Machine learning model of clinical laboratory data for 30-day mortality of patients with hodgkin's lymphoma in ICU: a retrospective study based on MIMIC-IV database.

Prognostic stratification of Hodgkin lymphoma (HL) patients in ICU remains challenging, with conventional scoring systems often overlooking pathophysiological biomarkers. This retrospective cohort study analyzed 1,908 HL patients from the MIMIC-IV database. Multivariate logistic regression and machine learning (ML, gradient-boosting (GBM) was optimized with LASSO regularization) were employed to identify 30-day mortality predictors, validated through SHAP interpretability, calibration curves, and decision curve analysis. Multi-organ dysfunction (AST, BUN, T-Bil), systemic inflammation (NLR, WBC) and APTT emerged as critical mortality determinants, and selected for model construction. GBM achieved superior discrimination (training AUC = 0.89; test AUC = 0.75), SHAP analysis, calibration curve and decision curve analysis (DCA) confirmed clinical utility, outperforming empirical intervention strategies. This study establishes a biomarker-driven ML framework for HL prognosis, integrating renal, hepatic, and inflammatory markers into actionable risk stratification. thereby providing a scientific basis for comprehensive HL management.