ArticleFrontiers in medicine2023
Development and validation of a dynamic online nomogram for predicting acute kidney injury in cirrhotic patients upon ICU admission.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 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
- Article
- A Nomogram to Predict Day-7 Total Bilirubin in Hepatitis B Virus-Related Cirrhosis: An Early Treatment-Response Assessment Tool.Infection and drug resistance · 2026Article
- Clinical diagnosis and biomarkers of acute kidney injury in liver cirrhosis: a systematic review.Therapeutic advances in gastroenterology · 2026Review
- Predicting in-hospital mortality in patients with alcoholic cirrhosis complicated by severe acute kidney injury: development and validation of an explainable machine learning model.Frontiers in medicine · 2025Article
- Machine learning modeling for the risk of acute kidney injury in inpatients receiving amikacin and etimicin.Frontiers in pharmacology · 2025Article
- Article
- A dynamic model to predict early occurrence of acute kidney injury in ICU hospitalized cirrhotic patients: a MIMIC database analysis.BMC gastroenterology · 2024Article
- Development and validation of a nomogram for predicting in-hospital death in cirrhotic patients with acute kidney injury.BMC nephrology · 2024Article
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Authors and funding
6 authors.
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Abstract
Background: Acute kidney injury (AKI) is one of the most common and deadly complications among cirrhotic patients at intensive care unit (ICU) admission. We aimed to develop and validate a simple and clinically useful dynamic nomogram for predicting AKI in cirrhotic patients upon ICU admission. Methods: We analyzed the admission data of 4,375 patients with liver cirrhosis in ICU from 2008 to 2019 in the intensive care unit IV (MIMIC-IV) database. The eligible cirrhotic patients were non-randomly divided into derivation ( Results: The incidence of AKI in 4,375 patients was 71.3%. Ascites, chronic kidney disease, shock, sepsis, diuretic drugs, hepatic encephalopathy, bacterial infections, vasoactive drugs, admission age, total bilirubin, and blood urea nitrogen were identified using the multivariate logistic regression analysis as significant predictors of AKI upon ICU admission. In the derivation cohort, the model showed good discrimination (C-index, 0.786; 95% CI, 0.765-0.806) and good calibration. The model in the validation cohort yielded good discrimination (C-index, 0.774; 95% CI, 0.753-0.795) and good calibration. Decision curve analysis demonstrated that the dynamic online nomogram was clinically useful. Conclusion: Our study presents a dynamic online nomogram that incorporates clinical predictors and can be conveniently used to facilitate the individualized prediction of AKI in cirrhotic patients upon ICU admission.
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