Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach

Ramón, Antonio; Bas, Andrés; Herrero, Santiago; Blasco, Pilar; Suárez, Miguel; Mateo, Jorge

doi:10.3390/jcm13071837

Open AccessArticle

Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach

by

Antonio Ramón

^1,2

,

Andrés Bas

¹,

Santiago Herrero

¹,

Pilar Blasco

^1,2,

Miguel Suárez

^2,3

and

Jorge Mateo

^2,4,*

¹

Department of Pharmacy, University General Hospital, 46014 Valencia, Spain

²

Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain

³

Department of Gastroenterology, Virgen de la Luz Hospital, 16002 Cuenca, Spain

⁴

Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain

^*

Author to whom correspondence should be addressed.

J. Clin. Med. 2024, 13(7), 1837; https://doi.org/10.3390/jcm13071837

Submission received: 21 February 2024 / Revised: 15 March 2024 / Accepted: 20 March 2024 / Published: 22 March 2024

(This article belongs to the Special Issue COVID-19 Treatments and Therapeutics)

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Abstract

Background: Despite advancements in vaccination, early treatments, and understanding of SARS-CoV-2, its impact remains significant worldwide. Many patients require intensive care due to severe COVID-19. Remdesivir, a key treatment option among viral RNA polymerase inhibitors, lacks comprehensive studies on factors associated with its effectiveness. Methods: We conducted a retrospective study in 2022, analyzing data from 252 hospitalized COVID-19 patients treated with remdesivir. Six machine learning algorithms were compared to predict factors influencing remdesivir’s clinical benefits regarding mortality and hospital stay. Results: The extreme gradient boost (XGB) method showed the highest accuracy for both mortality (95.45%) and hospital stay (94.24%). Factors associated with worse outcomes in terms of mortality included limitations in life support, ventilatory support needs, lymphopenia, low albumin and hemoglobin levels, flu and/or coinfection, and cough. For hospital stay, factors included vaccine doses, lung density, pulmonary radiological status, comorbidities, oxygen therapy, troponin, lactate dehydrogenase levels, and asthenia. Conclusions: These findings underscore XGB’s effectiveness in accurately categorizing COVID-19 patients undergoing remdesivir treatment.

Keywords: COVID-19; hospital stay; machine learning; mortality; SARS-CoV-2; remdesivir; XGB

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MDPI and ACS Style

Ramón, A.; Bas, A.; Herrero, S.; Blasco, P.; Suárez, M.; Mateo, J. Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach. J. Clin. Med. 2024, 13, 1837. https://doi.org/10.3390/jcm13071837

AMA Style

Ramón A, Bas A, Herrero S, Blasco P, Suárez M, Mateo J. Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach. Journal of Clinical Medicine. 2024; 13(7):1837. https://doi.org/10.3390/jcm13071837

Chicago/Turabian Style

Ramón, Antonio, Andrés Bas, Santiago Herrero, Pilar Blasco, Miguel Suárez, and Jorge Mateo. 2024. "Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach" Journal of Clinical Medicine 13, no. 7: 1837. https://doi.org/10.3390/jcm13071837

APA Style

Ramón, A., Bas, A., Herrero, S., Blasco, P., Suárez, M., & Mateo, J. (2024). Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach. Journal of Clinical Medicine, 13(7), 1837. https://doi.org/10.3390/jcm13071837

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Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach

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