Predicting survival post-cardiac arrest: An observational cohort study

• Published in: Resuscitation plus 2023-09, Vol.15, p.100447-100447, Article 100447
• Main Authors: Drennan, Ian R, Thorpe, Kevin E, Scales, Damon, Cheskes, Sheldon, Mamdani, Muhammad, Morrison, Laurie J
• Format: Article
• Language: English
• Subjects: Clinical Paper; Out-of-Hospital Cardiac Arrest; Post-Cardiac Arrest; Prediction; Resuscitation
• ISSN: 2666-5204; 2666-5204
• DOI: 10.1016/j.resplu.2023.100447

Over 400,000 out-of-hospital cardiac arrest (OHCA) occur each year in Canada and the United States with less than 10% survival to hospital discharge. Cardiac arrest is a heterogenous condition and patient outcomes are impacted by a multitude of factors. Prognostication is recommended at 72 hours after return of spontaneous circulation (ROSC), however there may be other factors that could predict patient outcome earlier in the post-arrest period. The objective of our study was to develop and internally validate a novel clinical prediction rule to risk stratify patients early in the post-cardiac arrest period. We performed a retrospective cohort study of adult (≥18 years) post-cardiac arrest patients between 2010 and 2015 from the Epistry Cardiac Arrest database in Toronto. Our primary analysis used ordinal logistic regression to examine neurologic outcome at discharge using the modified Rankin Scale (mRS). Our secondary analysis used logistic regression for neurologic outcome and survival to hospital discharge. Models were internally validated using bootstrap validation. A total of 3432 patients met our inclusion criteria. Our clinical prediction model was able to predict neurologic outcome on an ordinal scale using our predefined variables with an AUC of 0.89 after internal validation. The predictive performance was maintained when examining neurologic outcome as a binary variable and survival to hospital discharge. We were able to develop a model to accurately risk stratify adult cardiac arrest patients early in the post-cardiac arrest period. Future steps are needed to externally validate this model in other healthcare settings.