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European Heart Journal Advance Access originally published online on November 2, 2006
European Heart Journal 2006 27(23):2840-2845; doi:10.1093/eurheartj/ehl335
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© The European Society of Cardiology 2006. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org

Predicting survival with good neurological recovery at hospital admission after successful resuscitation of out-of-hospital cardiac arrest: the OHCA score

Christophe Adrie1,*, Alain Cariou2, Bruno Mourvillier3, Ivan Laurent4, Hala Dabbane1, Fatima Hantala1, Abdel Rhaoui5, Marie Thuong1 and Mehran Monchi4

1 Intensive Care Unit, Delafontaine Hospital, 2, rue du Dr Delafontaine, Saint Denis, France
2 Intensive Care Unit, Cochin Hospital, University of Paris V, Paris, France
3 Intensive Care Unit, Bichat-Claude Bernard Hospital, University of Paris VII, Paris, France
4 Intensive Care Unit, Jacques Cartier Institute, Massy, France
5 Intensive Care Unit, Troyes Hospital, Troyes, France

Received 27 June 2006; revised 29 September 2006; accepted 5 October 2006; online publish-ahead-of-print 2 November 2006.

* Corresponding author. Tel: +33 142 356 107; fax: +33 142 356 233. E-mail address: christophe.adrie{at}wanadoo.fr


   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 Appendix 1: Abbreviations and...
 Acknowledgement
 References
 
Aims Out-of-hospital cardiac arrest (OHCA) is common and carries a bleak prognosis. Early prediction of unfavourable outcomes is difficult but crucial to improve resource allocation. The aim of this study was to develop a simple tool for predicting survival with good neurological function in the overall population of patients with successfully resuscitated cardiac arrest.

Methods and results We used logistic regression analysis to identify clinical and laboratory variables that were both readily available at admission and predictive of poor outcomes (death or severe neurological impairment) in a development cohort of 130 consecutive OHCA patients admitted to a French intensive care unit (ICU) between 1999 and 2003. To test the prediction score built from these variables, we used a validation cohort of 210 patients recruited in four French ICUs between 2003 and 2005. Initial rhythm, estimated no-flow and low-flow intervals, blood lactate, and creatinine levels determined using whole blood analyzers were independently associated with poor outcomes and were used to build a continuous severity score. Goodness-of-fit tests indicated good performance (P=0.79 in the development cohort and P=0.13 in the validation cohort). The area under the receiver-operating characteristics curve was 0.82 in the development cohort and 0.88 in the validation cohort.

Conclusion The outcome can be accurately predicted after OHCA using variables that are readily available at ICU admission.

Key Words: Heart arrest • Resuscitation • Survival


   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 Appendix 1: Abbreviations and...
 Acknowledgement
 References
 
Every year, more than 225 000 people in the United States die suddenly from out-of-hospital cardiac arrest (OHCA), usually due to coronary heart disease.1,2 Despite advances in cardiac arrest resuscitation, considerable mortality and morbidity occur as a result of post-resuscitation disease,3 which is characterized by neurological impairments and other organ dysfunctions. In community-wide studies, overall survival rates ranged from 4 to 33% depending on the chain of survival.1,2 The mechanisms underlying post-resuscitation disease probably include both myocardial dysfunction and whole-body ischaemia/reperfusion syndrome responsible for a systemic inflammatory response similar to that seen in severe sepsis.4–6 Two recent studies showed higher survival rates in patients treated with mild hypothermia7,8 after successful cardiac arrest resuscitation. The beneficial effect of hypothermia shows that the outcome is determined not only by the time to circulation recovery, but also by pathogenic processes that continue to evolve after circulation recovery, causing damage to the nervous system and other organs. Both studies were conducted in a highly selected population that excluded up to 92% of the patients initially assessed for eligibility.8,9 Thus, the effectiveness of hypothermia in the overall population of patients with OHCA is unclear. In addition to hypothermia, several costly and time-consuming treatments hold promise for improving outcomes in patients with OHCA; they include high-volume haemofiltration and coronary angiography followed by angioplasty if appropriate.10,11 Tools are needed to help physicians choose among the increasing number of treatment options for patients who recover from spontaneous circulation after OHCA.

Most of the currently available scores for evaluating general severity in intensive care unit (ICU) patients12,13 require a 24 h wait for data collection and history taking. The time from collapse to initiation of cardiopulmonary resuscitation (CPR) referred to as ‘no-flow interval’ and the duration of CPR referred to as ‘low-flow interval’ are available at admission and have been evaluated as predictors of outcomes after cardiac arrest from ventricular fibrillation (VF).14–16 However, both variables performed poorly, perhaps in part, due to inaccurate recall or recording of times during this highly stressful event. A score that predicts outcomes based on variables available at ICU admission would help to optimize treatment decisions. Moreover, risk estimation at admission would assist in stratifying patients screened for randomized studies and in interpreting the results of epidemiological studies.

We developed a simple and generally applicable tool for predicting survival with good neurological function in unselected patients admitted to the ICU after successfully resuscitated OHCA. The tool is a score based on variables that are readily available at ICU admission.


   Methods
Top
 Abstract
 Introduction
 Methods
Results
 Discussion
 Appendix 1: Abbreviations and...
 Acknowledgement
 References
 
Patients and definitions
Cardiac arrest was defined as the absence of spontaneous respiration, palpable pulse, and responsiveness to stimuli. Consecutive patients older than 18 years of age who were successfully resuscitated after OHCA were prospectively included in the study. In France, patients with OHCA are managed by mobile emergency units, each staffed by a physician trained in emergency medicine. Successful resuscitation was defined as recovery of blood pressure and pulse for more than 1 h, with or without a continuous catecholamine infusion. Both the Simplified Acute Physiology Score (SAPS II)12 and the Logistic Organ Dysfunction (LOD) Score17 were calculated. To simulate everyday clinical practice, we prospectively collected data at ICU admission in all patients who recovered spontaneous circulation, before the initiation of further medical interventions. The emergency unit staff and ICU staff were not aware of the study. No-flow and low-flow intervals were estimated by the emergency unit staff. Outcomes were assessed using the Cerebral Performance Categories (CPC) of the Glasgow-Pittsburgh Outcome Categories: Category 1 is conscious and normal; Category 2 conscious with moderate disability; Category 3 conscious with severe disability; Category 4 coma or vegetative state; and Category 5 death.18 We defined a good outcome as conscious and CPC 1 or 2 at hospital discharge. We collected biochemical variables that can be obtained at admission using recently introduced whole-blood analyzers.

Statistical analysis
Descriptive statistics such as proportions, medians, and interquartile (IQR) ranges were used to summarize the results from the two cohorts. To develop the survival prediction model, we used a cohort of 130 patients admitted to a non-university hospital (Delafontaine Hospital, Saint Denis) between 1999 and 2003. In this cohort, variables known to be associated with outcome (initial rhythm, no-flow interval, and low-flow interval) and immediately available laboratory parameters (blood gases, sodium, potassium, urea, creatinine, and lactate) were screened for association with a poor outcome (CPC >2). We used {chi}2 tests to evaluate categorical data. To assess the relationship between each numerical variable and a poor outcome, we first plotted continuous variables against the dependent variable (CPC >2), and we used the Lowess smoothing function with locally weighted least squares to identify proportional relationships. The results of this procedure indicated that 1/creatinine was preferable over creatinine and that natural logarithms should be used for low-flow interval, no-flow interval, and plasma lactate. To increase ease of use, we substituted 1000/creatinine in µmol/L for 1/creatinine. Associations linking these numerical variables to poor outcomes (CPC 3–5) were evaluated using Mann–Whitney U tests. P-values less than 0.05 were considered statistically significant. All statistical tests were two-tailed.

Variables significantly associated with a poor outcome were included in a multiple logistic regression model.19–21 We used backward elimination of non-significant variables, without dropping any of the clinically important characteristics of cardiac arrest. Then, the beta-coefficient of each of the variables independently associated with a poor outcome was used to build a score: each beta-coefficient was multiplied by 10 and rounded to the nearest whole number.22

We validated this score in a second population of 210 patients (validation cohort) recruited between 2003 and 2005 in four French ICUs, including two in university hospitals (Bichat Hospital and Cochin Hospitals, both in Paris) and two in general hospitals (Delafontaine Hospital in Saint Denis and Troyes Hospital in Troyes).

Finally, we assessed score performances. To evaluate discrimination, the area under the receiver-operating characteristic (ROC) curve was determined, in both the development and the validation cohort, and values greater than 0.80 were taken as indicating good discrimination.23 ROC curves were compared using a non-parametric method.24 To evaluate calibration, Hosmer–Lemeshow goodness-of-fit tests using five quintiles of poor outcome were performed on the development and validation cohorts, and P-values greater than 0.1 were taken to indicate good agreement.22


   Results
Top
 Abstract
 Introduction
 Methods
 Results
Discussion
 Appendix 1: Abbreviations and...
 Acknowledgement
 References
 
The development cohort comprised 130 patients, including 95 men (72%), with a median age of 55 years (IQR: 47–69) and a SAPS II score of 67 (57–79). All patients received endotracheal mechanical ventilation. Ischaemic heart disease was the leading cause of OHCA (72 cases, 55%). Of the 130 patients, 28 (21%) met our criterion for a good outcome, i.e. were discharged alive from the hospital without major neurological impairments (CPC 1 or 2). Baseline characteristics at admission are described in Table 1. Good outcome rates varied widely, from 5 to 61%, depending on the duration of the no-flow (≤ or >5 min) and low-flow (≤ or >15 min) intervals (Figure 1). Early death from refractory shock occurred in 42 patients and later death from complications related to neurological failure in 60 patients. The following variables were independently associated with a poor outcome in the multivariable analysis: initial rhythm, no-flow interval, low-flow interval, lactate, and 1000/creatinine (Table 2). Using these variables, we developed an equation for a continuous score (Table 3). For converting the severity score to the probability of poor outcome (CPC 3–5), the following equations were determined:

Formula
and probability of hospital death=eLogit/(1+eLogit), where e is the mathematical constant 2.7182818. The area under the ROC curve was 0.82 (95% CI: 0.70–0.95) and the P-value for goodness-of-fit was 0.79.


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  		Table 1 Characteristics of the development cohort of patients who recovered spontaneous circulation after OHCA cardiac arrest

 

Figure 3351
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  		Figure 1 Survival with a good neurological outcome (%) (CPC 1 and 2) in the development cohort (130 patients) according to duration of the no-flow and low-flow intervals. Both variables were associated with large variations in poor outcomes (CPC >2).

 

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  		Table 2 Results of the multivariable analysis: independent predictors of outcomes in patients who recovered spontaneous circulation after OHCA cardiac arrest

 

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  		Table 3 Equation for the OHCA cardiac arrest score

 
The characteristics of the 210 OHCA patients in the validation cohort are reported in Table 4. In this cohort, the P-value for goodness-of-fit was 0.13, suggesting that the model faithfully reflected the outcome experienced in a group of patients independent from those used to develop the model. The area under the ROC curve was 0.88 (95% CI: 0.82–0.94) in the validation data set, suggesting good discrimination. Score performance for predicting a poor outcome in the two cohorts at different cut-offs is summarized in Table 5. The score performed far better than the SAPS II score (area under the ROC curve, 0.69; 95% CI, 0.61–0.77; P=0.0001), particularly in patients at low risk for death (Figure 2). Furthermore, the SAPS II score was available only 24 h after ICU admission.


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  		Table 4 Characteristics at ICU admission of the validation cohort of patients who recovered spontaneous circulation after OHCA cardiac arrest

 

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  		Table 5 Accuracy of the OHCA score at different cut-points (95% CI)

 

Figure 3352
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  		Figure 2 Relationship between the OHCA score and poor outcomes (CPC >2) in the validation cohort.

 

   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
Appendix 1: Abbreviations and...
 Acknowledgement
 References
 
The continuous score based on a logistic regression evaluation of variables that are readily available at ICU admission proved very accurate in predicting a poor outcome (CPC >2) at hospital discharge in unselected patients admitted after OHCA. The score was built over a 4-year period in one centre and was then validated over a 2-year period in four centres.

Meaningful neurological recovery cannot be reliably predicted from physical findings during the first 24 h after cardiac arrest.25–27 Absence after 24 h of the corneal reflex or pupillary reflexes and of the withdrawal response to pain and absence after 72 h of motor responses predict severe neurological impairment.27 However, the presence of these responses does not reliably predict a good neurological outcome. In addition, the recent introduction of hypothermia to treat patients with OHCA has complicated the evaluation of neurological function, as hypothermia requires the administration of sedatives and neuromuscular blockers. Sasser28 reported that a combination of neurological findings and demographic variables, co-morbidities, and CPR variables had 59% sensitivity and 93% specificity for predicting mortality. Although these performance characteristics are similar to those of our score, the neurological findings needed for prediction are available only after some time in the ICU. Our study shows that survival with minimal neurological impairment can be predicted at ICU admission, using variables that are easily obtained in everyday practice using a whole blood analyzer. Our score performed far better than previously described scores based only on the no-flow and low-flow intervals.14–16 The use in our score of other objective variables such as initial rhythm and laboratory variables may reduce the risk of error due to inaccurate estimation of time intervals, particularly of the no-flow interval.

We used logistic regression to select variables and to assign weights to values of those variables.19–21 This method allowed us to identify continuous variables that could easily be converted to mathematical functions in order to linearize their relationship to a poor outcome. The use of linear functions with no ceiling or floor avoids the ‘class jump’ phenomenon, in which small changes in the variable can lead to large changes in the predicted rate of poor outcomes. Although estimates of no-flow and low-flow intervals at ICU admission are known to be inaccurate, adding the natural logarithms of these variables improved the predictive performance of the other variables (initial rhythm and laboratory tests). Linearization of continuous variables has been used successfully to predict the prognosis of chronic liver disease (MELD score).29 Our use of this method may explain the good performance of our score [ROC curve, 0.88 (0.82–0.94)] in a validation sample recruited not only in the study centre, but also in three additional ICUs, during a period that differed from the development period.

The no-flow interval is notoriously difficult to estimate with accuracy. A small error has a large impact, since each additional minute is associated with a 7–10% increase in poor outcomes.30 In our study, log-transformation may have minimized this source of error, as a longer interval is associated with greater inaccuracy. We used the estimates made by the emergency unit staff, without telling them about the study. This allowed us to simulate real-life conditions.

Plasma lactate and creatinine levels probably reflect the severity of the whole-body ischaemia/reperfusion syndrome. In a study of dogs, the severity of lactic acidosis correlated with the lengths of the no-flow and low-flow intervals.31 Müllner et al.32 found that lactate at ICU admission was closely associated with unfavourable outcomes (CPC 3–5) after OHCA due to VF. However, lactate at admission was only weakly correlated with the duration of cardiac arrest and was unreliable for predicting unfavourable outcomes.32 This discrepancy between results in dogs and in humans may stem from the sensitivity of lactate levels to multiple factors, including pre-existing disease, substrate availability, and the cumulative amount of epinephrine administered in human studies. Müllner et al.32 concluded that lactate alone was of limited usefulness for predicting individual outcomes. However, their data were collected retrospectively, which may have led to substantial errors in estimated no-flow and low-flow intervals. In our prospective study, lactate elevation was closely correlated with unfavourable neurological outcomes, and lactate helped to predict the outcome when used in combination with other variables independently associated with outcome. To simulate the conditions of everyday practice, we chose not to exclude patients with pre-existing diseases known to affect laboratory variables and outcomes, such as renal failure or cirrhosis.

Because evidence of benefits from therapeutic hypothermia was published during the course of our study, we observed an increase in the use of hypothermia between the recruitment periods for the development cohort (14%) and validation cohort (74%). However, increased use of hypothermia did not affect score performance. Hypothermia is now widely used in the general population of patients with OHCA, although it was proved effective in a highly selected population excluding 92% of the patients who were initially assessed for eligibility.8,9 Whether this extension of the use of hypothermia to the overall OHCA population is beneficial remains unclear.9 Limited benefits in the overall population might explain the absence of change in score performance despite increased use of therapeutic hypothermia.

Our score performed far better than the SAPS II,12 which is widely used to evaluate severity based on variables collected within 24 h after ICU admission. Our score can be determined earlier, at ICU admission, since whole-blood analyzers available in ICUs can provide the laboratory variables within a few minutes.

The goodness-of-fit test results showed that our model was well calibrated, on average, to the wide range of clinical settings encountered in patients with OHCA. Importantly, as with all severity systems for ICU patients, probabilities estimated from our score are best interpreted in terms of patient populations, since our model was developed using a large population of patients with a wide variety of diagnoses and co-morbidities. The probability predicted by our score is the probability in the average patient, not the individual patient. Moreover, the organization of emergency medical services varies across countries, and our score may need to be calibrated for each specific system. To maximize the relevance of our score to everyday practice, we included consecutive patients and we refrained from telling the emergency unit staff members about the study. We believe our score may be helpful for identifying patient subgroups that require aggressive treatment and therefore for optimizing the use of finite healthcare resources. New costly interventions are being investigated as means of improving survival after OHCA. Should they prove effective, they will be subjected to cost-effectiveness evaluations, which will require quantification of the potential benefit, e.g. the number of additional lives saved. Our score may be helpful for this purpose.

In conclusion, we developed a score that accurately predicts outcomes of unselected groups of patients admitted to the ICU after OHCA. The score uses variables that are readily available at ICU admission. It may help to plan the use of costly time-consuming treatments and to optimize the design of epidemiological studies.


   Appendix 1: Abbreviations and Acronyms
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Appendix 1: Abbreviations and...
Acknowledgement
 References
 


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   Acknowledgement
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Appendix 1: Abbreviations and...
 Acknowledgement
References
 
We are indebted to Antoinette Wolfe MD for preparing the manuscript.

Conflict of interest: none declared.


   References
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Appendix 1: Abbreviations and...
 Acknowledgement
 References
 

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Corrigendum to: Predicting survival with good neurological recovery at hospital admission after successful resuscitation of out-of-hospital cardiac arrest: the OHCA score
Christophe Adrie, Alain Cariou, Bruno Mourvillier, Ivan Laurent, Hala Dabbane, Fatima Hantala, Abdel Rhaoui, Marie Thuong, and Mehran Monchi
EHJ 2007 28: 774. [Extract] [FREE Full Text]  



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Predicting survival with good neurologic recovery at hospital admission after successful resuscitation of out-of-hospital cardiac arrest: the OHCA score
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