Machine learning model to predict recurrent ulcer bleeding in patients with history of idiopathic gastroduodenal ulcer bleeding

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.

Conclusions

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.

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