Diagnostic value of Peptest™ combined with gastroesophageal reflux disease questionnaire in identifying patients with gastroesophageal reflux-induced chronic cough.

IF 2.3 3区 医学 Q2 RESPIRATORY SYSTEM
Chronic Respiratory Disease Pub Date : 2025-01-01 Epub Date: 2025-08-01 DOI:10.1177/14799731251364875
Jiaying Yuan, Xiao Luo, Lina Huang, Yaxing Zhou, Bingxian Sha, Tongyangzi Zhang, Shengyuan Wang, Li Yu, Xianghuai Xu
{"title":"Diagnostic value of Peptest™ combined with gastroesophageal reflux disease questionnaire in identifying patients with gastroesophageal reflux-induced chronic cough.","authors":"Jiaying Yuan, Xiao Luo, Lina Huang, Yaxing Zhou, Bingxian Sha, Tongyangzi Zhang, Shengyuan Wang, Li Yu, Xianghuai Xu","doi":"10.1177/14799731251364875","DOIUrl":null,"url":null,"abstract":"<p><p>ObjectivesGastroesophageal reflux-related chronic cough (GERC), an extraesophageal manifestation of gastroesophageal reflux disease (GERD). Although 24h MII-pH monitoring is the gold standard for diagnosing GERC, its invasiveness, high cost, and limited accessibility hinder widespread use in many clinical settings. This study aimed to develop a non-invasive machine learning model incorporating Peptest™ and GerdQ scores to facilitate GERC detection, particularly in primary care and resource-limited environments where MII-pH testing is not readily available.Methods210 chronic cough patients were enrolled between September 2022 and June 2024. GERC diagnosis followed established guidelines, and salivary pepsin levels were measured via Peptest™. Feature selection was performed using the Boruta algorithm (hereafter referred to as Boruta), a method based on random forest (RF), designed to identify relevant variables by comparing them to random shadow features. The selected optimal features were then evaluated using nine ML models, including logistic regression (LR), RF and others. Model performance was assessed through area under the curve (AUC), decision curve analysis (DCA), and calibration curves.Results73 (34.76%) patients had GERC. Peptest™ and GerdQ scores were key predictors. Logistic regression was selected for its balance of accuracy (AUC: 0.876) and clinical utility. The nomogram model showed excellent discrimination and calibration. DCA indicated high net benefit at prediction thresholds of 0.10-0.90. RCS analysis revealed non-linear relationships: GERC risk increased with GerdQ >8.66 and Peptest™ >54.791 ng/ml.ConclusionThe nomogram model provides a reliable, non-invasive tool for GERC diagnosis, aiding timely clinical intervention, especially for patients unsuitable for pH testing.</p>","PeriodicalId":10217,"journal":{"name":"Chronic Respiratory Disease","volume":"22 ","pages":"14799731251364875"},"PeriodicalIF":2.3000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12319193/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chronic Respiratory Disease","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/14799731251364875","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/8/1 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"RESPIRATORY SYSTEM","Score":null,"Total":0}
引用次数: 0

Abstract

ObjectivesGastroesophageal reflux-related chronic cough (GERC), an extraesophageal manifestation of gastroesophageal reflux disease (GERD). Although 24h MII-pH monitoring is the gold standard for diagnosing GERC, its invasiveness, high cost, and limited accessibility hinder widespread use in many clinical settings. This study aimed to develop a non-invasive machine learning model incorporating Peptest™ and GerdQ scores to facilitate GERC detection, particularly in primary care and resource-limited environments where MII-pH testing is not readily available.Methods210 chronic cough patients were enrolled between September 2022 and June 2024. GERC diagnosis followed established guidelines, and salivary pepsin levels were measured via Peptest™. Feature selection was performed using the Boruta algorithm (hereafter referred to as Boruta), a method based on random forest (RF), designed to identify relevant variables by comparing them to random shadow features. The selected optimal features were then evaluated using nine ML models, including logistic regression (LR), RF and others. Model performance was assessed through area under the curve (AUC), decision curve analysis (DCA), and calibration curves.Results73 (34.76%) patients had GERC. Peptest™ and GerdQ scores were key predictors. Logistic regression was selected for its balance of accuracy (AUC: 0.876) and clinical utility. The nomogram model showed excellent discrimination and calibration. DCA indicated high net benefit at prediction thresholds of 0.10-0.90. RCS analysis revealed non-linear relationships: GERC risk increased with GerdQ >8.66 and Peptest™ >54.791 ng/ml.ConclusionThe nomogram model provides a reliable, non-invasive tool for GERC diagnosis, aiding timely clinical intervention, especially for patients unsuitable for pH testing.

Abstract Image

Abstract Image

Abstract Image

Peptest™联合胃食管反流病问卷对胃食管反流性慢性咳嗽患者的诊断价值
目的胃食管反流相关慢性咳嗽(GERC)是胃食管反流病(GERD)的一种食管外表现。尽管24小时MII-pH监测是诊断GERC的金标准,但其侵入性、高成本和有限的可及性阻碍了在许多临床环境中的广泛应用。本研究旨在开发一种非侵入性机器学习模型,结合Peptest™和GerdQ评分,以促进GERC检测,特别是在初级保健和资源有限的环境中,不易获得MII-pH检测。方法在2022年9月至2024年6月期间招募210例慢性咳嗽患者。GERC诊断遵循既定指南,并通过Peptest™检测唾液胃蛋白酶水平。特征选择使用Boruta算法(以下简称Boruta),这是一种基于随机森林(RF)的方法,旨在通过将相关变量与随机阴影特征进行比较来识别相关变量。然后使用包括逻辑回归(LR), RF等在内的九种ML模型对所选的最佳特征进行评估。通过曲线下面积(AUC)、决策曲线分析(DCA)和校准曲线来评估模型的性能。结果73例(34.76%)患者有GERC。Peptest™和GerdQ评分是关键预测指标。选择Logistic回归以平衡其准确性(AUC: 0.876)和临床实用性。模态图模型具有良好的判别性和定标性。DCA表明,在0.10-0.90的预测阈值下,净效益较高。RCS分析显示出非线性关系:GerdQ >为8.66 ng/ml, Peptest™>为54.791 ng/ml时,GERC风险增加。结论nomogram模型为GERC的诊断提供了一种可靠的、无创的诊断工具,有助于临床及时干预,尤其是对不适合进行pH检测的患者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Chronic Respiratory Disease
Chronic Respiratory Disease RESPIRATORY SYSTEM-
CiteScore
5.90
自引率
7.30%
发文量
47
审稿时长
11 weeks
期刊介绍: Chronic Respiratory Disease is a peer-reviewed, open access, scholarly journal, created in response to the rising incidence of chronic respiratory diseases worldwide. It publishes high quality research papers and original articles that have immediate relevance to clinical practice and its multi-disciplinary perspective reflects the nature of modern treatment. The journal provides a high quality, multi-disciplinary focus for the publication of original papers, reviews and commentary in the broad area of chronic respiratory disease, particularly its treatment and management.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信