Since December 2019, a cluster of cases with unknown pneumonia with similar clinical manifestations suggesting viral pneumonia appeared in Wuhan city, Hubei Province, China. A new type of coronavirus was isolated from the lower respiratory tract samples, named severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) by the International Committee on Taxonomy of Viruses . The disease it causes was named coronavirus disease 2019 (COVID-19) by the World Health Organization on February 11, 2020 .
Patient testimony is showing that a considerable number of patients would not recover totally from the effects of the virus months after their initial illness. Symptoms are wide-ranging and can include breathlessness, chronic fatigue, “brain fog”, anxiety, and stress. The NICE guideline scope published on 30 October 2020 defines post-COVID syndrome as signs and symptoms that develop during or following an infection consistent with COVID-19 which continue for more than 10–12 weeks and are not explained by an alternative diagnosis. The definition says the condition usually presents with clusters of symptoms, often overlapping, which may change over time and can affect any system within the body. It also notes that many people with post-COVID syndrome can also experience generalized pain, fatigue, persisting high temperature, and psychiatric problems .
CT scan can be a useful tool in evaluating the individual disease burden . The severity can be assessed using a visual method (semi-quantitative, as in our study) or using a software that quantitatively determines the percentage of affected lung volumes using the deep learning algorithms [15,16,17].
In this study, we tried to assess the utility of the CT severity scoring system as a predictor for possible development of post-COVID syndrome in recovered patients.
From April 2020 to October 2020, 192 symptomatic COVID-19 patients were enrolled in this single-center study and high-resolution chest CT examinations were evaluated. A previously validated semi-quantitative CT score based on the lobar extent of disease as reported by Yang et al. and Pan et al. [6, 7] was calculated.
Following recovery from the acute stage of the disease, 77 patients out of 192 developed post-COVID syndrome (40.1%).
On reviewing previously published studies, the percentage of cases which developed post-COVID syndrome ranged from 32 to 60%. This wide range may be explained by the variability in study duration and number of cases [18,19,20].
CT-SSS in patients who developed post-COVID syndrome is significantly higher than in those who did not develop post-COVID syndrome (P value < 0.001).
We were able to demonstrate that a cut-off value of >7 in CT-SSS is highly predictive of long-term clinical status with a sensitivity, specificity, PPV, NPV, and accuracy of 95.9%, 96%, 95.92%, 96%, and 95.96% respectively. To our knowledge, and till the date of publication, no previous study assessed such a relationship.
We also found that either older patients or patients with at least one medical comorbidity (diabetes, hypertension, chronic chest, or heart diseases) were more likely to develop post-COVID syndrome. This could be explained by the significant correlation of those comorbidities with disease burden in COVID-19 patients and accordingly severity of lung affection and CT-severity score. This agrees with the study of Lu et al.  which stated that older age and increased blood glucose level were correlated with the severity of lung involvement and clinical prognosis in COVID-19 patients. There was a positive correlation between blood glucose level on admission and lung lesions.
However, our study had some limitations including limited number of patients. Furthermore, this is a hospital not a population-based study, yet, we could deduct the percentage of different CT imaging categories. It is thus recommended to perform future studies to confirm the generalizability of this study on a larger scale. We recommend also further studies to investigate other predictive parameters for development of post-COVID syndrome.