Diagnostic accuracy of bowel ultrasonography in patients with inflammatory bowel disease: a systematic review and meta-analysis

ANNALS OF GASTROENTEROLOGY(2024)

引用 0|浏览0
暂无评分
摘要
Background Bowel ultrasonography (BUS) is emerging as a promising noninvasive tool for assessing disease activity in inflammatory bowel disease (IBD) patients. We evaluated the diagnostic accuracy of BUS in IBD patients against the gold standard diagnostic method, standard colonoscopy. Methods Major databases were searched from inception to May 2023 for studies on BUS diagnostic accuracy in IBD. Outcomes of interest were pooled sensitivity, specificity, positive (PPV), and negative (NPV) predictive values. Endoscopic confirmation served as ground truth. Standard meta-analysis methods with a random-effects model and I2 statistics were applied. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Results Twenty studies (1094 patients) were included in the final analysis. The majority (75%) of studies considered bowel wall thickness >3 mm as abnormal. Endoscopic evaluation was performed between days 3 and 180. The pooled diagnostic accuracy of BUS in IBD was 66% (95% confidence interval [CI] 58-72%; I2=78%), sensitivity was 88.6% (95%CI 85-91%; I2=77%), and specificity 86% (95%CI 81-90%; I2=95%). PPV and NPV were 94% (95%CI 93-96%; I2=25%) and 74% (95%CI 66-80%; I2=95%), respectively. On subgroup analysis, small-intestine contrast-enhanced ultrasonography (SICUS) demonstrated high sensitivity (97%, 95%CI 91-99%; I2=83%), whereas BUS exhibited high specificity (94%, 95%CI 92-96%; I2=0%) and NPV (76%, 95%CI 68-83%; I2=80.9%). Meta-regression revealed a significant relation between side-to-side anastomosis and BUS specificity (P=0.02) and NPV (P=0.004). Conclusion The high diagnostic accuracy of BUS in detecting bowel wall inflammation suggests utilizing regular BUS as the primary modality, with subsequent consideration of SICUS if clinically warranted.
更多
查看译文
关键词
Bowel ultrasound,inflammatory bowel disease,meta-analysis
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要