The association between potential predictors and death of patients during the COVID-19 pandemic in Shiraz: a hierarchical multiple regression analysis

Author:

Mohebbi Zinat,Ghaemmaghami ParvinORCID,Rajaei Melika,Keshtkar Mohammad Mehdi,Ghanbarzadeh Sina,Khoram Bagher

Abstract

Abstract Introduction Identifying clinical factors that increase the risk of mortality in COVID-19 patients is crucial. This enables targeted screening, optimizing treatment, and prevention of severe complications, ultimately reducing death rates. This study aimed to develop prediction models for the death of patients (i.e., survival or death) during the COVID-19 pandemic in Shiraz, exploring the main influencing factors. Method We conducted a retrospective cohort study using hospital-based records of 1030 individuals diagnosed with COVID-19, who were hospitalized for treatment between March 21, 2021, and March 21, 2022, in Shiraz, Iran. Variables related to the final outcome were selected based on criteria and univariate logistic regression. Hierarchical multiple logistic regression and classification and regression tree (CART) models were utilized to explore the relationships between potential influencing factors and the final outcome. Additionally, methods were employed to identify the high-risk population for increased mortality rates during COVID-19. Finally, accuracy was evaluated the performance of the models, with the area under the receiver operator characteristic curve(AUC), sensitivity, and specificity metrics. Results In this study, 558 (54.2%) individuals infected with COVID-19 died. The final model showed that the type of medicine antiviral (OR: 11.10, p = 0.038) than reference (antiviral and corticosteroid), and discharge oxygen saturation(O2) (OR: 1.10, p < 0.001) had a positive association with the chance of survival, but other variables were not considered as predictive variables. Predictive models for the final outcome(death) achieved accuracies ranging from 81 to 87% for hierarchical multiple logistic regression and from 87 to 94% for the CART model. Therefore, the CART model performed better than the hirerical multiple logistic regression model. Conclusion These findings firstly elucidate the incidence and associated factors of the outcome (death) among patients in Shiraz, Iran. Furthermore, we demonstrated that antiviral medication alone (without corticosteroids) and high O2 increase the survival chances of COVID patients.

Publisher

Springer Science and Business Media LLC

Reference31 articles.

1. Sahin A-R, Erdogan A, Agaoglu PM, Dineri Y, Cakirci A-Y, Senel M-E, et al. 2019 novel coronavirus (COVID-19) outbreak: a review of the current literature. EJMO. 2020;4(1):1–7.

2. Suter F, Consolaro E, Pedroni S, Moroni C, Pastò E, Paganini MV et al. A simple, home-therapy algorithm to prevent hospitalisation for COVID-19 patients: a retrospective observational matched-cohort study. EClinicalMedicine. 2021:100941.

3. Ghebreyesus T. May. World Health Organization. WHO Director-General’s opening remarks at the media briefing on COVID-19-25 2020.

4. Hsieh Y-H, Lee J-Y, Chang H-L. SARS epidemiology modeling. Emerg Infect Dis. 2004;10(6):1165.

5. Rahman PNSN, Zaki R, Tan Z, Bibi S, Baghbanzadeh M, Aghamohammadi N, Zhang W, Haque U. Int J Epidemiol. 2020;49:717–26.

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