Gender Differences in Patients With COVID-19: Focus on Severity and Mortality

Jin, Jianmin; Bai, Peng; He, Wei; Wu, Fei; Liu, Xiaofang; Han, Demin; Liu, Shi; Jy, Yang

doi:10.3389/fpubh.2020.00152

Cited by 1,832 publications

(1,694 citation statements)

References 13 publications

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“…In addition, 'male' gender was revealed as a predictor of a positive result by the model, concurring with the observed gender bias 19,20 .…”

Section: Resultssupporting

confidence: 83%

COVID-19 diagnosis prediction by symptoms of tested individuals: a machine learning approach

Zoabi

Shomron

2020

Preprint

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Motivation: Effective screening of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and can mitigate the burden on healthcare systems. Prediction models that combine several features to estimate the risk of infection have been developed in hopes of assisting medical staff worldwide in triaging patients when allocating limited healthcare resources. Results: We established a machine learning approach that trained on records from 51,831 tested individuals

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“…In addition, 'male' gender was revealed as a predictor of a positive result by the model, concurring with the observed gender bias 19,20 .…”

Section: Resultssupporting

confidence: 83%

COVID-19 diagnosis prediction by symptoms of tested individuals: a machine learning approach

Zoabi

Shomron

2020

Preprint

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“…This is in agreement with other studies carried out by Li et aland Jian-Min et al . who showed that men's cases tended to be more serious than women's [19,30]. Therefore, gender may be considered a risk factor for higher severity and mortality in patients with COVID-19, independent of age and susceptibility.…”

Section: Discussionmentioning

confidence: 98%

The Epidemiological and Spatiotemporal Characteristics of 2019 novel coronavirus diseases (COVID-19) in Libya

Daw

El-Bouzedi

Ahmed

2020

Preprint

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Background: COVID-19 is a global pandemic that affectedaffects all aspects of life. Studies on understanding the geographical and epidemiological characteristics become particularly important in controlling the spread of the pandemic. Such studies are lacking in Northern African countries, particularly in Libya, which is considered being, the second largest country in Africa, with the longest coast facing Europe. The objectives of this study are to; determine the epidemiological parameters and spatiotemporal patterns of COVID-19 in this large country and outline the needed strategies to contain the spread and the consequences of the pandemic. Methods: This comprehensive study included all the confirmed cases of COVID-19 since its emergence in Libya, from early April 2020 until July 31th31. The epidemiological characteristics of COVID-19 were analyzed, and the spatial dynamic trends were explored. A region Region-based counts of weekly reported cases were used to characterize and quantify the spatial dynamics of COVID-19.Results: A total of 3695 of confirmed cases of OVID-19 were collected,; 2515 (68.1%) were males , and 1180 (31.9%) were females with a male-to-female (M:F) ratio of 2.1:1. Aged between 2 -and 78 years old. Older age patients infected with COVID-19 are at higher severity and mortality. A broad geographic variability and spatiotemporal spread variation of the COVID-19 pandemic in Libya was observed. Indicating. This indicates a more significant increase ofin COVID-19 from the middle of July, particularly in the West and Southern regions, although it was consistently observed in the Meddle and Southern regions.Conclusion: Evaluating the epidemiological data and the spatiotemporal dynamic trends of COVID-19 at early stages are particularly important in understanding the pandemic spread. These parameters are essential in designing effective prevention and control programs aimed at reducing the impact of the COVID- 19 pandemic, particularly in countries with limited resources.

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“…However, small percentage of those patients suffer from more sever illness that can rapidly progess into more critical form. This includes acute respiratory distress syndrome (ARDS) and acute respiratory failure, in addition to metabolic acidosis, coagulopathies, and septic shock [10]. Depending on patient characteristics and the studied population, ICU admission vary between 5 to 16% of the total number of patients [11].…”

Section: Discussionmentioning

confidence: 99%

Corona Virus Disease 2019 (COVID-19): Intensive Care Admission Prediction Model

Hachim

Naeem

et al. 2020

Preprint

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Background: Identifying clinical-features or a scoring-system to predict a benefit from hospital admission for patients with COVID-19 can be of great value for the decision-makers in the health-sector. We aim to identify differences in patients' demographic, clinical, laboratory and radiological findings of COVID-19 positive cases to develop and validate a diagnostic-model predicting who will develop severe-form and who will need critical-care in the future.Methodology: Patients were classified according to their clinical state into mild, moderate, severe, and critical. All their baseline clinical data, laboratory, and radiological results were used to construct a prediction-model that can predict if the COVID-19 patients will develop a severe condition that will necessitate their ICU-admission. An ensemble feature selection tool was used to identify the relative importance of each variable. The performance of the selected features compared to all features using logistic regression and area under curve test.Results: Patients with ICU admission showed a distinct clinical, demographic as well as laboratory features when compared to pattients that did not need ICU admission. This includes elder age group, male gender and presence of comorbidities like diabetis and history of hypertension.Out of the different demographic, clinical and laboratory charasteristics of these patients, Age at diagnosis, Lymphocyte count, C-reactive protein (CRP), lactate dehydrogenase (LDH), Albumin, Urea, and Procalcitonin levels were found to be able to predict which patients may need ICU admission. Conclusion: Higher CRP, LDH, Age at diagnosis, Urea, Procalcitonin, and lower Albumin, Lymphocyte count are significant determinant in ICU admission for COVID-19 patients.

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Gender Differences in Patients With COVID-19: Focus on Severity and Mortality

Cited by 1,832 publications

References 13 publications

COVID-19 diagnosis prediction by symptoms of tested individuals: a machine learning approach

COVID-19 diagnosis prediction by symptoms of tested individuals: a machine learning approach

The Epidemiological and Spatiotemporal Characteristics of 2019 novel coronavirus diseases (COVID-19) in Libya

Corona Virus Disease 2019 (COVID-19): Intensive Care Admission Prediction Model

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