Beyond binary AKI classification: development and external validation of a distributional model predicting serum...

Acute kidney injury (AKI) is a frequent, severe complication in the intensive care units (ICU). Existing machine learning models are typically inflexible, classification-based (i.e., predicting AKI occurrence as yes/no), and of limited clinical utility. This study proposes and externally validates the first multi-step, multivariate distributional regression model that directly predicts future distributions of serum creatinine (sCr) and urine output across multiple time horizons, thereby enhancing AKI risk stratification and personalized clinical decision support.

The model was developed using a training cohort of 4,118 adult ICU stays from the MIMIC-IV dataset and externally validated on four independent, diverse cohorts: MIMIC-IV (N=3,838), UZGent (N=4,442), eICU (N=10,760), and AmsterdamUMC (N=6,129). The model used clinical data to generate multivariate predictive distributions hourly for urine output and sCr (up to 48 hours ahead). Predictors included demographics, vital signs, laboratory results, medications, and recent urine output, with time-varying variables summarized over the preceding 72 hours (recent value, slope, minimum, maximum, variability). Performance was evaluated by comparing our predictive distributions with state-of-the-art tree-based classifiers for 24-hour ahead prediction of KDIGO stages 1-3 AKI and persistent stage 3 AKI.

Across all external cohorts, the distributional regression model demonstrated high discrimination (mean AUC-PR 0.774 for all stages) and excellent calibration, consistently outperforming the benchmark classifiers. By jointly predicting sCr and urine output distributions, a single model successfully enables flexible risk stratification across all stages, capturing AKI onset and persistence, and allowing changes to stage definitions.

This multi-step, multivariate distributional regression model is a reliable, more flexible, transparent, and clinically interpretable approach for AKI prediction compared to traditional classification methods. It represents a necessary step toward bedside implementation of predictive models for personalized AKI management in the ICU.