Derivation of a Clinical Decision Rule for the Discontinuation of In-Hospital Cardiac Arrest Resuscitations


Carl van Walraven, MD, FRCPC, MSc; Alan J. Forster, MD, FRCPC; Ian G. Stiell, MD, MSc, FRCPC

Background  Most patients undergoing in-hospital cardiac resuscitation will not survive to hospital discharge.

Objective  To derive a decision rule permitting the discontinuation of futile resuscitation attempts by identifying patients with no chance of surviving to hospital discharge.

Patients and Methods  Patient, arrest, and outcome data for 1077 adult patients undergoing in-hospital cardiac resuscitation was retrieved from 2 randomized clinical trials involving 5 teaching hospitals at 2 university centers. Recursive partitioning was used to identify a decision rule using variables significantly associated with death in hospital.

Results  One hundred three patients (9.6%) survived to hospital discharge. Death in hospital was significantly more likely if patients were older than 75 years (P<.001), the arrest was unwitnessed (P = .003), the resuscitation lasted longer than 10 minutes (P<.001), and the initial cardiac rhythm was not ventricular tachycardia or fibrillation (P<.001). All patients died if there was no pulse 10 minutes after the start of cardiopulmonary resuscitation, the initial cardiac rhythm was not ventricular tachycardia or fibrillation, and the arrest was not witnessed. As a resuscitation rule, these parameters identified all patients who survived to hospital discharge (sensitivity, 100%; 95% confidence interval, 97.1%-100%). Resuscitation could have been discontinued for 119 (12.1%) of 974 patients who did not survive, thereby avoiding 47 days of postresuscitative care.

Conclusions  A practical and highly sensitive decision rule has been derived that identifies patients with no chance of surviving in-hospital cardiac arrest. Prospective validation of the rule is necessary before it can be used clinically.

Arch Intern Med. 1999;159:129-134

CARDIOPULMONARY resuscitation (CPR) and advanced cardiac life support are used to resuscitate patients suffering cardiac arrest. With the exception of airway management and cardiac defibrillation, the effectiveness of interventions provided during resuscitation is uncertain.¹ Physicians and other health care personnel often expend prolonged amounts of time and effort in attempting to resuscitate patients. Resuscitated patients may be subjected to prolonged, intensive, and invasive therapy. Throughout this time, the patient's family and members of the health care team often deal with difficult emotions and decisions.

Several clinical factors have been associated with poor outcome following in-hospital cardiac arrest. Death has been shown to be independently associated with several prearrest clinical factors including hypotension,² renal failure,^3-5 pneumonia,² poor functional status,^{2, 6} and metastatic cancer.^{2, 5} Several factors identifiable during the arrest independently predict mortality including prolonged duration of resuscitation^{2, 6, 7} and initial cardiac rhythms other than ventricular tachycardia or fibrillation.^{6, 8, 9} Postarrest factors such as high Acute Physiology and Chronic Health Evaluation II or low Glasgow Coma Scale scores are associated with death after initially successful resuscitation.¹⁰ However, most of these studies were conducted within a single center, contained small numbers of arrests, or used data from unblinded and retrospective medical record review. Therefore, it is not surprising that some of the studies have conflicting results.

Several studies^{8, 10-12} have combined various prearrest, arrest, and postarrest factors into clinical decision rules for predicting survival following cardiac arrest. However, because the probability of survival from in-hospital cardiac arrest in most studies is less than 20%, a decision rule will not change management unless it can confidently predict the likelihood of survival to be nil. None of the published rules^{8, 10-12} have this level of accuracy. The rules can be impractical for common use because they require large amounts of clinical data,^{10, 11} complex mathematical calculations,⁹ or involve clinical information not available to the resuscitation team.

In this study, we used prospectively collected data for patients entered into 1 of 2 randomized controlled trials to derive a clinical decision rule for in-hospital cardiac arrest. Specifically, we wanted to determine if readily available patient and arrest characteristics could be combined during the resuscitation to identify which patients had no chance of being discharged from hospital. This rule would therefore allow the treating team to discontinue futile resuscitation efforts.

PATIENTS AND METHODS

Patients were eligible for this study if they suffered in-hospital cardiac arrest and were enrolled in 1 of 2 randomized controlled trials. These negative trials determined that neither high-dose epinephrine¹³ nor active compression-decompression cardiopulmonary resuscitation (CPR)¹⁴ increased resuscitation from cardiac arrest. To be included in these studies, patients had to undergo CPR in the hospital. Consequently, patients whose resuscitation required defibrillation only were not included. Patients resuscitated in the emergency department had a spontaneous pulse when presenting to the department and subsequently underwent an arrest while in the department. Patients were excluded if they were younger than 16 years; had a terminal illness; had been without CPR for more than 15 minutes after their collapse; had acute trauma or exsanguination; were in the operating, recovery, or delivery rooms at the time of the arrest; had a recent sternotomy; or were judged (as determined by a blinded chart review) to have received inappropriate CPR (eg, a respiratory arrest with detectable pulse). These studies took place at 5 university-associated, tertiary care teaching hospitals in Ottawa and London, Ontario, between 1989 and 1995.

During the resuscitations, nurses, respiratory therapists, orderlies, and physicians administered CPR. These hospital staff members were fully trained in standard CPR and required regular testing. Advanced cardiac life support was directed by staff physicians or senior medical residents using standard protocols published by the American Heart Association.¹⁵ During the epinephrine trial,¹³ patients were randomized to receive either high (7-mg) or standard (1-mg) dosages of epinephrine. During the active compression-decompression–CPR trial,¹⁴ patients were randomized to receive either standard CPR or a device for active chest compression-decompression. Otherwise, resuscitation efforts were identical in the 2 studies. Neither study showed a significant difference between treatment and control groups.

Following each cardiac arrest, the resuscitation team completed data collection forms. Physicians documented the suspected cause of the arrest as well as the cardiac rhythm noted at the start of advanced cardiac life support. Resuscitation nurses recorded all other factors. The arrest was considered "unwitnessed" if the patient lost spontaneous circulation in the absence of another person. The duration of the arrest was defined as the time from the start of CPR to either return of spontaneous circulation or cessation of resuscitative efforts. A study nurse abstracted current and past medical conditions of each patient from the medical record. All resuscitated patients were followed up until death or discharge from hospital. Prior to discharge, the neurologic status of all patients were evaluated with the Modified Mini-Mental State Examination¹⁶ and a 5-point scale of cerebral performance.¹⁷

Data from both studies were combined and all clinical variables were assessed for association with discharge from hospital using the ² test (for nominal data) or the unpaired separate-variance Student t test (for continuous data). Age and resuscitation duration was dichotomized at clinically reasonable cutoff points and analyzed as nominal variables. Variables that were significantly associated with survival-to-discharge (P<.05, 2-tailed) were analyzed by a ² recursive-partitioning technique using a P value threshold of .05 for each split.^{18, 19} Our goal was to generate a decision rule that would accurately identify patients with any chance of being discharged from hospital. For the decision rule, we chose the statistical model that offered a sensitivity of 1.0 (ie, no patients discharged from the hospital were predicted by the decision rule to die in hospital). Since several models meeting this standard were derived, that with the highest possible specificity was chosen as the final decision rule.

The performance of the derived decision rule for identifying patients with no chance of discharge from hospital was assessed by calculating sensitivity, specificity, and negative and positive predictive values with 95% confidence intervals (CIs).²⁰ A receiver operating characteristics curve was not calculated since the prediction rule is binary. To determine what effect the decision rule could have if used clinically, the outcome of patients identified by the decision rule as having no chance of discharge was determined. All analyses were performed using SPSS for Windows, Version 7.0 (SPSS Inc, Chicago, Ill) and KnowledgeSEEKER for Windows 3.1 (Angoss Software, Toronto, Ontario).

RESULTS

During the clinical trials, 1472 patients were treated for in-hospital cardiac arrest. However, 376 of them were excluded from analysis because of exclusion criteria for the randomized trials. Nineteen (1.3%) were excluded because of incomplete data.

For derivation of the prediction rule, 1077 patients were eligible (Table 1). Patients were more commonly men and had a mean age of 68 years, with 35% being older than 75 years. Cardiovascular disease was more common than respiratory disease as a current diagnosis, a pattern also seen for chronic diagnoses. Most arrests were witnessed and occurred on the hospital ward. For witnessed cases in which the time of collapse could be determined, delay to CPR and advanced cardiac life support was short. In 31.4% of arrests, the initial cardiac rhythm was either ventricular tachycardia or fibrillation, with the remainder of the patients having asystole or pulseless electrical activity. Most arrests had a cardiac cause; 9.6% of the patients survived to discharge. Survivors had good cognitive function with a median Modified Mini-Mental State Examination score only slightly lower than normal. Patients from the 2 trials were similar for most characteristics.

The association of patient and resuscitation variables with survival-to-discharge is shown in Table 2. Although mean age was not associated with survival, patients older than 75 years were significantly less likely to survive. Neither sex nor any of the current or past diagnoses were associated with survival. Cardiac arrests of survivors were more likely to be witnessed, to have either ventricular tachycardia or fibrillation as the initially recorded rhythm, and were shorter in duration. Groups did not differ with respect to treatment delays, or location and cause of arrest.

Using the variables significantly associated with survival-to-discharge, recursive partitioning identified 3 resuscitation factors that predicted no chance of survival (Figure 1). All patients died if there was no pulse 10 minutes after the start of CPR, the initial cardiac rhythm was not ventricular tachycardia or fibrillation, and the arrest was not witnessed. These factors were combined into the resuscitation rule, whereby continued resuscitation is unnecessary if all the following are true: (1) no pulse 10 minutes after the start of CPR; (2) initial cardiac rhythm is not ventricular tachycardia or fibrillation; and (3) the arrest was not witnessed. In our study population, this decision rule predicted 119 patients (11.0%) to die in hospital and correctly identified all patients who were discharged from hospital (sensitivity, 100%; 95% CI, 97.1%-100%) (Table 3). The low specificity (12.2%; 95% CI, 10.3%-14.4%) and positive predictive value (10.8%; 95% CI, 8.9%-12.8%) are the trade-offs for the high sensitivity.

Of the 119 patients identified by the decision rule to die in hospital, 99 (83.1%) could not be resuscitated with resuscitations lasting a mean of 25.6 minutes (range, 10-60 minutes). The remaining 20 patients survived a total of 1147 hours (mean, 57.4 hours; range, 1-240 hours), or 47.9 days in the intensive care unit. Details of the 7 patients identified by the rule to not survive who lived longer than 24 hours following the arrest are summarized in Table 4. All but 2 patients had multisystem disease prior to the arrest. Three of the patients (patients 3, 4, and 6) experienced new problems in hospital prior to the arrest and all except patient 4 experienced an arrest within the first week of hospitalization. Following the arrest, patients 1 through 4 had anoxic encephalopathy severe enough to preclude meaningful interaction with their environment; discussion with families resulted in life support being withdrawn. The remaining patients were able to participate in decisions regarding their treatment and all decided to not undergo further resuscitation in the event of cardiac arrest. Each patient died within 24 hours of extubation and only 1 (patient 6) was transferred out of the intensive care unit prior to death.

COMMENT

To our knowledge, this study represents the largest collection of prospectively gathered data for in-hospital cardiac arrests. With commonly available factors, our resuscitation rule identified all patients having any chance of being discharged from hospital following resuscitation. For patients identified by the rule as having no chance of survival, resuscitative efforts were often prolonged. At least 16.8% of patients predicted by the rule to die were treated in hospital for often extensive amounts of time following the arrest. If this decision rule is validated with prospectively collected data, it should be easy to use and avoid futile resuscitative efforts.

Decision rules can improve clinical decision making,²¹ decrease unnecessary investigations,²² and decrease the cost of health care delivery.²³ This study meets most methodological standards for the development of clinical decision rules.^{24, 25} The outcome predicted by the rule was clearly defined and clearly important. Since many characteristics of both the patients and study sites were described, the generalizability of the rule can be determined. The mathematical techniques used for the derivation of the rule were delineated and valid. Estimates for ramifications of the rule's application were given. The rule is clinically sensible since other studies have associated death after resuscitation with each variable in the rule including prolonged resuscitative efforts,^{2, 6, 7, 26} initial rhythms other than ventricular tachycardia or fibrillation,^{6-9, 27, 28} and unwitnessed arrest.⁷ Finally, the rule is easy to use, and suggests a course of action.

This study has several limitations. First, the rule has not been prospectively validated. The performance characteristics of the rule could change when applied to a different group of patients. Second, we did not determine the reliability of the variables included in the rule. Most likely, interobserver agreement for whether the arrest was witnessed is high. However, agreement may be lower for classification of the initial cardiac rhythm. Unreliable variables will decrease a rule's reproducibility. Prior to prospective validation, definitions for these and other parameters need to be developed and their interobserver agreement needs to be determined. Third, current and past diagnoses were grouped into relatively large categories (eg, respiratory disease), therefore making it impossible to determine the effect that particular diagnoses (eg, pneumonia) had on resuscitation outcome.

Fourth, the rule included factors concerning patient status at the start of the arrest and used them to predict prognosis 10 minutes later if resuscitation was unsuccessful. Therefore, the rule does not consider any factors arising within the first 10 minutes of the arrest. During this time, prognostically important cardiac rhythms and responses to therapy could occur. Also, the rule does not include recently described prognostic factors such as end-tidal carbon dioxide levels. Several studies^{29, 30} have suggested this to be a powerful predictor of survival from cardiac arrest. Future studies need to determine if these and other data can be added to the present prediction rule to improve its performance.

Finally, it is uncertain how effective this rule could be, even if validated. Situations could arise where a physician decides that the rule should not apply to a particular patient because of what are considered to be special, individual circumstances. This is valid since clinical judgment must always be used when applying any clinical decision rule. Further research is needed to determine how frequently this would occur with a particular rule. Also, the rule identified only 12% of all people dying after in-hospital cardiac arrest. Most of these patients were not successfully resuscitated. Using the decision rule to predict patient death minutes prior to it actually happening is not terribly useful. However, we believe the rule would help physicians with limited experience in cardiac resuscitation decide when continued resuscitation is futile.

Despite these uncertainties, we believe that this decision rule, if validated, has the potential to be helpful. It will decrease the amount of time and effort that staff expend on futile cardiac resuscitations. While the rule will not protect families from difficult emotions associated with a loved one's sudden death, stressful days of watching and waiting could be avoided. Finally, the costs saved by using the rule to avoid prolonged stays in the intensive care unit could be used to improve other areas of the health care system. However, these potential benefits can only be realized if the rule is validated in a prospective, multicenter study.

In summary, we have derived a simple and accurate decision rule that identifies all patients with a chance of surviving in-hospital cardiac arrest. The rule is explicit, easy to apply, uses objective data that are readily available at all cardiac arrests, and could significantly decrease the amount of futile postresuscitation care. If validated, the rule could be combined with astute clinical judgment to more effectively use cardiac resuscitation.

Author/Article Information


From the Department of Medicine (Drs van Walraven and Forster) and the Division of Emergency Medicine (Dr Stiell), University of Ottawa, Ottawa, Ontario.

Corresponding author: Carl van Walraven, MD, FRCPC, MSc, F-6, Ottawa Civic Hospital, 1053 Carling Ave, Ottawa, Ontario, Canada K1Y 4E9.

Accepted for publication May 28, 1998.

Dr van Walraven was an R. Samuel McLaughlin Foundation research fellow at the Institute for Clinical Evaluative Sciences, Toronto, Ontario, when this study was completed. Dr Stiell is a scientist of the Medical Research Council of Canada.

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Validation of a Clinical Decision Aid to Discontinue In-Hospital Cardiac Arrest Resuscitations


Carl van Walraven, MD, FRCPC, MSc; Alan J. Forster, MD, FRCPC, MSc; David C. Parish, MD, MPH; Francis C. Dane, PhD; K. M. Dinesh Chandra, MD; Marcus D. Durham, MA; Candace Whaley, BBA; Ian Stiell, MD, FRCPC, MSc

Context  Most patients undergoing in-hospital cardiac resuscitation do not survive to hospital discharge. In a previous study, we developed a clinical decision aid for identifying all patients undergoing resuscitation who survived to hospital discharge.

Objective  To validate our previously derived clinical decision aid.

Design, Setting, and Participants  Data from a large registry of in-hospital resuscitations at a community teaching hospital in Georgia were analyzed to determine whether patients would be predicted to survive to hospital discharge (ie, whether their arrest was witnessed or their initial cardiac rhythm was either ventricular tachycardia or ventricular fibrillation or they regained a pulse during the first 10 minutes of chest compressions). Data from 2181 in-hospital cardiac resuscitation attempts in 1987-1996 involving 1884 pulseless patients were analyzed.

Main Outcome Measure  Comparison of predictions based on the decision aid with whether patients were actually discharged alive from the hospital.

Results  For 327 resuscitations (15.0%), the patient survived to hospital discharge. For 324 of these resuscitations, the patients were predicted to survive to hospital discharge (sensitivity = 99.1%, 95% confidence interval, 97.1%-99.8%). In 269 resuscitations, patients did not satisfy the decision aid and were predicted to have no chance of being discharged from the hospital. Only 3 of these patients (1.1%) were discharged from the hospital (negative predictive value = 98.9%), none of whom were able to live independently following discharge from the hospital.

Conclusion  This decision aid can be used to help physicians identify patients who are extremely unlikely to benefit from continued resuscitative efforts.

JAMA. 2001;285:1602-1606

Cardiopulmonary resuscitation and advanced cardiac life support interventions are used to resuscitate patients having cardiac or respiratory arrest. Physicians and other health care workers follow algorithmic advanced cardiac life support guidelines from the American Heart Association¹ in an attempt to restore a spontaneous circulation in these patients. Despite these guidelines, decision making during resuscitation is difficult because cardiac arrest is a complex common pathway of a diverse collection of diseases.²

The decision to stop resuscitative efforts can be particularly difficult for physicians. In these situations, decision making must balance a "respect for human dignity" and clinical judgment.³ Prognostic factors that portend a poor outcome following arrest may help the latter. Death following resuscitation has been associated with both prearrest factors (including hypotension,⁴ renal failure,^{5, 6} pneumonia,⁴ low functional status,^{4, 7} and metastatic cancer^{4, 6}) and intra-arrest factors (including prolonged duration of resuscitation^{4, 7, 8} and initial cardiac rhythms other than pulseless ventricular tachycardia or ventricular fibrillation^{7, 9-11}). However, decision aids that use these and other factors to determine a patient's prognosis following resuscitation are complicated and difficult to use at the bedside. They have operated poorly when validated in distinct populations¹² and have been unable to definitively identify patients with no hope of survival.¹³ A simple decision aid that reliably identifies patients whoregardless of the cause of their arresthave a poor outcome would be helpful. This could help avoid the "tendency to try prolonged, excessive resuscitative efforts."³

For this reason, we previously derived a simple clinical decision aid to identify all patients who were eventually discharged from hospital after their arrest.¹⁴ Our goal, when deriving this decision aid, was to maximize sensitivity for identifying these patients. We studied 1077 adults undergoing in-hospital resuscitation who participated in 2 randomized clinical trials^{15, 16} involving 5 teaching hospitals. Using recursive partitioning, we found that all resuscitated patients who were eventually discharged from the hospital had a witnessed arrest, an initial cardiac rhythm of either ventricular tachycardia or ventricular fibrillation, or a pulse within the first 10 minutes of chest compressions. We proposed that physicians might safely withdraw resuscitative efforts on patients who did not satisfy the decision aid since none of these patients were discharged from the hospital.

In that report we stressed 2 issues. First, we emphasized that while 100% of the patients not satisfying the decision aid eventually died in hospital, the 95% confidence interval (CI) of this point estimate extended down to 97.1%. Second, we cautioned that validation of the decision aid in a separate patient population was required before the decision aid should be used clinically.¹⁴ This was the objective of our current study.

METHODS

This validation study was a secondary analysis of a resuscitation registry at the Medical Center of Central Georgia (MCCG) in Macon. The MCCG is a 550-bed community teaching hospital affiliated with the Mercer University School of Medicine. It is the major hospital for the metropolitan area as well as the tertiary center for the surrounding rural areas and has approximately 25 000 admissions per year.

Multidisciplinary teams consisting of nurses, residents, staff physicians, respiratory therapists, and pharmacists conducted the resuscitations. Staff physicians or senior medical residents directed the resuscitations using standard protocols published by the American Heart Association.³ Team members were all trained in basic life support and most were trained in advanced cardiac life support. All members underwent regular updates. During the study period, resuscitation was attempted for approximately 1.5% of admissions.

With the exception of arrests occurring in the neonatal intensive care unit or operating room, all in-hospital arrests occurring between 1987 and 1996 were entered into the registry. The resuscitation registry was reviewed and approved by the Institutional Review Board of Mercer University School of Medicine and MCCG. Resuscitation was defined, as has been suggested by the Utstein Conference,¹⁷ as "any effort to reverse a clinical death in progress." Code sheets were completed after each resuscitation attempt and were used to identify events. To ensure complete capture of resuscitations, the records of patients without a code sheet but whose hospital charges included cardioversion, defibrillation, or the administration of epinephrine were also reviewed to determine if a resuscitation attempt occurred. Data were entered into the registry by MCCG project staff and were checked prior to analysis. All data were cross-checked for accuracy through an extensive review of hospital records and death logs. Clinical categories, such as whether or not the arrest was witnessed and initial cardiac rhythm, used standardized definitions that had interrater values exceeding 0.9 before final classification of the data set was complete. Questionable cases were reviewed by 2 researchers (D.C.P., K.M.D.C.) for consensus definition in the final review. This was done before this study was conceived. A detailed description of the registry was previously published.¹¹

All resuscitations in the registry were eligible for this study. Resuscitations were excluded if they were performed on patients younger than 16 years old or were performed on patients in the operating room at the time of the arrest. Our decision aid applies to patients who were pulseless at the start of the resuscitation. Therefore, resuscitations were excluded if the initial rhythm was any other than pulseless ventricular tachycardia, ventricular fibrillation, pulseless electrical activity, or asystole. Resuscitations were also excluded if patients received no chest compressions, if information required by the decision aid was missing, or if time to initial chest compressions exceeded 15 minutes. Each of these exclusion criteria was used for patient selection in the decision aid derivation.¹⁴

To apply the decision aid, we determined the time from the start of chest compressions to the end of the resuscitation. This was the return of any spontaneous circulation lasting 2 or more minutes or the end of resuscitative efforts. The former criterion addressed a concern of our decision aid,² namely, that patients who regained a pulse for most of the resuscitation only to lose it before the 10-minute mark might have resuscitation stopped if the aid was applied. Patients who were directly seen to lose spontaneous circulation were classified as "witnessed." Also, all patients who had an arrest while on a cardiac monitor, in the intensive or coronary care unit or in the cardiac catheterization laboratory, were classified as "witnessed arrests." This was regardless of whether or not the patient was directly visualized to lose spontaneous circulation. Patients in the emergency department were included in the study only if they actually had an arrest after they arrived in the department. Finally, we applied the decision aid in this study in a more clinically intuitive order than that presented in the derivation study. This altered order does not affect the model's statistical significance or operating characteristics.

A 2 2 contingency table comparing actual discharge status to that predicted by the decision aid was used to calculate the classification performance of the clinical decision aid with 95% CIs. We reviewed the medical record of all patients who did not satisfy the decision aid but survived more than 24 hours to determine their course in hospital. Analyses are by resuscitation.

RESULTS

When this study was conducted, the registry recorded 3960 resuscitations. We excluded 1779 events for the following reasons: no chest compressions were given during the resuscitation (n = 1147), the patient was not pulseless at the start of the resuscitation (n = 254), the patient was younger than 16 years (n = 85), the patient was in the operating room when the arrest was called (n = 1), the time from arrest being called to the first chest compression exceeded 15 minutes (n = 9), or information required to apply the decision aid was missing (n = 283).

This left 2181 attempted resuscitations comprising 1884 patients. These patients had a mean age of 65 years (95% CI, 64.3-65.7) and 47.3% were female. Most arrests occurred off ward, had a cardiorespiratory cause, and were witnessed (Table 1). The predominant initial rhythms were asystole and pulseless electrical activity, and for 2094 (96.0%) resuscitations, chest compressions were delivered within 5 minutes of arrest. A spontaneous circulation was attained in almost half of resuscitations, and for 15.0% of resuscitations, patients survived to discharge from hospital.

Figure 1 illustrates how the decision aid identified patients who would be discharged from the hospital. Of the 327 resuscitations for which patients were discharged from hospital, 287 satisfied the first component of the decision aid ("arrest was witnessed"). Of the remaining 40 resuscitations, 10 satisfied the second component of the aid ("initial rhythm was ventricular tachycardia or ventricular fibrillation"). Twenty-seven of the remaining 30 resuscitations satisfied the final component of the decision aid ("pulse regained during the first 10 minutes of chest compressions").

Table 2 presents the classification performance of the decision aid. Of the 327 resuscitations for which patients were discharged from hospital, all but 3 satisfied the decision aid, resulting in a sensitivity of 99.1% (95% CI, 97.1%-99.8%). That is, the decision aid correctly identified all but 0.9% of those who were discharged from the hospital. The decision aid had a negative predictive value of 98.9% (95% CI, 96.5%-99.7%). That is, 1.1% of arrests that the decision aid predicted had no chance of survival were actually discharged from the hospital.

Likelihood ratios allow clinicians to measure the quantitative importance of test results.¹⁸ The negative likelihood ratio of the decision aid was 0.064. To put this into perspective, assume that we could accurately determine the probability that hospitalized patients will survive to discharge if they required resuscitation. Also, assume that we have 3 patients whose probability of surviving to hospital discharge, in the event of an arrest, is 30%, 15%, and 5%. If these patients did not satisfy the decision aid during their resuscitations, their probabilities of surviving to discharge would decrease to 2.7%, 1.1%, and 0.3%, respectively.

We determined the outcome of the 3 people whom the aid predicted had no chance of being discharged from the hospital. The first patient was a 76-year-old man with dementia, hypertension, and chronic obstructive pulmonary disease who was transferred to another hospital following resuscitation to continue inpatient medical therapy. When he was discharged he was in a very poor condition and required tracheostomy, gastrostomy, foley catheter, and rectal tube. These, however, were at least partially required for an obstructive oropharyngeal carcinoma as opposed to ischemic cerebral damage. He died 2 months following discharge from the hospital. The second patient was a 43-year-old man with chronic obstructive pulmonary disease and alcoholic cardiomyopathy. Although he had minimal ischemic damage from the arrest, he was discharged to a nursing home residence because of problems caring for himself. The final patient was a 65-year-old previously well woman who had an arrest following back surgery. She had no ischemic injury but required nursing home placement because of complications of her back surgery.

Of the 269 resuscitations in which patients were predicted to have no chance of surviving to hospital discharge, the mean resuscitation duration was 22.6 minutes (SD, 11.1 minutes; range, 10-72 minutes). In 53 of these resuscitations (19.7%), patients achieved a spontaneous circulation and were transferred to the intensive care unit. Twenty-six of these patients remained alive for at least 24 hours but died later during the hospitalization. These 26 patients survived a mean of 8.5 days following the resuscitation (range, 1-29 days; total, 213 days). Of the 20 patients whose chart was available, 15 (75%) never regained consciousness. For 9 (45%) of these 20 patients, a decision was made to withdraw active care.

COMMENT

Using one of the world's largest continuous registries of hospital resuscitations, we found that a simple clinical decision aid performed well to identify patients with any chance of discharge from hospital following resuscitation. All but 3 patients (1.1%) who did not meet criteria for the decision aid died following their arrest. We believe that this decision aid can be used with other clinical factors to help physicians identify patients who are extremely unlikely to benefit from ongoing resuscitation efforts.

Our aid meets the most important methodological standards for decision aids.^19-21 The outcome predicted by the aid is important and objective. The aid is clinically sensible since each component (ie, witnessed arrest,⁸ initial ventricular tachycardia or ventricular fibrillation,⁷ and duration of resuscitative efforts^{4, 7, 8, 22}) has been associated with survival in other studies. The potential effects of using the decision aidin terms of avoided intensive care unit dayshave been estimated in both the derivation¹⁴ as well as this study. Most important, the aid has been validated on a distinct population, a standard met by fewer than half of all decision aids published between 1991 and 1994 in 4 major medical journals.¹⁹ We hope that this aid will be tested further in other patient populations, preferably in a prospective fashion, to further ensure its validity.

Two factors regarding the decision aid require more detailed discussion. First, this study showed that the decision aid is very robust since it was valid in a separate patient group despite large differences between the derivation and validation populations. Compared with the derivation group,¹⁴ patients in this study were significantly younger (mean age, 65 vs 68 years, P<.001), and were less likely to have have had an arrest on the ward (43.9% vs 55.2%). Outcomes for the validation group were much better with a significantly greater number of patients surviving to 1 hour (48.8% vs 33%, P = .0001) and discharge (15.0% vs 9.6%, P<.001). Most important, the patients used for the derivation and validation studies were from very different health care systems. The observation that our decision aid performed so well in such a different patient population should give physicians confidence to apply it to their own patients.

Second, although each component of the aid is very objective, it must be used with care since difficulties in measuring time, classifying cardiac rhythms, and determining the presence of a pulse during the resuscitation have been well documented in the medical literature. All patients who had an arrest in the intensive care unit or while on a monitor are considered witnessed arrests when applying this aid, even if they are not actually visualized to become unstable. Similarly, physicians must be confident about the classification of the initial rhythm, and special care must be exercised to ensure that perfusing rhythms with hypotension are not classified as pulseless electrical activity. Also, physicians must ensure that resuscitative efforts have truly proceeded for a complete 10 minutes, without the return of a pulse that persisted for 2 or more minutes, before the last component of the aid is determined. Finally, this decision aid cannot be applied to out-of-hospital resuscitations without further research.

Although the resuscitations in this study spanned over 9 years, we do not believe that changes in resuscitation significantly affected our results. Between 1987 and 1996, the time period of the study, no pharmacological or mechanical intervention was introduced that reliably improved the patient outcomes following resuscitation. Although monitoring technology, such as telemetry, might have become more prevalent during the study, the decision aid would account for this since all such patients would be classified as having a witnessed arrest.

This study addresses several criticisms of our decision aid.² There was concern that patients who regained a pulse within the first 10 minutes of the resuscitation, only to lose it again at 10 minutes, would not satisfy the decision aid and would have resuscitative efforts withdrawn. Since the registry used for this study recorded when patients had any return of spontaneous rhythm, we ensured that patients who regained a pulse for longer than 2 minutes within the first 10 minutes of resuscitation satisfied the decision aid. Second, the decision aid only identified 12% of the study group as patients with no chance of survival. However, this is comparable to other decision aids used to identify patients with poor outcomes postarrest such as the Pre-Arrest Morbidity score,^{12, 23, 24} the Prognosis After Resuscitation score,^{12, 23} and the APACHE (Acute Physiology and Chronic Health Evaluation) III index,¹² each of which applied to between 5% and 20% of all resuscitations. Third, we were criticized for both ignoring prearrest patient factors that are associated with outcomes and attempting to derive a simple decision aid to be applied to such a diverse population of patients having a broad range of survival probabilities. As we demonstrated in this article, physicians could combine the negative likelihood ratio of the decision aid with estimated prearrest survival probabilities to improve prognostication.¹⁸ However, we believe that freeing physicians from having to calculate a pretest probability of survival based on various factors is a strength of the aid. Since many patients with a good prognosis have some return of spontaneous circulation within the first 10 minutes of the resuscitation, our aid innately accounts for these patients. Finally, we agree that any time patients are interactive with their environment after resuscitationeven if they end up dying lateris valuable. However, we do not feel that this advocates adopting a "never give up" mind-set during resuscitative efforts. Our data show that for each person who did not satisfy the decision aid but regained consciousness, approximately 15 people did not. In many of these cases, family members are put in the unenviable position of having to decide whether to withdraw care from a loved one. Given the stress that this causes,^{25, 26} it is arguably a high price to pay.

We believe that the decision aid validated in this study can be used by physicians to identify patients who are extremely unlikely to benefit from ongoing resuscitation efforts. Further validation of the aid in multiple sites with prospectively collected data would be welcome. In addition, this decision aid might also help patients be more directive regarding their resuscitation attempts. Our data could allow patients and physicians to precisely quantify when resuscitative efforts would be stopped, such as when our decision aid is not satisfied. Since patients are often afraid of being resuscitated only to remain on life support, our decision aid could be used to help patient decision making that might avoid such a situation.

Author/Article Information


Author Affiliations: Department of Medicine, University of Ottawa, Ottawa, Ontario (Drs van Walraven, Forster, and Stiell); Department of Internal Medicine, Mercer University School of Medicine, Macon, Ga (Drs Parish, Dane, Chandra, Mr Durham, and Ms Whaley); Clinical Epidemiology Unit, Loeb Health Research Institute, Ottawa Hospital, Ottawa, Ontario (Drs van Walraven and Stiell); and Institute for Clinical Evaluative Sciences, Toronto, Ontario (Dr van Walraven).

Corresponding Author: Carl van Walraven, MD, FRCPC, MSc, F-660, 1053 Carling Ave, Ottawa, Ontario, Canada K1Y 4E9 (e-mail: [log in to unmask]).

Author Contributions: Study concept and design: van Walraven, Stiell, Parish, Forster.

Acquisition of data: Dane, Parish, Whaley, Durham, Chandra.

Analysis and interpretation of data: van Walraven, Dane, Parish.

Drafting of the manuscript: van Walraven, Durham, Forster.

Critical revision of the manuscript for important intellectual content: van Walraven, Stiell, Dane, Parish, Whaley, Durham, Chandra.

Statistical expertise: van Walraven, Stiell, Dane, Forster.

Administrative, technical, or material support: Dane, Parish, Whaley, Durham, Chandra.

Funding/Support: Dr van Walraven was supported by the Physicians' Services Foundation Arthur Bond Scholarship and an Ontario Ministry of Health Career Scientist Award. Dr Forster is an R. Samuel McLaughlin Fellow with the Division of General Medicine, Brigham and Women's Hospital, Harvard Medical School. Dr Dane is the Kilpatrick Professor at Mercer University, Macon, Ga. Dr Stiell is a scientist of the Canadian Institute of Health Research.

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Edward E. Rylander, M.D.

D.A.B.F.P and D.A.B.P.M.