Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/18023
Title: Prediction models for the development of COPD: a systematic review.
Austin Authors: Matheson, Melanie C;Bowatte, Gayan;Perret, Jennifer L ;Lowe, Adrian J;Senaratna, Chamara V;Hall, Graham L;de Klerk, Nick;Keogh, Louise A;McDonald, Christine F ;Waidyatillake, Nilakshi T;Sly, Peter D;Jarvis, Deborah;Abramson, Michael J;Lodge, Caroline J;Dharmage, Shyamali C
Affiliation: Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
School of Public Health & Preventive Medicine, Monash University, Melbourne, Victoria, Australia
Murdoch Children's Research Institute, Melbourne, Victoria, Australia
National Institute of Fundamental Studies, Kandy, Sri Lanka
Department of Community Medicine, University of Sri Jayewardenepura, Nugegoda, Sri Lanka
Telethon Kids Institute, Perth, WA, Australia
School of Physiotherapy and Exercise Science, Curtin University, Perth, WA, Australia
Centre of Child Health Research, University of Western Australia, Perth, WA, Australia
Centre for Health Equity, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
Population Health and Occupational Diseases, National Heart and Lung Institute, Imperial College London, London, UK
Institute for Breathing and Sleep
Respiratory and Sleep Medicine
Issue Date: 14-Jun-2018
Date: 2018-06-14
Publication information: International Journal of Chronic Obstructive Pulmonary Disease 2018; 13: 1927-1935
Abstract: 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.
URI: https://ahro.austin.org.au/austinjspui/handle/1/18023
DOI: 10.2147/COPD.S155675
ORCID: 0000-0001-6481-3391
Journal: International Journal of Chronic Obstructive Pulmonary Disease
PubMed URL: 29942125
Type: Journal Article
Subjects: COPD
early detection
predictors and risk prediction models
Appears in Collections:Journal articles

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