Original
Clinical and social characterization of patients hospitalized for COPD exacerbation using Machine Learning tools
Manuel Casal-Guisande1,
, Cristina Represas-Represas2, Rafael Golpe-Gómez3, Alberto Fernández-García4, Almudena González-Montaos5, Alberto Comesaña-Campos6, Alberto Ruano-Raviña7, Alberto Fernández-Villar8
Corresponding author
manuel.casal.guisande@uvigo.es
Contact details of the corresponding author: Fundación Biomédica Galicia Sur (Vigo). Instituto de Investigación Sanitaria Galicia Sur, NeumoVigo I+i (Vigo), Spain
Contact details of the corresponding author: Fundación Biomédica Galicia Sur (Vigo). Instituto de Investigación Sanitaria Galicia Sur, NeumoVigo I+i (Vigo), Spain
1 Fundación Biomédica Galicia Sur (Vigo). Instituto de Investigación Sanitaria Galicia Sur, NeumoVigo I+i (Vigo), Spain
2 Pulmonary Department, Hospital Álvaro Cunqueiro (Vigo). Instituto de Investigación Sanitaria Galicia Sur, NeumoVigo I+i (Vigo). Centro de Investigación Biomédica en Red Enfermedades Respiratorias (CIBERES). Instituto de Salud Carlos III, Spain
3 Pulmonary Department, Hospital Lucus Augusti (Lugo), Spain
4 Servicio de Diagnóstico por Imagen, Hospital Ribera POVISA (Vigo). Instituto de Investigación Sanitaria Galicia Sur, NeumoVigo I+i (Vigo), Spain
5 Pulmonary Department, Hospital Álvaro Cunqueiro (Vigo). Instituto de Investigación Sanitaria Galicia Sur, NeumoVigo I+i (Vigo), Spain
6 Department of Design in Engineering, Universidade de Vigo (Vigo). Instituto de Investigación Sanitaria Galicia Sur, DESAINS (Vigo), Spain
7 Department of Preventive Medicine and Public Health, Universidade de Santiago de Compostela (Santiago de Compostela). Health Research Institute of Santiago de Compostela (IDIS). Centro de Investigación Biomédica en Epidemiología y Salud Pública (CIBERESP). Instituto de Salud Carlos III, Spain
8 Pulmonary Department, Hospital Álvaro Cunqueiro (Vigo). Instituto de Investigación Sanitaria Galicia Sur, NeumoVigo I+i (Vigo). Centro de Investigación Biomédica en Red Enfermedades Respiratorias (CIBERES). Instituto de Salud Carlos III, Spain
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Instituto de Investigación Sanitaria Galicia Sur, NeumoVigo I+i (Vigo), Spain" ] ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2024-07-04" "fechaAceptado" => "2024-10-26" "PalabrasClave" => array:1 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1903915" "palabras" => array:7 [ 0 => "Chronic Obstructive Pulmonary Disease" 1 => "Exacerbation" 2 => "Mortality" 3 => "Social determinants of health" 4 => "Machine Learning" 5 => "Clustering" 6 => "Intelligent Clinical Decision Support System" ] ] ] ] "tieneResumen" => true "resumen" => array:1 [ "en" => array:2 [ "titulo" => "Abstract" "resumen" => "<span id="abst0010" class="elsevierStyleSection elsevierViewall"><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Objective: This study aims to employ machine learning (ML) tools to cluster patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease (COPD) based on their diverse social and clinical characteristics. This clustering is intended to facilitate the subsequent analysis of differences in clinical outcomes.</p><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Methods: We analysed a cohort of patients with severe COPD from two Pulmonary Departments in north-western Spain using the k-prototypes algorithm, incorporating demographic, clinical, and social data. The resulting clusters were correlated with metrics such as readmissions, mortality, and place of death. Additionally, we developed an Intelligent Clinical Decision Support System (ICDSS) using a supervised ML model (Random Forest) to assign new patients to these clusters based on a reduced set of variables.</p><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Results: The cohort consisted of 524 patients, with an average age of 70.30 ± 9.35 years, 77.67% male, and an average FEV<span class="elsevierStyleInf">1</span> of 44.43 ± 15.4. Four distinct clusters (A-D) were identified with varying clinical-demographic and social profiles. Cluster D showed the highest levels of dependency, social isolation, and increased rates of readmissions and mortality. Cluster B was characterized by prevalent cardiovascular comorbidities. Cluster C included a younger demographic, with a higher proportion of women and significant psychosocial challenges. The ICDSS, using five key variables, achieved areas under the ROC curve of at least 0.91.</p><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Conclusions: ML tools effectively facilitate the social and clinical clustering of patients with severe COPD, closely related to resource utilization and prognostic profiles. The ICDSS enhances the ability to characterize new patients in clinical settings.</p></span>" ] ] ] "idiomaDefecto" => "en" "url" => "/03002896/unassign/S0300289624004137/v1_202411040408/en/main.assets" "Apartado" => null "PDF" => "https://static.elsevier.es/multimedia/03002896/unassign/S0300289624004137/v1_202411040408/en/main.pdf?idApp=UINPBA00003Z&text.app=https://archbronconeumol.org/" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0300289624004137?idApp=UINPBA00003Z" ]
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