ArticleGut pathogens2025
Risk stratification of spontaneous bacterial peritonitis recurrence: integrating acute kidney injury, biomarkers, composite scores, and machine learning models.
Article in Gut pathogens, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
19 authors.
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No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimRecurrent spontaneous bacterial peritonitis (SBP) is a major concern for cirrhotic patients with ascites. This study seeks to identify predictors of recurrent SBP using clinical factors, inflammatory markers, and machine learning models. PATIENTS AND
methodsThe study involved 347 patients with cirrhotic ascites and SBP. Receiver Operating Characteristic (ROC) curve analysis assessed the predictive ability of biomarkers. A composite score was created to evaluate the risk stratification model. Different machine learning models were compared for predictive accuracy.
resultsEighty-three patients (23.9%) experienced recurrent SBP. Independent predictors of recurrence in multivariable analysis included acute kidney injury (AKI), elevated C-reactive protein (CRP) levels, higher serum bilirubin levels, a higher model for end-stage liver disease (MELD) score, proton-pump inhibitor (PPI) use, and lack of β-blocker use. A composite 10-point score (including AKI, CRP > 50 mg/L, low albumin levels < 2.5 g/dL, ascitic protein < 1.0 g/dL, albumin/ascitic ratio < 2.5 [2 points], MELD ≥ 15, diabetes, multidrug-resistant organism [MDRO] infection, and non-use of β-blockers) stratified the risk of recurrence into low (0-3: 15%), moderate (4-6: 45%), and high (7-10: 80%) categories. Machine learning models outperformed supervised machine logistic regression in predicting recurrence. Logistic regression achieved 70% accuracy, 65% sensitivity, and 68% specificity. The decision tree model improved accuracy to 75%, sensitivity to 72%, and specificity to 71%. The random forest model showed the best performance with 78% accuracy, 77% sensitivity, and 76% specificity.
conclusionA composite score, combined with machine-learning models like random forest, enhances risk assessment for SBP recurrence. Clinical predictors such as AKI, CRP, bilirubin, MELD, PPI use, and β-blockers non-use help in targeted prevention.
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