Development and validation of prediction models for death within 6 months after cardiac arrest.

Background: Even in patients with a successful return of spontaneous circulation (ROSC), outcomes after cardiac arrest (CA) remain poor, with some eventually succumbing after several months of treatment. There is a need for early assessment of outcomes in patients with ROSC after CA. Therefore, we developed three models for predicting death within 6 months after CA using early post-arrest factors, performed external validation, and compared their efficiency. Methods: In this retrospective cohort study, 199 patients aged 18-80 years who experienced either in-hospital cardiac arrest or out-of-hospital cardiac arrest and achieved ROSC were included as the training set. Patients were divided into an "alive" group (95 cases) and a "dead" group (104 cases) according to their survival status 6 months after CA. Demographic data, medical history, and laboratory results were collected. Univariate and multivariate logistic regression analyses were used to identify risk factors. A risk prediction model was constructed using random forest methods, support vector machine (SVM), and a nomogram based on factors with Results: Duration of cardiac arrest, lactate level after ROSC, secondary infections, length of hospital stay, and ventilator support were the top five risk factors for death within 6 months after CA ( Conclusions: The prediction model established by early post-arrest factors performed well, which can aid in evaluating prognosis within 6 months after cardiac arrest. The predictive model constructed using random forest methods exhibited better predictive efficacy.