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

ABSTRACT

BACKGROUND AND 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 US. 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 cirrhosis patients between 2009-2019 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 two 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 (NPV) at 5,10 & 15% probability cutoffs were examined. Primary cohort n=9,643 (mean age 63.1±8.7 years, 97.2% men, SBP15.0%) 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 ML model showed the greatest net benefit on decision-curve analyses. CONCLUSIONS: A machine learning model generated using routinely collected variables excluded SBP with high negative predictive value. Applying this model could ease the need to provide paracentesis in resource-limited settings by excluding those unlikely to have SBP.