Sex-Disaggregated Data on Clinical Characteristics and Outcomes of Hospitalized Patients With COVID-19: A Retrospective Study

Wang, Mengdie; Jiang, Nan; Li, Changjun; Wang, Jing; Yang, Heping; Liu, Li; Tan, Xiaohong; Chen, Zhenyuan; Gong, Yanhong; Yin, Xiaoxv; Zong, Qiao; Xiong, Nian; Zhang, Guopeng

doi:10.3389/fcimb.2021.680422

Cited by 9 publications

(12 citation statements)

References 34 publications

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“…We assessed the association of age, sex, common co-morbidities, and β-thalassemia heterozygosity to clinical outcomes of participants from central Greece who were not vaccinated against SARS-CoV-2 and tested positive for COVID-19 during the last trimester of 2020. Our findings support earlier observations that male sex and older age are associated with poorer outcomes [9][10][11][12][13] and suggest that hyperlipidemia reduced participant mortality due to COVID-19 (p = 0.08, OR = 0.38), while β-thalassemia heterozygosity enhanced (p = 0.010, OR = 2.79) participant mortality due to COVID-19 (Table 3, Figure 2).…”

Section: Discussionsupporting

confidence: 91%

Susceptibility of β-Thalassemia Heterozygotes to COVID-19

Sotiriou

Samara

Vamvakopoulou

et al. 2021

JCM

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Background: β-Thalassemia is the most prevalent single gene blood disorder, while the assessment of its susceptibility to coronavirus disease 2019 (COVID-19) warrants it a pressing biomedical priority. Methods: We studied 255 positive COVID-19 participants unvaccinated against severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2), consecutively recruited during the last trimester of 2020. Patient characteristics including age, sex, current smoking status, atrial fibrillation, chronic respiratory disease, coronary disease, diabetes, neoplasia, hyperlipidemia, hypertension, and β-thalassemia heterozygosity were assessed for COVID-19 severity, length of hospitalization, intensive care unit (ICU) admission and mortality from COVID-19. Results: We assessed patient characteristics associated with clinical symptoms, ICU admission, and mortality from COVID-19. In multivariate analysis, severe-critical COVID-19 was strongly associated with male sex (p = 0.023), increased age (p < 0.001), and β-thalassemia heterozygosity (p = 0.002, OR = 2.89). Regarding the requirement for ICU care, in multivariate analysis there was a statistically significant association with hypertension (p = 0.001, OR = 5.12), while β-thalassemia heterozygosity had no effect (p = 0.508, OR = 1.33). Mortality was linked to male sex (p = 0.036, OR = 2.09), increased age (p < 0.001) and β-thalassemia heterozygosity (p = 0.010, OR = 2.79) in multivariate analysis. It is worth noting that hyperlipidemia reduced mortality from COVID-19 (p = 0.008, OR = 0.38). No statistically significant association of current smoking status with patient characteristics studied was observed. Conclusions: Our pilot observations indicate enhanced mortality of β-thalassemia heterozygotes from COVID-19.

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Section: Discussionsupporting

confidence: 91%

Susceptibility of β-Thalassemia Heterozygotes to COVID-19

Sotiriou

Samara

Vamvakopoulou

et al. 2021

JCM

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“…Other scientists observed unilateral pneumonia with ground-glass opacification mostly in women. They detected bilateral pneumonia more often in men (Wang et al, 2021).…”

Section: Sex Differences In Radiologic Findings In Covid-19mentioning

confidence: 96%

Impact of Age and Sex on COVID-19 Severity Assessed From Radiologic and Clinical Findings

Statsenko

Zahmi

Habuza

et al. 2022

Front. Cell. Infect. Microbiol.

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BackgroundData on the epidemiological characteristics and clinical features of COVID-19 in patients of different ages and sex are limited. Existing studies have mainly focused on the pediatric and elderly population.ObjectiveAssess whether age and sex interact with other risk factors to influence the severity of SARS-CoV-2 infection.Material and MethodsThe study sample included all consecutive patients who satisfied the inclusion criteria and who were treated from 24 February to 1 July 2020 in Dubai Mediclinic Parkview (560 cases) and Al Ain Hospital (605 cases), United Arab Emirates. We compared disease severity estimated from the radiological findings among patients of different age groups and sex. To analyze factors associated with an increased risk of severe disease, we conducted uni- and multivariate regression analyses. Specifically, age, sex, laboratory findings, and personal risk factors were used to predict moderate and severe COVID-19 with conventional machine learning methods.ResultsNeed for O2 supplementation was positively correlated with age. Intensive care was required more often for men of all ages (p < 0.01). Males were more likely to have at least moderate disease severity (p = 0.0083). These findings were aligned with the results of biochemical findings and suggest a direct correlation between older age and male sex with a severe course of the disease. In young males (18–39 years), the percentage of the lung parenchyma covered with consolidation and the density characteristics of lesions were higher than those of other age groups; however, there was no marked sex difference in middle-aged (40–64 years) and older adults (≥65 years). From the univariate analysis, the risk of the non-mild COVID-19 was significantly higher (p < 0.05) in midlife adults and older adults compared to young adults. The multivariate analysis provided similar findings.ConclusionAge and sex were important predictors of disease severity in the set of data typically collected on admission. Sexual dissimilarities reduced with age. Age disparities were more pronounced if studied with the clinical markers of disease severity than with the radiological markers. The impact of sex on the clinical markers was more evident than that of age in our study.

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“…For the analyses, patients were categorized into four groups: men without COVID-19 (12%), women without COVID-19 (14%), men with COVID-19 (68%) and women with COVID-19 (31%). Table 1 describes the study population stratified by sex, and in general the parameters are in line with previous reports [ 45 ]. Laboratory parameters showed decreased levels of lymphocytes in COVID-19 patients, especially among men.…”

Section: Resultsmentioning

confidence: 80%

Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach

et al. 2021

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Differences in clinical manifestations, immune response, metabolic alterations, and outcomes (including disease severity and mortality) between men and women with COVID-19 have been reported since the pandemic outbreak, making it necessary to implement sex-specific biomarkers for disease diagnosis and treatment. This study aimed to identify sex-associated differences in COVID-19 patients by means of a genetic algorithm (GALGO) and machine learning, employing support vector machine (SVM) and logistic regression (LR) for the data analysis. Both algorithms identified kynurenine and hemoglobin as the most important variables to distinguish between men and women with COVID-19. LR and SVM identified C10:1, cough, and lysoPC a 14:0 to discriminate between men with COVID-19 from men without, with LR being the best model. In the case of women with COVID-19 vs. women without, SVM had a higher performance, and both models identified a higher number of variables, including 10:2, lysoPC a C26:0, lysoPC a C28:0, alpha-ketoglutaric acid, lactic acid, cough, fever, anosmia, and dysgeusia. Our results demonstrate that differences in sexes have implications in the diagnosis and outcome of the disease. Further, genetic and machine learning algorithms are useful tools to predict sex-associated differences in COVID-19.

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Sex-Disaggregated Data on Clinical Characteristics and Outcomes of Hospitalized Patients With COVID-19: A Retrospective Study

Cited by 9 publications

References 34 publications

Susceptibility of β-Thalassemia Heterozygotes to COVID-19

Susceptibility of β-Thalassemia Heterozygotes to COVID-19

Impact of Age and Sex on COVID-19 Severity Assessed From Radiologic and Clinical Findings

Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach

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