Prediction models for the development of COPD: A systematic review

Melanie C. Matheson, Gayan Bowatte, Jennifer L. Perret, Adrian J. Lowe, Chamara V. Senaratna, Graham L. Hall, Nick de Klerk, Louise A. Keogh, Christine F. McDonald, Nilakshi T. Waidyatillake, Peter D. Sly, Deborah Jarvis, Michael J. Abramson, Caroline J. Lodge, Shyamali C. Dharmage

Research output: Contribution to journalReview ArticleResearchpeer-review

21 Citations (Scopus)


Early identification of people at risk of developing COPD is crucial for implementing preventive strategies. We aimed to systematically review and assess the performance of all published models that predicted development of COPD. A search was conducted to identify studies that developed a prediction model for COPD development. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was followed when extracting data and appraising the selected studies. Of the 4,481 records identified, 30 articles were selected for full-text review, and only four of these were eligible to be included in the review. The only consistent predictor across all four models was a measure of smoking. Sex and age were used in most models; however, other factors varied widely. Two of the models had good ability to discriminate between people who were correctly or incorrectly classified as at risk of developing COPD. Overall none of the models were particularly useful in accurately predicting future risk of COPD, nor were they good at ruling out future risk of COPD. Further studies are needed to develop new prediction models and robustly validate them in external cohorts.

Original languageEnglish
Pages (from-to)1927-1935
Number of pages9
JournalInternational Journal of Chronic Obstructive Pulmonary Disease
Publication statusPublished - 14 Jun 2018


  • COPD
  • Early detection
  • Predictors and risk prediction models

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