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A Machine Learning Algorithm Avoids Unnecessary Paracentesis for Exclusion of SBP in Cirrhosis in Resource-limited Settings. | LitMetric

AI Article Synopsis

  • Many patients with cirrhosis experience poor outcomes due to missed or delayed identification of spontaneous bacterial peritonitis (SBP), yet less than 15% receive timely diagnostic procedures like paracentesis, even with existing guidelines.
  • A study used data from the Veterans Health Administration to develop and validate a machine learning model that can non-invasively identify patients unlikely to have SBP, using clinical information from those who underwent paracentesis between 2009 and 2019.
  • The machine learning model demonstrated high negative predictive values, effectively ruling out SBP in various patient cohorts, suggesting it could reduce the need for invasive procedures in settings with limited resources.

Article Abstract

Background & Aims: Despite 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.

Methods: Using 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.

Results: Negative 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.

Conclusions: A 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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11588556PMC
http://dx.doi.org/10.1016/j.cgh.2024.06.015DOI Listing

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