Validation of a machine learning model to non-invasively exclude spontaneous bacterial peritonitis.

BACKGROUND: There remains a gap between guideline recommendations and real-world practice for the performance of timely paracentesis to diagnose spontaneous bacterial peritonitis (SBP). A machine learning (ML) model to non-invasively exclude SBP using twenty routinely collected clinical and laboratory values was previously developed and validated in a pre-COVID-19 era cohort. AIM: Because of the considerable health care delivery changes post-COVID-19 pandemic, we sought to validate this ML model in a contemporary cohort of admitted VA patients. METHODS: We included patients in the Veterans Health Administration Corporate Data Warehouse (VHA-CDW) admitted between 2020 and 2023 with cirrhosis and ascites who underwent timely paracentesis identified by ICD-10 codes (validation cohort), then performed manual chart review on a subset of patients admitted at 2 tertiary-care VA hospitals to confirm the SBP (validation subgroup). We evaluated the performance metrics of the previously developed ML model with this cohort and subgroup. RESULTS: The validation cohort included 4192 patients, of which 630 (15.0%) had SBP. At the <5%, <10%, and <15% thresholds for probability of SBP-negativity predicted by the ML model, negative predictive values (NPVs) were 93.6%, 92.0%, and 90.9%, respectively. In the validation subgroup, 7 (6.5%) of 107 patients had confirmed SBP. NPVs at the <5%, <10%, and <15% thresholds were 100%, 97.5%, and 95.4%, respectively. CONCLUSION: In a national cohort and manual confirmation subgroup of Veterans with cirrhosis admitted after 2020, a ML model non-invasively excluded SBP with high NPV. This could help in risk-stratification, especially in limited-resource settings.