European heart journal. Digital health · 2026
From haemodynamics to kidney risk: AI-based early prediction validated in general and burn ICU populations.
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Boutin Louis, Kadri Fedi, Chaftar Arij, Deniau Benjamin, Minani Sakura, Figueroa Stefanny M, Chadjichristos Christos E, Ghorbel Anis, Mebazaa Alexandre, Dépret François
Аннотация
AIMS: Acute kidney injury (AKI) is a frequent and severe complication in critically ill patients with cardiovascular instability. Current risk scores rely on delayed renal biomarkers such as serum creatinine (sCr) and blood urea nitrogen (BUN). We aimed to develop and validate machine learning (ML) models predicting AKI and major adverse kidney events (MAKE) exclusively from systemic physiological and haemodynamic data.
METHODS AND RESULTS: Two ML models were trained on the MIMIC-IV database: one including (sCr+/BUN+) and one excluding (sCr-/BUN-) renal parameters. External validation was performed in the eICU database and in a cohort of burn ICU patients from AP-HP. Model performance was assessed for early AKI and MAKE prediction up to 100 h before diagnosis. Systemic haemodynamic and physiological variables were the strongest predictors of AKI. In MIMIC-IV, the sCr-/BUN- model achieved auROC 0.78 at 72 h, approaching the sCr+/BUN+ model. In eICU, it outperformed the biomarker-based model at later time points (auROC 0.73). In the burn ICU cohort-representing a high-stress systemic environment-it maintained robust accuracy (auROC 0.75 at 24 h, 0.77 at 72 h). For MAKE prediction, the sCr-/BUN- model achieved auROC 0.87 (burn cohort), 0.67 (eICU), and 0.77 (MIMIC-IV). Median lead time for AKI prediction exceeded 70 h.
CONCLUSION: AI models based solely on non-renal parameters can accurately predict AKI and MAKE, even under extreme systemic stress such as severe burns. Haemodynamic signatures carry sufficient information to anticipate kidney dysfunction well in advance, opening the way to real-time, proactive cardio-renal risk stratification in ICU patients with acute heart failure, cardiogenic shock, and after cardiac surgery.
METHODS AND RESULTS: Two ML models were trained on the MIMIC-IV database: one including (sCr+/BUN+) and one excluding (sCr-/BUN-) renal parameters. External validation was performed in the eICU database and in a cohort of burn ICU patients from AP-HP. Model performance was assessed for early AKI and MAKE prediction up to 100 h before diagnosis. Systemic haemodynamic and physiological variables were the strongest predictors of AKI. In MIMIC-IV, the sCr-/BUN- model achieved auROC 0.78 at 72 h, approaching the sCr+/BUN+ model. In eICU, it outperformed the biomarker-based model at later time points (auROC 0.73). In the burn ICU cohort-representing a high-stress systemic environment-it maintained robust accuracy (auROC 0.75 at 24 h, 0.77 at 72 h). For MAKE prediction, the sCr-/BUN- model achieved auROC 0.87 (burn cohort), 0.67 (eICU), and 0.77 (MIMIC-IV). Median lead time for AKI prediction exceeded 70 h.
CONCLUSION: AI models based solely on non-renal parameters can accurately predict AKI and MAKE, even under extreme systemic stress such as severe burns. Haemodynamic signatures carry sufficient information to anticipate kidney dysfunction well in advance, opening the way to real-time, proactive cardio-renal risk stratification in ICU patients with acute heart failure, cardiogenic shock, and after cardiac surgery.