预测幽门螺杆菌治疗失败的机器学习模型:两国验证研究

IF 4.3 2区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY
Helicobacter Pub Date : 2024-01-25 DOI:10.1111/hel.13051
Fang Jiang, Thomas K. L. Lui, Chengsheng Ju, Chuan-Guo Guo, Ka Shing Cheung, Wallis C. Y. Lau, Wai K. Leung
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引用次数: 0

摘要

在全球范围内,含克拉霉素幽门螺杆菌治疗的成功率有所下降。本研究旨在探索不同的机器学习算法在预测幽门螺杆菌治疗失败中的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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.

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来源期刊
Helicobacter
Helicobacter 医学-微生物学
CiteScore
8.40
自引率
9.10%
发文量
76
审稿时长
2 months
期刊介绍: Helicobacter is edited by Professor David Y Graham. The editorial and peer review process is an independent process. Whenever there is a conflict of interest, the editor and editorial board will declare their interests and affiliations. Helicobacter recognises the critical role that has been established for Helicobacter pylori in peptic ulcer, gastric adenocarcinoma, and primary gastric lymphoma. As new helicobacter species are now regularly being discovered, Helicobacter covers the entire range of helicobacter research, increasing communication among the fields of gastroenterology; microbiology; vaccine development; laboratory animal science.
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