Stroke, a leading cause of death, requires precise predictions of life expectancy. Traditional scores focused on short-term outcomes and required labor-intensive data collection. Recent machine learning (ML)-based prediction models showed superior performance. However, they were predominantly geared towards classification tasks, omitting crucial temporal aspects. Thus, an unmet need arises for more convenient, time-aware predictive methodologies for long-term mortality in managing acute stroke patients. The developmental cohort consisted of 3,411 patients from two tertiary hospitals, while the external test cohort included 502 patients from a secondary cardiovascular center. We developed various ML- and deep learning (DL)-based models to predict post-ischemic stroke mortality, utilizing clinical data. Among these, we selected the best-performing model and compared it with traditional risk scores. Furthermore, we assessed key features based on their importance, determining their optimal number for practical application. The total number of deceased patients in the developmental cohort was 136, and 118 in the external test cohort. The Gradient Boosting Cox Proportional Hazards model demonstrated the best performance with C-index of 0.785 in internal validation set, and C-index of 0.845 in external test set. It surpassed the conventional risk score, PREMISE score with C-index of 0.783( p = 0.017) in the external dataset. The key features included age, National Institutes of Health Stroke Scale score, hemoglobin, and so on. The study developed and validated a machine learning-based model tailored for post-stroke survival prediction, outperforming existing scores. It could enhance the individual stroke patient’s survival predictions and optimize medical resource allocation.
Kim et al. (Sun,) studied this question.