ArticleMolecular biology reports2012
Risk stratification of spontaneous bacterial peritonitis in cirrhosis with ascites based on classification and regression tree analysis.
Article in Molecular biology reports, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- Diagnostic value of integrated albumin-to-leukocyte ratio risk modeling stratification and machine learning approaches for prediction of spontaneous bacterial peritonitis in cirrhotic patients.BMC infectious diseases · 2026Article
- Risk stratification of spontaneous bacterial peritonitis recurrence: integrating acute kidney injury, biomarkers, composite scores, and machine learning models.Gut pathogens · 2025Article
- Osmolality as a predictor of mortality in a community setting of heart failure population with a normal range of serum sodium: a classification and regression tree analysis.Journal of thoracic disease · 2025Article
- Validation of a new simple scoring system to predict spontaneous bacterial peritonitis in patients with cirrhosis and ascites.BMC gastroenterology · 2023Article
- A Novel Machine Learning-Based Point-Score Model as a Non-Invasive Decision-Making Tool for Identifying Infected Ascites in Patients with Hydropic Decompensated Liver Cirrhosis: A Retrospective Multicentre Study.Antibiotics (Basel, Switzerland) · 2022Article
- Article
- Explainable machine learning model for predicting spontaneous bacterial peritonitis in cirrhotic patients with ascites.Scientific reports · 2021Article
- Predictors of spontaneous bacterial peritonitis in Romanian adults with liver cirrhosis: Focus on the neutrophil-to-lymphocyte ratio.Experimental and therapeutic medicine · 2021Article
- Predictors of Spontaneous Bacterial Peritonitis in Patients with Cirrhotic Ascites.Journal of clinical and translational hepatology · 2018Article
- Review
- Using Classification and Regression Trees (CART) to Identify Prescribing Thresholds for Cardiovascular Disease.PharmacoEconomics · 2016Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Risk stratification for spontaneous bacterial peritonitis (SBP) in patients with cirrhosis and ascites helps guide care. Existing prediction models, such as end-stage liver disease (MELD) score, are accurate but controversial in clinical practice. We developed and validated a practical user-friendly bedside tool for SBP risk stratification of patients with cirrhosis and ascites. Using classification and regression tree (CART) analysis, a model was developed for prediction of SBP in cirrhosis with ascites. The CART model was derived on data collected from 676 patients admitted from January 2007 to December 2009 retrospectively, and then was prospectively tested in another independent 198 inpatients between January 2010 and December 2010. The accuracy of CART model was evaluated using the area under the receiver operating characteristic curve. The performance of the model was further validated by comparing its predictive accuracy with that of the MELD score. Furthermore, the model was used to stratify SBP among patients with MELD scores under 15. CART analysis identified four variables for prediction of SBP: creatinine, total bilirubin, prothrombin time and white blood cell count, and three risk groups: low (2.0%), intermediate (27.5-33.3%) and high (60.6-86.4%) risk. The accuracy of CART model (0.881) exceeded that of MELD (0.791). Subjects in the intermediate risk and high risk groups had 22.21-fold (95% confident interval (CI), 9.98-49.45) and 173.50-fold (95% CI, 77.68-634.33) increased risk of SBP, respectively, comparing with the low risk group. Similar results were found when this risk stratification was applied to the validation cohort. Cirrhotic patients with ascites at low, intermediate, and high risk for SBP can be easily identified using CART model, which provides clinicians with a validated, practical bedside tool for SBP risk stratification.
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