Prediction models for the development of COPD: a systematic review

Melanie C Matheson1 Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia;2 Murdoch Children’s Research Institute, Melbourne, VIC, Australia

Gayan Bowatte1 Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia;3 National Institute of Fundamental Studies, Kandy, Sri Lanka

Jennifer L Perret1 Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia;4 Department of Respiratory and Sleep Medicine, Institute for Breathing and Sleep, Austin Health, University of Melbourne, Melbourne, VIC, Australia

Adrian J Lowe1 Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia;2 Murdoch Children’s Research Institute, Melbourne, VIC, Australia

Chamara V Senaratna1 Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia;5 Department of Community Medicine, University of Sri Jayewardenepura, Nugegoda, Sri Lanka

Graham L Hall6 Telethon Kids Institute, Perth, WA, Australia;7 School of Physiotherapy and Exercise Science, Curtin University, Perth, WA, Australia;8 Centre of Child Health Research, University of Western Australia, Perth, WA, Australia

Nick de Klerk6 Telethon Kids Institute, Perth, WA, Australia;8 Centre of Child Health Research, University of Western Australia, Perth, WA, Australia

Louise A Keogh9 Centre for Health Equity, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia

Christine F McDonald4 Department of Respiratory and Sleep Medicine, Institute for Breathing and Sleep, Austin Health, University of Melbourne, Melbourne, VIC, Australia

Nilakshi T Waidyatillake1 Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia

Peter D Sly10 Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia

Deborah Jarvis11 MRC-PHE Centre for Environment and Health, Imperial College London, London, UK;12 Population Health and Occupational Diseases, National Heart and Lung Institute, Imperial College London, London, UK

Michael J Abramson13 School of Public Health & Preventive Medicine, Monash University, Melbourne, VIC, Australia

Caroline J Lodge1 Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia;2 Murdoch Children’s Research Institute, Melbourne, VIC, Australia

Shyamali C Dharmage1 Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia;2 Murdoch Children’s Research Institute, Melbourne, VIC, AustraliaCorrespondence[email protected]

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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.

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Acknowledgments

This project was funded by European Union’s Horizon 2020 research and innovation programme (Ageing Lungs in European Cohorts [ALEC] Study under grant agreement no 633212), and Australian National Health and Medical Research Council European Union collaboration grant ID1101313.

Author contributions

MC Matheson is the guarantor of the paper. MC Matheson, SC Dharmage, CJ Lodge, G Bowatte, and CV Senaratna: conception, design, literature search, data interpretation and writing the manuscript.

JL Perret, AJ Lowe, GL Hall, PD Sly, N de Klerk, L Keogh, NT Waidyatillake, CF McDonald, D Jarvis, and MJ Abramson: conception, design, and writing the manuscript. All authors contributed toward data analysis, drafting and revising the paper and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.

Prediction models for the development of COPD: a systematic review

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Prediction models for the development of COPD: a systematic review

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