{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T02:43:53Z","timestamp":1794278633412,"version":"4.9.0"},"reference-count":31,"publisher":"European Respiratory Society (ERS)","issue":"2","license":[{"start":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T00:00:00Z","timestamp":1674691200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009947","name":"Merck Sharp and Dohme","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009947","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["publications.ersnet.org"],"crossmark-restriction":true},"short-container-title":["ERJ Open Res"],"accepted":{"date-parts":[[2022,12,11]]},"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>The aim of this study was to develop and validate prediction models for risk of persistent chronic cough (PCC) in patients with chronic cough (CC). This was a retrospective cohort study.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Two retrospective cohorts of patients 18\u201385\u2005years of age were identified for years 2011\u20132016: a specialist cohort which included CC patients diagnosed by specialists, and an event cohort which comprised CC patients identified by at least three cough events. A cough event could be a cough diagnosis, dispensing of cough medication or any indication of cough in clinical notes. Model training and validation were conducted using two machine-learning approaches and 400+ features. Sensitivity analyses were also conducted. PCC was defined as a CC diagnosis or any two (specialist cohort) or three (event cohort) cough events in year 2 and again in year 3 after the index date.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>8581 and 52\u2009010 patients met the eligibility criteria for the specialist and event cohorts (mean age 60.0 and 55.5\u2005years), respectively. 38.2% and 12.4% of patients in the specialist and event cohorts, respectively, developed PCC. The utilisation-based models were mainly based on baseline healthcare utilisations associated with CC or respiratory diseases, while the diagnosis-based models incorporated traditional parameters including age, asthma, pulmonary fibrosis, obstructive pulmonary disease, gastro-oesophageal reflux, hypertension and bronchiectasis. All final models were parsimonious (five to seven predictors) and moderately accurate (area under the curve: 0.74\u20130.76 for utilisation-based models and 0.71 for diagnosis-based models).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The application of our risk prediction models may be used to identify high-risk PCC patients at any stage of the clinical testing\/evaluation to facilitate decision making.<\/jats:p><\/jats:sec>","DOI":"10.1183\/23120541.00471-2022","type":"journal-article","created":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T16:57:54Z","timestamp":1674752274000},"page":"00471-2022","update-policy":"https:\/\/doi.org\/10.1183\/ers-crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Prediction of persistent chronic cough in patients with chronic cough using machine learning"],"prefix":"10.1183","volume":"9","author":[{"given":"Wansu","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Schatz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichen","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fagen","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vishal","family":"Bali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amar","family":"Das","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Schelfhout","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julie A.","family":"Stern","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert S.","family":"Zeiger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"81","published-online":{"date-parts":[[2023,1,26]]},"reference":[{"key":"2024101711483190000_9.2.00471-2022.1","doi-asserted-by":"publisher","DOI":"10.1378\/chest.129.1_suppl.59S"},{"key":"2024101711483190000_9.2.00471-2022.2","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1016\/j.jaip.2019.04.012","article-title":"What is chronic cough? 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New diagnosis codes effective Oct. 1. Here are some family physicians should know. https:\/\/www.aafp.org\/pubs\/fpm\/blogs\/gettingpaid\/entry\/new_diagnosis_codes.html Date last updated: 1 August 2021. Date last accessed: 30 January 2023."}],"container-title":["ERJ Open Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1183\/23120541.00471-2022","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T05:23:16Z","timestamp":1740374596000},"score":1,"resource":{"primary":{"URL":"https:\/\/publications.ersnet.org\/lookup\/doi\/10.1183\/23120541.00471-2022"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,26]]},"references-count":31,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,3,29]]},"published-print":{"date-parts":[[2023,3]]}},"alternative-id":["10.1183\/23120541.00471-2022"],"URL":"https:\/\/doi.org\/10.1183\/23120541.00471-2022","relation":{"has-part":[{"id-type":"doi","id":"https:\/\/doi.org\/10.1183\/23120541.00471-2022.supp1","asserted-by":"subject"}]},"ISSN":["2312-0541"],"issn-type":[{"value":"2312-0541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,26]]}}}