Tawheed Ahmed, Yalniz Mehmet, Ozercan Mubin, Bahcecioglu Ibrahim Halil
Department of Endemic Medicine, Faculty of Medicine, Helwan University, Cairo 11795, Egypt.
Department of Gastroenterology, Firat University, Elazig 23119, Türkiye.
World J Hepatol. 2025 Mar 27;17(3):102044. doi: 10.4254/wjh.v17.i3.102044.
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.
自发性细菌性腹膜炎（SBP）是肝衰竭的常见并发症。它是肝硬化患者腹水的急性细菌感染。由于其相关并发症，SBP给肝病学家带来了重大挑战。虽然通过多形核细胞计数进行诊断性腹腔穿刺术准确性很高，但由于它是一种有相关风险的侵入性操作，对一些患者来说可能会很麻烦。几项研究提出了新的诊断方法以改进当前的做法，其中许多方法仍然具有侵入性。尽管一些血清检测在SBP的诊断中显示出前景，但结果仍处于初步阶段。人工智能和机器学习的最新进展引入了用于诊断的预测模型和评分系统。然而，这些模型仍然缺乏足够的敏感性、特异性以及有效评估治疗反应的能力。
