ArticleBMC medical informatics and decision making2026
Development and external validation of a prediction model for major adverse kidney events within 30 days in sepsis associated acute kidney injury: a multi-center retrospective clinical study.
Article in BMC medical informatics and decision making, 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
backgroundMajor adverse kidney events within 30 days (MAKE30) are associated with poor outcomes in patients with sepsis-associated acute kidney injury (SA-AKI). This study aimed to develop and validate a nomogram-based prediction model for MAKE30 in SA-AKI patients.
methodsClinical and laboratory data were collected from SA-AKI patients admitted to eight tertiary Grade-A hospitals in Shanghai between January 2021 and October 2022, forming the development cohort. External validation was performed using data from SA-AKI patients treated at Ruijin Hospital between January 2017 and December 2019. A predictive nomogram was constructed using LASSO regression followed by multivariate logistic regression. Model performance was assessed using area under the curve (AUC), calibration plots, decision curve analysis (DCA), and clinical impact curves (CIC). The model was subsequently validated in the external validation cohort.
resultsA total of 531 SA-AKI patients were included, with 372 in the development cohort and 159 in the validation cohort. The incidence of MAKE30 was 55.6% in the development cohort and 62.9% in the external validation cohort. Seven independent predictors of MAKE30 were identified: AKI for 7 days, AKI stage, combination antimicrobial therapy, nutritional risk, maximum heart rate (HRmax), cumulative fluid balance D3, Glasgow Coma Scale (GCS) score. The nomogram achieved an AUC of 0.829 (95% CI 0.787-0.870) in the development cohort and 0.776 (95% CI 0.698-0.855) in the validation cohort. Calibration plots, DCA, and CIC demonstrated favorable clinical applicability of the model.
conclusionsA prediction model incorporating seven readily available risk indicators can effectively predict the risk of MAKE30 in SA-AKI patients, facilitating early risk stratification and potential intervention.
trial registrationNone (Retrospective cohort study).
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