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Machine learning model to predict recurrent ulcer bleeding in patients with history of idiopathic gastroduodenal ulcer bleeding
Author(s) -
Wong Grace LaiHung,
Ma Andy Jinhua,
Deng Huiqi,
Ching Jessica YuetLing,
Wong Vincent WaiSun,
Tse YeeKit,
Yip Terry CheukFung,
Lau Louis HoShing,
Liu Henry HinWai,
Leung ChiMan,
Tsang Steven WoonChoy,
Chan ChunWing,
Lau James YunWong,
Yuen PongChi,
Chan Francis KaLeung
Publication year - 2019
Publication title -
alimentary pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.308
H-Index - 177
eISSN - 1365-2036
pISSN - 0269-2813
DOI - 10.1111/apt.15145
Subject(s) - medicine , cohort , gastroenterology , retrospective cohort study , population , cohort study , receiver operating characteristic , surgery , environmental health
Summary 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.