Prognostic Factors and Risk Stratification in Malignant Tumor Patients With Sepsis: A Retrospective Cohort Study

BACKGROUND Sepsis is a life-threatening complication in cancer patients and a major contributor to ICU admission and mortality. Although tumor-related factors affect outcomes, the prognostic value of ICU-specific clinical parameters remains insufficiently explored. This study aimed to identify ICU-related predictors of mortality beyond tumor characteristics in cancer patients with sepsis. MATERIAL AND METHODS We conducted a single-center retrospective cohort study including 4301 cancer patients with sepsis. Independent predictors of 28-day mortality were identified using multivariable logistic regression. Model performance was evaluated using discrimination, calibration, decision curve analysis (DCA), and clinical impact curves. RESULTS Nine independent predictors of 28-day mortality were identified (all P<0.05). A nomogram incorporating these predictors demonstrated strong discriminative performance (original cohort AUC, 0.741; 95% CI, 0.722-0.759; validation cohort AUC, 0.758; 95% CI, 0.730-0.786), outperforming SAPS III (original AUC, 0.726; 95% CI, 0.707-0.745; validation AUC, 0.740; 95% CI, 0.710-0.770) and SOFA scores (original AUC, 0.696; 95% CI, 0.676-0.716; validation AUC, 0.723; 95% CI, 0.692-0.753). Calibration analysis demonstrated good agreement between predicted and observed mortality (original cohort: calibration intercept, 0.036; slope, 0.868; Brier score, 0.185; validation cohort: intercept, 0.014; slope, 0.940; Brier score, 0.174). DCA showed superior net clinical benefit across a wide range of threshold probabilities compared with "treat-all" and "treat-none" strategies. Clinical impact curves confirmed effective risk stratification, with predicted high-risk cases closely aligning with observed mortality events. CONCLUSIONS In this large cohort of cancer patients with combined sepsis, we identified independent predictors of 28-day mortality, offering a practical tool for individualized mortality risk stratification in this vulnerable population.