Document Type : Original Article

Authors

• Ahmad Mohtashami ¹
• Milad Ahmadi Gohari ²
• Abbas Bahrampour ¹
• mohamadhossein mehrolhasani ³
• Yunes Jahani ¹

¹ Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

² Modeling in Health Research Center, Institute for Futures Studies in Health, Department of Biostatistics and Epidemiology, Faculty of Health, Kerman university of Medical Sciences, Kerman, Iran.

³ Health Services Management Research Center, Institute for Futures Studies in Health, Kerman University of medical sciences, Kerman, Iran.

Abstract

Introduction: This study aims to identify the risk factors affecting mortality among Covid-19 patients in the southeast of Iran.
Methods: This cross-sectional study used data from the Covid-19 patients admitted to Afzalipur Teaching Hospital in Kerman, Iran, from February 2020 to September 2021. The demographic and clinical data of 6,057 patients were analyzed using Bayesian network and logistic regression models.
Findings: Out of 6,057 patients, 333 patients (5.5%) died. The most important risk factors for Covid-19 mortality were age, gender, fever, headache, decreased level of consciousness (LoC), chronic liver disease, blood oxygen level (BOL), admission season, and length of stay (LoS). Fever, headache, and longer LoS were protective and mortality-reducing variables.
Conclusion: Following model estimation results, it is recommended that old male patients with low oxygen levels and a lower LoC, as well as patients with chronic liver disease, receive additional medical care and not be discharged prematurely. Early medical interventions for high-risk patients may reduce the Covid-19 mortality risk.

Keywords

• Bayesian network
• logistic regression
• mortality
• Covid-19

Main Subjects

• Biostatistics