Complicity of the pre-existing clinical phenotype with the outcome of death in COVID-19 patients
DOI:
https://doi.org/10.35366/107508Keywords:
COVID-19, epidemiology, factors riskAbstract
Introduction: The Coronavirus Disease (COVID-19) has been a public health problem worldwide
for a considerable time. According to the COVID-19 dashboard at Johns Hopkins University (JHU),
Mexico is in the fourteenth place of reported cases. Some studies have described some risk factors
associated with having COVID-19. However, the risk to develop different COVID-19 outcomes is
unclear. Objective: To describe the risk factors for develop different COVID-19 outcomes. Material
and methods: We carried out a multicenter cross-sectional study, from June 2020 to March2021. A
non-probabilistic sampling study design was used. For continuous variables Kruskal-Wallis test was
carried out for comparing nonparametric distribution among studied groups. χ2 test was performed for
the categorical variables. Univariated logistic analysis was performed to determine the associations
of risk factors to COVID-19 outcomes. The analysis was performed using the STATA v.13 software.
Results: We analyzed 713 patients and were classified as mild (N = 193, 27%); severe (N = 232,
32%); critical (N =169, 24%) and deceased (N = 119, 17%). Critical and deceased group had a
highest percentage of males with 121 (72%) and 75 (63%) respectively. The main comorbidities
were overweight (N = 221, 31%), obesity (N = 215, 30%) and type 2 diabetes (N = 208, 29%). Others
comorbidities were smoking (17%), cardiopathies (3%), alcoholism (2%) neumopathies (1.6%) and
nefropahties (1.5%). Conclusion: The main risk factors in deceased group were overweight and
type 2 diabetes.
References
Organization WH. Novel coronavirus -Disease outbreak
2022.
Lai CC, Shih TP, Ko WC, Tang HJ, Hsueh PR. Severe
acute respiratory syndrome coronavirus 2 (SARS-
CoV-2) and coronavirus disease-2019 (COVID-19): the
epidemic and the challenges. Int J Antimicrob Agents.
; 55 (3): 105924.
Hopkins J. Coronavirus resource center. 2022. Available
in: https://coronavirus.jhu.edu/
Kadam SB, Sukhramani GS, Bishnoi P, Pable AA,
Barvkar VT. SARS-CoV-2, the pandemic coronavirus:
molecular and structural insights. J Basic Microbiol.
; 61 (3): 180-202.
Palacios Cruz M, Santos E, Velázquez Cervantes MA,
León Juárez M. COVID-19, a worldwide public health
emergency. Revista Clínica Española. 2021; 221 (1):
-61.
Martinez-Fierro ML, Diaz-Lozano M, Alvarez-Zuñiga C,
Ramirez-Hernandez LA, Araujo-Espino R, Trejo-Ortiz
PM et al. Population-Based COVID-19 Screening in
Mexico: Assessment of Symptoms and Their Weighting
in Predicting SARS-CoV-2 Infection. Medicina (Kaunas).
; 57 (4): 363.
Mesta F, Coll AM, Ramírez M, Delgado-Roche L.
Predictors of mortality in hospitalized COVID-19
patients: a Mexican population-based cohort study.
Biomedicine (Taipei). 2021; 11 (2): 1-4.
Gandhi RT, Lynch JB, Del Rio C. Mild or moderate
Covid-19. N Engl J Med. 2020; 383 (18): 1757-1566.
Onder G, Rezza G, Brusaferro S. Case-fatality rate and
characteristics of patients dying in relation to COVID-19
in Italy. JAMA. 2020; 323 (18): 1775-1776.
Fernández-Rojas MA, Luna-Ruiz Esparza MA, Campos-
Romero A, Calva-Espinosa DY, Moreno-Camacho JL,
Langle-Martínez AP et al. Epidemiology of COVID-19
in Mexico: symptomatic profiles and presymptomatic
people. Int J Infect Dis. 2021; 104: 572-579.
Li AJ, Li X. Sex-dependent immune response and
lethality of COVID-19. Stem Cell Res. 2020; 50:
Hernández-Garduño E. Obesity is the comorbidity
more strongly associated for Covid-19 in Mexico. A
case-control study. Obes Res Clin Pract. 2020; 14 (4):
-379.
López-Reyes A, Martinez-Armenta C, Espinosa-
Velázquez R, Vázquez-Cárdenas P, Cruz-Ramos M,
Palacios-González B et al. NLRP3 inflammasome:
the stormy link between obesity and COVID-19. Front
Immunol. 2020; 11: 570251.
Becerra-Sánchez A, Rodarte-Rodríguez A, Escalante-
García NI, Olvera-González JE, De la Rosa-Vargas JI,
Zepeda-Valles G et al. Mortality analysis of patients
with COVID-19 in Mexico based on risk factors applying
machine learning techniques. Diagnostics (Basel).
; 12 (6): 1396.
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Copyright (c) 2022 Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra
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© Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra under a Creative Commons Attribution 4.0 International (CC BY 4.0) license which allows to reproduce and modify the content if appropiate recognition to the original source is given.
