Radiologic Abnormalities in Prolonged SARS-CoV-2 Infection: A Systematic Review.
Kyongmin Sarah BeckJeong Hwa YoonSoon-Ho YoonPublished in: Korean journal of radiology (2024)
We systematically reviewed radiological abnormalities in patients with prolonged SARS-CoV-2 infection, defined as persistently positive polymerase chain reaction (PCR) results for SARS-CoV-2 for > 21 days, with either persistent or relapsed symptoms. We extracted data from 24 patients (median age, 54.5 [interquartile range, 44-64 years]) reported in the literature and analyzed their representative CT images based on the timing of the CT scan relative to the initial PCR positivity. Our analysis focused on the patterns and distribution of CT findings, severity scores of lung involvement on a scale of 0-4, and the presence of migration. All patients were immunocompromised, including 62.5% (15/24) with underlying lymphoma and 83.3% (20/24) who had received anti-CD20 therapy within one year. Median duration of infection was 90 days. Most patients exhibited typical CT appearance of coronavirus disease 19 (COVID-19), including ground-glass opacities with or without consolidation, throughout the follow-up period. Notably, CT severity scores were significantly lower during ≤ 21 days than during > 21 days ( P < 0.001). Migration was observed on CT in 22.7% (5/22) of patients at ≤ 21 days and in 68.2% (15/22) to 87.5% (14/16) of patients at > 21 days, with rare instances of parenchymal bands in previously affected areas. Prolonged SARS-CoV-2 infection usually presents as migrating typical COVID-19 pneumonia in immunocompromised patients, especially those with impaired B-cell immunity.
Keyphrases
- coronavirus disease
- sars cov
- end stage renal disease
- computed tomography
- ejection fraction
- newly diagnosed
- chronic kidney disease
- magnetic resonance imaging
- dual energy
- contrast enhanced
- image quality
- prognostic factors
- patient reported outcomes
- deep learning
- stem cells
- depressive symptoms
- acute myeloid leukemia
- convolutional neural network
- electronic health record
- smoking cessation
- big data
- multiple myeloma