Development and validation of a scoring system to predict esophagogastroduodenoscopy necessity

ObjectiveThis study aimed to develop and validate a scoring system for predicting the need for esophagogastroduodenoscopy (EGD) in clinical practice to enhance accuracy and reduce misapplications.MethodsFrom February 2021 to April 2022, outpatients scheduled for EGD at the Department of Gastroenterology in our hospital were recruited. Patients completed the system evaluation by providing clinical symptoms, relevant medical history, and endoscopic findings. Patients were randomly divided into the training and validation cohorts (at 2:1 ratio). The optimal algorithm was selected from five alternatives including a parallel test. Six physicians participated in a human-computer comparative validation. Sensitivity and negative likelihood ratio (-LR) were used as the primary indicators.ResultsAltogether 865 patients were enrolled, with 578 in the training cohort and 287 in the validation cohort. The scoring system comprised 21 variables, including age, 13 typical clinical symptoms, and seven medical history variables. The parallel test was selected as the final algorithm. Positive EGD findings were reported in 54.5% of the training cohort and 62.7% of the validation cohort. The scoring system demonstrated a sensitivity of 79.0% in the training cohort and 83.9% in the validation cohort, with -LR being 0.627 and 0.615, respectively. Compared to physicians, the scoring system exhibited higher sensitivity (84.0% vs 68.7%, P = 0.02) and a lower -LR (1.11 vs 2.41, P = 0.439).ConclusionsWe developed a scoring system to predict the necessity of EGD using a parallel test algorithm, which was user-friendly and effective, as evidenced by single-center validation. A scoring system to predict the necessity of esophagogastroduodenoscopy was developed, and showed greater performance than physicians. The results of the single-center validation showed that it was an easy-to-use and effective tool for pre-endoscopic evaluation of the individuals.image