Association Between Artificial Intelligence Based Chest Computed Tomography and Clinical/Laboratory Characteristics with Severity and Mortality in COVID-19 Hospitalized Patients - Ye J; Huang Y; Chu C; Li J; Liu G; Li W; Gao C (Dove Medical Press, 2024)
Jiawei Ye,1,* Yingying Huang,2,* Caiting Chu,3 Juan Li,1 Guoxiang Liu,1 Wenjie Li,1 Chengjin Gao1 1Department of Emergency Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People's Republic of China; 2Dementia Research Centre, Faculty of Medicine, Health and Human Sciences, Macquarie University Sydney, Australia; 3Department of Radiology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People's Republic of China*These authors contributed equally to this workCorrespondence: Chengjin Gao; Wenjie Li, Email gaochengjin@xinhuamed.com.cn; kingnever@hotmail.comBackground: Some patients with COVID-19 rapidly develop respiratory failure or mortality, underscoring the necessity for early identification of those prone to severe illness. Numerous studies focus on clinical and lab traits, but only few attend to chest computed tomography. The current study seeks to numerically quantify pulmonary lesions using early-phase CT scans calculated through artificial intelligence algorithms in conjunction with clinical and laboratory helps clinicians to early identify the development of severe illness and death in a group of COVID-19 patients.Methods: From December 15, 2022, to January 30, 2023, 191 confirmed COVID-19 patients admitted to Xinhua Hospital Affiliated with Shanghai Jiao Tong University School of Medicine were consecutively enrolled. All patients underwent chest CT scans and serum tests within 48 hours prior to admission. Variables significantly linked to critical illness or mortality in univariate analysis were subjected to multivariate logistic regression models post collinearity assessment. Adjusted odds ratio, 95% confidence intervals, sensitivity, specificity, Youden index, receiver-operator-characteristics (ROC) curves, and area under the curve (AUC) were computed for predicting severity and in-hospital mortality.Results: Multivariate logistic analysis revealed that myoglobin (OR = 1.003, 95% CI 1.001- 1.005), APACHE II score (OR = 1.387, 95% CI 1.216- 1.583), and the infected CT region percentage (OR = 113.897, 95% CI 4.939- 2626.496) independently correlated with in-hospital COVID-19 mortality. Prealbumin stood as an independent safeguarding factor (OR = 0.965, 95% CI 0.947- 0.984). Neutrophil counts (OR = 1.529, 95% CI 1.131- 2.068), urea nitrogen (OR = 1.587, 95% CI 1.222- 2.062), SOFA score(OR = 3.333, 95% CI 1.476- 7.522), qSOFA score(OR = 15.197, 95% CI 3.281- 70.384), PSI score(OR = 1.053, 95% CI 1.018- 1.090), and the infected CT region percentage (OR = 548.221, 95% CI 2.615- 114,953.586) independently linked to COVID-19 patient severity.Keywords: COVID-19, chest CT, artificial intelligence, mortality, severity