Abstract 111: External Validations of Clinical Predictive Models for Patients with Sudden Cardiac Arrest

Background: Sudden cardiac arrest is associated with high morbidity and mortality. A number of clinical predictive models (CPMs) have been introduced to help with patient-specific prognostication of survival and neurologic outcomes. Here, we evaluate the performance of these CPMs with attention to key variables and models with rigorous validation. Methods: We performed a systematic review and citation search of cardiac arrest CPMs in the Tufts Predictive Analytics and Comparative Effectiveness (PACE) CPM Registry and identified external validations of these models through September 2018. We extracted information on CPM performance from both original reports and external validations. For external validations, we calculated the percent change in discrimination. Results: We identified 65 unique cardiac arrest CPMs (median n=611, IQR=781) published between 1981 and 2018. Thirty-eight of the 65 models (58%) reported a c-statistic (ROC AUC) (median=0.82, IQR=0.09). The median number of predictive variables was 4 (IQR=3), and the three most common variables were 1) initial cardiac rhythm (n=41 of 65; 63%), 2) age (n=33 of 65; 51%), and 3) witnessed arrest (n=24 of 65; 37%). We identified external validations for 26 of 65 (40%) CPMs. All validations (n=44) reported discrimination, but only 21 of 44 (48%) validations reported information regarding calibration. Of the CPMs that reported discrimination and were externally validated at least once (n=15), we noted a median percent change in discrimination of -1.9% (IQR = 11.3%). The three most rigorously validated cardiac arrest CPMs were 1) the OHCA score (n=5, median AUC=0.79), 2) the CAHP score (n=3, median AUC=0.85), and 3) the GO-FAR score (n=3, median AUC=0.82). Conclusions: While few cardiac arrest CPMs have been externally validated, those that have demonstrate stable discriminatory power.