Abstract
OBJECTIVES: To identify independent risk factors for multiple organ failure (MOF) and construct a clinically applicable predictive nomogram. METHODS: We retrospectively analyzed 418 patients with acute kidney failure (AKF) and severe sepsis treated between January 2020 and September 2024. Demographic data, clinical features, and laboratory parameters were collected. Patients were randomly assigned to a training cohort (n=293) and a validation cohort (n=125). Independent risk factors for MOF were identified using logistic regression analysis, and a nomogram was subsequently developed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). RESULTS: Five independent predictors of MOF were identified: abdominal infection, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, neutrophil count (NEU), lactate (Lac), and heparin-binding protein (HBP). The nomogram showed good discrimination, with an AUC of 0.756 (95% CI: 0.701-0.811) in the training cohort and 0.816 (95% CI: 0.743-0.889) in the validation cohort. Calibration curves demonstrated good agreement between predicted and observed outcomes, and DCA indicated a favorable net clinical benefit. CONCLUSIONS: A nomogram incorporating abdominal infection, APACHE II score, NEU, Lac, and HBP effectively predicts the risk of MOF in AKF patients with severe sepsis. This model may aid in early risk stratification and clinical decision-making.