| Authors | Matheson, M.C. Bowatte, G. Perret, J.L. Lowe, A.J. Senaratna, C.V. Hall, G.L. de Klerk, N. Keogh, L.A. McDonald, C.F. Waidyatillake, N.T. Sly, P.D. Jarvis, D. Abramsom, M.J. Lodge, C.J. Dharmage, S.C. |
|---|---|
| Type | Journal Article (Review) |
| Journal | International Journal of COPD |
| Year of Publication | 2018 |
| URL | https://www.dovepress.com/prediction-models-for-the-development-of-copd-a-systematic-review-peer-reviewed-article-COPD |
| DOI | https://doi.org/10.2147/COPD.S155675 |
| Download |  copd-155675-prediction-models-for-the-development-of-copd-a-systematic-061318.pdf (539.2 KB) |
| Abstract | Background: 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.Methods: A search was conducted to identify studies that developed a new prediction model for COPD development. The Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist was followed when extracting data and appraising the selected studies.Results: Of the 4481 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.The overall predictive performance of the models was unable to be fully assessed due to limitations in the data presented. Two of the models had good ability to discriminate between people who were correctly or incorrectly classified as at risk of developing COPD (concordance statistic 0.830-0.845).Conclusions: 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. |
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