ArticleDigital health
Interpretable early mortality prediction in oncology ICU patients: A dual-cohort validation of a LASSO-XGBoost-SHAP framework.
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Abstract
Background: Early risk stratification for critically ill cancer patients remains challenging, and conventional severity scores are frequently miscalibrated. We developed and externally validated an interpretable first-day (0-24 h) risk-reassessment model for ICU mortality during the index ICU stay. Methods: We performed a retrospective dual-cohort study using MIMIC-IV for derivation/internal validation (n=9,532; training n=6,673, internal test n=2,859) and eICU-CRD for external validation (n=7,821) among adult cancer patients with an index ICU stay >=24 h. Candidate predictors were restricted to the first 0-24 h after ICU admission. LASSO selected sparse features, nine algorithms were benchmarked, and the final model was chosen by integrated assessment of discrimination, calibration, and decision-curve net benefit. Performance was evaluated using ROC-AUC, PR-AUC, sensitivity, PPV, F2, Brier score, ECE, calibration plots, and decision-curve analysis over p_t=0.01-0.50. TreeSHAP provided global, cohort-level, feature-level, and directional interpretation. Results: ICU mortality rates were 9.45% in MIMIC and 7.39% in eICU. XGBoost was retained as the locked model. In the internal test set, XGBoost achieved ROC-AUC 0.864 (95% CI 0.844-0.885), PR-AUC 0.428, sensitivity 0.807, PPV 0.249, and F2 0.558. In eICU, performance remained stable (ROC-AUC 0.848, 95% CI 0.832-0.864; PR-AUC 0.367; sensitivity 0.794; PPV 0.192; F2 0.487). Calibration remained clinically acceptable, and decision-curve analysis showed positive net benefit across plausible thresholds. SHAP highlighted treatment-intensity indicators and acute physiologic stressors, including vasopressors, sedation, mechanical ventilation, HR_max, SpO2_min, BUN_max, potassium_max, and RR_max, without implying causal effects. Conclusions: This LASSO-XGBoost-SHAP framework supports first-day reassessment after completion of the 24-h predictor window, subsequent monitoring prioritization, resource allocation, and goal-concordant decision-making in oncology ICUs. It should not be interpreted as an admission-time triage model.
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