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Evaluation of Factors Related to the Survival of Hospitalized Patients With COVID-19: Survival Analysis With Frailty Approach

Published online by Cambridge University Press:  09 August 2021

Parvin Sarbakhsh Open the ORCID record for Parvin Sarbakhsh [Opens in a new window] ,
Saba Ghaffary  and
Elnaz Shaseb
Parvin Sarbakhsh*
Affiliation:
Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran
Saba Ghaffary
Affiliation:
Hematology and Oncology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
Elnaz Shaseb
Affiliation:
Faculty of Pharmacy, Tabriz University of Medical Sciences, Drug Applied Research Center, Tabriz, Iran
*
Corresponding author: Parvin Sarbakhsh, Email: p.sarbakhsh@gmail.com.
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Abstract

Objective:

Considering that coronavirus disease 2019 (COVID-19) is an emerging disease and results in very different outcomes, from complete recovery to death, it is important to determine the factors affecting the survival of patients. Given the lack of knowledge about effective factors and the existence of differences in the outcome of individuals with similar values of the observed covariates, this study aimed to investigate the factors affecting the survival of patients with COVID-19 by the parametric survival model with the frailty approach.

Methods:

The data of 139 patients with COVID-19 hospitalized in Imam Reza Hospital in Tabriz were analyzed by the Gompertz survival model with gamma frailty effect. At first, variables with P < 0.1 in univariable analysis were included in the multivariable analysis, and then the stepwise method was used for variable selection.

Results:

Diabetes mellitus was significantly related to the survival of hospitalized patients (P = 0.021). The rest of the investigated variables were not significant. The frailty effect was significant (P = 0.019).

Conclusions:

In the investigated sample of patients with COVID-19, diabetes was an important variable related to patient survival. Also, the significant frailty effect indicates the existence of unobserved heterogeneity that causes individuals with a similar value of the observed covariates to have different survival distributions.

Keywords

COVID-19survival analysispopulation characteristics
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 17 , 2023 , e30
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc

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