"MURAL" model to predict bleeding from mural-based lesions in potential small bowel bleeding may improve diagnostic capability and decrease cost. Issue 48 (2nd December 2022)

• Record Type: Journal Article
• Authors: Limsrivilai, Julajak; Chaemsupaphan, Thanaboon; Khamplod, Sipawath; Srisajjakul, Sitthipong; Kositamongkol, Chayanis; Phisalprapa, Pochamana; Maipang, Kochakon; Kaosombatwattana, Uayporn; Pausawasdi, Nonthalee; Charatcharoenwitthaya, Phunchai; Leelakusolvong, Somchai; Pongprasobchai, Supot
• Is Part Of: Medicine. Volume 101:Issue 48(2022)
• Page Start: e31989
• Publication Date: 2022-12-02
• Subjects: cost effectiveness -- computed tomography enterography -- mural -- small bowel bleeding -- video capsule endoscopy; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000031989
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract : In potential small bowel bleeding, video capsule endoscopy (VCE) is excellent to detect mucosal lesions, while mural-based lesions are better detected by computed tomography enterography (CTE). A predictive tool to identify mural-based lesions should guide selecting investigations. In this retrospective study, we developed and validated the "MURAL" model based on logistic regression to predicts bleeding from mural-based lesions. Cost-effectiveness analysis comparing diagnostic strategy among VCE, CTE, and MURAL model was performed. Of 296 patients, 196 and 100 patients were randomly included in the derivative and validation cohorts, respectively. The MURAL model comprises 5 parameters: age, presence of atherosclerosis, chronic kidney disease, antiplatelet use, and serum albumin level. The area under the receiver operating characteristic curve was 0.778 and 0.821 for the derivative and validation cohorts, respectively. At a cutoff value of 24.2%, the model identified mural-based lesions with 70% sensitivity and 83% specificity in the validation cohort. Cost-effectiveness analysis revealed that application of the MURAL model demonstrated a comparable missed lesion rate but had a lower missed tumor rate, and lower cost compared to VCE strategy. The model for predicting mural-based lesions provide some guidance in investigative decision-making, which may improve diagnostic efficiency and reduce costs.