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

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
• comorbidity
• cluster analysis
• machine learning

Transparency

Declaration of funding

There was no sponsorship/funding for this manuscript.

Declaration of financial/other relationships

The authors of the present manuscript declare they have not any conflict of interest. Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

JC Arevalo-Lorido, Juana Carretero-Gomez, José Manuel Casas-Rojo and Ricardo Gómez-Huelgas were involved in the conception, design and interpretation of the data. José Manuel Casas-Rojo was additionally involved in data curation; José Carlos Arévalo-Lorido was additionally involved in the analysis of the data. All the authors were involved in the drafting of the paper or in its critical review for intellectual content; and the final approval of the version to be published. All authors agree to be accountable for all aspects of the work.

Acknowledgements

Authors gratefully acknowledge all the investigators who participate in the SEMI-COVID-19 registry.