ArticleClinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association2024
A Machine Learning Algorithm Avoids Unnecessary Paracentesis for Exclusion of SBP in Cirrhosis in Resource-limited Settings.
Article in Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Mid-regional pro-adrenomedullin: A rapid sepsis biomarker for diagnosing spontaneous bacterial peritonitis in cirrhosis.European journal of clinical investigation · 2025Article
- Exploring the next frontier in diagnosing spontaneous bacterial peritonitis.World journal of hepatology · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
BACKGROUND &
aimsDespite the poor prognosis associated with missed or delayed spontaneous bacterial peritonitis (SBP) diagnosis, <15% get timely paracentesis, which persists despite guidelines/education in the United States. Measures to exclude SBP non-invasively where timely paracentesis cannot be performed could streamline this burden.
methodsUsing Veterans Health Administration Corporate Data Warehouse (VHA-CDW) we included patients with cirrhosis between 2009 and 2019 who underwent timely paracentesis and collected relevant clinical information (demographics, cirrhosis severity, medications, vitals, and comorbidities). XGBoost-models were trained on 75% of the primary cohort, with 25% reserved for testing. The final model was further validated in 2 cohorts: Validation cohort #1: In VHA-CDW, those without prior SBP who received 2nd early paracentesis, and Validation cohort #2: Prospective data from 276 non-electively admitted University hospital patients.
resultsNegative predictive values (NPVs) at 5%,10%, and 15% probability cutoffs were examined. Primary cohort: n = 9643 (mean age, 63.1 ± 8.7 years; 97.2% men; SBP, 15.0%) received first early paracentesis. Testing-set NPVs for SBP were 96.5%, 93.0%, and 91.6% at the 5%, 10%, and 15% probability thresholds, respectively. In Validation cohort #1: n = 2844 (mean age, 63.14 ± 8.37 years; 97.1% male; SBP, 9.7%) with NPVs were 98.8%, 95.3%, and 94.5%. In Validation cohort #2: n = 276 (mean age, 56.08 ± 9.09; 59.6% male; SBP, 7.6%) with NPVs were 100%, 98.9%, and 98.0% The final machine learning model showed the greatest net benefit on decision-curve analyses.
conclusionsA machine learning model generated using routinely collected variables excluded SBP with high NPV. Applying this model could ease the need to provide paracentesis in resource-limited settings by excluding those unlikely to have SBP.
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