Validation of the CaRdiac Arrest Survival Score (CRASS) for predicting good neurological outcome after out-of-hospital cardiac arrest in an Asian emergency medical service system

Abstract

Background

Survival with favorable neurological outcomes is an important indicator of successful resuscitation in out-of-hospital cardiac arrest (OHCA). We sought to validate the CaRdiac Arrest Survival Score (CRASS), derived using data from the German Resuscitation Registry, in predicting the likelihood of good neurological outcomes after OHCA in Singapore.

Methods

We conducted a retrospective population-based validation study among EMS-attended OHCA patients (≥18 years) in Singapore, using data from the prospective Pan-Asian Resuscitation Outcomes Study registry. Good neurological outcome was defined as a cerebral performance category of 1 or 2. To evaluate the CRASS score in light of the difference in patient characteristics, we used the default constant coefficient (0.8) and the adjusted coefficient (0.2) to calculate the probability of good neurological outcomes.

Results

Out of 11,404 analyzed patients recruited between April 2010 and December 2018, 260 had good and 11,144 had poor neurological function. The CRASS score demonstrated good discrimination, with an area under the curve of 0.963 (95% confidence interval: 0.952–0.974). Using the default constant coefficient of 0.8, the CRASS score consistently overestimated the predicted probability of a good outcome. Following adjustment of the coefficient to 0.2, the CRASS score showed improved calibration.

Conclusion

CRASS demonstrated good discrimination and moderate calibration in predicting favorable neurological outcomes in the validation Singapore cohort. Our study established a good foundation for future large-scale, cross-country validations of the CRASS score in diverse sociocultural, geographical, and clinical settings.

Introduction

Out-of-hospital cardiac arrest (OHCA) is a time-critical medical emergency, with an average incidence of 67 to 170 per 100,000 persons per year in Europe1., 2. and a 2010 global incidence of 96 emergency medical services (EMS)-attended OHCAs per 100,000 population.³ A more recent study by the International Liaison Committee on Resuscitation (ILCOR) found that this incidence ranged from 30.0 to 97.1 individuals per 100,000 population across its surveyed registries.⁴ Due to its poor prognosis, OHCA is one of the leading causes of death in adults worldwide.⁵ Timely intervention and effective prehospital emergency care are vital for saving lives.6., 7. Many factors associated with successful management of OHCA have been described in conjunction with the chain of survival.⁵ The most important indicators of successful resuscitation are overall survival and survival with favorable neurological outcomes, therefore early and accurate prediction of such outcomes are key to efficient OHCA management.8., 9., 10..

Numerous prognostic models have been developed to predict survival,8., 9., 11. neurologic recovery,¹² and the return of spontaneous circulation (ROSC).13., 14. These models served as clinical scoring tools to provide valuable support in decision-making regarding the proper allocation of resources, optimal treatment and communication with families.⁹ Clinical prognostic models are commonly formulated to quantify the likelihood that an event will occur, for which both traditional logistic regression13., 15., 16. and sophisticated machine learning techniques (e.g., random forest and deep learning)17., 18. have been employed. There are a variety of OHCA scoring models, some of which serve as operational tools that can be used by EMS personnel at the scene,16., 19. while others are designed for comparison between EMS services or hospitals as quality assessment tools.13., 15. However, none of these scoring models have been widely adopted in clinical practice, even though some have been externally validated.⁸ The gap between validation and adoption is then predominantly driven by how clinical decisions in OHCA need to be made with an extremely high degree of certainty.⁸ Bringing scoring models that were developed and validated based on a specific cohort into clinical practice then raises questions about their validity in other clinical cohorts of diverse cultures, patient populations, geographical locations and EMS systems. OHCA survival rates, for instance, differ dramatically between regions,2., 20. therefore a score developed using data from one region may not be able to accurately reflect the survival rate in another region. As a remedy for the wide variations in patient outcomes and characteristics, Liu et al.²¹ proposed adjusting clinical scores with local historical information before actual implementations. It is therefore essential to ensure that clinical scoring models are validated, calibrated, and adjusted appropriately.

Among the scores that predict neurological outcomes following OHCA, the CaRdiac Arrest Survival Score (CRASS) is one of the newest additions.¹⁵ CRASS is a tool developed with data from the German Resuscitation Registry²² (EMS in Germany involves both paramedics and an EMS physician being dispatched to the scene) that uses patient demographics and OHCA-related details to estimate the probability of good neurological survival. CRASS was originally developed not as a triage scoring system but rather as a quality assessment tool for comparing treatment regimens in EMS and hospital systems. This is unlike other related scoring systems like the UB-ROSC score, which was created with the intention of being directly utilized by EMS personnel.¹⁶ However, it should be noted that in its development and validation, the score was still evaluated based on individual, patient-level prediction.¹⁵ As CRASS has not been externally validated in other countries, its performance in different EMS systems and cultural settings is unknown. This study aims to evaluate CRASS in predicting good neurological outcomes using data collected from OHCA patients over eight years in Singapore.