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Research article
Predicting the COVID-19 patients’ status using chest CT scan findings: A risk assessment model based on Decision tree
Amir Vahedian-Azimi
Baqiyatallah University of Medical Sciences
Corresponding Author
ORCiD: https://orcid.org/0000-0002-1678-7608
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: The role of chest computed tomographic (CT) to diagnosis coronavirus disease-2019 (COVID-19) is still an open field to be explored. The aim of this study was to apply the decision tree (DT) model to predict critical or non-critical status of patients infected with COVID-19 based on available information on non-contrast CT scan.
Method: This study was performed onpatients with COVID-19 who underwent chest CTscan atBaqiyatallahHospital, Tehran, Iran. In this retrospectivestudy, the medical records of 1078 patients with COVID-19 were evaluated. The classification and regression tree (CART) of decision treemodeland k-fold cross validation were used to predict the status of patients and to measure their sensitivity, specificity and area under curve (AUC).
Results: Data included 169 critical cases and 909 non-critical cases. The bilateral distribution and multifocal lung involvement were165 (97.6%) and 766 (84.3%) in critical patients, respectively.According to DT model, total opacity score, age, lesion types and gender were statistically significant predictors incritical patients.Moreover, the results showed that the accuracy, sensitivity and specificity of the DT model were 93.3%, 72.8% and 97.1%, respectively.
Conclusions: The presented algorithm demonstrates the factors affecting the patient's condition. In addition, this model has the potential characteristics for clinical applicationsand canalso identify high-risk subpopulations that need specific prevention.
Keywords
Chest CT scan without contrast, coronavirus disease, COVID-19, disease outcome, decision tree
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This is a preprint. It has not completed peer review.
Research article
Predicting the COVID-19 patients’ status using chest CT scan findings: A risk assessment model based on Decision tree
Amir Vahedian-Azimi
Baqiyatallah University of Medical Sciences
Corresponding Author
ORCiD: https://orcid.org/0000-0002-1678-7608
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 3
Posted 13 Jan, 2021
No community comments so far
No comments provided
No comments provided
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: The role of chest computed tomographic (CT) to diagnosis coronavirus disease-2019 (COVID-19) is still an open field to be explored. The aim of this study was to apply the decision tree (DT) model to predict critical or non-critical status of patients infected with COVID-19 based on available information on non-contrast CT scan.
Method: This study was performed onpatients with COVID-19 who underwent chest CTscan atBaqiyatallahHospital, Tehran, Iran. In this retrospectivestudy, the medical records of 1078 patients with COVID-19 were evaluated. The classification and regression tree (CART) of decision treemodeland k-fold cross validation were used to predict the status of patients and to measure their sensitivity, specificity and area under curve (AUC).
Results: Data included 169 critical cases and 909 non-critical cases. The bilateral distribution and multifocal lung involvement were165 (97.6%) and 766 (84.3%) in critical patients, respectively.According to DT model, total opacity score, age, lesion types and gender were statistically significant predictors incritical patients.Moreover, the results showed that the accuracy, sensitivity and specificity of the DT model were 93.3%, 72.8% and 97.1%, respectively.
Conclusions: The presented algorithm demonstrates the factors affecting the patient's condition. In addition, this model has the potential characteristics for clinical applicationsand canalso identify high-risk subpopulations that need specific prevention.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6