing or in error. A multi-pass rnatching process

enables the programmer to discriminate correct matches from unmatched pairs, despite

errors in critical blocking variables. This process involves assigning weights to fields, which

are then used to measure the contribution of each field to the probability of making an

accurate match. A record pair is then classified as a match if the composite weigh is above

a set threshold value, and a non-match if the composite weight is below another threshold

value. An undecided situation (possible match) occurs when the composite weight is

between the two threshold values (upper, lower). These marginal cases can be reviewed

and incorrect matches can be corrected. This process is performed using a computer-

assisted review process. Three key components to the matching process are: Blocking;

Probability matching of variables; and Matching algorithm.

Blocking

Blocking is a method by which the number of pairs being examined is limited. It involves

the partitioning of the two data sets into mutually exclusive blocks. There is no pre-set

number of blocks necessary to perform data linkage. This goal of this process is to

increase the proportion of matched pairs while decreasing the number of record pairs to

compare.

For example. if the variable age was used. and there were 100 different ages recorded.

then the data sets could be partitioned into 100 subsets. Furthermore. if the age

distribution was uniforrn within in the two datasets, then using datasets A and B. each with

1 .O00 individuals. there would be 10 observations in each group for a total of 100 blocks.

The resulting combination of matches would be 10 x 10 or 100 pairs per block. On the

other hand, if genderwas used to block, there would be only two subsets resulting in 500

x 500 or 250,000 pairs in each block.

Blocking leads to a cornparison between those records having the same value in the

blocking field. Those records not matching on the blocking variable are classified as no-

matches. To accommodate errors in blocking variables, multiple passes are performed

using different blocking variables. In this example. al1 records that did not match in which

age was the blocking variable or those with a missing value for the age variable can be

rematched using another blocking variable (eg. postal code). Once again, al1 those that

do not match can be rematched using another blocking variable. This process of multiple

passes accommodates errors in values recorded and missing values. The choice of

blocking variables is important. Preference should be given to those with a large number

of values that can be grouped and those which are considered "reliable" by the

investigator.

C3

Probability matching of variables

In the setting where no one unique identifer exists between two datasets being linked. al1

fields must be considered. In such situation al1 fields contain information, even those with

some miçsing data will. Alone, no one field can detemine a match; however, together

multiple fields can help determine "matchesn and "non-matches" for each pair of records.

Characteristically some variables provide more information than others. Gender or age

alone provide very little information, and it highly improbable that a record from one

database can be matched to its corresponding record on the second based on one of

these variables alone. However, observations agreeing on health insurance number would

be highly suggestive of a match. This introduces two probabilities (m and u) associated

with each variable. The m probability is the probability that a field agrees given that the

record pair being exarnined is a matched pair. This is equivalent to one minus the error

rate of the field when the record pair is in fact a true match. For example, in a sample of

records, if the variable gender disagrees 1 0 % of the time due to either keystroke errors,

or misreporting, then the m probability for this field is 0.9 (1-0.1). The more reliable a field,

the greater the m probability.

The u probability is the probability that a variable agrees given that the record pair being

examined is an unmatched pair. Since there are many more unmatched pairs possible

than matched pairs, this probability can be considered the probability that the field agrees

at random. In the example of the two files A and 6 of 1,000 observations, there are

999,000 unmatched pairs.

The weight for a particular variable is computed as the logarithm to the base 2 (log3 of the

ratio of m and u probabilities. In the above example, gender has a 10% error rate while

the health insurance number has a 40% error rate. The rn probability for sex is 0.9; while

the u probability is 0.5 in situations with equal number of males and females. Therefore,

the weig ht for the variable gender is: log, (mlu) = In (rn/u)An (2) = In (0.910.5)lln (2) = 0.85.

If we assume that the probability of a chance agreement between hospital insurance

number is 1 in 2 million (assuming no number is entered preferentially); given m as 0.6

(40% error rate in matched pairs), then the weight assigned to health insurance number

will be 1og2(0.610.0000005) = 20.19. The rn probability should always be greater than the

u probability; otherwise, the probability of a chance agreement is greater than the

probability that the field agrees in a rnatched pair.

For each record pair, a composite weight is computed as the sum of al1 the individual

weights for ail variables used in matching. When a variable in the pair being compared

agree, the agreement weight is assigned; similarly, if the variables disagree, the

disagreement weight is computed, log,[(l-m)l(l -u)] and assigned. This computation of

weights leads to assigning negative weights for disagreements (unmatched). Agreements

add to the composite weight, while disagreement subtract from the composite weight. The

higher the composite weight value the greater the agreement.

Matching algorithm

The following algorithm is used by the Automatch program to match records in 2 files.

1. Blocks are forrned by using variables common in the two files A and B.

2. Weights are computed from the m and u probabilities.

m l = prob { fleld r agrees Ir E M 1 u, = prob { field i agrees Ir E (I 1

m l

w 4 = log,; if field agrees, else

where r is any given record pair and W is the composite weight for the record pair.

3. Composite weight are computed for al1 record pairs within each block being

considered, [mihi] when field agrees on both records or [(1 - mb/(l - ui)] if not.

Matched fields contribute positive weights while unmatched fields contribute

negative weights to the composite weight calculated for each record.

C 6

4. From the matrix foned using composite weights generated from records in both

files A and 6, a linear sum assignment procedure is used to compute an optimal

assig nrnent:

2 x,, = i , i = 1 , 2 .,... p

and 2 x,, = 1 . j = 1.2 *,... q 1.1

C, = the weight of matching record 1 on file A with record j o n file B.

X, = an indicator variable equal to 1 if record I is assigned to record j; otherwise,

equal to O if not

p = number of records in the block belonging to file A.

q = number of records in the block belonging to file B.

Z, = maximum weight for each block.

The linear sum assignment identify rnatched pairs and duplicates on either file. In the

situation where one row or column has more than one elernent above the match cutoff

weight, that record is then considered a potential duplicate match to the record assigned

for that row or column. Potential duplicates can be viewed interactively during the clerical

C7

review process. These duplicates can either be dropped if they are considered to be not

true duplicates, swapped with matched the record or retained as a separate file.

Estimating probabilities

In a situation where the investigator has no idea about the value to assign the m and u

probabilities, a guess can be made. Assigning 0.9 to the m probability is a good start,

since the

higher the value assigned to the m probability. the greater the disagreement weight.

Therefore, if a variable is considered "key" to the linking process (e.g. date of event) its' m

probability can be assigned a high value. By assigning a high value, disagreement of this

variable will be a rare event in matched pairs, and in the event of a non-match negative

weights will be contributed to the composite weight. The weig hts that are computed based

on these probabilities can be reviewed following the match.

The investigator can approximate the u probability, since the frequency analysis perfoned

by Automatch will replace the guess with an actual value. U probabilities can be estimated

as 1 ln values, where n is assigned a value derived for the number of unique values for the

variable. For example, the variable "sex" has only two possible values, therefore, the u

probability could be estimated as % = 0.5.

CS

The frequency analysis component of the program examines individual values to obtain

a u probability for each value. This is important in situations where the variables have a

non- unifom distribution. The m probabilities can be modified once the first match is run.

The "PROB" component of the program calculates an estimated m value from the given

values. This estimate is computed so that separate probabilities are computed for each

value. This balances the probability model, since if u probabilities are conditional upon

specific values, the m probability must also be identically conditional.

References

Jaro MA. Advances in record-linkage methodology as applied to matching the 1985 census

of Tampa, Florida. J Am Statist Assoc l989;84(406):414-420.

Jaro MA. Automatch. Generalized record linkage system. Version 2.9.

APPENDIX D

AMBULANCE CALL REPORT FORM

Treafment prior to ambulance airival O Poliœ O Fire a Citizen ûther

';@narai Appearance

DND/RCMP social ~ns NO 1 Registration NO Heahh No

I l l l l t l l l

Disposition In Hospbl EmergbnCy Dspt

Refuseâ ûeatmml& released Treated (obsemd) b releriried Transfered to Ach. DePt. 0 Tranefered to another hspitd 0 Morgue O

Ver.

1 Charge

O Patient O D.N.D.

O EWoyer O coroner W.C.B. ChargeaMe welfare

a D.V.A. 00th~

No charge

Problem Location (if trauma) I I

cd1 f e ~ d ~ e d ~ m m o b i r krkdc~an ~ v d p . t k n ( ûqmtod- k r h n d d w h d m ~ ~ t . . ~

?me of Cal1 Events I I I I I I I I I I l t I l l I I I I I I 1 1 1 ,

ase hosQital -mcpdai)

; .. . . b n , d ~-/Rrrt,<&.y :--. 8 p l o ~ m k w: 3..:ir5 .. . :?.:< -- dl, - : r . w r @ $ fer Codes BL8PIocsdunGaim

Pick Up Cad«r R - ResMence S - StreWRoedltibhY P - Public mace D - ~octor's OtRceSlink N- Nwelng Horne H- Ho3pitd c - ~ o b site -Construction O- Jobsite-- F- Jobsite-Fadœy T - T r a m patient A- A i r fW X- Other

Patient Transport Codes

Patient statu8 at destination as compsmd ta patient stahrs at first c o n t a t

Return Priority Codes 7 - DetenaMe 2 - . Schûd~led 3- Prompt 4- Ufg8nt 5 - Obviously ûead 6- LegaiSr Oead 7 - No Patient C a M

Case Severity 1- Minor 2- Moderate 3- Severe 4- UfeThreatening 5- V.SA. 6 - Pmounced dead by Base

hospM p w a a n 7 - Obvioulydead

H=d/-lNeck

Muitiple sites: tlead/FaWEye/EarFled<

Shoulder Ami (upper)lO~- Hand ringers Multiple &8a: Upper 8-itiûû

Bac)<

Chest Abdomen - G.I. Multiple sites: Upper iorso

Pelvis Buttocks - penneum Hi p Multiple sites: tower lm

Thigh Kneelbg (i0wer)lAnkfe w m s Muttiple sites: b w e r extremitiea

Multiple sites Genitourfnary

'Code trauma kwatim ibrtheseprobiems. 62 - Uppw respi'ratory tract - airway

70 - Spine - muitiple Ievels 71 - Spine -m'ai 72 - Spine - thora& 7 3 - Spine - turnbar 74- spine-sacml

CNS - CSF - CVA - D i - Gr - LAC- LOA - L O C - MI - 00 - V A - URI - UTf - R W - WQ - RLQ - LLQ -

centralnenrouseystem ceiebral anal fluid cerebrovascularm delerflml tremens g-- laceraam I d ofawarenese l a of- myocarâlal Infadiai oveidoee ttanabnttschemic- upperreapiraaory~ion urinery trad lnku4km fieMupperq- left upgm quadrant nem-suadrant lefi lower quadrant reoardhrg

A L S ~ n ~ - . - . - -- 23o-r4wal3- - 240- N e e d k w 250- R e s a u e ~ W a b s d 260- C a r d i a ~ m i t a 270- D&iMlatkn-mmualdevice ni- mmlatkri--- 272- Oeltibiliatbn-su$malicd~ 280- Nasa l endohaches i lm 282- Un-lnasd ETT 290- OralcmdcmdwlnhibaISori 292- U~èWcœSdUoralETT 300- Catdiovlersion 310- V a a o v a g l d ~ 32U- Cleatairwayvialeryngoecope 330- m - i q w t s 340- ~hodyrarmvalviaMad3alforr;eps 350- OtherALSpmeeditre 580- DmwMood 590 - TeSr sample (e.0. Visidex)

bmgs 410- Atmpine 420- Benadryi 430- CWum chbride 44û- Diazeparn 450- Dopamine 460- €pinephrine 470- Furosemide a- le- m- Lidocaille 500- Morphine 510- N&XOM Sm- Sodium Bicarboriate 530 - SaJbütomd 540- Otherdq 550- Nitiwsoxide SIO- Nitrogfycerin

ECGI Codes 10 - N.S.R 11 - S.V.T. 12 - Seamd degree kart bbd< 13- C q h e e r t b k c k 14 - P.V.C.'s 15- V.t& 16 - V.F. 17- Asystdû 18 - E.M.D. 19- SimusBtadycafdia 20- Juncb'mal Rhythm 21 - Other

I R s i u s s l d ~ d e ~ œ i have been advlsed awt I 8hould have treabnent and that tmafmûnt is availaMe brmediw. I refuse wch matment and transpsrtation to hospitd baving been infomsed of the ifaics imdvrxf. I assume Ml mpodbi!ity arising out of sud, reW- J'ei 8de in- que je &wab me fabe edgner et que je pouvais rscevoir des eobie bmn4diat8m8n1.38 refuse les soins et le transport B lndpital en louie maîssance des daque8 amquelg cette dedaion rn'e%pos%. J'asewne i'eritièfe mpcmmhiR4 de ce refis.

Patie11~8ubsdMe dedsbrt maigr* narne and addregelPatient(8) ou personne perrant les cWaion8 en son rwm-Nm et-admsee en leîtms mou&

I -

Relatiorrship to patient Uen de patente a m le (la) patient@) I Signature of Pab'ent a substftine Oeoisiorr MalcetSignature du (de la) paîient(e) ou de la

persorme prenant la declsiais en son m

Signature of Sannd wftnesslSignaîure du 28 W i n

I have adviaed thie patient a ttie party reepo- fb the above noted aLtiori d ü p riab to the s health mat are invohred. Jai a l c e ~ o u ~ l a ~ ~ d o t a d 6 d a i m d .desau8dsr~qus-~ea tmTnerpour1a~dumsl&de .

I 1 w- 1 ~ a t e 19 l

l~witrWWtotheabave-mentkned bei~ghreri~&aispiesentkrequelepatkntadteinromiedeefaHe~a -ours/Hem I

APPENDIX E

MEDICAL DEATH CERTlF ICATE AND STATEMENT OF DEATH

F m 16 PROVINCE OF (VSA 1370) GN~ARIO (Canada)

Office of the Registrar-General

To be cornpkted by anenâing physiuan or coroner

MEDICAL CERTIFICAT€

OF

DEATH

FIegistration No. (Deparmient use mly1 1 NAME OF

DECEASED

DATE OF DEATH

PLACE OF DEATH

1 Surname of deceased (pnht or type) Aü given names

5 Name of hospital or institution (otherwise grve exact localion where deafh occurred) 1 CHECK (J) IF I

2. SU(

I

1 D.O.A. 0 1

3. Month (by name). &y. year of death

Barough. city. town. wlhge or township (by name) I

Regional municipality. cwnty or district

MEDICAL CERTIFICATE OF DEATH

4. AG€ (years)

CAUSE OF

DEATH

AUT OPSY PARTI-

CULARS

If under 1 year (Monlhsl ; IOaysl

1 1 1

ACCIDENTAL OR

VIOLENT DEATH

(if applicable)

If under f day (Houn) ; (Minutes)

+ 1 t

CERTIFI- CATION

(attending ph ysician,

coroner, etc.)

6 Approx. interval be- hveen onsel

8 dealh

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . lmmediate cause of death (a) due 10. or as a consetauence of

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Antecedent causes. (b)i"i C ; . i i . . if any givin rise to

the i~mediaye cause (a) above. staling the under- lying Cause Iast

(c)

Part II II

Other signHicant conditions contributing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . to the death but not -CI

causally related to the irnmediate cause (a) abOW

C

- -- -- -- - -- - - - - -

7 H deceased was a female. did the death occur eithef during pregnancy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (including abortion and ectopic pregnancy] or within 42 &ys thereaftep

(Yes or No) 8 Autopsy yes NO 9. Does the cause of death yes NO 10- May.further information Yes No

stated above take account O O

rehtin R the cause of ? O of autopss f ind iw? deathtemi lablehteP G I

1 1 If accident. suicide. homicide or 12. Phce O injury (e.g. home. 13. Dale of inlury (Month (by name). day. yearJ undetemined (specdyl lam. hghway. etc. j

1 1

14 Haw did injury occufl (descnbe clrcumstances)

Signature (attending physician. coroner. etc.) 1 6. Designation. 1 5 . l cerli aiat Io the

best O? fn knowledge Attending ûîher and belie? the above physician Coroner (speufy) Mmed person died on the date and frorn the causes stated herein: X O 17 O

.. . . . . . . . . . . 17 Narne of physician or coroner (prlnt or type) Date signed - Month (by narne). day. year

CERTIFÇ CATION

OF

I am sat$fied as ta the correctness and suniciency of this medicai cerbfmte of death and the statement of death and I register the death by slgning this certifite and the staternent of death.

1 1 I

For Departmental Usa Only

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Signature of Division Registrar

Do Nc Write

In This

Space

Date: Month (by name). Qy. year Regsrration Number Code Number

H E A L T H

Dr. Sohail Waien et aL Clinical Epidemiology G-22 1

C E N T R E TO:

FROM: Thomas W. Paton Pham-D.

DATE: September 28, 1994

SUBJECR Patient Outmmes FoUowing Cardiac Arrest in Metmpolitan Toronto

Thank you for your response to the concerns of the Research Ethics Board with respect to the above titled research protocol ahd the Consent Fom. The study is now ethicaliy acceptable for performance at Sunnybrook Health Science Centre.

We would ask that the Board be notified if there are any deviations h m the approved protaml in the future or if it is tenninated prematurely.

Thomas W. Paton, Phatm-D. Chair, Research Ethics Board

2075 Boyview Avenue North York , Ontarlo Canada. M4N 3M5

U n i v e r s i t y of T o r o n t o

Ministry of Ministère de la Registiation Office of the c a h u m -O'.'---

@ U I n d R m 6 b d

Consumer and Consommation Division Registrar General -, ,, ='----u--.~h~u

Commercial et du Division des Bureau du ~ c u ~ ~ iLOQumime#(Imr

Ontario Relations Commerce ~nregistrements regislrarre générai zw

Dr. Sohail Waien ICES G106-2075 Bayview Ave North York, Ont. M4N 3M5

August 19, 1994

Dear Dr. Sohail Waien:

Sub j ect : Research

1 conf i r m receipt of your telephone of July 26, 1994 in which you request third party access to the records of the O f f i c e of the Registrar General (ORG) . Your request for access has a special access f i l e number: WiIEN/940727. Please quote this number should you have any future communication w i t h us on this matter.

Your request for access has been approved.

The information you request is enclosed. Please note that the information provided can only be used for the purpose for which it was requested.

Thank you for your interest in the records of the office of the Registrar General.

Approval for Access to Image Terminal (DRG)

Approval is provided subject to the following special conditions:

1) Access is not to be used for any other purpose. This approval is a l so given with the clear understanding no contac t w i l l be permitted with the individuals or families whose i d e n t i t i e s have been facilitated through t h i s special access.

August 19, 1994 Dr. oha ail Waien P a g e 2

2) Access to records on the Vi ta l S t a t i s t i c s ~nformation System ( V S I S ) and on the ~egistrar General information System (RGIS) is for a maximum period of one year commencing Aug.20,94. Any request for renewal of access must be made in writing to the Office of the Registrar General.

Access to our information system is provided through the Ontario Cancer and Research Foundationns registered terminal located at 900 Bay Street. To arrange training on this terminal, scheduling of tirne, and estimation of fees please contact Darlene Dale, Manager, Operations of the Ontario Cancer Research Foundation ( 4 16) 971- 9800.

Please report to M s Rosa Ventresca, Manager, Toronto Front Counter, Office of the Registrar General, 2nd Floor Macdonald Block, 900 Bay, f o r a system user identification account. Activation of your user account will conçt i tute acknowledgement of your acceptance of these conditions of access.

Please accept m y b e s t wishes for success in this research.

Deputy Registrar General

ccs: Rosa Ventresca Darlene Dale Brian Vegnaduzzo Walter B i l y k

John man awmkbmf

a3oDufferinStreet Dowrisu(ew* ON.

F ~ x (4 16) ?92-2115 T-,W 6) 392-22a)

fi!- Sahail Wnicn, 1C 3, Su nnybroak Hcailh Science m r e , 2U75 Usyvicw Avenue, Toronto, Ontririo, M l N 3'MS'

Dcnr Dr. Waicn,

1 am witing to conîirm our agreement rcgarding the confidentiality and S. .i 1-if y of patient information accuxnu1~ted for your project on out-of-hospitnl CS:-diac an-esfs.

Metro Ambulonce is cntirely satinicd with your nrrangcmcnts for preveiiting unnutharlzed accm to patlcnt-speo*fic dam. We agree with your iiitcntioii to produce results which cannot ùc msociatcd with any ùidlvidiial p i t ient.

F u ~ l i e r , we arc pleased to a g m to maintain the strictcst confidentiality of ::::y infoimation shared bctween us. As yon know, our agency handled :t= -:-rands of confidenLial patient records pcr yem without iiicident.

I trust this is the informalion you requtred.

Alan Craie, 1 Senim EMS Development Oficcr

Sunnybmok Heaith Science Centre UnÏversity of Toronto H € A L TH

0.106. 2075 ï+kœ A-. rJd> Y d , Omrb, C o d a WU(S W 1416) kr: W b ) 40*4018

SCIENCE cC€%TiT€ C L I N I C A L E P l D E M l O L O G Y U N I T

Sohail A Waien G - 214,2075 Bayview Ave. North Yorlc, Ontario M N 3M5

Walter Bi& Oftice of the Regatrar General 189 Red R'mr Road Thunder Bay, Ontario P7V 6L8

ûear Mr. Bilyk:

I am cornpleting a Masler's thesis at the University of Toronto entitled "Ouicomes of Cardiac ARea Patients in Metropolitan Toronto". I woJd Iike permission to allow indusion of the following materhl in the thesis and permission for the National Library to make use of the thesis (Le., to reproduœ, laan. or tell copies of the thesis by any means and in any fom or format).

These rights will in now way restrict republication of the material in any other fom by you or by &ers authorized by you.

The exœpts to be pnnted are: Medical death œnirtcate and Statement of death (starnped as 'Spechen")~ an Appendk

if these arrangements met with your approval, piease sign where indicated belw. Thank you for your assistance in this matter.

PERwlSSlON GRANTED FOR THE USE REQUESIED ABOVE:

Sohail A Walen G - 214,2075 Bayview Ave. Norai York, Ontario M4N 3M5

Toni Campeau Training and Develapment sedion Minisby of Health 5700 Young Street

Dear Mr. Campeau:

I am tornpleting a Master's thesis at the University of Toronto entitkd uOutcornes of Cardiac Arrest Patients in Matroplitan Toronto'. I would like permission to allow induskn of the follovving material In the thesis and p e lon for Me National Libnry to make use of the thesis 0.e.. to ieproduce. loan. *pies af the thesis by an y means and in any fom or format). ( - .C .

These righhi wlll in now way fastrict mpublicaüon of the material In any other form by you or by oüters authorired by you.

The excerpta to be printed is the Ontario Ministry of Heeith, Ambulance Call Report f ~ n n as an Appendk

If these arrangements meet with your approval. please sign where iiidicated below. Thank you for your assistance in thls matter.

sigpkure Print N a m

H E A L T H SCIENCE CENTRE

Division of Prehospital Care

To Whorn I t May Concem:

This is to give permission to Dr. Sohail Waien to use Standing Orders in Cardiac Arrest and an Ambulance Cal1 Report for purposes of his thesis. i w .- vf~ w f l ) Sincerely, &'

Dr. B. Schwartz Medical Director

ch Metro Toronto Am bulance 4330 Dufferin St., Downsview, Ontario M3H 5R9

Tel: 4 16-392-3884 Fax: 4 16-397-9060

S~nnybrwkilealthSEienceCentreUniveni~ofToronta H E A L T H

G106, 2075 €bpi- Avenue, North Y&, Onbrio, C m d o M4N 3M5 a: (416) 480-4055 Fax: (416) 480-6048

SCIENCE C E N T R E C L I N I C A L E P I D E M I O L O G Y U N I T

m ""t7F Anaon

Sohail A Waien G - 21 4.2075 Bayview Ave. North York, Ontario M4N 3M5

January 22. 1997

Terri Burton Division of prehospital care 4330 Dufferin Road Toronto. Ontario M3H 5R9

Dear Ms. Burton:

I am cornpleting a Master's thesis at the University of Toronto entitled "Outcomes of Cardiac Arrest Patients in Metropolitan Toronto". I would like permission to allow inclusion of the following material in the thesis and permission for the National Library to make use of the thesis (Le., to reproduce. loan. or sel1 copies of the thesis by any means and in any fom or format).

These rights will in now way restrict republication of the material in any other f om by you or by others authorized by you.

The excerpts to be printed are Metropolitan Toronto Paramedic prograrn, standing orders, issued March 1994 (pages 7-9 inclusive) as an Appendix.

If these arrangements meet with your approval. please sign where indicated below. Thank you for your assistance in this matter.

Yours ss&rely

X

PERMISSION GRANTED FOR THE USE REQUESTED ABOVE:

PROGRAM MATCH DICTA waien DICTB registry BLOCKl CHAR NYSIIS NYSIIS BLOCKl CHAR SEX SEX MATCHl ARRAY UNCERT SUR-NAME SUR-NAME 0.93 0.05 700.0 MATCH1 UNCERT NAMEl NAME10.90 0.05 700.0 MATCHl PRORATED L A S W DEATHYY 0.90 0.08 3 MATCHl PRORATED LASTMM DEATHMM 0.90 0-08 3 MATCH1 PRORATED LASTDD DEATHDD 0.90 0.03 5 MATCHl DELTA-PER AGE AGE-DTH 0.9 0.02 10.0 BLOCK2 CHAR S U T 1 SURINT1 BLOCK2 CHAR NiTl N T 1 BLOCK2 NUMERIC LASTYY DEATHYY MATCH2 ARRAY UNCERT SUR-NAME SUR-NAME 0.93 0.05 700.0 MATCH2 UNCERT NAMEf NAME10.90 0.05 700.0 MATCH2 PRORATED LASTMM DEATHMM 0.90 0.08 3 MATCH2 PRORATED LASTDD DEATHDD 0.90 0.03 5 MATCH2 CHAR SEX SEX 0.90 0.5 MATCH2 DELTA-PER AGE AGE-DTH 0.9 0.02 10.0 BLOCK3 NUME:RIC LASTjrYMM DEA'MYYMM MATCH3 ARRAY UNCERT SUR-NAME SUT-NAME 0.95 0.05 700.0 MATCH3 UNCERT NAMEl NAME10.90 0.05 700.0 MATCH3 PRORATED LASTDD DEATHDD 0.90 0.03 7 MATCH3 CHAR SEX SEX 0.90 0.5 MATCH3 DELTA-PER AGE AGE-Dm 0.90 0.02 10.0 BLOCK4 CHAR SURNAME SURNAME MATCH4 UNCERT NAMEl NAME1 0.90 0.05 700.0 MATCH4 PROWTED LASTYY DEATHYY 0.90 0.08 3 MATCH4 PRORATED LASTMM DEATHMM 0.90 0.08 3 MATCH4 PRORATED LASTDD DEATHDD 0.90 0.03 5 MATCH4 CHAR SEX SEX 0.90 0.5 MATCH4 DELTA-PER AGE AGE-DTH 0.9 0.02 10.0 CUTOFFl 32 20 CUTOFF2 35 20 CUTOFF3 40 18 CUTOFF4 50 8

APPENDIX H

MISSING DATA AND RE-ABSTRACTION RESULTS

Missing data for lin king variables among records not iïnked (n = 485)

Admitting hospital identifier 1 IO0 (20.6)

Variables with rnissing values

Incorrect hospital identifier

Last name

n (%)

First name

First and last name

Hospital identifier, last and first name / 314 (64.7)

Re-abstraction results - n = 455

Missing variables Variables missing in I Missing variables re-a bstracted MTA database found upon re-abstraction

1 n of ACR forms n (%)

Last name I 255 O

First name I 250 3 (1 -2)

Sex I 60 20 (32.3)

ACR = Ambulance Cail Report forrn MTA = Metro Toronto Ambulance

7.1 % of the CA cohort (n=6.448)

Accuracy of narnes between records linked using the MCC code - results for two hospitals (n = 124) *

MCC code = al1 others 1 37 5 (14)

MCC code = 1 & 5

MCC = Major Clinical Category No patient name available from metro Toronto Ambulance database: n = 15 No hospital chart number available from Canadian lnstitute of Health Information database: n = ?O

Number of Agreement between records names by MCC code

n (%)

62 56 (90)

MCC code distribution for Iinkage 2

MCC code

01, Diseases and disorders of the nervous system

02, Diseases and disorders of the eye

03, Diseases and disorders of the ear, nose, mouth, and throat

04, Diseases and disorders of the respiratory system

05, Diseases and disorders of the circulatory system

06, Diseases and disorders of the digestive system

07, Diseases and disorders of the hepatobiliary system and pancreas

08, Diseases and disorders of the musculoskletal system and connective tissue

09, Diseases and disorders of the skin, subcutaneous tissue and breast

10, Endocrine, nutritional and metabolic diseases and disorders

11, Diseases and disorders of the kidney and urinary tract

15, Newborns and other neonates with conditions originating in the perinatal period

16, Diseases and disorders of blood and blood fonning organs

17, Lymphoma, Leukemia or unspecified site neoplasms

18, Multisystemic or unspecified site infections

19, Mental diseases and disorders

20, AlcohollDrug use

21, Injury, poisoning and toxic affects of drugs

23, Other reasons for hospitalization

25, Multiple significant trauma

98, Unrelated O.R. procedures

Before*

n (%)

121 (12.4)

1 (O.?)

11 (1.1)

87 (8.9)

477 (48.7)

59 (6.0)

18 ( A -8)

' Before = linkage 2 ' After = linkage 2 after rematch of 229 records " Not recorded = 1 MCC = Major Clinical Category

MCC code distribution for re-matched records (n = 229)

MCC Code -- - - - -

01, Diseases and disorders of the nervous system

03, Diseases and disorders of the ear, nose, mouth, and throat

04, Diseases and disorders of the respiratory system

05, Diseases and disorders of the circulatory system

06, Diseases and disorders of the digestive system

08, Diseases and disorders of the musculoskletal system and connective tissue

1 1, Diseases and disorders of the kidney and urinary tract

20, AlcohollDrug use

23, Other reasons for hospitalization

98, Ungroupable data

MCC = Major Clinical Category ' not recorded = 1

J I

RECOMMENDATIONS

In order to achieve higher survival rates among those individuals experiencing an out-of-

hospital cardiac arrest (CA) in Toronto a nurnber of initiatives are necessary. The following

is a brief list of proposals which should help improve the current 8% survival rate.

Data Acquisition

Data acquisition must become a major focus of Metro Toronto Ambulance (MTA); complete

and accurate data is a vital component necessary to manage and evaluate a large

emergency medical service (EMS) program such as Toronto's. The data requirements

outlined by the Utstein guidelines represent a minimum standard by which prograrns can

be evaluated. Ambulance cal1 report (ACR) forms and the MTA database should be

modified to collect as a minimum data representative of the Utstein guidelines. Routine

collection and entry of data from al1 ACR forms should be initiated, thus allowing for an

ongoing evaluation of both the EMS program and other community initiatives being

undertaken.

Cardiopulmonary Resuscitation Training

Cardiopulmonary resusitation (CPR) training is the cheapest fint-line therapy available to

CA victims. It is therefore imperaiive that its benefits are promoted. Initial programs

should be designed to target "high risk" populations and those who are open to behaviour

modification. Since CA survivors are at a greater risk of a subsequent event. thus

52

representing a high risk group. Their spouses. cornpanions or care givers could be

identified through physicians and encouraged to learn CPR.

High school students represent a second group. CPR training can be provided to students

through school programs which can reach a large number of individuals for a minimum

cost. The public at large represents a third and potentially the most difficult group to reach.

This should not be an excuse to ignore this group and awareness/education programs

could be directed through the mass media, major events and perhaps through employers.

ln general. al1 programs wili have to be tailored to the age ievel and expectations of the

trainees.

Emergency Department Data

Emergency department data should be routinely collected in a manner similar to that

currently employed for collecting in-hospital data. These data will provide further insights

into the care of CA victims and other emergencies managed in the pre-hospital arena by

EMS and in the emergency department.

I MAGE NALUATIO N TEST T A R E T (QA-3)

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