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Sex Difference in Characteristics and Predictors of In-Hospital Mortality among Patients with COVID-19
Shirui Meng1, Zechun Zeng2, Huijuan Zuo2
1Queen Mary School, Jiangxi Medical College, Nanchang University, Nanchang, China, mengshirui2002@163.com.
Summary
Lymphocyte percentage (LYM%) predicts COVID-19 in-hospital mortality in both males and females. Other risk factors include age, obesity, and markers of inflammation and organ injury, aiding in patient risk stratification.
Area of Science:
- Clinical Medicine
- Infectious Diseases
- Critical Care
Background:
- The surge in coronavirus disease-2019 (COVID-19) cases in Beijing led to increased hospitalizations.
- Understanding factors associated with in-hospital mortality is crucial for managing COVID-19 patients.
Purpose of the Study:
- To evaluate sex-based differences in clinical characteristics, management, and mortality among COVID-19 patients.
- To identify predictive factors for in-hospital mortality in a cohort of COVID-19 patients.
Main Methods:
- Retrospective analysis of 1,091 patients hospitalized with COVID-19 at Beijing Anzhen Hospital (October 2022 - January 2023).
- Sex-based comparisons of clinical data and in-hospital outcomes.
- Multifactorial logistic regression and Receiver Operating Characteristic (ROC) curve analysis to identify mortality predictors.
Main Results:
- Overall in-hospital mortality was 14.9% for males and 10.4% for females.
- Lymphocyte percentage (LYM%), uric acid, and high-sensitivity C-reactive protein were independent predictors of mortality for all patients.
- For female patients, older age, obesity, and high high-sensitivity troponin I levels were also identified as risk factors.
Conclusions:
- Lymphocyte percentage (LYM%) is a consistent predictor of in-hospital mortality across both sexes.
- Older age, systemic inflammation, myocardial injury, and metabolic dysregulation markers are associated with increased mortality risk.
- These findings can guide closer monitoring and tailored interventions for high-risk COVID-19 patients.
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