RT Journal Article
SR Electronic
T1 Prediction of persistent chronic cough in patients with chronic cough using machine learning
JF ERJ Open Research
JO erjor
FD European Respiratory Society
SP 00471-2022
DO 10.1183/23120541.00471-2022
VO 9
IS 2
A1 Wansu Chen
A1 Michael Schatz
A1 Yichen Zhou
A1 Fagen Xie
A1 Vishal Bali
A1 Amar Das
A1 Jonathan Schelfhout
A1 Julie A. Stern
A1 Robert S. Zeiger
YR 2023
UL http://openres.ersjournals.com/content/9/2/00471-2022.abstract
AB Introduction 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.Methods Two retrospective cohorts of patients 18–85 years of age were identified for years 2011–2016: 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.Results 8581 and 52 010 patients met the eligibility criteria for the specialist and event cohorts (mean age 60.0 and 55.5 years), 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: 0.74–0.76 for utilisation-based models and 0.71 for diagnosis-based models).Conclusions 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.Prediction of persistent chronic cough https://bit.ly/3V3vVzf