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Investigating the Association Between Covid-19 Prognosis and Demographic and Clinical Features, Underlying Diseases, and Drug and Supplement Use in Patients Hospitalized in Zabol, Iran: A Single-Center Retrospective Study Publisher



Hemat Jouy S1 ; Lotfi F2 ; Akrami R3 ; Shahraki J2 ; Sheervalilou R4 ; Shahraki O4 ; Ghaznavi H4
Authors
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Authors Affiliations
  1. 1. Departmentof Exercise Physiology, Faculty of Physical Education and Sport Sciences, Central Tehran Branch, Islamic Azad University, Tehran, Iran
  2. 2. Department of Pharmacology and Toxicology, Faculty of Pharmacy, Zabol University of Medical Sciences, Zabol, Iran
  3. 3. Department of Epidemiology & Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Pharmacology Research Center, Zahedan University of Medical Sciences, Zahedan, Iran

Source: Infection# Epidemiology and Microbiology Published:2023


Abstract

Backgrounds: The primary goal of this study was to identify the potential association between COVID-19 prognosis and demographic and clinical features, underlying diseases, and drug and supplement use in patients admitted to Amir al-Momenin hospital in Zabol. Materials & Methods: This retrospective study surveyed the electronic health records of 848 COVID-19 patients hospitalized in a tertiary referral hospital in southeastern Iran from the beginning of the COVID-19 outbreak until the end of February 2021. Univariate and multiple analytical tests including unconditional and penalized logistic regressions were used for statistical analysis. Findings: Out of a total of 848 patients, 371 (43.75%) patients were female, and 477 (56.25%) patients were male. Age, underlying pulmonary and cardiovascular diseases, and loss of consciousness predicted a higher mortality rate. On the contrary, a negative chest X-ray was associated with a lower risk of death. Conclusion: Identifying predisposing factors of mortality in COVID-19 patients will help physicians provide more intensive care to those at higher risk of death by classifying patients based on risk factors and underlying diseases. © 2023, TMU Press.
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