Afreixo, Vera

Enes, Vera

Machado, Ana Filipa

Marques, Alda

Tavares, Ana Helena

English

Repositório Institucional da Universidade de Aveiro

Clinical characteristics of patients with chronicobstructive pulmonary disease (COPD): are theydifferent?Vera Enes1, Ana Helena Tavares2, Vera Afreixo2, Filipa Machado3, AldaMarques1,31 Institute of Biomedicine (iBiMED), University of Aveiro, vera.enes@ua.pt2 Center for Research & Development in Mathematics and Applications (CIDMA),University of Aveiro3 Respiratory Research and Rehabilitation Laboratory (Lab3R), School of HealthSciences (ESSUA), University of AveiroChronic Obstructive Pulmonary Disease is a major public health problem knownto affect 800.000 people in Portugal. Symptoms include breathing difficulty,cough, fatigue and sputum. Although known that the disease progresses differ-ently in patients with the same level of airway obstruction, the clinical charac-teristics of patients that may be associated with different disease phenotypesare not fully understood. This study aims to enhance our knowledge on theclinical characteristics of patients with COPD. A clustering procedure was per-formed, based on lung function, oxygen saturation, muscle strength and impactof the disease on patients’ daily life and well-being.Keywords: COPD, Clustering, Principal Component AnalysisChronic Obstructive Pulmonary Disease (COPD) is a condition characterized by progres-sive and persistent airflow limitation resulting from a chronic inflammatory response of theairways and lungs in response to inhaled harmful gases and particles. Clinical diagnosis isbased on airflow obstruction (assessed with lung function test-spirometry) and symptoms.Its prognosis depends on several factors including acute exacerbations (define as worseningof symptoms that result in additional therapy), environmental exposures, comorbiditiesand genetic predisposition [2]. COPD is burdensome not only for economic and socialsystems but most importantly to patients since it significantly affect their quality of life.It is known that the disease does not progress in the same way in all patients and thatlung function, symptoms and reduction of quality of life may not be correlated. In fact,the interplay between patients’ clinical characteristics and different disease phenotypes isnot fully understood.This study aims to enhance our knowledge on the clinical characteristics of patients withCOPD. We retrospectively reviewed 394 patients with COPD. A clustering procedureis designed to stratify patients with COPD. From the 70 registered variables, we focus13 April, 10:30 - 10:50, Room A3XXVI Meeting of the Portuguese Association of Classification and Data Analysis, Viseu, 11-13 April 2019105on the most commonly assessed clinical variables: body mass index (BMI), age (AGE),the modified British Medical Research Council questionnaire (mMRC), number of acuteexacerbations (AECOPD), number of hospitalization by respiratory cause (nHosp), theCharlson comorbidity index (CCI), Peripheral oxygen saturation (SpO2), forced expiratoryvolume in one second (FEV1pp), quadriceps muscle strength (QMSpp), 1-minute sit-to-stand test (1STS), COPD assessment test total score (CAT), St. George’s RespiratoryQuestionnaire (SGRQ), dyspnoea and fatigue Borg scores (dysp.Borg and fat.Borg), andHospital Anxiety and Depression Scale (anx.HADS and dep.HADS). Other variables wereexcluded due to missing values.Clustering aims to find groups in a dataset. Since k-means looks for spherical clusters,it works best when the input variables are uncorrelated and have similar scales. In ourdataset several variables were strongly correlated, e.g. the Pearson correlation betweenCAT and SGRQ was 0.79. We apply Principal Component (PC) Analysis on these vectors,obtaining a set of values of linearly uncorrelated variables. The number of components toretain is selected such that at least a given percentage of the variance is explained. Thescores associated to those first PCs yield a data matrix, on which the k-means clusteringalgorithm is applied. The result of k-means depends on the number of clusters k, whichis often hard to choose a priori. Therefore it is common practice to run the method forseveral values of k, and then select the ‘best’ value of k as the one which optimizes a certaincriterion called a validity index. Many such indices have been proposed in the literature.Here we consider the Calinski-Harabasz index [1] and the GAP statistic [3].Our procedure retained 6 principal components that explained 70% of the total variance ofthe dataset. Carrying out k-means clustering for different numbers of clusters yields andevaluating the obtained validation indices it appears that 3 or 5 clusters are appropriate.By looking at the composition of each cluster we define a patient prototype of each cluster.Acknowledgements This work was funded by Programa Operacional de Competitividade e Interna-cionalizac¸a˜o – POCI, through Fundo Europeu de Desenvolvimento Regional - FEDER (POCI-01-0145-FEDER-007628 and POCI-01-0145-FEDER-028806), Fundac¸a˜o para a Cieˆncia e Tecnologia (PTDC/DTP-PIC/2284/2014 and PTDC/SAU-SER/28806/2017). Moreover, the costs resulting from the FCT hirings isfunded by national funds (OE), through FCT, I.P., in the scope of the framework contract foreseen in thenumbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017,of July 19. The work of VA and AT is partially funded by FCT under project UID/MAT/04106/2019.References[1] Tadeusz Calin´ski and Jerzy Harabasz. A dendrite method for cluster analysis. Communica-tions in Statistics - Theory and Methods, 3(1):1–27, 1974.[2] Global Initiative for Chronic Obstructive Lung Disease. Global strategy for the diagnosis,management, and prevention of chronic obstructive pulmonary disease (2019 report), 2019.[3] Robert Tibshirani, Guenther Walther, and Trevor Hastie. Estimating the number of clustersin a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (StatisticalMethodology), 63(2):411–423, 2001.XXVI Meeting of the Portuguese Association of Classification and Data Analysis, Viseu, 11-13 April 2019106

Clinical characteristics of patients with chronic obstructive pulmonary disease (COPD): are they different

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Clinical characteristics of patients with chronic obstructive pulmonary disease (COPD): are they different

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