Volume 14, Issue 3 (May & Jun 2024)                   J Research Health 2024, 14(3): 217-230 | Back to browse issues page


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Mohammadzadeh F, Delshad Noughabi A, Sabeti Bilondi S, Tavakolizadeh M, Hajavi J, Aalami H et al . Risk Score Model for Predicting COVID-19 Progression in Iranian Patients: Development and Validation Study. J Research Health 2024; 14 (3) :217-230
URL: http://jrh.gmu.ac.ir/article-1-2400-en.html
1- Department of Epidemiology and Biostatistics, Social Development and Health Promotion Research Center, School of Health, Gonabad University of Medical Sciences, Gonabad, Iran.
2- Social Development and Health Promotion Research Center, Gonabad University of Medical Sciences, Gonabad, Iran.
3- Department of Nursing, Faculty of Nursing and Midwifery, Gonabad Branch, Islamic Azad University, Gonabad, Iran.
4- Unit of Clinical Research Development, Bohlool Hospital, Gonabad University of Medical Sciences, Gonabad, Iran.
5- Department of Microbiology, Infectious Disease Research Center, School of Medicine, Gonabad University of Medical Sciences, Gonabad, Iran.
6- Unit of Clinical Research Development, Gonabad University of Medical Sciences, Gonabad, Iran.
7- Health Promotion Research Center, Bohlool Hospital, Gonabad University of Medical Sciences, Gonabad, Iran. , Dr.saheban@yahoo.com
Abstract:   (828 Views)
Background: The recent novel coronavirus disease 2019 (COVID-19) pandemic has underlined the importance of risk score models in public health emergencies. This study aimed to develop a risk prediction score to identify high-risk hospitalized patients for disease progression on admission.
Methods: This prospective cohort study included 171 COVID-19 patients, identified through the reverse transcription polymerase chain reaction test, admitted to Bohlool Hospital in Gonabad City, Iran, between April 4 and June 5, 2021. The patients’ demographic, clinical, and laboratory data were collected upon admission, and clinical outcomes were monitored until the end of the study. The discovery dataset (80% of the data) was used to develop the risk score model based on clinical and laboratory features and patient characteristics to predict COVID-19 progression. An additive risk score model was developed based on the regression coefficients of the significant variables in a multiple logistic regression model. The performance of the risk score model was evaluated on the validation dataset (20% of the data) using the receiver operating characteristic (ROC) curve. Statistical analyses were performed with SPSS software, version 21.
Results: The Mean±SD for age of participants was 59.54±20.52 years, and 48.6% were male. Most patients (82.5%) fully recovered or showed improvement, while 5.2% experienced disease progression and 12.3% died. Three variables, interleukin-6, neutrophil-to-lymphocyte ratio, and lung involvement, were found to be significant in predicting risk, with a good discriminatory ability, having an area under the ROC curve of 0.970 (95% CI, 0.935%, 1.00%) in the discovery set and 0.973 (95% CI, 0.923%, 1.00%) in the validation set.
Conclusion: The developed risk score model in this study can be used as a clinical diagnostic tool to identify COVID-19 patients at higher risk of disease progression and aid in informed decision-making and resource utilization in similar situations, such as respiratory disease outbreaks in the post-corona era. 
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Type of Study: Orginal Article | Subject: ● Disease Control
Received: 2023/07/31 | Accepted: 2023/10/7 | Published: 2024/05/1

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