医学
穿孔
内镜黏膜下剥离术
优势比
直肠
单变量分析
内科学
多元分析
病变
胃肠病学
外科
结肠镜检查
结直肠癌
癌症
材料科学
冶金
冲孔
作者
Attila Ülkücü,Mariano Laporte,Imran Khan,Scott Steele,David Liska,Joshua Sommovilla,Emre Görgün
标识
DOI:10.1097/sla.0000000000006838
摘要
Objective: This study aimed to develop a predictive model for perforation to improve patient safety during Endoscopic Submucosal Dissection (ESD). Summary Background Data: ESD provides a less invasive approach for colorectal lesion removal but carries perforation risks that can cause considerable morbidity. Method: Data of patients who underwent ESD for colorectal lesions at a tertiary care center between March 2011 and November 2023 were reviewed from a prospectively maintained database. We stratified the subjects into derivation and validation sets using 50-50 allocation. Predictors of perforation identified in univariate analysis ( P <0.22) were evaluated for independent association using multivariate analysis. Model efficacy was assessed using the ROC curve. Results: The study involved 1051 patients (515 males and 536 females) with lesions mainly in right colon (638; 61%), left colon (181; 17%), rectum (137; 13%), and transverse colon (98; 9%). Perforation occurred in 108 (10%) patients. Significant ( P <0.05) perforation predictors; first 100 procedures (63.3% vs. 36.7%, P <0.001), lesion size >35 mm (59.2% vs. 38.8%, P =0.00369), extended operative time (mean 169 vs. 76.9 min, P <0.001), higher lift degree ( P =0.00114), and incomplete resections (18.4% vs. 2.9%, P <0.001). In multivariate analysis, fibrosis (odds ratio [OR]:2.67, P =0.010), the first 100 procedures (OR:14.6, P <0.001), and lesion size >35 mm (OR: 3.50; P <0.001) emerged as significant independent preoperative. ESD perforation risk score validation achieved adequate performance (AUC: 0.7078; accuracy: 80.19%). Conclusion: This study introduces a validated ESD perforation risk model, providing clinicians with reliable tool for patient risk assessment and potentially improving preprocedural strategies.
科研通智能强力驱动
Strongly Powered by AbleSci AI