ArticleBMC medical informatics and decision making2024
Machine learning for the prediction of 1-year mortality in patients with sepsis-associated acute kidney injury.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.Frontiers in medicine · 2025Pooled it
- Early identification of acute kidney injury progression in critically ill patients with sepsis: interpretable machine learning approach.Clinical kidney journal · 2026Article
- A multicenter machine learning model for predicting ICU mortality in mechanically ventilated patients: development and external validation.BMC medical informatics and decision making · 2026Article
- Review of research progress in sepsis-associated acute kidney injury.Frontiers in molecular biosciences · 2025Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
introductionSepsis-associated acute kidney injury (SA-AKI) is strongly associated with poor prognosis. We aimed to build a machine learning (ML)-based clinical model to predict 1-year mortality in patients with SA-AKI.
methodsSix ML algorithms were included to perform model fitting. Feature selection was based on the feature importance evaluated by the SHapley Additive exPlanations (SHAP) values. Area under the receiver operating characteristic curve (AUROC) was used to evaluate the discriminatory ability of the prediction model. Calibration curve and Brier score were employed to assess the calibrated ability. Our ML-based prediction models were validated both internally and externally.
resultsA total of 12,750 patients with SA-AKI and 55 features were included to build the prediction models. We identified the top 10 predictors including age, ICU stay and GCS score based on the feature importance. Among the six ML algorithms, the CatBoost showed the best prediction performance with an AUROC of 0.813 and Brier score of 0.119. In the external validation set, the predictive value remained favorable (AUROC = 0.784).
conclusionIn this study, we developed and validated a ML-based prediction model based on 10 commonly used clinical features which could accurately and early identify the individuals at high-risk of long-term mortality in patients with SA-AKI.
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