Application of the fencovid phenotype-probability calculator in Covid-19 in-patients in a latin american population

Aplicación de la calculadora de probabilidad fenotípica FEN-COVID en pacientes hospitalizados por COVID-19 en una población latinoamericana

Introduction: the clinical variability of COVID-19 may be one of the determinants limiting therapeutic decisions. This study aims to classify a cohort of Latin American in-patients using the FENCOVID tool to identify clinical phenotypes and predict likelihood of mortality and need of intensive care unit (ICU) admission. Methods: a retrospective cohort observational study, in hospitalized adults with COVID-19 confirmed between September 2020 and March 2021, at two tertiary health care centers. Phenotype assignment in selected patients was performed by applying the FENCOVID calculator. A multivariate analysis was conducted to document the associations between phenotype, in-hospital complications, and clinical outcomes. Results: a total of 126 COVID-19 in-patients were identified. The median age was 58 years, 45 were females (35.7%), 23% were diabetic, 45% had hypertension and 20% were obese. One-hundred-eight (85.7%) patients were phenotype B and 18 (14.2%) phenotype C. Although the latter had worse outcomes (ICU admission in 77.8% vs 45.4% and death in 66% vs 22%, OR 1.408, IC95% 3.191-5.243, p <0.007), this association was not maintained in the multivariate analysis OR 1.110 (IC95% 0.780 - 1.581, p de 0.555) Conclusion: FENCOVID phenotypes appear to discriminate a subset showing poor clinical behavior, although a milder phenotype was not described. The bivariate analysis documented an association with death or ICU admission which was not maintained in the multivariate model.

1. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020;20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1
2. Chams N, Chams S, Badran R, Shams A, Araji A, Raad M, et al. COVID-19: A Multidisciplinary Review. Front Public Health. 2020;8:383. doi: 10.3389/fpubh.2020.00383
3. Truog RD, Mitchell C, Daley GQ. The Toughest Triage — Allocating Ventilators in a Pandemic. N Engl J Med. 2020;382(21):1973-1975. doi: 10.1056/NEJMp2005689
4. Firth P, Eyal N. Allocating Medical Resources in the Time of Covid-19. N Engl J Med. 2020;382(22):e79. doi: 10.1056/NEJMc2009666
5. Robinson PN. Deep phenotyping for precision medicine. Hum Mutat. 2012;33(5):777-780. doi: 10.1002/humu.22080
6. Azoulay E, Zafrani L, Mirouse A, Lengliné E, Darmon M, Chevret S. Clinical phenotypes of critically ill COVID-19 patients. Intensive Care Med. 2020;46(8):1651-1652. doi: 10.1007/s00134-020-06120-4
7. Schinkel M, Appelman B, Butler J, Schuurman A, Wiersinga WJ, Douma RA, et al. Association of clinical sub-phenotypes and clinical deterioration in COVID-19: further cluster analyses. Intensive Care Med. 2021;47(4):482-484. doi: 10.1007/s00134-021-06363-9
8. Gutiérrez-Gutiérrez B, del Toro MD, Borobia AM, Carcas A, Jarrín I, Yllescas M, et al. Identification and validation of clinical phenotypes with prognostic implications in patients admitted to hospital with COVID-19: a multicentre cohort study. Lancet Infect Dis. 2021;21(6):783-792. doi: 10.1016/S1473-3099(21)00019-0
9. Gu Z, Gu L, Eils R, Schlesner M, Brors B. Circlize implements and enhances circular visualization in R. Bioinformatics. 2014;30(19):2811-2. doi: 10.1093/bioinformatics/btu393
10. Update to living systematic review on prediction models for diagnosis and prognosis of covid-19. BMJ. 2021;372:n236. doi: 10.1136/bmj.n236
11. Adamidi ES, Mitsis K, Nikita KS. Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review. Comput Struct Biotechnol J. 2021;19:2833-2850. doi: 10.1016/j.csbj.2021.05.010
12. Sudre CH, Lee KA, Lochlainn MN, Varsavsky T, Murray B, Graham MS, et al. Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app. Sci Adv. 2021;7(12):eabd4177. doi: 10.1126/sciadv.abd4177
13. Wang X, Jehi L, Ji X, Mazzone PJ. Phenotypes and Subphenotypes of Patients With COVID-19: A Latent Class Modeling Analysis. Chest. 2021;159(6):2191-2204. doi: 10.1016/j.chest.2021.01.057
14. Lusczek ER, Ingraham NE, Karam BS, Proper J, Siegel L, Helgeson ES, et al. Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles. PLoS One. 2021;16(3):e0248956. doi: 10.1371/journal.pone.0248956
15. Teng C, Thampy U, Bae JY, Cai P, Dixon RAF, Liu Q, et al. Identification of phenotypes among covid-19 patients in the united states using latent class analysis. Infect Drug Resist. 2021;14:3865-3871. doi: 10.2147/IDR.S331907
16. Data Science Collaborative Group. Differences in clinical deterioration among three sub-phenotypes of COVID-19 patients at the time of first positive test: results from a clustering analysis. Intensive Care Med. 2021;47(1):113-115. doi: 10.1007/s00134-020-06236-7
17. Vasquez CR, Gupta S, Miano TA, Roche M, Hsu J, Yang W, et al. Identification of Distinct Clinical Subphenotypes in Critically Ill Patients With COVID-19. Chest. 2021;160(3):929-943. doi: 10.1016/j.chest.2021.04.062
18. Bos LDJ, Sjoding M, Sinha P, Bhavani S v, Lyons PG, Bewley AF, et al. Longitudinal respiratory subphenotypes in patients with COVID-19-related acute respiratory distress syndrome: results from three observational cohorts. Lancet Respir Med. 2021;S2213-2600(21) 00365-9. doi: 10.1016/S2213-2600(21)00365-9
19. Zhang J, Whebell SF, Sanderson B, Retter A, Daly K, Paul R, et al. Phenotypes of severe COVID-19 ARDS receiving extracorporeal membrane oxygenation. Br J Anaesth. 2021;126(3):e130-e132. doi: 10.1016/j.bja.2020.12.023
20. Su C, Xu Z, Hoffman K, Goyal P, Safford MM, Lee J, et al. Identifying organ dysfunction trajectory-based subphenotypes in critically ill patients with COVID-19. Sci Rep. 2021;11(1):15872. doi: 10.1038/s41598-021-95431-7