Derivation and evaluation of a termination of resuscitation clinical prediction rule for advanced life support providers,
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1 Resuscitation (2007) 74, CLINICAL PAPER Derivation and evaluation of a termination of resuscitation clinical prediction rule for advanced life support providers, Laurie J. Morrison a,b,d,, P. Richard Verbeek b,c, Marian J. Vermeulen d,e, Alex Kiss e, Katherine S. Allan a, Lisa Nesbitt f, Ian Stiell f,g a Prehospital and Transport Medicine Research Program, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, Ontario, M4N 3M5 Canada b Division of Emergency Medicine, Department of Medicine, University of Toronto, Toronto, Ontario, Canada c Sunnybrook Osler Centre for Prehospital Care, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada d Department of Health Administration, University of Toronto, Toronto, Ontario, Canada e Department of Research Design and Biostatistics, Sunnybrook Health Sciences Centre, Institute for Clinical and Evaluative Sciences, Toronto, Ontario, Canada f Ottawa Health Research Institute, Ottawa, Ontario, Canada g Department of Emergency Medicine, University of Ottawa, Ottawa, Ontario, Canada Received 20 October 2006; received in revised form 20 December 2006; accepted 1 January 2007 KEYWORDS Emergency medical services; Cardiopulmonary resuscitation; Resuscitation orders; Medical ethics; Paramedic Summary Objectives: The primary aim was to derive a new termination of resuscitation (TOR) clinical prediction rule for advanced life support paramedics (ALS) and to measure both its pronouncement rate and diagnostic test characteristics. Secondary aims included measuring the test characteristics of a previously derived and published basic life support termination of resuscitation (BLS TOR) clinical prediction rule [Morrison LJ, Visentin LM, Kiss A, et al. Validation of a rule for termination of resuscitation in out-of-hospital cardiac arrest. N Engl J Med 2006;355(5):478 87] on the same cohort of patients for comparison purposes. Methods: Secondary data analysis of adult cardiac arrests treated by ALS in rural and urban EMS systems participating in the OPALS study (data extracted from Phase A Spanish translated version of the summary of this article appears as Appendix in the final online version at /j.resuscitation Presented at the Society for Academic Emergency Medicine Annual Meeting, Orlando, Florida, May 2004; Canadian Association of Emergency Physicians, Montreal, Canada June 2004; NAEMSP Meeting, Naples, FL, January 2005, where it received the National Center for Early Defibrillation award for Best Cardiac Arrest Presentation. Corresponding author at: Prehospital and Transport Medicine Research Program, C753 Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, Ontario, M4N 3M5 Canada. Tel.: ; fax: address: laurie.morrison@sunnybrook.ca (L.J. Morrison) /$ see front matter 2007 Elsevier Ireland Ltd. All rights reserved. doi: /j.resuscitation
2 TOR ALS clinical prediction rule 267 III). A previous study for a basic life support termination of resuscitation (BLS TOR) clinical prediction rule proposed Termination of Resuscitation if the patient had no return of spontaneous circulation (ROSC) before transport; no shock administered; EMS personnel did not witness the arrest [Morrison LJ, Visentin LM, Kiss A, et al. Validation of a rule for termination of resuscitation in out-of-hospital cardiac arrest. N Engl J Med 2006;355(5):478 87]. Multivariable logistic regression was used to examine the relationship between these variables, additional Utstein variables, and the primary outcome of survival to hospital discharge. Diagnostic test characteristics were measured for both the ALS TOR and BLS TOR models on this derivation cohort. Results: Four thousand six hundred and seventy-three cardiac arrest patients were included; 3098 (66%) were male, mean (S.D.) age 69 (15); 239 (5.1%; 95% CI ) survived to hospital discharge; 3841 patients had no ROSC (82%) and of these only three survived (0.08%; 95% CI 0.02, 0.23). The final ALS TOR model associated with survival, included: ROSC (OR 260.9; 95% CI 96.3, 706.7), bystander witnessed (OR 2.0; 95% CI 1.3, 3.1), bystander CPR (OR 2.8; 95% CI 1.9, 4.1), EMS witnessed (OR 12.3; 95% CI 7.1, 21.3) and shock prior to transport (OR 6.4; 95% CI 4.1, 10.1). A new ALS TOR clinical prediction rule based on these variables was 100% sensitive (95% CI ) for survival and had 100% negative predictive value (95% CI ) for death. Under the ALS TOR clinical prediction rule, 30% of patients would be pronounced in the field. The BLS TOR clinical prediction rule, was 100% sensitive (95% CI 99.9, 100), had 100% negative predictive value (95% CI ) and the field pronouncement rate was 48%. Conclusion: Cardiac arrest patients may be considered for prehospital ALS TOR when there is no ROSC prior to transport, no shock delivered, no bystander CPR and the arrest was not witnessed by bystanders or EMS. A single EMS termination clinical prediction rule for all levels of providers would be optimal for EMS systems to implement. Prospective evaluation of the ALS TOR clinical prediction rule in the hands of ALS providers will be required before implementation Elsevier Ireland Ltd. All rights reserved. Introduction According to a large meta analysis, the median survival rate to hospital discharge from out of hospital cardiac arrest, for all presenting rhythms, is 6.4%. 2 Traditionally, within the Ontario EMS system, Emergency Medical Technician-Defibrillation (EMT- D) providers transport cardiac arrest patients to the closest emergency department (ED) once basic life support (BLS) resuscitation protocols (cardiopulmonary resuscitation and automatic defibrillation) have been exhausted. In contrast, Emergency Medical Technician-Paramedic (EMT-P) providers are trained in advanced life support (ALS) procedures, including defibrillation, intubation and drug administration for cardiac arrest. 3 In addition, EMT-P providers may contact a base hospital physician who may then delegate to continue or to terminate advance life support (ALS) resuscitation. The National Association of EMS Physicians has published a position paper on the termination of resuscitation in the prehospital setting for adult patients suffering non-traumatic cardiac arrest. Bailey et al. recommended that termination should be considered when a series of criteria are met, including failure to respond to min of advanced life support care in the prehospital setting. 4 The position paper also recommends consideration be given to response and treatment intervals; return of spontaneous circulation; other less tangible endpoints such as logistics, education, leadership, and interface with support services for the provider, the family and the legal system. This paper and its recommendations were based on retrospective research identifying various signs and treatment responses as predictors of survival; however, these recommendations have not been studied prospectively. 5 9 A study by Cone et al. attempted to test the safety of the protocol in the NAEMSP position paper, with survival to discharge as the primary endpoint. 10 Using both prospective and retrospective cases, the study found the proposed protocol to have 100% specificity for lack of survival to discharge. No TOR eligible patients survived to discharge but a small number did survive to admission. EMS policy directing paramedics to terminate resuscitative efforts has been implemented in some advance life support services with this level of evidence. 11
3 268 L.J. Morrison et al. We previously derived a basic life support Termination of Resuscitation clinical prediction rule (BLS TOR) in a retrospective sample of cardiac arrest patients who did not respond to basic life support resuscitation by EMT-Ds. 12 Methods of prospective validation were used to test the predictive value of the BLS TOR clinical prediction rule on a cohort of patients treated only by BLS providers. 1 Out of the 1240 patients enrolled, 776 patients with cardiac arrest for whom the rule recommended termination, only four survived (0.5%). The rule had a specificity of 90.2% for recommending transport of survivors to the emergency department and had a positive predictive value for death of 99.5% when termination was recommended. Implementation of this rule would result in a field pronouncement rate of 62.6%. The addition of other criteria (a response time interval greater than 8 min or a cardiac arrest witnessed by a bystander) would improve both the specificity and positive predictive value of the rule further but would result in the transportation of a larger proportion of patients. It would be simpler for the EMS services and both advanced and basic life support providers if a universal termination of resuscitation clinical prediction rule could be established. However, the concern is that the criteria for termination may be different for those patients who receive advance life support care versus basic life support care. We propose to derive a new ALS TOR clinical prediction rule by determining the relationship between out-of-hospital cardiac arrest variables and the primary outcome of survival to hospital discharge. In addition, the pronouncement rate and diagnostic test characteristics of the ALS TOR clinical prediction rule will be measured and compared to the BLS TOR clinical prediction rule measures in the same patient cohort. Methods Theoretical model A previously derived and validated BLS TOR clinical prediction rule 1 suggests continued resuscitation and transportation of the patient if any of the following occur at the scene of initial resuscitation of an out-of-hospital cardiac arrest: a shockable rhythm, a return of spontaneous circulation (ROSC) before transport, or if the cardiac arrest was witnessed by an EMS provider. The ALS TOR study seeks to derive a Termination of Resuscitation (TOR) clinical prediction rule for ALS paramedics from a prospective sample of cardiac arrests receiving ALS care. The predictive validity (sensitivity and specificity) of selected characteristics in cardiac arrest patients will be established by identifying those who did not survive to discharge from hospital. These diagnostic test characteristics will be compared to those measured for the BLS TOR clinical prediction rule when applied to the same patient cohort. Study design This investigation is a secondary analysis of data originally collected for the Ontario Prehospital Advanced Life Support (OPALS) study The OPALS study was a prospective cohort study using a three-phase, before-after design of all eligible cardiac arrest patients. Data were extracted from Phase III of the existing OPALS study database, implementing the methods described previously. All variables were defined using the Utstein style for reporting cardiac arrest data. 16 The outcome of each prehospital cardiac arrest pronounced dead in the Emergency Department, pronounced dead while in hospital, or survived to hospital discharge was determined by chart review or through contact with the family doctor. Setting and selection of participants The OPALS study was implemented in 21 urban and rural communities 2,13,14 and included all cardiac arrest patients who received advanced cardiac care. Cardiac arrests cases were excluded for the following reasons: it was determined that the arrest had a non-cardiac etiology, including trauma; the patient was obviously dead (rigor mortis, lividity, decomposition or decapitation) 17 the patient was under the age of 16; advanced life support was available at the scene before the arrival of paramedics (i.e., cardiac arrest occurred in a hospital parking lot or clinic where physicians were attending to the patient); a DNAR order was presented to the paramedics; or the event was determined to be a non-arrest after review by the steering committee. Outcome measures The primary outcome was defined as survival to discharge from hospital. An additional 15 Utstein variables 16 were collected as potential predictors of survival following an out-of-hospital cardiac arrest: (1) a system response interval of less than 8 min (defined as the interval from the 911 call to the time the ambulance arrives on scene); (2) a patient response interval of less than 8 min (defined as the interval from the 911 call to the time the
4 TOR ALS clinical prediction rule 269 paramedic arrives at the patient); (3) a cardiac arrest witnessed by a bystander; (4) a cardiac arrest witnessed by EMS personnel (notably, a member of the fire department, an EMT-D or a paramedic); (5) an initial rhythm of either ventricular fibrillation (VF) or ventricular tachycardia (VT); (6) CPR started by a bystander; (7) CPR started by fire department or police; (8) CPR started by an EMT- D crew; (9) CPR started by an EMT-P crew; (10) first defibrillation administered by public access defibrillation (PAD); (11) first defibrillation administered by a first responder (fire department); (12) first defibrillation administered by an EMT-D; (13) first defibrillation administered by an EMT-P; (14) any return of spontaneous circulation (ROSC); (15) any defibrillation administered during the entire ambulance call. The BLS TOR decision clinical prediction rule 12 variables were cardiac arrest witnessed by EMS personnel (first responder, EMT-D or EMT-P), a return of spontaneous circulation, and a shock delivered by EMS personnel during the resuscitation process. Primary data analyses Derivation of the new ALS TOR clinical prediction rule Associations between each of the predictor variables and survival to hospital discharge were assessed using logistic regression. The initial set of covariates for model-building were chosen as those found to be significant in bivariate analyses. We examined the predictor variables for multicolinearity and if the tetrachoric correlation coefficient was greater than 0.80, only one member of the pair was included in the model. The number of Utstein variables 16 was reduced further to a final model using regression modeling techniques outlined by Harrell. 18 The reduction technique involved looking initially at a model with only the key predictor variable of interest (return of spontaneous circulation) and the dependent variable (survival to hospital discharge). A return of spontaneous circulation was chosen as the key predictor variable as numerous studies have shown that cardiac arrest patients who fail to achieve return of spontaneous circulation after advanced cardiac life support do not survive to hospital discharge. 4 6,19,20 The parameter estimate signifying the relationship between return of spontaneous circulation and the single dependent variable was noted, and then one of the covariates was added to this model. Whether this new covariate changed the parameter estimate for the key variable return of spontaneous circulation by more than 10% was noted. If so, this variable was retained for the final multivariable model. If the parameter estimate did not change appreciably (<10%) this variable was not included in the final model. A subsequent covariate was then similarly entered into the model with return of spontaneous circulation in order to determine whether the covariate should be retained in the final model. This procedure was repeated for each individual covariate until a final group of covariates remained for use in the final model (Table 1). Because all of the defibrillation variables were associated with survival, we used a single variable representing any defibrillation administered to the Table 1 Unadjusted odds ratios for survival to discharge (prior probability (survival) = 0.051) Variable Odds ratio (95% confidence interval) Sensitivity Specificity Positive predictive value Negative predictive value System response interval <8 min 1.7 (0.7, 3.9) 96 (92, 99) 6 (5, 7) 4 (3, 5) 98 (95, 99) Patient response interval <8 min 1.7 (1.2, 2.4) 64 (56, 72) 49 (47, 50) 5 (4, 6) 97 (96, 98) Bystander witnessed 1.9 (1.4, 2.4) 55 (49, 62) 60 (59, 62) 7 (6, 8) 96 (95, 97) EMS witnessed 4.8 (3.6, 6.5) 31 (25, 37) 91 (91, 92) 16 (13, 20) 96 (95, 97) Initial rhythm ventricular 10.7 (7.6, 15.0) 82 (76, 86) 70 (69, 72) 13 (12, 15) 99 (98, 99) fibrillation or ventricular tachycardia CPR started by bystander 2.8 (2.1, 3.8) 30 (24, 36) 87 (86, 88) 11 (9, 14) 96 (95, 96) First shock by EMT-D 2.9 (2.0, 4.4) 13 (9, 17) 95 (95, 96) 13 (9, 18) 95 (95, 96) First shock by fire 2.4 (1.8, 3.3) 24 (19, 30) 89 (88, 90) 10 (8, 13) 96 (95, 96) First shock by paramedic 2.8 (2.2, 3.7) 45 (38, 51) 78 (76, 79) 10 (8, 12) 96 (96, 97) First shock by Public Access 11.5 (5.0, 26.6) 4 (2, 7) 100 (99, 100) 38 (19, 59) 95 (94, 96) Defibrillation Program (PAD) Shocked (by PAD or provider) 8.8 (6.2, 12.7) 85 (80, 89) 61 (60, 63) 11 (9, 12) 99 (98, 99) Any return of spontaneous circulation (161.7, >999.9) 99 (96, 100) 87 (86, 88) 28 (25, 32) 100 (100, 100)
5 270 L.J. Morrison et al. Table 2 Adjusted odds ratios for survival to discharge (model with initial rhythm ventricular fibrillation or ventricular tachycardia) (prior probability (survival) = 0.051) Variable OR (95% CI) Bystander witnessed 2.0 (1.2, 3.1) EMS witnessed 10.8 (6.2, 18.8) CPR started by bystander 2.4 (1.6, 3.6) Initial rhythm: ventricular 6.8 (4.4, 10.4) fibrillation or tachycardia Any return of spontaneous (86.9, 638.3) circulation OR: odds ratio; EMS: emergency medical services; CPR: cardiopulmonary resuscitation. patient before, or during the call in the multivariable analyses. We also found that an initial rhythm of ventricular fibrillation or ventricular tachycardia was strongly correlated with shock administration (r = 0.97) and therefore estimated separate models for these variables (Tables 2 and 3). Bootstrap simulations were carried out to validate the regression model. One thousand bootstrap simulations were carried out producing parameter estimates for the five variables in the regression model. These parameter estimates and their corresponding risk ratios were found to mirror those of the original regression model thereby supporting its conclusions. All variables had bootstrapped confidence limits that clearly excluded 1. Diagnostic test characteristic measures of the ALS and BLS TOR clinical prediction rules The sensitivity (Se), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV) of each variable was estimated independently, and the final model comprised a combination of variables that were found to be significantly associated with survival in the multi- Table 3 Adjusted odds ratios for survival to discharge (model with any defibrillation administered) (prior probability (survival) = 0.051) Variable OR (95% CI) Bystander witnessed 2.0 (1.3, 3.2) EMS witnessed 12.3 (7.1, 21.3) CPR started by bystander 2.8 (1.9, 4.1) Shocked (by PAD or provider) 6.4 (4.1, 10.1) Any return of spontaneous (96.3, 706.8) circulation OR: odds ratio; EMS: emergency medical services; CPR: cardiopulmonary resuscitation; PAD: Public Access Defibrillation. variable analysis. In addition, we calculated these diagnostic test characteristic measures for the previously derived BLS termination of resuscitation clinical prediction rule. 1,12 Goodness of fit of each of the models was evaluated using the Hosmer- Lemeshow statistic. All analyses were conducted using SAS version 8.0 or higher (SAS Institute, 1996, Cary, NC). Statistical significance was defined a priori as a two-tailed p-value of <0.05. The adequacy of the sample size was evaluated against the criterion of 10 observations per variable for multivariable logistic regression. 18 Results Initially we identified 5274 cardiac arrest patients attended to by paramedics. Of these, 599 (11%) were excluded for the following reasons: noncardiac etiology (233); trauma (199); obvious death (3); under age 16 (101); advance life support (physician) available on scene (24); DNAR order presented to paramedics (37); outcome defined as a nonarrest upon review by the steering committee (2). An additional two patients were excluded as defibrillation was administered following transport from the scene, leaving a total of Of the patients who were excluded, 374 (62%) were male with a mean (S.D.) age was 66 (18). The number of males included in the study was 3098 (66%) and the mean (S.D.) age was 69 (15). Response times are not applicable to the 452 EMS witnessed arrest cases and therefore were not recorded. Times were missing for 113 (2.4%) of cases. In addition, 172 (4%) cases were missing data on the initial ECG rhythm. Of the 4673 patients, 671 (14.4%; 95% CI 13.4, 15.4) were admitted and 239 (5.1%; 95% CI 4.5, 5.8) survived to hospital discharge. There was less than 1% loss to follow up. The majority of patients (3841, or 82%) did not achieve ROSC during resuscitation efforts and, among this group, only three survived to discharge (0.08%; 95% CI 0.02, 0.23). In bivariate analyses, a patient response time interval of less than 8 min, cardiac arrest witnessed by a bystander, cardiac arrest witnessed by an EMS personnel, an initial rhythm of ventricular fibrillation or ventricular tachycardia, CPR started by a bystander, CPR started by a paramedic crew, first defibrillation administered by a public access defibrillator, first defibrillation administered by a first responder, first defibrillation administered by an EMT-D, first defibrillation administered by a paramedic, and return of spontaneous circulation variables were associated with an increased probability of survival to discharge (Table 1). The system
6 TOR ALS clinical prediction rule 271 Table 4 Diagnostic test statistics of current and previously derived clinical prediction rules Variables resuscitation is terminated if none of the following criteria are present EMT-P clinical prediction rule Bystander witnessed, bystander CPR, EMS witnessed, shocked (by PAD or provider), or ROSC prior to transport EMT-D Clinical prediction rule EMS witnessed, shocked by treating EMT-D, or ROSC prior to transport Sensitivity Specificity Positive predictive value Negative predictive value 100 (99.9, 100) 32 (30, 33) 7 (6.6, 8.1) 100 (99.9, 100) 100 (99.9, 100) 50 (49, 52) 9.7 (9, 11) 100 (99.9, 100) EMT-P: Emergency Medical Technician-Paramedic; CPR: cardiopulmonary resuscitation; EMS: emergency medical services; PAD: Public Access Defibrillation; ROSC: return of spontaneous circulation; EMT-D: Emergency Medical Technician-Defibrillator. response time interval and the patient response interval were also correlated r = However, the system response interval of <8 min was not significant in the univariable analysis for predicting survival whereas the patient response interval was significant. In subsequent modeling only the patient response time interval variable was used. There were no differences in the model according to whether this interval was defined as a binary or a continuous variable. From the factors that were independently associated with survival to discharge, we examined the properties of a clinical prediction rule (Figure 1, Table 3) that included bystander witnessed (adjusted OR 2.0; 95% CI 1.3, 3.1), EMS witnessed (OR 12.3; 95% CI 7.1, 21.3), bystander CPR (OR 2.8; 95% CI 1.9, 4.1), shock prior to transport (OR 6.4; 95% CI 4.1, 10.1), or ROSC (OR 260.9; 95% CI 96.3, 706.8). Given that an initial rhythm of VF/VT (OR 6.8; 95% CI 4.4, 10.4) (Table 2) and defibrillation were both independently asso- ciated with survival, we chose defibrillation on the basis of sensibility. In each model, bystander witnessed, bystander CPR, return of spontaneous circulation, as well as an initial rhythm of VF/VT or defibrillation, remained significantly associated with survival. The sensitivity, or the ability of such a clinical prediction rule to identify patients that would require transport (i.e., survivors), was 100% (95% CI 99.9, 100). The specificity, or the ability of such a clinical prediction rule to identify patients that should not be transported (i.e., patients who died) was 32% (95% CI 30, 33). The negative predictive value, or the probability that a patient who would be pronounced in the field according to the clinical prediction rule would not survive to discharge, was also 100% (95% CI 99.9, 100) (Table 4). Similar test characteristics were observed for the BLS TOR clinical prediction rule 1,12 based on EMS witnessed arrest, any defibrillation, or the presence of ROSC before transport in an ambulance. Sensitivity was 100% (95% CI 99.9, 100), specificity was 50% (95% CI 49, 52) and the negative predictive value was 100% (95% CI 99.9, 100) (Table 4). The pronouncement rate in the field would be 30% (1425 cases) if the ALS TOR clinical prediction rule is applied by EMT-P providers to the derivation cohort. Whereas, if the BLS TOR clinical prediction rule is applied by EMT-P providers to the same derivation cohort, the pronouncement rate in the field would increase to 48% (2263 cases) without missing any additional survivors. Figure 1 ALS TOR clinical prediction rule criteria. Limitations The ALS TOR clinical prediction rule performed well in this cohort of patients. However, it should not be
7 272 L.J. Morrison et al. applied to the clinical setting without prospective validation in the hands of ALS providers. 21 The study population consisted of only adult patients of age 18 years or older treated for out-of-hospital cardiac arrest (presumed cardiac etiology). 16 Thus, the ALS TOR clinical prediction rule is limited in its application to only those patients who fit this Utstein etiology category. The return of spontaneous circulation variable in the OPALS data set includes the occurrence of return of spontaneous circulation in the ED as well as in the prehospital setting, and the data were coded in such a way that it was not possible to differentiate between locations. This may inflate the accuracy of the clinical prediction rule and the diagnostic test characteristics in this derivation phase when compared to the validation phase, which should include return of spontaneous circulation in the out-of-hospital setting prior to transfer to the ambulance stretcher (i.e., prior to deciding to transport). The intent of termination of resuscitation is to avoid transfer of futile cases that can be pronounced dead in the field; hence, in the validation phase of the ALS TOR clinical prediction rule all variables, including return of spontaneous circulation, will be defined as occurring before transfer to an ambulance. Discussion This study derived an ALS clinical prediction rule with high sensitivity and negative predictive values for EMT-Ps to terminate futile cardiac resuscitation in the out-of-hospital setting. The factors that were independently associated with survival to discharge included: bystander witnessed, EMS witnessed, bystander CPR, shock before transport, or return of spontaneous circulation. The sensitivity of the rule to identify patients that would require transport was 100% and the specificity of the rule to identify patients that should not be transported was 32%. The negative predictive value was 100%. None of the traditional EMS time intervals (system response interval or patient response interval) proved to be a significant contribution to the clinical prediction rule. The system response interval and the patient response interval were split into binary variables by the standard EMS benchmark of 8 min. De Maio et al. conducted an analysis of survival as a function of the system response interval on the Phase I OPALS data set (EMT-D providers) and found that the 8-min target was not the optimal defibrillation response interval for out-of-hospital cardiac arrest. This work suggested that more lives could be saved by optimizing the system response interval well below a 90th percentile of 8 min. 22 Indeed, Petrie et al. suggested that a system response interval of more than 8 min was predictive of death when the presenting rhythm was asystole in the OPALS phase I data set. 23 The National Association of EMS Physicians position paper on TOR clinical prediction rules states that system response intervals are often hard to define and although they are associated with poor outcomes, should be used as considerations, not as criteria for termination of resuscitation. 4,24 Regardless of the clinical and operational significance of these intervals, none of them were statistically significant in the derivation of the ALS TOR clinical prediction rule. It may be that the intervals are too long, or too few cases have sufficiently short intervals to have the power to demonstrate a significant effect in a multivariable analysis. There have been other attempts to perform multivariable analysis of survival correlates However, these studies all demonstrated associations with an interval, and were limited to patients with a witnessed cardiac arrest presenting in ventricular fibrillation. None of the studies derived and tested a clinical prediction rule. The ALS TOR clinical prediction rule derived in this study employed a large sample size of urban and rural out-of-hospital cardiac arrests, both witnessed and unwitnessed, inclusive of all presenting rhythms. The data was collected prospectively and based on Utstein definitions. The large data set and the rigor in defining variables permitted precision in the odds ratios and a more robust multivariable analysis for survival. The OPALS data represented a broad range of populations including urban and sub-urban EMS systems with populations of less than 1 million, and thus the ALS TOR clinical prediction rule may be generaliseable to all out-of-hospital cardiac arrests occurring in other similar sized EMS systems. The test characteristics of the ALS TOR clinical prediction rule were similar to those of a previously derived and validated BLS TOR clinical prediction rule 1 when applied to the same EMT-P data set. In fact, the BLS TOR clinical prediction rule 1 did have a higher specificity (50%) and higher pronouncement rate (48%) without changing the number of survivors. This is not surprising given that the only differences between the two rules are the inclusion of two additional variables; bystander witnessed and bystander CPR, in the ALS TOR clinical prediction rule. Ong et al. 28 applied the BLS TOR clinical prediction rule 1 to the entire OPALS dataset (Phases I III) and found similar diagnostic test characteristics. Optimally, a single clinical prediction rule for both providers would facilitate implementa-
8 TOR ALS clinical prediction rule 273 tion in large EMS systems employing both levels of providers. Interestingly, the addition of the post hoc variable witnessed by a bystander in the BLS TOR validation study 1 would have decreased the field pronouncement rate from 62.6% to 38.4%, approaching the anticipated field pronouncement rate of the ALS TOR clinical prediction rule (30%). This suggests that the ALS TOR clinical prediction rule could be evaluated prospectively as a single termination of resuscitation rule in both provider groups. A universal clinical prediction rule to terminate resuscitation would help to reliably define the cohort of patients least likely to show a benefit in controlled trials evaluating efficacy of interventions or systems of care for out of hospital cardiac arrest. The major drawback of employing the ALS TOR clinical prediction rule in a BLS system is the 25% absolute increase in the number of futile cases that would be transported (from 37.4% to 61.6%). 1 The ALS TOR clinical prediction rule defined in this paper is similar to the clinical decision aid derived by Van Walraven et al. to discontinue in-hospital cardiac arrest resuscitation. 29 In hospital patients were more likely to be discharged from hospital if they suffered a witnessed cardiac arrest, the initial rhythm was ventricular tachycardia or fibrillation, or they regained a pulse within 10 min of CPR (sensitivity = 99.1%, 95% CI [ ]; negative predictive value = 98.9%, 95% CI [ ]). 30 The ALS TOR clinical prediction rule has better sensitivity and negative predictive values than the in-hospital termination clinical prediction rule, which is reassuring since the amount of information and time to acquire detailed information is limited in the prehospital setting, and any clinical prediction rule should be highly selective about discontinuing care. The rate of autonomous pronouncement of death in the field would be 30% with ALS TOR clinical prediction rule. Although field pronouncement transfers the burden of care and the task of death notification to the paramedics, several studies have shown that non-physician personnel convey the message of death effectively to family members. 31,32 Surveys conducted on family members witnessing field pronouncement suggest that they are comfortable with the decision to terminate the resuscitation in the field. 33,34 The impact of pronouncement of death and informing the relatives etc in the field on the paramedic however, has not yet been determined. Previous work in the United States and in Canada has shown that it is less expensive to pronounce death in futile cases in the field than it is to transfer the patient to the ED Transport and in-hospital advanced life support is associated with considerable expense; American estimates approach $1 billion annually. Clinical prediction rules that allow termination of resuscitative efforts by both EMT-D and EMT-P providers may have a positive impact on minimizing health care costs of continued resuscitation and transfer of patients for whom resuscitation is futile. In fact a comparison between the resources associated with ALS field pronouncement and ED physician pronouncement found that BLS transport and ED physician pronouncement were associated with higher overall costs compared to paramedic field pronouncement. 3 The ALS TOR clinical prediction rule identified a large number of patients (1425, 30%) who could be pronounced dead at the scene without medical delegation, thus reducing the number of patients transported to the ED. Conclusions We conducted a multivariable analysis to define clinical variables of an ALS Termination of Resuscitation clinical prediction rule for out of hospital cardiac arrests attended by EMT-P paramedics. Cardiac arrest patients may be considered for prehospital ALS TOR when there is no return of spontaneous circulation prior to transport, no shock delivered, no bystander CPR and the arrest was not witnessed by bystanders or EMS. The test characteristics of the clinical prediction rule proved to be highly sensitive and have a high negative predictive value for survival. Termination of resuscitation clinical prediction rules may minimize health care costs and focus EMS resources on patients for whom resuscitation may be helpful, without compromising the family s ability to grieve and their comfort with field pronouncement. A single EMS termination clinical prediction rule for all levels of providers would be optimal for EMS systems as it would be easier to implement and oversee; however if the ALS TOR rule is applied in a BLS systems this may result in an increased transport rate of futile cases to the emergency department. Prospective evaluation of the ALS TOR clinical prediction rule in the hands of ALS providers will be required prior to implementation. Conflict of interest None.
9 274 L.J. Morrison et al. Acknowledgements The authors wish to acknowledge the OPALS Steering Committee for permitting the use of the OPALS phase III data by our research program and their interest (Ian Stiell and Lisa Nesbitt) in collaborating to complete this study. We would also like to thank the OPALS Study site investigators: the Program and Medical Directors from the 11 participating Ontario OPALS Base Hospital Programs [Cambridge, Halton, Kingston, London, Niagara, Ottawa, Peterborough, Sarnia, Sudbury, Thunder Bay, and Windsor]. References 1. Morrison LJ, Visentin LM, Kiss A, et al. Validation of a rule for termination of resuscitation in out-of-hospital cardiac arrest. N Engl J Med 2006;355(5): Nichol G, Stiell IG, Laupacis A, Pham B, De Maio VJ, Wells GA. A cumulative meta-analysis of the effectiveness of defibrillator-capable emergency medical services for victims of out-of-hospital cardiac arrest. Ann Emerg Med 1999;34(4 Pt. 1): Cheung MC, Morrison LJ, Verbeek PR. Prehospital vs. emergency department pronouncement of death: a cost analysis. Can J Emerg Med 2001;3(1): Bailey ED, Wydro GC, Cone DC. Termination of resuscitation in the prehospital setting for adult patients suffering nontraumatic cardiac arrest. National Association of EMS Physicians Standards and Clinical Practice Committee. Prehosp Emerg Care 2000;4(2): Bonnin MJ, Pepe PE, Kimball KT, Clark Jr PS. Distinct criteria for termination of resuscitation in the out-of-hospital setting. JAMA 1993;270(12): Kellermann AL, Hackman BB, Somes G. Predicting the outcome of unsuccessful prehospital advanced cardiac life support. JAMA 1993;270(12): Schoenenberger RA, von Planta M, von Planta I. Survival after failed out-of-hospital resuscitation. Are further therapeutic efforts in the emergency department futile? Arch Intern Med 1994;154(21): Stratton S, Niemann JT. Effects of adding links to the chain of survival for prehospital cardiac arrest: a contrast in outcomes in 1975 and 1995 at a single institution. Ann Emerg Med 1998;31(4): Stratton SJ, Niemann JT. Outcome from out-of-hospital cardiac arrest caused by nonventricular arrhythmias: contribution of successful resuscitation to overall survivorship supports the current practice of initiating out-of-hospital ACLS. Ann Emerg Med 1998;32(4): Cone DC, Bailey ED, Spackman AB. The safety of a field termination-of-resuscitation protocol. Prehosp Emerg Care 2005;9(3): Faine PG, Willoughby PJ, Koenigsberg M, Manczko TJ, Ward S. Implementation of an out-of-hospital termination of resuscitation policy. Prehosp Emerg Care 1997;1(4): Verbeek PR, Vermeulen MJ, Ali FH, Messenger DW, Summers J, Morrison LJ. Derivation of a termination-ofresuscitation guideline for emergency medical technicians using automated external defibrillators. Acad Emerg Med 2002;9(7): Stiell IG, Wells GA, Spaite DW, et al. The Ontario Prehospital Advanced Life Support (OPALS) Study: rationale and methodology for cardiac arrest patients. Ann Emerg Med 1998;32(2): Stiell IG, Wells GA, Field BJ, et al. Improved out-of-hospital cardiac arrest survival through the inexpensive optimization of an existing defibrillation program: OPALS study. Phase II. Ontario Prehospital Advanced Life Support. JAMA 1999;281(13): Stiell IG, Wells GA, Field B, et al. Advanced cardiac life support in out-of-hospital cardiac arrest. N Engl J Med 2004;351(7): Cummins RO, Chamberlain DA, Abramson NS, et al. Recommended guidelines for uniform reporting of data from out-of-hospital cardiac arrest: the Utstein Style. Task Force of the American Heart Association, the European Resuscitation Council, the Heart and Stroke Foundation of Canada, and the Australian Resuscitation Council. Ann Emerg Med 1991;20(8): Strategy CoH. Health manual of operational directives and guidelines. Ontario Ministry of Health, Emergency Health Services; Harrell Jr FE. Regression modeling strategies with applications to linear models, logistic regression, and survival analysis. New York: Springer-Verlag; Bonnin MJ, Swor RA. Outcomes in unsuccessful field resuscitation attempts. Ann Emerg Med 1989;18(5): Kellermann AL, Hackman BB. Terminating unsuccessful advanced cardiac life support in the field. Am J Emerg Med 1987;5(6): Laupacis A, Sekar N, Stiell IG. Clinical prediction rules. A review and suggested modifications of methodological standards. JAMA 1997;277(6): De Maio VJ, Stiell IG, Wells GA, Spaite DW. Optimal defibrillation response intervals for maximum out-of-hospital cardiac arrest survival rates. Ann Emerg Med 2003;42(2): Petrie D, De Maio VJ, Stiell I, et al. Factors affecting survival after prehospital asystolic cardiac arrest in a basic life support-defibrillation system. Can J Emerg Med 2001;3(3): Lateef F, Anantharaman V. Delays in the EMS response to and the evacuation of patients in high-rise buildings in Singapore. Prehosp Emerg Care 2000;4(4): Larsen MP, Eisenberg MS, Cummins RO, Hallstrom AP. Predicting survival from out-of-hospital cardiac arrest: a graphic model. Ann Emerg Med 1993;22(11): Spaite DW. Outcome analysis in EMS systems. Ann Emerg Med 1993;22(8): Valenzuela TD, Roe DJ, Cretin S, Spaite DW, Larsen MP. Estimating effectiveness of cardiac arrest interventions: a logistic regression survival model. Circulation 1997;96(10): Ong ME, Jaffey J, Stiell I, Nesbitt L. Comparison of termination-of-resuscitation guidelines for basic life support: defibrillator providers in out-of-hospital cardiac arrest. Ann Emerg Med 2006;47(4): Van Walraven C, Forster AJ, Stiell IG. Derivation of a clinical decision rule for the discontinuation of. Archives of Internal Medicine 1999;159(2): van Walraven C, Forster AJ, Parish DC, et al. 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10 TOR ALS clinical prediction rule Delbridge TR, Fosnocht DE, Garrison HG, Auble TE. Field termination of unsuccessful out-of-hospital cardiac arrest resuscitation: acceptance of family members. Ann Emerg Med 1996;27(5): Kellermann AL, Staves DR, Hackman BB. In-hospital resuscitation following unsuccessful prehospital advanced cardiac life support: heroic efforts or an exercise in futility? Ann Emerg Med 1988;17(6): Gray WA, Capone RJ, Most AS. Unsuccessful emergency medical resuscitation are continued efforts in the emergency department justified? N Engl J Med 1991;325(20): Suchard JR, Fenton FR, Powers RD. Medicare expenditures on unsuccessful out-of-hospital resuscitations. J Emerg Med 1999;17(5): Cheung MC, Morrison LJ. Cost-Identification analysis of field pronouncement comparing the prehospital setting to the emergency department. CJEM 2001;3(1):19 25.
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