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Multicenter Study
. 2022 Apr;38(4):501-510.
doi: 10.1080/03007995.2022.2029382. Epub 2022 Feb 1.

The importance of association of comorbidities on COVID-19 outcomes: a machine learning approach

José Carlos Arévalo-Lorido  1 , Juana Carretero-Gómez  1 , Jose Manuel Casas-Rojo  2 , Juan Miguel Antón-Santos  2 , José Antonio Melero-Bermejo  2 , Maria Dolores López-Carmona  3 , Lidia Cobos Palacios  3 , Jaime Sanz-Cánovas  3 , Paula Maria Pesqueira-Fontán  4 , Andrés Alberto de la Peña-Fernández  5 , Navas-Maria de la Sierra Alcántara  6 , Gema Maria García-García  1 , José David Torres Peña  7   8   9 , Jeffrey Oskar Magallanes-Gamboa  10 , Rosa Fernández-Madera-Martinez  11 , Javier Fernández-Fernández  12 , Manuel Rubio-Rivas  13   14 , Guillermo Maestro-de la Calle  15 , Eva Cervilla-Muñoz  16 , Antonio Ramos-Martínez  17 , Manuel Méndez-Bailón  18 , José Manuel Ramos-Rincón  19 , Ricardo Gómez-Huelgas  3   20 ; SEMI-COVID-19 Network
Affiliations
Multicenter Study

The importance of association of comorbidities on COVID-19 outcomes: a machine learning approach

José Carlos Arévalo-Lorido et al. Curr Med Res Opin. 2022 Apr.

Abstract

Background: The individual influence of a variety of comorbidities on COVID-19 patient outcomes has already been analyzed in previous works in an isolated way. We aim to determine if different associations of diseases influence the outcomes of inpatients with COVID-19.

Methods: Retrospective cohort multicenter study based on clinical practice. Data were taken from the SEMI-COVID-19 Registry, which includes most consecutive patients with confirmed COVID-19 hospitalized and discharged in Spain. Two machine learning algorithms were applied in order to classify comorbidities and patients (Random Forest -RF algorithm, and Gaussian mixed model by clustering -GMM-). The primary endpoint was a composite of either, all-cause death or intensive care unit admission during the period of hospitalization. The sample was randomly divided into training and test sets to determine the most important comorbidities related to the primary endpoint, grow several clusters with these comorbidities based on discriminant analysis and GMM, and compare these clusters.

Results: A total of 16,455 inpatients (57.4% women and 42.6% men) were analyzed. According to the RF algorithm, the most important comorbidities were heart failure/atrial fibrillation (HF/AF), vascular diseases, and neurodegenerative diseases. There were six clusters: three included patients who met the primary endpoint (clusters 4, 5, and 6) and three included patients who did not (clusters 1, 2, and 3). Patients with HF/AF, vascular diseases, and neurodegenerative diseases were distributed among clusters 3, 4 and 5. Patients in cluster 5 also had kidney, liver, and acid peptic diseases as well as a chronic obstructive pulmonary disease; it was the cluster with the worst prognosis.

Conclusion: The interplay of several comorbidities may affect the outcome and complications of inpatients with COVID-19.

Keywords: COVID-19; SARS-CoV-2; cluster analysis; comorbidity; machine learning.

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