Oxygen markers for predicting COVID-19 - related pneumonia mortality
Keywords:
COVID-19, oxygen markers, COVID-19 pneumonia, mortality, predictive factorsAbstract
Introduction: the value of oxygen as a prognostic maker of mortality due to COVID-19 pneumonia has not been evaluated at the Hospital General Docente "Dr. Agostinho Neto".
Objective: to identify the values of oxygenation markers for prognosing mortality caused by COVID-19 pneumonia at the Hospital General Docente "Dr. Agostinho Neto" de Guantánamo, Cuba, throughout period 2020-2021.
Method: a cohort of 276 patients with COVID-19 pneumonia was studied. Peripheral oxygen saturation (SpO2), arterial oxygen saturation (SaO2), the difference between the oxygen concentration in the alveoli and arterial system (DA-aO2), arterial oxygen pressure ratio (PaO2) and inspired oxygen fraction (FiO2) [PaO2/FiO2] were studied. The association between variables and deceased discharge was determined using the Chi-square technique and the Odds Ratio (OR) calculation.
Results: the variable with the highest positive predictive value was SpO2 (87.3 %) with a value lower than 90 mmHg at admission. The highest negative predictive value was recorded for the DA-aO2 variable (95.6%), less than 20 mmHg at 48 hours after admission. Attributable risk was higher for PaO2/FiO2 ratio, less than 300 mmHg (0.59), at admission (0.52). Attributable risk percent was higher for the variable DA-aO2 20 mmHg at admission (95.8 %) and at 48 hours after admission (95.3 %).
Conclusions: abnormal DA-aO2, PaO2/FiO2 ratio, SaO2 and SpO2, at admission and 48 hours after admission, are predictive markers of mortality in patients with COVID-19.
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References
2. Liang W, Liang H, Ou L, Chen B, Chen A, Li C, et al. Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. JAMA Intern Med [Internet] 2020 [citado 15 Mayo 2022]; 180(8):1081–1089. DOI: https://doi.org/10.1001/jamainternmed.2020.2033
3. Zhou Y, He Y, Yang H, Yu H, Wang T, Chen Z, et al. Development and validation a nomogram for predicting the risk of severe COVID-19: A multi-center study in Sichuan, China. PloS one [Internet] 2020 [citado 15 Mayo 2022]; 15(5):e0233328. DOI: https://doi.org/10.1371/journal.pone.0233328
4. Gong J, Ou J, Qiu X, Jie Y, Chen Y, Yuan L, et al. A tool for early prediction of severe coronavirus disease 2019 (COVID-19): A multicenter study using the risk nomogram in Wuhan and Guangdong, China. Clin Infec Dis [Internet]. 2020 [citado 15 Mayo 2022]; 71(15):833–840. DOI: https://doi.org/10.1093/cid/ciaa443
5. Marmanillo Mendoza G, Zuñiga Manrique R, Cornejo Del Valle O, Portilla Canqui L. Índice SatO2/FiO2 versus PaO2/FiO2 para predecir mortalidad en pacientes con COVID-19 en un hospital de altura. Acta Med Perú [Internet]. 2021 [citado 15 Mayo 2022]; 38(4):273-278. DOI: https://doi.org/10.35663/amp.2021.384.2033
6. Esteban Ronda V, Ruiz Alcaraz S, Ruiz Torregrosa P, Giménez Suau M, Nofuentes Pérez E, León Ramírez JM, et al. Aplicación de escalas pronósticas de gravedad en la neumonía por SARS-CoV-2 Med Clin (Barc) [Internet] 2021 [citado 15 Mayo 2022]; 157(3):99-105. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7843026/
7. Ministerio de Salud Pública. Protocolo de actuación nacional para la COVID-19. Versión 1.7. [Internet]. La Habana: MINSAP; 2021. [citado 15 Mayo 2022]. Disponible en: https://covid19cubadata.github.io/protocolos/protocolo-version-6.pdf
8. Medina Mendieta J, Cortés Cortés M, Cortés Iglesias M, Pérez Fernández A, Manzano Cabrera M. Estudio sobre modelos predictivos para la COVID-19 en Cuba. Medisur [Internet]. 2020 [citado 15 Mayo 2022]; 18(3):431-442. Disponible en: http://medisur.sld.cu/index.php/medisur/article/view/4703/3164
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 15 Mayo 2022]; 369:m1328. DOI: https://doi.org/10.1136/bmj.m1328
10. Gupta RK, Marks M, Samuels T, Luintel A, Rampling T, Chowdhury H, et al. Systematic evaluation and external validation of 22 prognostic models among hospitalized adults with COVID-19: an observational cohort study. Eur Resp J [Internet]. 2020 [citado 15 Mayo 2022]; 56(6):2003498. DOI: https://doi.org/10.1183/13993003.03498-2020
11. Herrera Cartaya CE, Lage Dávila A, Betancourt Cervantes J, Barreto Fiu EE, Sánchez Valdés L, Crombet Ramos T. Nomograma de predicción para la estratificación del riesgo en pacientes con COVID-19. Eur J Health Res [Internet]. 2021 [citado 15 Mayo 2022]; 7(2):1-19. DOI: https://doi.org/10.32457/ejhr.v7i2.1592
12. Lippi G, lebani M. Laboratory abnormalities in patients with COVID-2019 infection. Clinical chemistry and laboratory medicine [Internet]. 2020 [citado 15 Mayo 2022]; 58(7):1131–1134. DOI: https://doi.org/10.1515/cclm-2020-0198
13. Carsana L, Sonzogni A, Nasr A, Rossi RS, Pellegrinelli A, Zerbi P, et al. Pulmonary post-mortem findings in a series of COVID-19 cases from northern Italy: a two-centre descriptive study. Lancet [Internet]. 2020 [citado 15 Mayo 2022]; 20(10):1135-1140. DOI: https://doi.org/10.1097/lancet.00003245
14. López Reyes R, Oscullo G, Jiménez D, Cano I, García Ortega A. Riesgo trombótico y COVID-19: revisión de la evidencia actual para una mejor aproximación diagnóstica y terapéutica. Arch Bronconeumol [Internet]. 2021 [citado 15 Mayo 2022];57(S1):55-64. DOI: https://doi.org/10.1016/j.arbres.2020.07.033
15. Connors JM, Levy JH. COVID-19 and its implications for thrombosis and anticoagulation. Blood. [Internet]. 2020 [citado 15 Mayo 2022]; 135(23):2033-2040. DOI: http://doi.org/10.1182/blood.2020006000
