ArticleIntensive care medicine experimental2026
Prospective and external evaluation of an AI model for continuous and early prediction of moderate and severe AKI in critically ill patients.
Article in Intensive care medicine experimental, 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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18 authors.
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Abstract
backgroundAcute kidney injury (AKI) is a major complication in critically ill patients, burdening both patients and healthcare systems. We previously introduced an AI-based model for early and continuous prediction of ICU-acquired AKI (ICU-A-AKI-2/3). In this study, we enhanced the model to better handle missing data, a common challenge in clinical settings. The upgraded model was validated in both retrospective and prospective cohorts, demonstrating improved robustness and predictive performance.
methodsThe model was validated in retrospective cohorts from three countries (US, Netherlands, Italy; N = 70,107 ICU admissions) across 176 ICUs. It was then prospectively validated in three European hospitals (Italy, Spain; N = 329) from May to October 2023. Using an XGBoost classifier, the model analyzes clinical data from ICU patients to predict hourly risk probabilities for AKI stages 2 and 3, as defined by KDIGO.
resultsIn retrospective cohorts, the AI model achieved an auROC greater than 0.89 for early detection of ICU-A-AKI-2/3. Prospective validation showed auROCs between 0.82 (95% CI 0.73-0.92) and 0.96 (95% CI 0.92-0.99) across hospitals, with a mean lead time of approximately 14 h.
conclusionsThis enhanced AI model offers timely prediction of ICU-A-AKI-2/3 episodes, as demonstrated across diverse cohorts. Its high predictive performance represents a significant advancement in integrating AI into clinical workflows, enhancing AKI management and improving clinical outcomes in ICU settings.
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