Initial CT features of COVID-19 predicting clinical category

Area of Science:

• Radiology
• Infectious Diseases
• Medical Imaging

Background:

• Coronavirus disease 2019 (COVID-19) presents with diverse clinical manifestations.
• Accurate assessment of COVID-19 severity is crucial for patient management.
• Computed Tomography (CT) is a key imaging modality for diagnosing COVID-19.

Purpose of the Study:

• To analyze the initial CT imaging characteristics of COVID-19 patients across different clinical severity categories.
• To identify CT features that correlate with the severity of COVID-19.

Main Methods:

• Retrospective analysis of 86 COVID-19 patients.
• Evaluation of initial CT features including lesion characteristics, distribution, and associated signs.
• Statistical analysis using Chi-square, Fisher's exact, and Mann-Whitney U tests.
• Binary logistic regression to predict severe and critical COVID-19 categories.

Main Results:

• Significant differences in age and sex were observed between mild/moderate and severe/critical COVID-19 groups.
• Multifocal lesions were present in the majority of cases (91.8%).
• Ground-glass opacities (GGO) were more prevalent in severe/critical cases (57.8%) compared to mild/moderate cases (31.7%).
• Lymphocyte count emerged as a significant predictor of severe and critical COVID-19.

Conclusions:

• Initial CT findings alone may overlap between different COVID-19 clinical categories.
• Integrating CT imaging with laboratory markers, particularly lymphocyte count, enhances the prediction of COVID-19 severity.
• This combined approach can aid in early identification of patients at risk for severe outcomes.