External Validation of Updated Prediction Models for Neurological Outcomes at 90 Days in Patients With Out-of-Hospital Cardiac Arrest.

Abstract

Few prediction models for individuals with early-stage out-of-hospital cardiac arrest (OHCA) have undergone external validation. This study aimed to externally validate updated prediction models for OHCA outcomes using a large nationwide dataset. We performed a secondary analysis of the JAAM-OHCA (Comprehensive Registry of In-Hospital Intensive Care for Out-of-Hospital Cardiac Arrest Survival and the Japanese Association for Acute Medicine Out-of-Hospital Cardiac Arrest) registry. Previously developed prediction models for patients with cardiac arrest who achieved the return of spontaneous circulation were updated. External validation was conducted using data from 56 institutions from the JAAM-OHCA registry. The primary outcome was a dichotomized 90-day cerebral performance category score. Two models were updated using the derivation set (n=3337). Model 1 included patient demographics, prehospital information, and the initial rhythm upon hospital admission; Model 2 included information obtained in the hospital immediately after the return of spontaneous circulation. In the validation set (n=4250), Models 1 and 2 exhibited a C-statistic of 0.945 (95% CI, 0.935-0.955) and 0.958 (95% CI, 0.951-0.960), respectively. Both models were well-calibrated to the observed outcomes. The decision curve analysis showed that Model 2 demonstrated higher net benefits at all risk thresholds than Model 1. A web-based calculator was developed to estimate the probability of poor outcomes (https://pcas-prediction.shinyapps.io/90d_lasso/). The updated models offer valuable information to medical professionals in the prediction of long-term neurological outcomes for patients with OHCA, potentially playing a vital role in clinical decision-making processes.