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Machine learning models in predicting failure of Helicobacter pylori treatment: A two country validation study

Jiang, Fang; Lui, Thomas KL; Ju, Chengsheng; Guo, Chuan-Guo; Cheung, Ka Shing; Lau, Wallis CY; Leung, Wai K; (2024) Machine learning models in predicting failure of Helicobacter pylori treatment: A two country validation study. Helicobacter , 29 (1) , Article e13051. 10.1111/hel.13051. Green open access

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Abstract

Background: The success rate of clarithromycin-containing Helicobacter pylori treatment had declined globally. This study aims to explore the role of different machine learning algorithms in predicting failure of H. pylori treatment. // Materials and Methods: We included 84,609 adult patients who had received the first course of clarithromycin-containing triple therapy for H. pylori in Hong Kong from 2003 to 2013 as training set. Results were validated in patients who had received similar triple therapy with 27,736 Hong Kong patients between 2014 and 2017 (internal cohort); and 18,050 UK patients between 2012 and 2017 (external cohort). The performance of 11 available machine learning algorithms were used to predict the failure of triple therapy. The performance was determined by the area under receiver operating characteristic curve (AUC). // Results: The treatment failure rates in the training, internal and external validation cohort was 5.9%, 9.5%, and 6.1%, respectively. In the internal validation set, Extra-Tree (ET) Classifier had the best AUC (0.88; 95% CI, 0.87–0.88), sensitivity (79.6%; 95% CI, 79.0–80.2) and specificity (79.4%; 95% CI, 79.0–79.8). In the external validation set, ET Classifier also had the best AUC (0.85; 95% CI, 0.85–0.86), sensitivity (80.1%; 95% CI, 79.5–80.9), and specificity (80.2%; 95% CI, 78.8–81.3). Top features of importance used by ET Classifier in predicting treatment failure included time interval between antibiotic use and triple therapy (48.8%), age (29.1%) and triple therapy regime (6.28%). // Conclusions: Machine learning algorithm, based on simple baseline clinical parameters, could help to identify patients at high risk of failure from clarithromycin-containing triple therapy for H. pylori.

Type: Article
Title: Machine learning models in predicting failure of Helicobacter pylori treatment: A two country validation study
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/hel.13051
Publisher version: https://doi.org/10.1111/hel.13051
Language: English
Additional information: Copyright © 2024 The Authors. Helicobacter published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, https://creativecommons.org/licenses/by-nc-nd/4.0/, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Keywords: Artificial intelligence, Helicobacter pylori, machine learning algorithms, triple therapy
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy > Practice and Policy
URI: https://discovery.ucl.ac.uk/id/eprint/10185379
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