Grace Lai-Hung Wong1,2, Andy Jinhua Ma3,4, Huiqi Deng3,5, Jessica Yuet-Ling Ching1,2, Vincent Wai-Sun Wong1,2, Yee-Kit Tse1,2, Terry Cheuk-Fung Yip1,2, Louis Ho-Shing Lau1,2, Henry Hin-Wai Liu6, Chi-Man Leung7, Steven Woon-Choy Tsang8, Chun-Wing Chan9, James Yun-Wong Lau10, Pong-Chi Yuen3, Francis Ka-Leung Chan1,2. 1. Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China. 2. Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China. 3. Department of Computer Science, Hong Kong Baptist University, Hong Kong, China. 4. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China. 5. School of Mathematics, Sun Yat-Sen University, Guangzhou, China. 6. Department of Medicine, Queen Elizabeth Hospital, Hong Kong, China. 7. Department of Medicine, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China. 8. Department of Medicine, Tseung Kwan O Hospital, Hong Kong, China. 9. Department of Medicine, Yan Chai Hospital, Hong Kong, China. 10. Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China.
Abstract
BACKGROUND: Patients with a history of Helicobacter pylori-negative idiopathic bleeding ulcers have an increased risk of recurring ulcer complications. AIM: To build a machine learning model to identify patients at high risk for recurrent ulcer bleeding. METHODS: Data from a retrospective cohort of 22 854 patients (training cohort) diagnosed with peptic ulcer disease in 2007-2016 were analysed to build a model (IPU-ML) to predict recurrent ulcer bleeding. We tested the IPU-ML in all patients with a diagnosis of gastrointestinal bleeding (n = 1265) in 2008-2015 from a different catchment population (independent validation cohort). Any co-morbid conditions which had occurred in >1% of study population were eligible as predictors. RESULTS: Recurrent ulcer bleeding developed in 4772 patients (19.5%) in the training cohort, during a median follow-up period of 2.7 years. IPU-ML model built on six parameters (age, baseline haemoglobin, and presence of gastric ulcer, gastrointestinal diseases, malignancies, and infections) identified patients with bleeding recurrence within 1 year with an area under the receiver operating characteristic curve (AUROC) of 0.648. When we set the IPU-ML cutoff value at 0.20, 27.5% of patients were classified as high risk for rebleeding with a sensitivity of 41.4%, specificity of 74.6%, and a negative predictive value of 91.1%. In the validation cohort, the IPU-ML identified patients with a recurrence ulcer bleeding within 1 year with an AUROC of 0.775, and 84.3% of overall accuracy. CONCLUSION: We developed a machine-learning model to identify those patients with a history of idiopathic gastroduodenal ulcer bleeding who are not at high risk for recurrent ulcer bleeding.
BACKGROUND:Patients with a history of Helicobacter pylori-negative idiopathic bleeding ulcers have an increased risk of recurring ulcer complications. AIM: To build a machine learning model to identify patients at high risk for recurrent ulcer bleeding. METHODS: Data from a retrospective cohort of 22 854 patients (training cohort) diagnosed with peptic ulcer disease in 2007-2016 were analysed to build a model (IPU-ML) to predict recurrent ulcer bleeding. We tested the IPU-ML in all patients with a diagnosis of gastrointestinal bleeding (n = 1265) in 2008-2015 from a different catchment population (independent validation cohort). Any co-morbid conditions which had occurred in >1% of study population were eligible as predictors. RESULTS: Recurrent ulcer bleeding developed in 4772 patients (19.5%) in the training cohort, during a median follow-up period of 2.7 years. IPU-ML model built on six parameters (age, baseline haemoglobin, and presence of gastric ulcer, gastrointestinal diseases, malignancies, and infections) identified patients with bleeding recurrence within 1 year with an area under the receiver operating characteristic curve (AUROC) of 0.648. When we set the IPU-ML cutoff value at 0.20, 27.5% of patients were classified as high risk for rebleeding with a sensitivity of 41.4%, specificity of 74.6%, and a negative predictive value of 91.1%. In the validation cohort, the IPU-ML identified patients with a recurrence ulcer bleeding within 1 year with an AUROC of 0.775, and 84.3% of overall accuracy. CONCLUSION: We developed a machine-learning model to identify those patients with a history of idiopathic gastroduodenal ulcer bleeding who are not at high risk for recurrent ulcer bleeding.
Authors: Alanna Ebigbo; Christoph Palm; Andreas Probst; Robert Mendel; Johannes Manzeneder; Friederike Prinz; Luis A de Souza; João P Papa; Peter Siersema; Helmut Messmann Journal: Endosc Int Open Date: 2019-11-25