Elsevier

Resuscitation

Volume 146, 1 January 2020, Pages 66-73
Resuscitation

CaRdiac Arrest Survival Score (CRASS) — A tool to predict good neurological outcome after out-of-hospital cardiac arrest

https://doi.org/10.1016/j.resuscitation.2019.10.036Get rights and content

Abstract

Aim

The aim of this study was to develop a score to predict the outcome for patients brought to hospital following out-of-hospital cardiac arrest (OHCA).

Methods

All patients recorded in the German Resuscitation Registry (GRR) who suffered OHCA 2010–2017, who had ROSC or ongoing CPR at hospital admission were included. The study population was divided into development (2010–2016: 7985) and validation dataset (2017: 1806). Binary logistic regression analysis was used to derive the score. The probability of hospital discharge with good neurological outcome was defined as 1/(1 + e−X), where X is the weighted sum of independent variables.

Results

The following variables were found to have a significant positive (+) or negative (−) impact: age 61–70 years (−0·5), 71–80 (−0·9), 81–90 (−1·3) and > = 91 (−2·3); initial PEA (−0·9) and asystole (−1·4); presumable trauma (−1·1); mechanical CPR (−0·3); application of adrenalin > 0 − < 2 mg (−1·1), 2 − <4 mg (−1·6), 4 − < 6 mg (−2·1), 6 − < 8 mg (−2·5) and > = 8 mg (−2·8); pre emergency status without previous disease (+0·5) or minor disease (+0·2); location at nursing home (−0·6), working place/school (+0·7), doctor’s office (+0·7) and public place (+0·3); application of amiodarone (+0·4); hospital admission with ongoing CPR (−1·9) or normotension (+0·4); witnessed arrest (+0·6); time from collapse until start CPR 2 − < 10 min (−0·3) and > = 10 min (−0·5); duration of CPR <5 min (+0·6). The AUC in the development dataset was 0·88 (95% CI 0·87–0·89) and in the validation dataset 0·88 (95% CI 0·86–0·90).

Conclusion

The CaRdiac Arrest Survival Score (CRASS) represents a tool for calculating the probability of survival with good neurological function for patients brought to hospital following OHCA.

Introduction

Outcome of attempted resuscitation following out-of-hospital cardiac arrest (OHCA) is dependent on additional variables, including those which are related to the condition of the individual patient as well post-resuscitation treatment in hospital. For example, the interval from collapse to initiation of CPR and initial ECG rhythm have been evaluated as predictors of outcome,1 but both variables performed poorly as predictors for their own. However, it may be possible to create an outcome scoring system that predicts individual patient prognosis by taking into account multiple independent factors.

Previously reported scores to predict outcome after OHCA have limitations. The OHCA score reported 2006 by Adrie and colleagues was developed in a small cohort of only 130 patients with a median age of 55 years, and included only patients who sustained blood pressure and pulse for more than one hour after return of spontaneous circulation (ROSC).2 The Cardiac Arrest Hospital Prognosis (CAHP) score showed a good discrimination, but excluded a lot of cases particularly ongoing CPR and arrests caused by asphyxia, and therefore is not necessarily applicable to the OHCA population in general.3

The purpose of our study was to develop a simple and generally applicable tool for predicting resuscitation success, defined as survival with good neurological function at hospital discharge, by using different independent variables that are available at hospital admission. The purpose of the score will be to enable comparison between different hospitals worldwide, for example, within the European Registry of Cardiac Arrest4 Additionally, it is hoped that such a score might help with analysing the effects of different resuscitation strategies and post-resuscitation interventions in patients.

Section snippets

Methods

This was a retrospective analysis of prospectively collected data from the German Resuscitation Registry (GRR).5 The GRR includes approximately 160 EMS, who record data on OHCA resuscitation attempts, and serve approximately 30 million of the total German population of 82 million.

The German Resuscitation Registry for OHCA is divided into two different datasets:

1 The 'Pre-hospital' dataset is focussed on documentation of pre-hospital logistic issues, presumed aetiology, resuscitation treatment

Results

The characteristics of both the development and validation cohorts are described in Table 1.

Discussion

The study presents an easy-to-calculate cardiac arrest survival score. It is the first score developed for prediction of hospital discharge with good neurological function for all patients admitted to a hospital after OHCA, regardless of ROSC or ongoing CPR, and without excluding cases because of missing variables. The following variables were found to have a significant positive impact on survival to hospital discharge with good neurological outcome: pre emergency status without previous or

Conclusions

Treatment after out-of-hospital cardiac arrest is a common issue in intensive care medicine. Sufficient quality management tools for risk adjusted analysis in cardiac arrest centers are missing at the moment. The recently published scores (out-of-hospital cardiac arrest score (OHCA-score) and cardiac arrest hospital prognosis score (CAHP-score)) are limited due to selection and inclusion bias.

The CaRdiac Arrest Survival Score (CRASS) developed in this study allows quality management based on

Conflicts of interests

SS, JW, MF, AB, TJ, SB, BB, JTG are members of the steering committee of the German Resuscitation Registry. All members receive travel cost from the German Resuscitation Registry. JTG receive grants from the German Resuscitation Registry for the Institute for emergency medicine. All other authors have nothing to disclose.

Acknowledgements

The GRR is financed by German Society of Anesthesiology and Intensive Care Medicine.

The authors are indebted to all the active participants in the GRR who registered cardiac arrest patients on a voluntary basis. Further, the authors would like to thank all professionals involved in pre-hospital emergency medical care and intensive care.

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Both first authors contributed equally.

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