Abstract
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.
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All data generated or analyzed during this study are included in this article. The datasets used and/or analyzed in the study are available from the corresponding author on reasonable request.
References
Angeli, P., Garcia-Tsao, G., Nadim, M. K. & Parikh, C. R. News in pathophysiology, definition and classification of hepatorenal syndrome: A step beyond the International Club of Ascites (ICA) consensus document. J. Hepatol. 71 (4), 811–822 (2019).
Desai, A. P. et al. Changing epidemiology and outcomes of acute kidney injury in hospitalized patients with cirrhosis - a US population-based study. J. Hepatol. 73 (5), 1092–1099 (2020).
Adebayo, D. & Wong, F. Review article: Recent advances in ascites and acute kidney injury management in cirrhosis. Aliment. Pharmacol. Ther. 59 (10), 1196–1211 (2024).
Chaney, A. A review for the practicing clinician: Hepatorenal syndrome, a form of acute kidney injury, in patients with cirrhosis. Clin. Exp. Gastroenterol. 14, 385–396 (2021).
Akbas, M. M., Uzunlulu, M., Torun, C. & Bahadir, O. Comparison of hepatorenal syndrome incidence and outcomes using previous and current diagnostic criteria in cirrhotic patients. Hepatol. Forum. 6 (1), 5–10 (2025).
Rudler, M. et al. Multiple Concomitant Precipitating Factors of Hepatic Encephalopathy Are Associated With a Poor Prognosis in Patients With Cirrhosis Admitted to Intensive Care Unit. United Eur. Gastroenterol. J. 13 (5), 738–749 (2025).
Allegretti, A. S. et al. Urinary NGAL as a Diagnostic and Prognostic Marker for Acute Kidney Injury in Cirrhosis: A Prospective Study. Clin. Transl Gastroenterol. 12 (5), e00359 (2021).
Hong, C. et al. Acute kidney injury defined by cystatin C may be superior for predicting the outcomes of liver cirrhosis with acute gastrointestinal bleeding. Ren. Fail. 44 (1), 398–406 (2022).
Lei, L. et al. Value of urinary KIM-1 and NGAL combined with serum Cys C for predicting acute kidney injury secondary to decompensated cirrhosis. Sci. Rep. 8 (1), 7962 (2018).
Liu, S. W. et al. Recent advances in machine learning for precision diagnosis and treatment of esophageal disorders. World J. Gastroenterol. 31 (23), 105076 (2025).
Hadweh, P. et al. Machine learning and artificial intelligence in intensive care medicine: Critical recalibrations from rule-based systems to frontier models. J. Clin. Med. https://doi.org/10.3390/jcm14124026 (2025).
Sun, M. et al. Predicting in-hospital mortality in patients with alcoholic cirrhosis complicated by severe acute kidney injury: development and validation of an explainable machine learning model. Front. Med. (Lausanne). 12, 1570928 (2025).
Tian, J., Cui, R., Song, H., Zhao, Y. & Zhou, T. Prediction of acute kidney injury in patients with liver cirrhosis using machine learning models: evidence from the MIMIC-III and MIMIC-IV. Int. Urol. Nephrol. 56 (1), 237–247 (2024).
European Association for the Study of the L. EASL Clinical Practice Guidelines for the management of patients with decompensated cirrhosis. J. Hepatol. 69 (2), 406–460 (2018).
Angeli, P. et al. Diagnosis and management of acute kidney injury in patients with cirrhosis: revised consensus recommendations of the International Club of Ascites. J. Hepatol. 62 (4), 968–974 (2015).
Singer, M. et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 315(8), 801–810 (2016).
Biggins, S. W. et al. Diagnosis, Evaluation, and Management of Ascites, Spontaneous Bacterial Peritonitis and Hepatorenal Syndrome: 2021 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatology 74 (2), 1014–1048 (2021).
Dri, E. et al. Inflammatory mediators of endothelial dysfunction. Life (Basel) https://doi.org/10.3390/life13061420 (2023).
Villa, G. et al. Hemodynamic Instability during Acute Kidney Injury and Acute Renal Replacement Therapy: Pathophysiology and Clinical Implications. Blood Purif. 50 (6), 729–739 (2021).
Suzuki, N. et al. Altered expression of alkaline phosphatase (ALP) in the liver of primary biliary cirrhosis (PBC) patients. Hepatol. Res. 35 (1), 37–44 (2006).
Poupon, R. Liver alkaline phosphatase: A missing link between choleresis and biliary inflammation. Hepatology 61(6), 2080–2090 (2015).
Mousa, N. et al. Neutrophil Percentage-to-Albumin Ratio as a Predictor of Acute Kidney Injury in Cirrhotic Patients: A Novel Approach Utilizing Artificial Intelligence. Am J. Nephrol 2025:1–13 .
Nguyen, N. N. et al. A novel risk-predicted nomogram for acute kidney injury progression in decompensated cirrhosis: a double-center study in Vietnam. Int. Urol. Nephrol. 57 (7), 2279–2290 (2025).
Zheng, L. et al. Development and validation of the PHM-CPA model to predict in-hospital mortality for cirrhotic patients with acute kidney injury. Dig. Liver Dis. 57 (2), 485–493 (2025).
Chen, B. et al. Elevated D-Dimer levels correlate with the development of hepatorenal syndrome and a poor outcome in patients with cirrhosis. Scand. J. Gastroenterol. 57 (12), 1486–1493 (2022).
Duah, A. et al. Acute kidney injury in patients with liver cirrhosis: Prevalence, predictors, and in-hospital mortality at a district hospital in Ghana. BioMed. Res. Int. 2022, 4589767 (2022).
Li, M. et al. Impact of albumin infusion on prognosis in ICU patients with cirrhosis and AKI: insights from the MIMIC-IV database. Front. Pharmacol. 15, 1467752 (2024).
Tang, X. W. et al. Development and validation of a nomogram for predicting in-hospital mortality of intensive care unit patients with liver cirrhosis. World J. Hepatol. 16 (4), 625–639 (2024).
Ramspek, C. L., Jager, K. J., Dekker, F. W., Zoccali, C. & van Diepen, M. External validation of prognostic models: what, why, how, when and where? Clin. Kidney J. 14 (1), 49–58 (2021).
Hammerton, G. & Munafo, M. R. Causal inference with observational data: The need for triangulation of evidence. Psychol. Med. 51(4), 563–578 (2021).
Saha, R. et al. Evaluation of Acute Kidney Injury (AKI) Biomarkers FABP1, NGAL, Cystatin C and IL-18 in an Indian Cohort of Hospitalized Acute-on-chronic Liver Failure (ACLF) Patients. J. Clin. Exp. Hepatol. 15 (3), 102491 (2025).
Altran, W. S., de Sousa, L. F., Dos Santos Cortinhas, R. & Ponce, D. The role of urinary biomarkers in the diagnosis of acute kidney injury in patients with liver cirrhosis. Sci. Rep. 15 (1), 11575 (2025).
Funding
This study was partially supported by research funding from the Nanning Jiangnan District Scientific Research and Technological Development Project (No. 20240826-11); Internal Project of Nanning Second People’s Hospital (No. 202407). The National Natural Science Foundation of China Project (No. 81860116, 82260109), the Natural Science Foundation of the Guangxi (No. 2024GXNSFAA010132; 2024AB17099), the Self-funded Project of Guangxi Administration of Traditional Chinese Medicine (No. XZYA20230264).
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Study concept and design: LW, and LZY; Collection and assembly of data: PXY, YHL, DT and YB; Performed the experiment: WJH, QFY and PXY; Data analysis and interpretation: PXY, DT, WJH and QFY; Manuscript writing and review: All authors. All authors have read and approved the manuscript in its current state.
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This study was approval by the Ethics Committee of First Affiliated Hospital of Guangxi Medical University. Written informed consent was obtained from participants to participate by the Ethics Committee. All the procedures were carried out in accordance with institutional guidelines.
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Pan, Xy., Yang, Hl., Du, T. et al. Clinical prediction of the mortality for acute kidney injury in decompensated cirrhosis. Sci Rep (2026). https://doi.org/10.1038/s41598-026-43918-6
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DOI: https://doi.org/10.1038/s41598-026-43918-6
