A machine learning-based prediction model for poor prognosis in sepsis using lymphocyte count: a national, multicenter prospective cohort.

Sepsis-induced immunosuppression leads to poor prognosis. Circulating lymphocyte count (LC), as an easily accessible clinical marker, closely reflects the immune status of sepsis. The study aims to perform immune phenotyping of sepsis patients using dynamic LC for early identification of high-risk individuals. A latent class trajectory model (LCTM) was used to analyze the dynamic trajectories of lymphocyte count (LC) based on repeated measurements obtained within at least two measurements of lymphocyte count (LC) within the first 24 h after sepsis diagnosis, followed by two more between day 2 and day 7. Survival differences among subphenotypes were assessed using Kaplan-Meier curves and Cox regression. Feature selection was conducted via the Boruta algorithm, and a high-precision machine learning model was developed to predict the target trajectory. Model interpretability was ensured through SHapley Additive exPlanations (SHAP). The predictive performance of the model for ICU mortality was assessed using the receiver operating characteristic (ROC) curve. The derivation cohort included 2085 sepsis patients from the China Multicenter Sepsis database, and the external validation cohort of 1299 sepsis patients. We identified four trajectory patterns of LC dynamics, among which the persistent lymphopenia (PL) subgroup exhibited the highest disease severity and poorest prognosis. The trajectory model demonstrated consistent patterns in external validation. Six machine learning models were utilized to determine the best model to identify the PL subphenotype, and an online prediction tool was developed for clinical application. Incorporating the PL trajectory subphenotype significantly improved the predictive performance for ICU mortality. Dynamic LC trajectories effectively capture immunological heterogeneity in sepsis, encompassing immunocompromised and immunocompetent hosts. These findings underscore the importance of early identification of patients with persistent lymphopenia to better target populations for future sepsis immunotherapy.