An artificial intelligence‐driven predictive model for pediatric allogeneic hematopoietic stem cell transplantation using clinical variables

Background Hematopoietic stem cell transplantation (HSCT) is a procedure with high morbidity and mortality. Identifying patients for maximum benefit and risk assessment is crucial in the decision‐making process. This has led to the development of predictive risk models for HSCT in adults, which have limitations when applied to pediatric population. Our goal was to develop an automatic learning algorithm to predict survival in children with malignant disorders undergoing HSCT. Methods We studied allogenic HSCTs performed on children with malignant disorders at a third‐level hospital between 1991 and 2021. Survival was analyzed using the Kaplan–Meier method, log‐rank test for the univariate analysis, and Cox regression for the multivariate analysis. A prognostic index was constructed based on these findings. Lastly, we constructed a predictive model using a random forest algorithm to forecast 1‐year survival after HSCT. Results We analyzed 229 HSCTs in 201 patients with a median follow‐up of 1.64 years. Variables that impacted on the multivariate analysis were older age (hazard ratio [HR] 1.40, 95% confidence interval [CI] 1.12–1.76, p = .003), oldest period of HSCT (HR 0.46, 95% CI 0.29–0.73, p