Self-expanding metal stents versus TIPS in treatment of refractory bleeding esophageal varices: a systematic review and meta-analysis
Endoscopy International Open. 2020;8(3):E291-e300
Background and study aims Refractory and recurrent esophageal variceal (EV) bleeding can be life threatening. Self-expanding metal stents (SEMS) have been used as a "bridge" therapy. However, their role in the treatment protocol is not established due to paucity in data. Methods We searched multiple databases from inception through May 2019 to identify studies that reported on SEMS and TIPS in refractory EV hemorrhage. Our primary goals were to analyze and compare the pooled all-cause mortality, immediate bleeding control and rebleeding rates. Results Five hundred forty-seven patients from 21 studies were analyzed (SEMS: 12 studies, 176 patients; TIPS 9 studies, 398 patients). The pooled rate of all-cause mortality with SEMS was 43.6 % (95 % CI 28.6-59.8, I (2) = 38) and with TIPS was 27.9 % (95 % CI 16.3-43.6, I (2) = 91). The pooled rate of immediate bleeding control with SEMS was 84.5 % (95 % CI 74-91.2, I (2) = 40) and with TIPS was 97.9 % (95 % CI 87.7-99.7, I (2) = 0). The pooled rate of rebleeding with SEMS was 19.4 % (95 % CI 11.9-30.4, I (2) = 32) and with TIPS was 8.8 % (95 % CI 4.8-15.7, I (2) = 40). Conclusion Use of SEMS in refractory EV hemorrhage demonstrates acceptable immediate bleeding control with good technical success rate. Mortality and rebleeding rates were lesser with TIPS, however, its superiority and/ or inferiority cannot be validated due to limitations in the comparison methodology.
High pooled performance of convolutional neural networks in computer-aided diagnosis of GI ulcers and/or hemorrhage on wireless capsule endoscopy images: a systematic review and meta-analysis
Gastrointest Endosc. 2020
BACKGROUND AND AIMS Diagnosis of gastrointestinal (GI) ulcers and/or hemorrhage by wireless capsule endoscopy (WCE) is limited by the physician-dependent, tedious, time-consuming process of image and/ or video classification. Computer-aided diagnosis (CAD) by convolutional neural networks (CNN) based machine learning may help reduce this burden. Our aim was to conduct a meta-analysis and appraise the reported data. METHODS Multiple databases were searched (from inception to November 2019) and studies that reported on the performance of CNN in the diagnosis of GI ulcerations and/ or hemorrhage on WCE were selected. Random effects model was used to calculate the pooled rates. In cases where multiple 2X2 contingency tables were provided for different thresholds, we assumed the data tables as independent from each other. Heterogeneity was assessed by I(2)% and 95% prediction intervals. RESULTS Nine studies were included in our final analysis that evaluated the performance of CNN based CAD of GI ulcers and/ or hemorrhage by WCE. The pooled accuracy was 95.4% (95% CI, 94.3-96.3), sensitivity was 95.5% (95% CI, 94-96.5), specificity was 95.8% (95% CI, 94.7-96.6), positive predictive value was 95.8% (95% CI, 90.5-98.2) and negative predictive value was 96.8% (95% CI, 94.9-98.1). I(2)% heterogeneity was negligible except for the pooled positive predictive value. CONCLUSIONS Based on our meta-analysis, CNN based CAD of GI ulcerations and/ or hemorrhage on WCE achieves high-level performance. The quality of evidence is robust and therefore CNN based CAD has the potential to become the first-choice of machine learning to optimize WCE image/video reading.