Exploring the next frontier in diagnosing spontaneous bacterial peritonitis.

Abstract

Spontaneous bacterial peritonitis (SBP) is a common complication of liver failure. It is an acute bacterial infection of the ascitic fluid in patients with liver cirrhosis. SBP presents a significant challenge for hepatologists owing to its associated complications. While diagnostic paracentesis with polymorphonuclear count is highly accurate, it can be troublesome for some patients as it is an invasive procedure with associated risks. Several studies have proposed new diagnostic methods to improve current practices, many of which remain invasive. Although some serum tests show promise in the diagnosis of SBP, the results are still preliminary. Recent advancements in artificial intelligence and machine learning have introduced predictive models and scoring systems for diagnosis. However, these models still lack sufficient sensitivity, specificity, and the ability to effectively assess treatment response.