Machine learning for the prediction of mortality in patients with sepsis-associated acute kidney injury: a systematic review and meta-analysis

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BackgroundPredicting mortality in sepsis-related acute kidney injury facilitates early data-driven treatment decisions. Machine learning is predicting mortality in S-AKI in a growing number of studies. Therefore, we conducted this systematic review and meta-analysis to investigate the predictive value of machine learning for mortality in patients with septic acute kidney injury.MethodsThe PubMed, Web of Science, Cochrane Library and Embase databases were searched up to 20 July 2024 This was supplemented by a manual search of study references and review articles. Data were analysed using STATA 14.0 software. The risk of bias in the prediction model was assessed using the Predictive Model Risk of Bias Assessment Tool.ResultsA total of 8 studies were included, with a total of 53 predictive models and 17 machine learning algorithms used. Meta-analysis using a random effects model showed that the overall C index in the training set was 0.81 (95% CI: 0.78–0.84), sensitivity was 0.39 (0.32–0.47), and specificity was 0.92 (95% CI: 0.89–0.95). The overall C-index in the validation set was 0.73 (95% CI: 0.71–0.74), sensitivity was 0.54 (95% CI: 0.48–0.60) and specificity was 0.90 (95% CI: 0.88–0.91). The results showed that the machine learning algorithms had a good performance in predicting sepsis-related acute kidney injury death prediction.ConclusionMachine learning has been shown to be an effective tool for predicting sepsis-associated acute kidney injury deaths, which has important implications for enhancing risk assessment and clinical decision-making to improve sepsis patient care. It is also eagerly anticipated that future research efforts will incorporate larger sample sizes and multi-centre studies to more intensively examine the external validation of these models in different patient populations, allowing for a more in-depth exploration of sepsis-associated acute kidney injury in terms of accurate diagnostic efficacy across a diverse range of model and predictor types.Trial registrationThis study was registered with PROSPERO (CRD42024569420).