Computed Tomography Severity Score As a Predictor of Disease Severity and Mortality in COVID-19 Patients: A Systematic Review and Meta-analysis

Overview

Journal J Med Imaging Radiat Sci

Publisher Elsevier

Specialty Radiology

Date 2023 Mar 12

PMID 36907753

Authors

Jay Prakash

Naveen Kumar

Khushboo Saran

Arun Kumar Yadav

Amit Kumar

Pradip Kumar Bhattacharya

Anupa Prasad

Affiliations

Soon will be listed here.

Abstract

Background: Prediction of outcomes in severe COVID-19 patients using chest computed tomography severity score (CTSS) may enable more effective clinical management and early, timely ICU admission. We conducted a systematic review and meta-analysis to determine the predictive accuracy of the CTSS for disease severity and mortality in severe COVID-19 subjects.

Methods: The electronic databases PubMed, Google Scholar, Web of Science, and the Cochrane Library were searched to find eligible studies that investigated the impact of CTSS on disease severity and mortality in COVID-19 patients between 7 January 2020 and 15 June 2021. Two independent authors looked into the risk of bias using the Quality in Prognosis Studies (QUIPS) tool.

Results: Seventeen studies involving 2788 patients reported the predictive value of CTSS for disease severity. The pooled sensitivity, specificity, and summary area under the curve (sAUC) of CTSS were 0.85 (95% CI 0.78-0.90, I =83), 0.86 (95% CI 0.76-0.92, I =96) and 0.91 (95% CI 0.89-0.94), respectively. Six studies involving 1403 patients reported the predictive values of CTSS for COVID-19 mortality. The pooled sensitivity, specificity, and sAUC of CTSS were 0.77 (95% CI 0.69-0.83, I = 41), 0.79 (95% CI 0.72-0.85, I = 88), and 0.84 (95% CI 0.81-0.87), respectively.

Discussion: Early prediction of prognosis is needed to deliver the better care to patients and stratify them as soon as possible. Because different CTSS thresholds have been reported in various studies, clinicians are still determining whether CTSS thresholds should be used to define disease severity and predict prognosis.

Conclusion: Early prediction of prognosis is needed to deliver optimal care and timely stratification of patients. CTSS has strong discriminating power for the prediction of disease severity and mortality in patients with COVID-19.

Citing Articles

Can there be calm during a cytokine storm? Immune checkpoint pathways affecting the severity of COVID-19 disease.

Meggyes M, Nagy D, Toth I, Feik T, Peterfalvi A, Polgar B Front Microbiol. 2025; 15:1508423.

PMID: 39764446 PMC: 11700970. DOI: 10.3389/fmicb.2024.1508423.

The severity assessment and nucleic acid turning-negative-time prediction in COVID-19 patients with COPD using a fused deep learning model.

Liu Y, Zhang W, Sun M, Liang X, Wang L, Zhao J BMC Pulm Med. 2024; 24(1):515.

PMID: 39402509 PMC: 11476205. DOI: 10.1186/s12890-024-03333-x.

Association of chest computed tomography severity score at ICU admission and respiratory outcomes in critically ill COVID-19 patients.

Esper Treml R, Caldonazo T, Hohmann F, Lima da Rocha D, Filho P, Mori A PLoS One. 2024; 19(5):e0299390.

PMID: 38696477 PMC: 11065208. DOI: 10.1371/journal.pone.0299390.

References

Xiao J, Li X, Xie Y, Huang Z, Ding Y, Zhao S . Maximum chest CT score is associated with progression to severe illness in patients with COVID-19: a retrospective study from Wuhan, China. BMC Infect Dis. 2020; 20(1):953. PMC: 7729688. DOI: 10.1186/s12879-020-05683-3. View

Raoufi M, Safavi Naini S, Azizan Z, Jafar Zade F, Shojaeian F, Boroujeni M . Correlation between Chest Computed Tomography Scan Findings and Mortality of COVID-19 Cases; a Cross sectional Study. Arch Acad Emerg Med. 2020; 8(1):e57. PMC: 7305634. View

Li Y, Yang Z, Ai T, Wu S, Xia L . Association of "initial CT" findings with mortality in older patients with coronavirus disease 2019 (COVID-19). Eur Radiol. 2020; 30(11):6186-6193. PMC: 7283986. DOI: 10.1007/s00330-020-06969-5. View

Bernheim A, Mei X, Huang M, Yang Y, Fayad Z, Zhang N . Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection. Radiology. 2020; 295(3):200463. PMC: 7233369. DOI: 10.1148/radiol.2020200463. View

Lal A, Mishra A, Sahu K . CT chest findings in coronavirus disease-19 (COVID-19). J Formos Med Assoc. 2020; 119(5):1000-1001. PMC: 7129384. DOI: 10.1016/j.jfma.2020.03.010. View

Machnicki S, Patel D, Singh A, Talwar A, Mina B, Oks M . The Usefulness of Chest CT Imaging in Patients With Suspected or Diagnosed COVID-19: A Review of Literature. Chest. 2021; 160(2):652-670. PMC: 8056836. DOI: 10.1016/j.chest.2021.04.004. View

Li K, Fang Y, Li W, Pan C, Qin P, Zhong Y . CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19). Eur Radiol. 2020; 30(8):4407-4416. PMC: 7095246. DOI: 10.1007/s00330-020-06817-6. View

Venugopalan Nair A, Kumar D, Yadav S, Nepal P, Jacob B, Al-Heidous M . Utility of visual coronary artery calcification on non-cardiac gated thoracic CT in predicting clinical severity and outcome in COVID-19. Clin Imaging. 2021; 74:123-130. PMC: 7834505. DOI: 10.1016/j.clinimag.2021.01.015. View

Abbasi B, Akhavan R, Ghamari Khameneh A, Zandi B, Farrokh D, Pezeshki Rad M . Evaluation of the relationship between inpatient COVID-19 mortality and chest CT severity score. Am J Emerg Med. 2020; 45:458-463. PMC: 7521211. DOI: 10.1016/j.ajem.2020.09.056. View

10.

Higgins J, Thompson S . Quantifying heterogeneity in a meta-analysis. Stat Med. 2002; 21(11):1539-58. DOI: 10.1002/sim.1186. View

11.

Liu J, Yu H, Zhang S . The indispensable role of chest CT in the detection of coronavirus disease 2019 (COVID-19). Eur J Nucl Med Mol Imaging. 2020; 47(7):1638-1639. PMC: 7118704. DOI: 10.1007/s00259-020-04795-x. View

12.

Lessmann N, Sanchez C, Beenen L, Boulogne L, Brink M, Calli E . Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence. Radiology. 2020; 298(1):E18-E28. PMC: 7393955. DOI: 10.1148/radiol.2020202439. View

13.

Xu B, Xing Y, Peng J, Zheng Z, Tang W, Sun Y . Chest CT for detecting COVID-19: a systematic review and meta-analysis of diagnostic accuracy. Eur Radiol. 2020; 30(10):5720-5727. PMC: 7227176. DOI: 10.1007/s00330-020-06934-2. View

14.

Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z . Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020; 395(10229):1054-1062. PMC: 7270627. DOI: 10.1016/S0140-6736(20)30566-3. View

15.

Salahshour F, Mehrabinejad M, Toosi M, Gity M, Ghanaati H, Shakiba M . Clinical and chest CT features as a predictive tool for COVID-19 clinical progress: introducing a novel semi-quantitative scoring system. Eur Radiol. 2021; 31(7):5178-5188. PMC: 7809225. DOI: 10.1007/s00330-020-07623-w. View

16.

Bellos I, Tavernaraki K, Stefanidis K, Michalopoulou O, Lourida G, Korompoki E . Chest CT severity score and radiological patterns as predictors of disease severity, ICU admission, and viral positivity in COVID-19 patients. Respir Investig. 2021; 59(4):436-445. PMC: 7972804. DOI: 10.1016/j.resinv.2021.02.008. View

17.

Tan C, Huang Y, Shi F, Tan K, Ma Q, Chen Y . C-reactive protein correlates with computed tomographic findings and predicts severe COVID-19 early. J Med Virol. 2020; 92(7):856-862. PMC: 7262341. DOI: 10.1002/jmv.25871. View

18.

Li K, Wu J, Wu F, Guo D, Chen L, Fang Z . The Clinical and Chest CT Features Associated With Severe and Critical COVID-19 Pneumonia. Invest Radiol. 2020; 55(6):327-331. PMC: 7147273. DOI: 10.1097/RLI.0000000000000672. View

19.

Sun D, Li X, Guo D, Wu L, Chen T, Fang Z . CT Quantitative Analysis and Its Relationship with Clinical Features for Assessing the Severity of Patients with COVID-19. Korean J Radiol. 2020; 21(7):859-868. PMC: 7289692. DOI: 10.3348/kjr.2020.0293. View

20.

McRae M, Simmons G, Christodoulides N, Lu Z, Kang S, Fenyo D . Clinical decision support tool and rapid point-of-care platform for determining disease severity in patients with COVID-19. Lab Chip. 2020; 20(12):2075-2085. PMC: 7360344. DOI: 10.1039/d0lc00373e. View