Predicting the Covid-19 Patients Status Using Chest Ct Scan Findings: A Risk Assessment Model Based on Decision Tree Analysis Publisher Pubmed



Talebi A¹ ; Borumandnia N² ; Jafari R³ ; Pourhoseingholi MA⁴ ; Jafari NJ⁵ ; Ashtari S⁴ ; Roozpeykar S³ ; Rahimibashar F⁶ ; Karimi L⁷ ; Guest PC^{8, 9, 10} ; Jamialahmadi T^{11, 12} ; Vahedianazimi A¹³ ; Goharimoghadam K¹⁴ ; Sahebkar A^{15, 16, 17} Show Affiliations
Source: Advances in Experimental Medicine and Biology Published:2023

Abstract

Background: The role of chest computed tomography (CT) to diagnose 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 scans. Methods: This retrospective study was performed on patients with COVID-19 who underwent chest CT scans. Medical records of 1078 patients with COVID-19 were evaluated. The classification and regression tree (CART) of decision tree model and k-fold cross-validation were used to predict the status of patients using sensitivity, specificity, and area under the curve (AUC) assessments. Results: The subjects comprised of 169 critical cases and 909 non-critical cases. The bilateral distribution and multifocal lung involvement were 165 (97.6%) and 766 (84.3%) in critical patients, respectively. According to the DT model, total opacity score, age, lesion types, and gender were statistically significant predictors for critical outcomes. 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 health conditions in COVID-19 disease patients. This model has the potential characteristics for clinical applications and can identify high-risk subpopulations that need specific prevention. Further developments including integration of blood biomarkers are underway to increase the performance of the model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.