Ten-year prediction model for post-bronchodilator airflow obstruction and early detection of COPD: Development and validation in two middle-aged population-based cohorts

Jennifer L. Perret, Don Vicendese, Koen Simons, Debbie L. Jarvis, Adrian J. Lowe, Caroline J. Lodge, Dinh S. Bui, Daniel Tan, John A. Burgess, Bircan Erbas, Adrian Bickerstaffe, Kerry Hancock, Bruce R. Thompson, Garun S. Hamilton, Robert Adams, Geza P. Benke, Paul S. Thomas, Peter Frith, Christine F. Mcdonald, Tony BlakelyMichael J. Abramson, E. Haydn Walters, Cosetta Minelli, Shyamali C. Dharmage, TAHS and ECRHS Investigator Groups

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Abstract

Background: Classifying individuals at high chronic obstructive pulmonary disease (COPD)-risk creates opportunities for early COPD detection and active intervention. Objective: To develop and validate a statistical model to predict 10-year probabilities of COPD defined by post-bronchodilator airflow obstruction (post-BD-AO; forced expiratory volume in 1 s/forced vital capacity<5th percentile). Setting: General Caucasian populations from Australia and Europe, 10 and 27 centres, respectively. Participants: For the development cohort, questionnaire data on respiratory symptoms, smoking, asthma, occupation and participant sex were from the Tasmanian Longitudinal Health Study (TAHS) participants at age 41-45 years (n=5729) who did not have self-reported COPD/emphysema at baseline but had post-BD spirometry and smoking status at age 51-55 years (n=2407). The validation cohort comprised participants from the European Community Respiratory Health Survey (ECRHS) II and III (n=5970), restricted to those of age 40-49 and 50-59 with complete questionnaire and spirometry/smoking data, respectively (n=1407). Statistical method: Risk-prediction models were developed using randomForest then externally validated. Results: Area under the receiver operating characteristic curve (AUC ROC) of the final model was 80.8% (95% CI 80.0% to 81.6%), sensitivity 80.3% (77.7% to 82.9%), specificity 69.1% (68.7% to 69.5%), positive predictive value (PPV) 11.1% (10.3% to 11.9%) and negative predictive value (NPV) 98.7% (98.5% to 98.9%). The external validation was fair (AUC ROC 75.6%), with the PPV increasing to 17.9% and NPV still 97.5% for adults aged 40-49 years with ≥1 respiratory symptom. To illustrate the model output using hypothetical case scenarios, a 43-year-old female unskilled worker who smoked 20 cigarettes/day for 30 years had a 27% predicted probability for post-BD-AO at age 53 if she continued to smoke. The predicted risk was 42% if she had coexistent active asthma, but only 4.5% if she had quit after age 43. Conclusion: This novel and validated risk-prediction model could identify adults aged in their 40s at high 10-year COPD-risk in the general population with potential to facilitate active monitoring/intervention in predicted 'COPD cases' at a much earlier age.

Original languageEnglish
Article numbere001138
JournalBMJ Open Respiratory Research
Volume8
Issue number1
DOIs
Publication statusPublished - 2 Dec 2021

Bibliographical note

Funding Information:
The TAHS was supported by the National Health and Medical Research Council (NHMRC) of Australia, research grants 299901 and 1021275; the University of Melbourne; Clifford Craig Foundation; the Victorian, Queensland and Tasmanian Asthma Foundations; Royal Hobart Hospital; Helen MacPherson Smith Trust; GlaxoSmithKline; and John L Hopper. JP, AL and SCD are funded through the NHMRC of Australia. The ECRHS was supported by grants from the European Commission (018996) and Medical Research Council (ECRHS III no. 92091), and multiple local grants that supported study testing centres of ECRHS II and III which have been listed in the acknowledgement section. These sponsors of the study had no role in study design, data collection, data analysis, data interpretation or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Publisher Copyright:
© 2021 Author(s) (or their employer(s)).

Keywords

  • clinical epidemiology
  • COPD epidemiology

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