A machine-learning-derived online prediction model based on inflammatory and nutritional composite indicators for acute kidney injury in sepsis patients with multiple myeloma

Abstract

Background Acute kidney injury (AKI) frequently complicates sepsis in patients with multiple myeloma (MM), yet no validated predictive tool integrates the inflammatory and nutritional disturbances specific to this dual-disease population. We aimed to develop and externally validate an interpretable machine learning (ML) model—based on composite inflammatory-nutritional indices—to predict in-hospital AKI in septic patients with MM, and to deploy it as a web-based point-of-care tool. Methods In this retrospective cohort study, 362 septic MM patients (development cohort: 253 training, 109 internal test) and 71 temporal validation patients were enrolled from Shaanxi Provincial People’s Hospital (2020–2025). Moreover, an independent external validation cohort from a separate tertiary hospital (n = 118, Xi’an No. 3 Hospital). Candidate predictors included 8 composite indices (e.g., NLR, SII, LAR, AAPR) alongside routine clinical and laboratory variables. Boruta and recursive feature elimination (RFE) identified a parsimonious predictor set. Six ML algorithms (logistic regression, SVM, MLP, LightGBM, XGBoost, Random Forest) were compared using AUC, calibration, and decision curve analysis. The final model was interpreted with SHAP and implemented as a public Streamlit application. Results AKI occurred in 206/362 (56.9%) patients. Eight predictors were selected: NLR, LAR, creatinine, SII, PTA, cystatin C, CRP, and AAPR. Random Forest demonstrated the most robust and generalizable performance, achieving a temporal validation AUC of 0.8574 (accuracy 0.7465, sensitivity 0.8205, specificity 0.6562). Moreover, Random Forest also achieved an external validation AUC of 0.9650 (accuracy 0.8983, sensitivity 0.9000, specificity 0.8971), with favorable calibration and consistent net clinical benefit. SHAP analysis identified higher NLR, LAR, SII, cystatin C, creatinine, and CRP, together with lower PTA and AAPR, as the most influential predictors in the model. The model is freely available at (https://gftcs94ast7omb44cg2izq.streamlit.app/). Conclusion An interpretable Random Forest model employing eight routinely available indicators accurately predicts AKI in septic MM patients, bridging algorithmic complexity and bedside applicability. This tool may facilitate early risk stratification and personalized renal protection in this high-risk group.