Construction and evaluation of prediction model for renal function recovery in acute kidney injury patients...

Background: The primary objective of this study is to employ machine learning (ML) algorithms to develop predictive models for renal function recovery in critically ill patients undergoing continuous renal replacement therapy (CRRT) due to acute kidney injury (AKI).

Methods: Data were retrospectively collected from patients with AKI who underwent CRRT during their first Intensive Care Unit (ICU) admission from the Medical Information Mart for Intensive Care-IV database and at Huzhou Central Hospital. The evaluation of model performance was conducted utilizing metrics including the area under the receiver operating characteristic curve (AUROC), calibration curves and decision curve analysis (DCA).

Results: A total of 1078 AKI patients undergoing CRRT were included, with a renal function recovery rate of 18.18% (n = 196). Six features that significantly affect renal function recover rate were identified through least absolute shrinkage and selection operator (LASSO) regression with cross-validation. The Lasso-LR model was chosen as the definitive clinical risk prediction model for this study and served as the basis for constructing the nomogram. Its AUROCs in the training and external validation cohorts were 0.774 (95% CI: 0.735 ∼ 0.814) and 0.748 (95% CI: 0.685 ∼ 0.812), respectively. The calibration curve and ideal curve fit were generally found to be suboptimal. Simultaneously, the DCA curve suggested that the nomogram has clinical value.

Conclusions: Among the ML models developed using data from the two datasets to predict renal function recovery in critically ill AKI patients undergoing CRRT, the Lasso-LR model demonstrated the best performance. It can serve as a valuable tool for more efficient clinical disease management and prognostic evaluation.