Predicting the 30-day Adverse Outcomes of Non-Critical New-Onset COVID-19 Patients in Emergency Departments based on their Lung CT Scan Findings; A Pilot Study for Derivation an Emergency Scoring Tool
Introduction: Coronavirus Disease (COVID‐19) has become the most important global health issue, and chest computed tomography (CT) scan can help determine the severity of the infection. Objectives: This study aimed to provide an emergency scoring tool for predicting 30-day adverse outcomes in non-critical new-onset COVID-19 patients. Methods: This derivation study was conducted on new-onset COVID-19 patients presenting to the emergency department of an urban teaching hospital in Tehran, Iran, between 20 February and 20 March 2020. The total lobe severity score (TSS), age, history of comorbidities, and 30-day adverse outcomes (death, ICU admission or intubation) were taken into account to produce three prediction models. Results: Overall, 137 patients were included in the study. Their mean age was 59.9±16.8 years and 62% were male. The ground glass nodule, patch B/punctate ground-glass opacity, fibrous stripes, and air bronchogram sign with perihilar distribution, bilateral and ≥ 2 affected lobes were the most common findings. The mean TSS (model 1) was significantly higher in patients with an adverse outcome (9.4±3.2) compared to the discharged patients (7.2±3.3) (p<0.001, AUC: 0.703, sensitivity: 64.4% and specificity: 74.1%). The optimal cut-off point of model 2 (TSS and age) had the following parameters: AUC: 0.721, sensitivity: 71.2% and specificity: 67.2%. The optimal cut-off point of model 3 (TSS, age, comorbidities) had: AUC: 0.755, sensitivity: 79.7% and specificity: 65.5%. The discrimination achieved with model 3 based on Bonferroni’s test was significantly better than that achieved with TSS (p<0.001). Conclusion: TSS combined with age and history of at least one comorbidity had a better predictive value for adverse outcomes with a cut-off point above 8.
2. Fu L, Wang B, Yuan T-w, Chen X, Ao Y, Fitzpatrick T, et al. Clinical characteristics of coronavirus disease 2019 (COVID-19) in China: A systematic review and meta-analysis. J Infect. 2020;80(6):656-65.
3. Suleyman G, Fadel RA, Malette KM, Hammond C, Abdulla H, Entz A, et al. Clinical Characteristics and Morbidity Associated With Coronavirus Disease 2019 in a Series of Patients in Metropolitan Detroit. JAMA Netw Open. 2020;3(6):e2012270.
4. Sadeghi M, Saberian P, Hasani-Sharamin P, Dadashi F, Babaniamansour S, Aliniagerdroudbari E. The possible factors correlated with the higher risk of getting infected by COVID-19 in emergency medical technicians; a case-control study. Bull Emerg Trauma. 2021;In press.
5. Khoshnood RJ, Ommi D, Zali A, Ashrafi F, Vahidi M, Azhide A, et al. Epidemiological Characteristics, Clinical Features, and Outcome of COVID-19 Patients in Northern Tehran, Iran; a Cross-Sectional Study. Adv J Emerg Med. 2020;5(1):e11.
6. Li LQ, Huang T, Wang YQ, Wang ZP, Liang Y, Huang TB, et al. COVID-19 patients' clinical characteristics, discharge rate, and fatality rate of meta-analysis. J Med Virol. 2020;92(6):577-83.
7. Dehghani Firouzabadi F, Firouzabadi M, Ghalehbaghi B, Jahandideh H, Roomiani M, Goudarzi S. Have the symptoms of patients with COVID-19 changed over time during hospitalization? Med Hypotheses. 2020;143:110067.
8. Dehghani Firouzabadi M, Dehghani Firouzabadi F, Goudarzi S, Jahandideh H, Roomiani M. Has the chief complaint of patients with COVID-19 disease changed over time? Med hypotheses. 2020;144:109974.
9. Gavriatopoulou M, Korompoki E, Fotiou D, Ntanasis-Stathopoulos I, Psaltopoulou T, Kastritis E, et al. Organ-specific manifestations of COVID-19 infection. Clin Exp Med. 2020;20(4):493-506.
10. Naderpour Z, Saeedi M. A primer on covid-19 for clinicians: clinical manifestation and natural course. Adv J Emerg Med. 2020;4(2s):e62.
11. Lv M, Wang M, Yang N, Luo X, Li W, Chen X, et al. Chest computed tomography for the diagnosis of patients with coronavirus disease 2019 (COVID-19): a rapid review and meta-analysis. Ann Transl Med. 2020;8(10):622.
12. Bao C, Liu X, Zhang H, Li Y, Liu J. Coronavirus Disease 2019 (COVID-19) CT Findings: A Systematic Review and Meta-analysis. J Am Coll Radiol. 2020;17(6):701-9.
13. Pan Y, Guan H, Zhou S, Wang Y, Li Q, Zhu T, et al. Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): a study of 63 patients in Wuhan, China. Eur Radiol. 2020;30(6):3306-9.
14. Asefi H, Safaie A. The role of chest CT scan in diagnosis of COVID-19. Adv J Emerg Med. 2020;4(2s):e64.
15. Poortahmasebi V, Zandi M, Soltani S, Jazayeri SM. Clinical performance of RT-PCR and chest CT scan for COVID-19 diagnosis; a systematic review. Adv J Emerg Med. 2020;4(2s):e57.
16. Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, et al. Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology. 2020;296(2):E32-40.
17. Jalaber C, Lapotre T, Morcet-Delattre T, Ribet F, Jouneau S, Lederlin M. Chest CT in COVID-19 pneumonia: A review of current knowledge. Diagn Interv Imaging. 2020;101(7):431-7.
18. Caruso D, Zerunian M, Polici M, Pucciarelli F, Polidori T, Rucci C, et al. Chest CT Features of COVID-19 in Rome, Italy. Radiology. 2020;296(2):E79-85.
19. Cheng Z, Lu Y, Cao Q, Qin L, Pan Z, Yan F, et al. Clinical Features and Chest CT Manifestations of Coronavirus Disease 2019 (COVID-19) in a Single-Center Study in Shanghai, China. Am J Roentgenol. 2020;215(1):121-6.
20. Dai H, Zhang X, Xia J, Zhang T, Shang Y, Huang R, et al. High-resolution Chest CT Features and Clinical Characteristics of Patients Infected with COVID-19 in Jiangsu, China. Int J Infect Dis. 2020;95:106-12.
21. Pan F, Ye T, Sun P, Gui S, Liang B, Li L, et al. Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19). Radiology. 2020;295(3):715-21.
22. Aguiar D, Lobrinus JA, Schibler M, Fracasso T, Lardi C. Inside the lungs of COVID-19 disease. Int J Legal Med. 2020;134(4):1271-4.
23. Chen HJ, Qiu J, Wu B, Huang T, Gao Y, Wang ZP, et al. Early chest CT features of patients with 2019 novel coronavirus (COVID-19) pneumonia: relationship to diagnosis and prognosis. Eur Radiol. 2020;30(11):6178-85.
24. Li Y, Xia L. Coronavirus Disease 2019 (COVID-19): Role of Chest CT in Diagnosis and Management. Am J Roentgenol. 2020;214(6):1280-6.
25. Chung M, Bernheim A, Mei X, Zhang N, Huang M, Zeng X, et al. CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV). Radiology. 2020;295(1):202-7.
26. Shi H, Han X, Jiang N, Cao Y, Alwalid O, Gu J, et al. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis. 2020;20(4):425-34.
27. Mo P, Xing Y, Xiao Y, Deng L, Zhao Q, Wang H, et al. Clinical characteristics of refractory COVID-19 pneumonia in Wuhan, China. Clin Infect Dis. 2020;Online ahead of print.
28. Chen T, Wu D, Chen H, Yan W, Yang D, Chen G, et al. Clinical characteristics of 113 deceased patients with coronavirus disease 2019: Retrospective study. BMJ. 2020;368:m1091.
29. Pozzilli P, Lenzi A. Commentary: Testosterone, a key hormone in the context of COVID-19 pandemic. Metabolism. 2020;108:154252.
30. Francone M, Iafrate F, Masci GM, Coco S, Cilia F, Manganaro L, et al. Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis. Eur Radiol. 2020;30(12):6808-17.
31. Zeinab IS, Hajar V, Mojdeh M, Fatemeh S, Mohsen G. Variable Clinical Manifestations of COVID-19: Viral and Human Genomes Talk. Iran J Allergy Asthma Immunol. 19(5):456-70.
32. Jalali A, Karimialavije E, Aliniagerdroudbari E, Babaniamansour S. Incidentally Diagnosed COVID-19 in the Emergency Department: A Case Series. CRCP. 5(Covid- 19):145-8.
33. Yang S, Shi Y, Lu H, Xu J, Li F, Qian Z, et al. Clinical and CT features of early-stage patients with COVID-19: a retrospective analysis of imported cases in Shanghai, China. Eur Respir J. 2020;55(4):2000407.
|Issue||Vol 5 No 4 (2021): Autumn (October)|
|COVID-19 Patient Outcome Assessment Prognosis Scoring System X-Ray Computed Tomography|
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