Clinical prediction of the mortality for acute kidney injury in decompensated cirrhosis.

Acute kidney injury (AKI) is a severe complication in patients with decompensated cirrhosis, associated with increased mortality. This study aimed to identify key indicators associated with AKI in patients with decompensated cirrhosis and to develop a predictive model for outcome assessment. A total of 487 patients with cirrhosis were enrolled and divided into decompensated and compensated groups. We found that decompensated cirrhosis patients had significantly higher rates of AKI compared to compensated patients. Patients with AKI exhibited worse clinical outcomes (28-day mortality) and significant differences in multiple laboratory parameters. Three machine learning algorithms identified four common indicators, including activated partial thromboplastin time (APTT), alkaline phosphatase (ALP), total bilirubin (TBil), and maximum creatinine (Cr_max) were associated with AKI outcomes. Logistic regression modeling based on these variables yielded an AUC of 0.811 in the derivation cohort and 0.824 in the external validation cohort with 61 patients, indicating strong predictive accuracy. The nomogram demonstrated good calibration and clinical utility based on decision curve analysis. This study identifies four clinically relevant biomarkers significantly linked to adverse outcomes in patients with decompensated cirrhosis and AKI. A predictive model incorporating these markers demonstrates high accuracy and generalizability, offering a valuable tool for early risk stratification.