ArticleBMC gastroenterology2024
A dynamic model to predict early occurrence of acute kidney injury in ICU hospitalized cirrhotic patients: a MIMIC database analysis.
Article in BMC gastroenterology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A nomogram model for predicting acute kidney injury in critically ill patients with upper gastrointestinal bleeding: a study based on the MIMIC-IV database and external validation.BMC gastroenterology · 2026Article
- Clinical diagnosis and biomarkers of acute kidney injury in liver cirrhosis: a systematic review.Therapeutic advances in gastroenterology · 2026Review
- The Kidney in the Shadow of Cirrhosis: A Critical Review of Renal Failure.Biomedicines · 2025Review
- Review
- A novel risk-predicted nomogram for acute kidney injury progression in decompensated cirrhosis: a double-center study in Vietnam.International urology and nephrology · 2025Article
- miR-769-5p has diagnostic value in acute kidney injury in intensive care unit patients and mediates disease development by targeting SIRT6.BMC nephrology · 2025Article
- Association between the blood urea nitrogen to serum albumin ratio and the risk of mortality in patients with chronic kidney disease: a cohort study.BMC nephrology · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundThis study aimed to develop a tool for predicting the early occurrence of acute kidney injury (AKI) in ICU hospitalized cirrhotic patients.
methodsEligible patients with cirrhosis were identified from the Medical Information Mart for Intensive Care database. Demographic data, laboratory examinations, and interventions were obtained. After splitting the population into training and validation cohorts, the least absolute shrinkage and selection operator regression model was used to select factors and construct the dynamic online nomogram. Calibration and discrimination were used to assess nomogram performance, and clinical utility was evaluated by decision curve analysis (DCA).
resultsA total of 1254 patients were included in the analysis, and 745 developed AKI. The mean arterial pressure, white blood cell count, total bilirubin level, Glasgow Coma Score, creatinine, heart rate, platelet count and albumin level were identified as predictors of AKI. The developed model had a good ability to differentiate AKI from non-AKI, with AUCs of 0.797 and 0.750 in the training and validation cohorts, respectively. Moreover, the nomogram model showed good calibration. DCA showed that the nomogram had a superior overall net benefit within wide and practical ranges of threshold probabilities.
conclusionsThe dynamic online nomogram can be an easy-to-use tool for predicting the early occurrence of AKI in critically ill patients with cirrhosis.
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