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The cardiovascular phenotype of Chronic Obstructive Pulmonary Disease (COPD): Applying machine learning to the prediction of cardiovascular comorbidities - 21/09/21

Doi : 10.1016/j.rmed.2021.106528 
Vasilis Nikolaou a, , Sebastiano Massaro a, b, Wolfgang Garn a, Masoud Fakhimi a, Lampros Stergioulas c, David Price d, e, f
a University of Surrey, Surrey Business School, Guildford, GU2 7HX, United Kingdom 
b The Organizational Neuroscience Laboratory, London, WC1N 3AX, United Kingdom 
c The Hague University of Applied Sciences, Johanna Westerdijkplein, 75, 2521, EN Den Haag, Netherlands 
d Optimum Patient Care, Cambridge, UK 
e Observational and Pragmatic Research Institute, Singapore 
f Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, United Kingdom 

Corresponding author. University of Surrey, Surrey Business School, Alexander Fleming Rd, Guildford, GU2 7XH, United Kingdom.University of SurreySurrey Business SchoolAlexander Fleming RdGuildfordGU2 7XHUnited Kingdom

Abstract

Background

Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous group of lung conditions that are challenging to diagnose and treat. As the presence of comorbidities often exacerbates this scenario, the characterization of patients with COPD and cardiovascular comorbidities may allow early intervention and improve disease management and care.

Methods

We analysed a 4-year observational cohort of 6883 UK patients who were ultimately diagnosed with COPD and at least one cardiovascular comorbidity. The cohort was extracted from the UK Royal College of General Practitioners and Surveillance Centre database. The COPD phenotypes were identified prior to diagnosis and their reproducibility was assessed following COPD diagnosis. We then developed four classifiers for predicting cardiovascular comorbidities.

Results

Three subtypes of the COPD cardiovascular phenotype were identified prior to diagnosis. Phenotype A was characterised by a higher prevalence of severe COPD, emphysema, hypertension. Phenotype B was characterised by a larger male majority, a lower prevalence of hypertension, the highest prevalence of the other cardiovascular comorbidities, and diabetes. Finally, phenotype C was characterised by universal hypertension, a higher prevalence of mild COPD and the low prevalence of COPD exacerbations. These phenotypes were reproduced after diagnosis with 92% accuracy. The random forest model was highly accurate for predicting hypertension while ruling out less prevalent comorbidities.

Conclusions

This study identified three subtypes of the COPD cardiovascular phenotype that may generalize to other populations. Among the four models tested, the random forest classifier was the most accurate at predicting cardiovascular comorbidities in COPD patients with the cardiovascular phenotype.

Le texte complet de cet article est disponible en PDF.

Highlights

A large observational study that characterizes the COPD cardiovascular phenotype.
Three phenotypes were identified and reproduced to another population.
These phenotypes were characterised by different COPD severity and treatments.
Random Forest was highly accurate at predicting cardiovascular comorbidities.

Le texte complet de cet article est disponible en PDF.

Keywords : Cardiovascular subtypes, Machine learning, Cluster analysis, Random forest


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Vol 186

Article 106528- septembre 2021 Retour au numéro
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