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Vol. 61. Issue 5.
Pages 264-273 (May 2025)
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Vol. 61. Issue 5.
Pages 264-273 (May 2025)
Original Article
Clinical and Social Characterization of Patients Hospitalized for COPD Exacerbation Using Machine Learning Tools
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Manuel Casal-Guisandea,b,d,
Corresponding author
manuel.casal.guisande@uvigo.es

Corresponding author.
, Cristina Represas-Represasb,c,d, Rafael Golpee, Alberto Fernández-Garcíab,f, Almudena González-Montaosb,c, Alberto Comesaña-Camposg,h, Alberto Ruano-Raviñai,j,k, Alberto Fernández-Villarb,c,d
a Fundación Pública Galega de Investigación Biomédica Galicia Sur, Hospital Álvaro Cunqueiro (Vigo), Spain
b NeumoVigo I+i, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO , Spain
c Pulmonary Department, Hospital Álvaro Cunqueiro (Vigo), Spain
d Centro de Investigación Biomédica en Red Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III (ISCIII), Spain
e Pulmonary Department, Hospital Lucus Augusti (Lugo), Spain
f Department of Radiodiagnosis, Hospital Ribera POVISA (Vigo), Spain
g Department of Design in Engineering, Universidade de Vigo (Vigo), Spain
h DESAINS, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
i Department of Preventive Medicine and Public Health, Universidade de Santiago de Compostela (Santiago de Compostela), Spain
j Health Research Institute of Santiago de Compostela (IDIS), Spain
k Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III (ISCIII), Spain
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Tables (2)
Table 1. Cohort and Cluster Characteristics.
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Table 2. Comparison of Indicators of Medical Resource Consumption and Prognostic Indicators Between Clusters.
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Abstract
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.

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.

Results

The cohort consisted of 524 patients, with an average age of 70.30±9.35 years, 77.67% male, and an average FEV1 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.

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.

Keywords:
Chronic obstructive pulmonary disease
Exacerbation
Mortality
Social determinants of health
Machine learning
Clustering
Intelligent clinical decision support system
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