Longitudinal Chest CT Features in Severe/Critical COVID-19 Cases and the Predictive Value of the Initial CT for Mortality

Abstract

Purpose

To evaluate longitudinal computed tomography (CT) features and the predictive value of the initial CT and clinical characteristics for mortality in patients with severe/critical coronavirus disease 2019 (COVID-19) pneumonia.

Methods

A retrospective analysis was performed on patients with COVID-19 pneumonia confirmed by laboratory. By excluding mild and common patients, 155 severe/critical patients with definite outcome were finally enrolled. A total of 516 CTs of 147 patients were divided into four stages according to the time after onset (stage 1, 1–7 days; stage 2, 8–14 days; stage 3, 15–21 days, and stage 4, >21 days). The evolving imaging features between the survival and non-survival groups were compared by using Chi-square, Fisher’s exact test, student’s t-test or Mann–Whitney U-test, as appropriate. The predictive value of clinical and CT features at admission for mortality was analysed through logistic regression analysis. To avoid overfitting caused by CT scores, CT scores were divided into two parts, which were combined with clinical variables, respectively, to construct the models.

Results

Ground-glass opacities (GGO) patterns were predominant for stages 1 and 2 for both groups (both P>0.05). The numbers of consolidation lesions increased in stage 3 in both groups (P=0.857), whereas the linear opacity increased in the survival group but decreased in the non-survival group (P=0.0049). In stage 4, the survival group predominantly presented linear opacity patterns, whereas the non-survival group mainly showed consolidation patterns (P=0.007). Clinical and imaging characteristics correlated with mortality; multivariate analyses revealed age >71 years, neutrophil count >6.38 × 10⁹/L, aspartate aminotransferase (AST) >58 IU/L, and CT score (total lesions score >17 in model 1, GGO score >14 and consolidation score >2 in model 2) as independent risk factors (all P<0.05). The areas under the curve of the six independent risk factors alone ranged from 0.65 to 0.75 and were 0.87 for model 2, 0.89 for model 1, and 0.92 for the six variables combined. Statistical differences were observed between Kaplan Meier curves of groups separated by cut-off values of these six variables (all P<0.01).

Conclusion

Longitudinal imaging features demonstrated differences between the two groups, which may help determine the patient’s prognosis. The initial CT score combined with age, AST, and neutrophil count is an excellent predictor for mortality in COVID-19 patients.

Keywords:

• COVID-19
• SARS-CoV-2
• mortality
• multivariate combined analysis
• chest CT score
• risk factors

Abbreviations

COVID-19, coronavirus disease 2019; CT, computed tomography; GGO, ground-glass opacities; AST, aspartate aminotransferase; RT-PCR, reverse-transcription polymerase chain reaction; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; ROC, receiver operating characteristic; IQR, interquartile ranges; EPV, event per variable.

Data Sharing Statement

The authors commit to making the relevant anonymized patient-level data available on reasonable request.

Ethics Approval and Consent to Participate

This study was conducted in accordance with the principles of the Declaration of Helsinki and Institutional Review Board approval has been obtained. The patients’ consent to review their medical records was not required by the ethics committee, the reasons for the waiver are as follows. Firstly, this was a retrospective study, the relevant clinical features and CT images of the patients were anonymized or maintained with confidentiality. Secondly, there was no risk to the subjects.

Acknowledgments

We thank the frontline medical staff for their hard work and selfless dedication in the face of the pandemic, despite the risk of infection to themselves and their families. They make an important contribution to controlling the spread of this outbreak.

Author Contributions

All authors contributed to data analysis, drafting or revising the article, have agreed on the journal to which the article will be submitted, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that they have no conflict of interest.

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.