Volume 14, Issue 3 (May & Jun-InPress 2024)
J Research Health 2024, 14(3): 1-1 |
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Mohammadzadeh F, Delshad Noughabi A, Sabeti Bilondi S, Tavakolizadeh M, Hajavi J, Aalami H et al . Development and Validation of a Risk Score Model for Predicting the Progression of COVID-19 Among Iranian Patients. J Research Health 2024; 14 (3) :1-1
URL: http://jrh.gmu.ac.ir/article-1-2400-en.html
URL: http://jrh.gmu.ac.ir/article-1-2400-en.html
Fatemeh Mohammadzadeh1 
, Ali Delshad Noughabi2 , Sina Sabeti Bilondi3 , Mitra Tavakolizadeh4 , Jafar Hajavi5 , Hosein Aalami6 , Mohsen Sahebanmaleki 7
, Ali Delshad Noughabi2 , Sina Sabeti Bilondi3 , Mitra Tavakolizadeh4 , Jafar Hajavi5 , Hosein Aalami6 , Mohsen Sahebanmaleki 7
1- Department of Epidemiology & Biostatistics, School of Health, Social Development & Health Promotion Research Center, Gonabad University of Medical Sciences, Gonabad, Iran.
2- Social Development and Health Promotion Research Center, Gonabad University of Medical Sciences, Gonabad, Iran.
3- Islamic Azad University of Gonabad, Clinical Reasearch Development Unit, Bohlool Hospital, Gonabad University of Medical Sciences, Gonabad, Iran.
4- Clinical Reasearch Development Unit, Bohlool Hospital, Gonabad University of Medical Sciences, Gonabad, Iran.
5- Department of Microbiology, School of Medicine, Infectious Disease Research Center, Gonabad University of Medical Sciences, Gonabad, Iran.
6- Clinical Reasearch Development Unit, Gonabad University of Medical Sciences, Gonabad, Iran.
7- Bohlool Hospital, Health Promotion Research Center, Gonabad University of Medical Sciences, Gonabad, Iran. , Dr.saheban@yahoo.com
2- Social Development and Health Promotion Research Center, Gonabad University of Medical Sciences, Gonabad, Iran.
3- Islamic Azad University of Gonabad, Clinical Reasearch Development Unit, Bohlool Hospital, Gonabad University of Medical Sciences, Gonabad, Iran.
4- Clinical Reasearch Development Unit, Bohlool Hospital, Gonabad University of Medical Sciences, Gonabad, Iran.
5- Department of Microbiology, School of Medicine, Infectious Disease Research Center, Gonabad University of Medical Sciences, Gonabad, Iran.
6- Clinical Reasearch Development Unit, Gonabad University of Medical Sciences, Gonabad, Iran.
7- Bohlool Hospital, Health Promotion Research Center, Gonabad University of Medical Sciences, Gonabad, Iran. , Dr.saheban@yahoo.com
Abstract: (201 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 Bohlol 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 (version 21.0).
Results: The mean age of participants was 59.54 (SD=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.
Methods: This prospective cohort study included 171 COVID-19 patients, identified through the reverse transcription polymerase chain reaction test, admitted to Bohlol 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 (version 21.0).
Results: The mean age of participants was 59.54 (SD=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.
Type of Study: Orginal Article |
Subject:
● Disease Control
Received: 2023/07/31 | Accepted: 2023/10/7 | Published: 2024/05/17
Received: 2023/07/31 | Accepted: 2023/10/7 | Published: 2024/05/17
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