Intensive care medicine experimental · 2026
Prospective and external evaluation of an AI model for continuous and early prediction of moderate and severe AKI in critically ill patients.
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Alfieri Francesca, Zappalà Simone, Bacci Alessandro, Cauda Valentina, Basso Marco, Musso Gaia, Cochelli Luca, Votta Carmine Domenico, Mariconti Laura, Maderna Laura, Russo Gianluca, Gomez Josep, Esteban-Reboll Federico, Gilavert Cuevas Mari Carmen, Bodì Maria, Finazzi Stefano, Kashani Kianoush, Ancona Andrea
Аннотация
BACKGROUND: Acute 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.
METHODS: The 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.
RESULTS: In 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.
CONCLUSIONS: This 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.
METHODS: The 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.
RESULTS: In 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.
CONCLUSIONS: This 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.