Fast decliner phenotype of chronic obstructive pulmonary disease (COPD): applying machine learning for predicting lung function loss.

Autor:	Nikolaou V; University of Surrey, Surrey Business School, Guildford, UK v.nikolaou@surrey.ac.uk., Massaro S; University of Surrey, Surrey Business School, Guildford, UK.; The Organizational Neuroscience Laboratory, London, UK., Garn W; University of Surrey, Surrey Business School, Guildford, UK., Fakhimi M; University of Surrey, Surrey Business School, Guildford, UK., Stergioulas L; Hague University of Applied Sciences, Den Haag, The Netherlands., Price DB; Academic Primary Care, University of Aberdeen, Aberdeen, UK.; Optimum Patient Care, Cambridge, UK.; Observational and Pragmatic Research Institute, Singapore.
Jazyk:	angličtina
Zdroj:	BMJ open respiratory research [BMJ Open Respir Res] 2021 Oct; Vol. 8 (1).
DOI:	10.1136/bmjresp-2021-000980
Abstrakt:	Background: Chronic obstructive pulmonary disease (COPD) is a heterogeneous group of lung conditions challenging to diagnose and treat. Identification of phenotypes of patients with lung function loss may allow early intervention and improve disease management. We characterised patients with the 'fast decliner' phenotype, determined its reproducibility and predicted lung function decline after COPD diagnosis. Methods: A prospective 4 years observational study that applies machine learning tools to identify COPD phenotypes among 13 260 patients from the UK Royal College of General Practitioners and Surveillance Centre database. The phenotypes were identified prior to diagnosis (training data set), and their reproducibility was assessed after COPD diagnosis (validation data set). Results: Three COPD phenotypes were identified, the most common of which was the 'fast decliner'-characterised by patients of younger age with the lowest number of COPD exacerbations and better lung function-yet a fast decline in lung function with increasing number of exacerbations. The other two phenotypes were characterised by (a) patients with the highest prevalence of COPD severity and (b) patients of older age, mostly men and the highest prevalence of diabetes, cardiovascular comorbidities and hypertension. These phenotypes were reproduced in the validation data set with 80% accuracy. Gender, COPD severity and exacerbations were the most important risk factors for lung function decline in the most common phenotype. Conclusions: In this study, three COPD phenotypes were identified prior to patients being diagnosed with COPD. The reproducibility of those phenotypes in a blind data set following COPD diagnosis suggests their generalisability among different populations. Competing Interests: Competing interests: VN is an employee of Parexel. SM is the director of the Organisational Neuroscience Laboratory. DBP declares advisory board membership with Aerocrine, Amgen, AstraZeneca, Boehringer Ingelheim, Chiesi, Mylan, Mundipharma, Napp Pharmaceuticals, Novartis and Teva; consultancy agreements with Almirall, Amgen, AstraZeneca, Boehringer Ingelheim, Chiesi, GlaxoSmithKline, Mylan, Mundipharma, Napp Pharmaceuticals, Novartis, Pfizer, Teva and Theravance; grants and unrestricted funding for investigator-initiated studies (conducted through Observational and Pragmatic Research Institute) from Aerocrine, AKL Research and Development, AstraZeneca, Boehringer Ingelheim, British Lung Foundation, Chiesi, Mylan, Mundipharma, Napp Pharmaceuticals, Novartis, Pfizer, Respiratory Effectiveness Group, Teva, Theravance, UK National Health Service and Zentiva; payment for lectures/speaking engagements from Almirall, AstraZeneca, Boehringer Ingelheim, Chiesi, Cipla, GlaxoSmithKline, Kyorin, Mylan, Merck, Mundipharma, Novartis, Pfizer, Skyepharma and Teva; payment for manuscript preparation from Mundipharma and Teva; payment for the development of educational materials from Mundipharma and Novartis; payment for travel/accommodation/meeting expenses from Aerocrine, AstraZeneca, Boehringer Ingelheim, Mundipharma, Napp Pharmaceuticals, Novartis and Teva; funding for patient enrolment or completion of research from Chiesi, Novartis, Teva and Zentiva; stock/stock options from AKL Research and Development, which produces phytopharmaceuticals; owns 74% of the social enterprise Optimum Patient Care (Australia and UK) and 74% of Observational and Pragmatic Research Institute (Singapore);); 5% shareholding in Timestamp, which develops adherence monitoring technology; is peer reviewer for grant committees of the Efficacy and Mechanism Evaluation programme and Health Technology Assessment; and was an expert witness for GlaxoSmithKline. (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
Databáze:	MEDLINE
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