ArticleFrontiers in nutrition2026
A machine-learning-derived online prediction model based on inflammatory and nutritional composite indicators for acute kidney injury in sepsis patients with multiple myeloma.
Article in Frontiers in nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 ( 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.
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