Abstract Su804: Use of Machine Learning Models to Predict Neurologically Intact Survival for Advanced Age Adults Following Out-of-hospital Cardiac Arrest

Background: We recently published a multivariable logistic regression model that successfully predicted neurologic outcomes in advanced age adults (≥ 65 years old) who achieve return of spontaneous circulation following out-of-hospital cardiac arrest (OHCA). Prior to externally validating this model, we sought to compare the predictive performance of our logistic regression model against common machine learning (ML) algorithms using the same dataset. Methods: We performed a retrospective observational analysis of the Cardiac Arrest Registry to Enhance Survival database from 2013-2021. All non-traumatic OHCA occurring in adults (≥65 years) who survived to hospital admission were included. The primary outcome measure was neurologically intact survival defined as a cerebral performance category (CPC) score of 1 or 2 at hospital discharge. Our original logistic regression model was compared to Boosted Trees and Decision Tree ML algorithms and assessed using accuracy, Area Under the Receiver Operating Characteristic Curve (AUC-ROC) and Area Under the Precision-Recall Curve (AUC-PR). The model variables included age, gender, race/ethnicity, location, witnessed status, who initiated cardiopulmonary resuscitation, whether an automated external defibrillator was applied prior to emergency medical services arrival, and first monitored rhythm. Results: A total of 83,561 OHCA were included. Median age was 75 years (IQR 69-82), 58.9% were male, 53% were White, 67% experienced an OHCA at home, 52% were witnessed by a bystander, 44% received bystander CPR, and 34% were found in shockable rhythm. Neurologically intact survival (CPC 1 or 2) occurred in 23% of patients. The logistic regression model had an accuracy of 79.6%, AUC-PR 0.542, and AUC-ROC of 0.774. The Boosted Trees model had an accuracy of 79.2%, AUC-PR 0.555, and AUC-ROC of 0.774. The Decision Tree Model had an accuracy of 79.3%, AUC-PR 0.490, and AUC-ROC of 0.717. Conclusion: Boosted Trees and Decision Tree ML models performed comparably to the logistic regression model in predicting favorable versus unfavorable neurological outcomes following successful resuscitation from OHCA in advanced-age adults. Given their similar performance, logistic regression may be preferred for its simplicity and interpretability over alternative ML models. Future efforts to improve and ultimately externally validate the model are warranted.