Background: Spontaneous bacterial peritonitis (SBP) is a detrimental infection of the ascitic fluid in liver cirrhosis patients, with high mortality and morbidity. Early diagnosis and timely antibiotic administration have successfully decreased the mortality rate to 20%-25%. However, many patients cannot be diagnosed in the early stages due to the absence of classical SBP symptoms. Early diagnosis of asymptomatic SBP remains a great challenge in the clinic.

Aim: To establish a multivariate predictive model for early diagnosis of asymptomatic SBP using positive microbial cultures from liver cirrhosis patients with ascites.

Methods: A total of 98 asymptomatic SBP patients and 98 ascites liver cirrhosis patients with negative microbial cultures were included in the case and control groups, respectively. Multiple linear stepwise regression analysis was performed to identify potential indicators for asymptomatic SBP diagnosis. The diagnostic performance of the model was estimated using the receiver operating characteristic curve.

Results: Patients in the case group were more likely to have advanced disease stages, cirrhosis related-complications, worsened hematology and ascites, and higher mortality. Based on multivariate analysis, the predictive model was as follows: y () = 0.018 + 0.312 × MELD (model of end-stage liver disease) + 0.263 × PMN (ascites polymorphonuclear) + 0.184 × N (blood neutrophil percentage) + 0.233 × HCC (hepatocellular carcinoma) + 0.189 × renal dysfunction. The area under the curve value of the established model was 0.872, revealing its high diagnostic potential. The diagnostic sensitivity was 73.5% (72/98), the specificity was 86.7% (85/98), and the diagnostic efficacy was 80.1%.

Conclusion: Our predictive model is based on the MELD score, polymorphonuclear cells, blood N, hepatocellular carcinoma, and renal dysfunction. This model may improve the early diagnosis of asymptomatic SBP.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7422546PMC
http://dx.doi.org/10.3748/wjg.v26.i29.4316DOI Listing

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