MORTALITY RISK PREDICTION IN PATIENTS TREATED WITH REMDESIVIR FOR SARS-CoV-2 OMICRON INFECTION

Background. Remdesivir is used for antiviral treatment in patients with COVID-19. In this population, early quantitative assessment of mortality risk remains a clinical priority.

Aim of the study is to develop and validate prognostic model for predicting risk of lethal outcomes in hospitalized COVID-19 patients treated with remdesivir.

Methods. The study included patients aged 18 years and older with a diagnosis of COVID-19 and treatment with remdesivir (N = 1428). The observations were divided into training, internal validation and external validation sets. Following selection using LASSO regression, predictors were included in multivariable logistic regression model. Optimal classification thresholds were determined with Youden’s metric on internal validation set. Model performance was assessed in the external validation cohort (n = 487) using the AUC, sensitivity, and specificity. Clinical utility of the model was assessed using decision curves analysis. Statistical analysis was performed in R version 4.4.1 with libraries tidyr, dplyr, comorbidity, glmnet, caret, boot, calibrationCurves, pROC, rms, dcurves.

Results. LASSO regression selected C-reactive protein, lactate dehydrogenase, and D-dimer as most stable predictors. According to the multivariable logistic regression model, C-reactive protein (per 10 mg/L), OR 1.098, p < 0.001; lactate dehydrogenase (per 50 U/L), OR = 1.276, p < 0.001; and D-dimers > 400 ng/mL, OR = 5.829, p < 0.001, were independently associated with lethal outcome. Optimal classification threshold on internal validation set was 4.9 %. On external validation cohort, the model achieved AUC of 89.5 %, with sensitivity of 93.8 % and specificity of 72.9 %. Decision curve analysis demonstrated that the model provides a clinical net benefit across all threshold probabilities compared to alternative management strategies.

Conclusion. We developed and validated prognostic model for predicting lethal outcomes in COVID-19 patients receiving remdesivir treatment. The model is presented as a nomogram for convenient clinical application.

1. WHO COVID-19 dashboard / World Health Organization. – Electronic data. – Geneva: WHO, 2026. – Available at: https://data.who.int/dashboards/covid19/cases. – Accessed: 09.03.2026.

2. COVID-19 epidemiological update – 14 March 2025 / World Health Organization. – Geneva: WHO, 2025. – 31 p. – Available at: https://www.who.int/publications/m/item/covid-19epidemiological-update-edition-177. – Accessed: 09.03.2026.

3. Remdesivir for the Treatment of Covid-19 – Final Report /j. H. Beigel, K. M. Tomashek, L. E. Dodd [et al.] // The New England Journal of Medicine. – 2020. – Vol. 383, № 19. – P. 1813–1826. – doi: 10.1056/NEJMoa2007764.

4. Infectious Diseases Society of America Guidelines on the Treatment and Management of Patients With COVID-19 (September 2022) / A. Bhimraj, R. L. Morgan, A. H. Shumaker [et al.] // Clinical Infectious Diseases. – 2024. – Vol. 78, № 7. – P. e250–e349. – doi: 10.1093/cid/ciac724.

5. Рекомендации (временные) об организации оказания медицинской помощи пациентам с инфекцией COVID-19: [утв. Приказом Министерства здравоохранения Респ. Беларусь, 21 июля 2021 г., № 900]. – Минск, 2021. – 75 с. Rekomendacii (vremennye) ob organizacii okazaniya medicinskoj pomoshchi pacientam s infekciej COVID-19 [Recommendations (temporary) on the organization of medical care for patients with COVID-19 infection]: [utv. Prikazom Ministerstva zdravoohraneniya Resp. Belarus', 21 iyulya 2021 g., № 900]. – Minsk, 2021. – 75 s. [In Russian].

6. Remdesivir for 5 or 10 Days in Patients with Severe Covid-19 /j. D. Goldman, D. C. B. Lye, D. S. Hui [et al.] // The New England Journal of Medicine. – 2020. – Vol. 383, № 19. – P. 1827–1837. – doi: 10.1056/NEJMoa2015301.

7. Bálint, G. Omicron: increased transmissibility and decreased pathogenicity / G. Bálint, B. Vörös-Horváth, A. Széchenyi // Signal Transduction and Targeted Therapy. – 2022. – Vol. 7, № 1. – P. 151. – doi: 10.1038/s41392-022-01009-8.

8. Impact of SARS-CoV-2 Omicron and Delta variants in patients requiring intensive care unit (ICU) admission for COVID-19, Northern Italy, December 2021 to January 2022 / A. Piralla, F. Mojoli, L. Pellegrinelli [et al.] // Respiratory Medicine and Research. – 2023. – Vol. 83. – P. 100990. – doi: 10.1016/j.resmer.2023.100990.

9. Prediction of in-hospital mortality with machine learning for COVID-19 patients treated with steroid and remdesivir / T. Kuno, Y. Sahashi, S. Kawahito [et al.] //journal of Medical Virology. – 2022. – Vol. 94, № 3. – P. 958–964. – doi: 10.1002/jmv.27393.

10. A Systematic Review of Predictor Composition, Outcomes, Risk of Bias, and Validation of COVID-19 Prognostic Scores / K. S. Appel, R. Geisler, D. Maier [et al.] // Clinical Infectious Diseases. – 2024. – Vol. 78, № 4. – P. 889–899. – doi: 10.1093/cid/ciad618.

11. Biomarkers and short-term prognosis in COVID-19 / Ó. M. Peiró, A. Carrasquer, R. Sánchez-Gimenez [et al.] // Biomarkers. – 2021. – Vol. 26, № 2. – P. 119–126. – doi: 10.1080/1354750X.2021.1874052.

12. Prognostic implications of neutrophil-lymphocyte ratio in COVID-19 / S. Jimeno, P. S. Ventura, J. M. Castellano [et al.] // European Journal of Clinical Investigation. – 2021. – Vol. 51, № 1. – P. e13404. – doi: 10.1111/eci.13404.

13. Abnormal Liver Tests in COVID-19: A Retrospective Observational Cohort Study of 1,827 Patients in a Major U. S. Hospital Network / M. A. Hundt, Y. Deng, M. M. Ciarleglio [et al.] // Hepatology. – 2020. – Vol. 72, № 4. – P. 1169–1176. – doi: 10.1002/hep.31487.

14. Association between thrombocytopenia and 180-day prognosis of COVID-19 patients in intensive care units: A twocenter observational study / Y. Zhu, J. Zhang, Y. Li [et al.] // PLoS One. – 2021. – Vol. 16, № 3. – P. e0248671. – doi: 10.1371/journal.pone.0248671.

15. Anemia is associated with severe illness in COVID-19: A retrospective cohort study / Z. Tao, J. Xu, W. Chen [et al.] //journal of Medical Virology. – 2021. – Vol. 93, № 3. – P. 1478–1488. – doi: 10.1002/jmv.26444.

16. Preexisting Comorbidities Predicting COVID-19 and Mortality in the UK Biobank Community Cohort /j. L. Atkins, J. A. H. Masoli, J. Delgado [et al.] // The Journals of Gerontology. Series A: Biological Sciences and Medical Sciences. – 2020. – Vol. 75, № 11. – P. 2224–2230. – doi: 10.1093/gerona/glaa183.

17. Krasko, O. V., Reutovich M. Yu., Ivanov A. V. Prediction and decision-making based on nonlinear risks model in stomach cancer treatment // Informatics. – 2024. – Т. 21, № 1. – С. 65–82. – doi: 10.37661/1816-0301-2024-21-1-65-82.

18. Mehta, P. Is severe COVID-19 a cytokine storm syndrome: a hyperinflammatory debate / P. Mehta, D. C. Fajgenbaum // Current Opinion in Rheumatology. – 2021. – Vol. 33, № 5. – P. 419–430. – doi: 10.1097/BOR.0000000000000822.

19. Lactate dehydrogenase, an independent risk factor of severe COVID-19 patients: a retrospective and observational study / Y. Han, H. Zhang, S. Mu [et al.] // Aging (Albany NY). – 2020. – Vol. 12, № 12. – P. 11245–11258. – doi: 10.18632/aging.103372.