Endoscopic ultrasound‐based application system for predicting endoscopic resection‐related outcomes and diagnosing subepithelial lesions: Multicenter prospective study

Abstract

ObjectivesSubepithelial lesions (SELs) are associated with various endoscopic resection (ER) outcomes and diagnostic challenges. We aimed to establish a tool for predicting ER‐related outcomes and diagnosing SELs and to investigate the predictive value of endoscopic ultrasound (EUS).MethodsPhase 1 (system development) was performed in a retrospective cohort (n = 837) who underwent EUS before ER for SELs at eight hospitals. Prediction models for five key outcomes were developed using logistic regression. Models with satisfactory internal validation performance were included in a mobile application system, SEL endoscopic resection predictor (SELERP). In Phase 2, the models were externally validated in a prospective cohort of 200 patients.ResultsAn SELERP was developed using EUS characteristics, which included 10 models for five key outcomes: post‐ER ulcer management, short procedure time, long hospital stay, high medication costs, and diagnosis of SELs. In Phase 1, 10 models were derived and validated (C‐statistics, 0.67–0.99; calibration‐in‐the‐large, −0.14–0.10; calibration slopes, 0.92–1.08). In Phase 2, the derived risk prediction models showed convincing discrimination (C‐statistics, 0.64–0.73) and calibration (calibration‐in‐the‐large, −0.02–0.05; calibration slopes, 1.01–1.09) in the prospective cohort. The sensitivities and specificities of the five diagnostic models were 68.3–95.7% and 64.1–83.3%, respectively.ConclusionWe developed and prospectively validated an application system for the prediction of ER outcomes and diagnosis of SELs, which could aid clinical decision‐making and facilitate patient–physician consultation. EUS features significantly contributed to the prediction.Trial registrationChinese Clinical Trial Registry, http://www.chictr.org.cn (ChiCTR2000040118).