Identification of patients with low risk of severity in confirmed COVID-19 cases. Cuba, 2020- 2021
Keywords:
Prognosis, triage, theoretical models, COVID-19, SARS-CoV-2.Abstract
Introduction: The COVID-19 pandemic, a treat to global health, overwhelmed health systems in many countries. Performing and adequate identification of subjects with very low risk to develop a severe form of the disease is of vital importance.
Objective: To identify patients with very low risk of transition to severity in confirmed COVID-19 cases in Cuba.
Material and Methods: A cohort retrospective study was carried out based on the Cuban National Database of confirmed COVID-19 patients. A total of 10 600 persons were analyzed between March 11th, 2020 and May 31st, 2021. Admission to the Intensive Care Units and mortality related to COVID-19, both measured 30 days after disease confirmation, were the selected response variables. As predictors, age and comorbidities were selected. A classification tree algorithm was applied to identify risk strata. For every stratum, its volume, the risk of transition to severity and the explained risk were calculated.
Results: Subjects under 65 years old with no comorbidities (85.8% of the total sample) had a very low risk (0.2%) of admission to ICU and death (1.2%). Adjusted models showed a good calibration for both variables (accuracy= 0.88) and discriminant ability (area under the ROC curve: 0.89 and 0.88).
Conclusions: The study allowed the identification of subjects with very low risk of transition to severity who were followed up at primary healthcare services, under home surveillance and symptomatic treatment.
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References
1. WHO. Pulse survey on continuity of essential health services during the COVID-19 pandemic: interim report, 27 August 2020 [Internet]. Ginebra: WHO; 2020 [Citado 10/06/2022]. Disponible en: https://www.who.int/publications/i/item/WHO-2019-nCoV-EHS_continuity-survey-2020.1
2. Silva SJR da, Pena L. Collapse of the public health system and the emergence of new variants during the second wave of the COVID-19 pandemic in Brazil. One Health [Internet]. 2021 [Citado 10/06/2022];13:100287. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S23527714210007
3. Faggioni MP, González-Melado FJ, Di Pietro ML. National health system cuts and triage decisions during the COVID-19 pandemic in Italy and Spain: ethical implications. J Med Ethics [Internet]. 2021 [Citado 10/06/2022];medethics-2020-106898. Disponible en: https://doi.org/10.1136/medethics-2020-106898
4. Hasan Z, Narasimhan M. Preparing for the COVID-19 Pandemic. Chest [Internet]. 2020 [Citado 10/06/2022];157(6):1420-2. Disponible en: https://doi.org/10.1016/j.chest.2020.03.027
5. Pathak EB, García RB, Menard JM, Salemi JL. Out-of-Hospital COVID-19 Deaths: Consequences for Quality of Medical Care and Accuracy of Cause of Death Coding. Am J Public Health [Internet]. 2021 [Citado 10/06/2022];111(S2):S101-6. Disponible en: https://doi.org/10.2105/AJPH.2021.306428
6. Hirner S, Pigoga JL, Naidoo AV, Calvello Hynes EJ, Omer YO, Wallis LA, et al. Potential solutions for screening, triage, and severity scoring of suspected COVID-19 positive patients in low-resource settings: a scoping review. BMJ Open [Internet]. 2021 [Citado 10/06/2022];11(9):e046130. Disponible en: https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2020-046130
7. Struyf T, Deeks JJ, Dinnes J, Takwoingi Y, Davenport C, Leeflang MM, et al. Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19 disease. Cochrane Infectious Diseases Group, editor. Cochrane Database Syst Rev [Internet]. New Jersy: Cochrane Database Syst Rev; 2020 [Citado 10/06/2022]. Disponible en: https://doi.wiley.com/10.1002/14651858.CD013665
8. MINSAP. Protocolo de Actuación Nacional para la COVID-19. versión 5 [Internet]. La Habana: MINSAP; 2020 [Citado 10/06/2022]. Disponible en: https://files.sld.cu/editorhome/files/2020/08/VERSION-5-DEL-PROTOCOLO-PARA-PUBLICAR-13-DE-AGOSTO-2020.pdf
9. Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ [Internet]. 2020 [Citado 10/06/2022]; 369:1328. Disponible en: https://www.bmj.com/content/bmj/369/bmj.m1328.full.pdf
10. World Health Organization. Living guidance for clinical management of COVID-19: living guidance, 23 November 2021 [Internet]. Geneva: WHO; 2021 [Citado 10/06/2022]. Disponible en: https://apps.who.int/iris/bitstream/handle/10665/349321/WHO-2019-nCoV-clinical-2021.2-eng.pdf
11. Miranda-Portal JÁ. Respuesta de Cuba para enfrentar la COVID-19 y sus retos. Rev Cubana Med Trop [Internet]. 2023 [Citado 10/06/2023];74(3). Disponible en: https://revmedtropical.sld.cu/index.php/medtropical/article/view/973/641
12. Riley RD, Ensor J, Snell KIE, Harrell FE, Martin GP, Reitsma JB, et al. Calculating the sample size required for developing a clinical prediction model. BMJ [Internet]. 2020 [Citado 10/06/2022];368:m441. Disponible en: https://www.bmj.com/lookup/doi/10.1136/bmj.m441
13. Charlson ME, Charlson RE, Peterson JC, Marinopoulos SS, Briggs WM, Hollenberg JP. The Charlson comorbidity index is adapted to predict costs of chronic disease in primary care patients. J Clin Epidemiol. 2008;61(12):1234-40.
14. Hothorn T, Zeileis A. partykit: A modular toolkit for recursive partytioning in R. J Mach Learn Res [Internet]. 2015 [Citado 10/06/2022];16(1):3905-9. Disponible en: https://www.jmlr.org/papers/volume16/hothorn15a/hothorn15a.pdf
15. Lunardon N, Menardi G, Torelli N. ROSE: A Package for Binary Imbalanced Learning. The R Journal [Internet]. 2014 [Citado 10/06/2022];6(1):11. Disponible en: https://journal.r-project.org/archive/2014-1/menardi-lunardon-torelli.pdf
16. Kuhn M, Vaughan D, Hvitfeldt E, Hvitfeldt ME. Package ‘yardstick’ [Internet].Canadá: Yardstick; 2022 [Citado 10/06/2022]. Disponible en: https://cran.uni-muenster.de/web/packages/yardstick/yardstick.pdf
17. Kuhn M, Wickham H. Tidymodels: Easily install and load the’tidymodels’ packages. R Package Version 1.1.0 [Internet]. EE UU: Tidymodel; 2020 [Citado 10/06/2022]. Disponible en: https://tidymodels.tidymodels.org/
18. Clift AK, Coupland CAC, Keogh RH, Diaz-Ordaz K, Williamson E, Harrison EM, et al. Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study. BMJ [Internet]. 2020 [Citado 10/06/2022];371:m3731. Disponible en: https://www.bmj.com/lookup/doi/10.1136/bmj.m3731
19. Goodacre S, Thomas B, Sutton L, Burnsall M, Lee E, Bradburn M, et al. Derivation and validation of a clinical severity score for acutely ill adults with suspected COVID-19: The PRIEST observational cohort study. PLOS ONE [Internet]. 2021 [Citado 10/06/2022];16(1):e0245840. Disponible en: https://dx.plos.org/10.1371/journal.pone.0245840
20. Gupta RK, Harrison EM, Ho A, Docherty AB, Knight SR, van Smeden M, et al. Development and validation of the ISARIC 4C Deterioration model for adults hospitalised with COVID-19: a prospective cohort study. Lancet Respir Med [Internet]. 2021 [Citado 10/06/2022];9(4):349-59. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S2213260020305592
21. Carr E, Bendayan R, Bean D, Stammers M, Wang W, Zhang H, et al. Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study. BMC Med [Internet]. 2021 [Citado 10/06/2022];19(1):23. Disponible en: https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01893-3
22. Gupta RK, Marks M, Samuels THA, Luintel A, Rampling T, Chowdhury H, et al. Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: an observational cohort study. Eur Respir J [Internet]. 2020 [Citado 10/06/2022];56(6):2003498. Disponible en: http://erj.ersjournals.com/lookup/doi/10.1183/13993003.03498-2020
23. Monzón-Pérez M, Sánchez-Valdés L, Lage-Dávila A. Estado y tendencias en la investigación sobre modelos de predicción clínica para la estratificación del riesgo de severidad en pacientes confirmados de Covid-19 [Internet]. Brazil: ScieOPrepints; 2023 [Citado 10/06/2023]. Disponible en: https://doi.org/10.1590/SciELOPreprints.5795
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