Raw data.

Objective
This study aimed to develop an innovative early prediction model for acute kidney injury (AKI) following cardiac surgery in intensive care unit (ICU) settings, leveraging preoperative and postoperative clinical variables, and to identify key risk factors associated with AKI.
Methods
Retrospective data from 1,304 cardiac surgery patients (1,028 AKI cases and 276 non-AKI controls) were extracted from the MIMIC-IV database. We analyzed three datasets: preoperative 48-hour averages, preoperative 48-hour maxima, and postoperative 24-hour maxima of critical physiological parameters. Using logistic regression, LASSO regression, and random forest (RF) algorithms, we constructed nine prediction models, evaluating their performance via AUROC, sensitivity, specificity, Youden’s index, decision curve analysis (DCA), and calibration curves.
Results
Our findings demonstrate that all models achieved AUROC values >0.7, with three models exceeding 0.75. Notably, the logistic regression model incorporating preoperative 48-hour maximum values and postoperative 24-hour maximum values exhibited the highest predictive accuracy (AUROC = 0.755, 95%CI: 0.7185–0.7912), outperforming other configurations. This model’s superiority lies in its integration of dynamic preoperative and postoperative variables, capturing both baseline risks and acute postoperative changes. By systematically comparing multiple machine learning approaches, our study highlights the utility of combining temporal physiological metrics to enhance AKI risk stratification. These results offer a robust, clinically applicable tool for early AKI prediction, enabling proactive interventions to improve outcomes in cardiac surgery patients.