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01-12-2020 | Computed Tomography | Original Article

Qualitative and quantitative chest CT parameters as predictors of specific mortality in COVID-19 patients

Authors: Davide Colombi, Gabriele D. Villani, Gabriele Maffi, Camilla Risoli, Flavio C. Bodini, Marcello Petrini, Nicola Morelli, Pietro Anselmi, Gianluca Milanese, Mario Silva, Nicola Sverzellati, Emanuele Michieletti

Published in: Emergency Radiology | Issue 6/2020

Abstract

Purpose

To test the association between death and both qualitative and quantitative CT parameters obtained visually and by software in coronavirus disease (COVID-19) early outbreak.

Methods

The study analyzed retrospectively patients underwent chest CT at hospital admission for COVID-19 pneumonia suspicion, between February 21 and March 6, 2020. CT was performed in case of hypoxemia or moderate-to-severe dyspnea. CT scans were analyzed for quantitative and qualitative features obtained visually and by software. Cox proportional hazards regression analysis examined the association between variables and overall survival (OS). Three models were built for stratification of mortality risk: clinical, clinical/visual CT evaluation, and clinical/software-based CT assessment. AUC for each model was used to assess performance in predicting death.

Results

The study included 248 patients (70% males, median age 68 years). Death occurred in 78/248 (32%) patients. Visual pneumonia extent > 40% (HR 2.15, 95% CI 1.2–3.85, P = 0.01), %high attenuation area – 700 HU > 35% (HR 2.17, 95% CI 1.2–3.94, P = 0.01), exudative consolidations (HR 2.85–2.93, 95% CI 1.61–5.05/1.66–5.16, P < 0.001), visual CAC score > 1 (HR 2.76–3.32, 95% CI 1.4–5.45/1.71–6.46, P < 0.01/P < 0.001), and CT classified as COVID-19 and other disease (HR 1.92–2.03, 95% CI 1.01–3.67/1.06–3.9, P = 0.04/P = 0.03) were significantly associated with shorter OS. Models including CT parameters (AUC 0.911–0.913, 95% CI 0.873–0.95/0.875–0.952) were better predictors of death as compared to clinical model (AUC 0.869, 95% CI 0.816–0.922; P = 0.04 for both models).

Conclusions

In COVID-19 patients, qualitative and quantitative chest CT parameters obtained visually or by software are predictors of mortality. Predictive models including CT metrics were better predictors of death in comparison to clinical model.

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Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, Zhao X, Huang B, Shi W, Lu R, Niu P, Zhan F, Ma X, Wang D, Xu W, Wu G, Gao GF, Tan W (2020) A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 382:727–733. https://doi.org/10.1056/NEJMoa2001017CrossRefPubMedPubMedCentral

World Health Organization. https://covid19.who.int/?gclid=CjwKCAjw0_T4BRBlEiwAwoEiAW7pjLqt2R1sX2-3bSqkFtK27ne_nhUUSo9yiFdZIgG_5T2YYLG-SBoCuK4QAvD_BwE. Accessed 10 Aug 2020

Kim L, Garg S, O’Halloran A, Whitaker M, Pham H, Anderson EJ, Armistead I, Bennett NM, Billing L, Como-Sabetti K, Hill M, Kim S, Monroe ML, Muse A, Reingold AL, Schaffner W, Sutton M, Talbot HK, Torres SM, Yousey-Hindes K, Holstein R, Cummings C, Brammer L, Hall AJ, Fry AM, Langley GE (2020) Risk factors for intensive care unit admission and in-hospital mortality among hospitalized adults identified through the U.S. Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET). Clin Infect Dis 2019:1–27. https://doi.org/10.1093/cid/ciaa1012CrossRef

Francone M, Iafrate F, Masci GM, Coco S, Cilia F, Manganaro L, Panebianco V, Andreoli C, Colaiacomo MC, Zingaropoli MA, Ciardi MR, Mastroianni CM, Pugliese F, Alessandri F, Turriziani O, Ricci P, Catalano C (2020) Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis. Eur Radiol. https://doi.org/10.1007/s00330-020-07033-y

Pan F, Zheng C, Ye T, Li L, Liu D, Li L, Hesketh RL, Yang L (2020) Different computed tomography patterns of coronavirus disease 2019 (COVID-19) between survivors and non-survivors. Sci Rep 10:11336. https://doi.org/10.1038/s41598-020-68057-4CrossRefPubMedPubMedCentral

Yuan M, Yin W, Tao Z, Tan W, Hu Y (2020) Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in Wuhan, China. PLoS One 15:1–10. https://doi.org/10.1371/journal.pone.0230548CrossRef

Liu F, Zhang Q, Huang C, Shi C, Wang L, Shi N, Fang C, Shan F, Mei X, Shi J, Song F, Yang Z, Ding Z, Su X, Lu H, Zhu T, Zhang Z, Shi L, Shi Y (2020) CT quantification of pneumonia lesions in early days predicts progression to severe illness in a cohort of COVID-19 patients. Theranostics 10:5613–5622. https://doi.org/10.7150/thno.45985CrossRefPubMedPubMedCentral

Sun D, Li X, Guo D, Wu L, Chen T, Fang Z, Chen L, Zeng W, Yang R (2020) Ct quantitative analysis and its relationship with clinical features for assessing the severity of patients with COVID-19. Korean J Radiol 21:859–868. https://doi.org/10.3348/kjr.2020.0293CrossRefPubMedPubMedCentral

Rubin GD, Haramati LB, Kanne JP, Schluger NW, Yim J-J, Anderson DJ, Altes T, Desai SR, Goo JM, Inoue Y, Luo F, Prokop M, Richeldi L, Tomiyama N, Leung AN, Ryerson CJ, Sverzellati N, Raoof S, Volpi A, Martin IBK, Kong C, Bush A, Goldin J, Humbert M, Kauczor H-U, Mazzone PJ, Remy-Jardin M, Schaefer-Prokop CM, Wells AU (2020) The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the Fleischner Society. Radiology 201365:172–180. https://doi.org/10.1148/radiol.2020201365CrossRef

10.

Colombi D, Bodini FC, Petrini M, Maffi G, Morelli N, Milanese G, Silva M, Sverzellati N, Michieletti E (2020) Well-aerated lung on admitting chest CT to predict adverse outcome in COVID-19 pneumonia. Radiology 296(2):E86–E96. https://doi.org/10.1148/radiol.2020201433

11.

Kampf G, Todt D, Pfaender S, Steinmann E (2020) Persistence of coronaviruses on inanimate surfaces and their inactivation with biocidal agents. J Hosp Infect 104:246–251. https://doi.org/10.1016/j.jhin.2020.01.022CrossRefPubMedPubMedCentral

12.

Hansell DM, Bankier AA, MacMahon H, McLoud TC, Müller NL, Remy J (2008) Fleischner Society: glossary of terms for thoracic imaging. Radiology 246:697–722. https://doi.org/10.1148/radiol.2462070712CrossRefPubMed

13.

Roberton BJ, Hansell DM (2011) Organizing pneumonia: a kaleidoscope of concepts and morphologies. Eur Radiol 21:2244–2254. https://doi.org/10.1007/s00330-011-2191-6CrossRefPubMed

14.

Ciccarese F, Coppola F, Spinelli D, Galletta GL, Lucidi V, Paccapelo A, De Benedittis C, Balacchi C, Golfieri R (2020) Diagnostic accuracy of North America Expert Consensus Statement on reporting ct findings in patients with suspected COVID-19 infection: an Italian single center experience. Radiol Cardiothorac Imaging 2:e200312. https://doi.org/10.1148/ryct.2020200312CrossRefPubMedCentralPubMed

15.

Azour L, Kadoch MA, Ward TJ, Eber CD, Jacobi AH (2017) Estimation of cardiovascular risk on routine chest CT: Ordinal coronary artery calcium scoring as an accurate predictor of Agatston score ranges. J Cardiovasc Comput Tomogr 11:8–15. https://doi.org/10.1016/j.jcct.2016.10.001CrossRefPubMed

16.

Sverzellati N, Milanese G, Milone F, Balbi M, Ledda RE, Silva M (2020) Integrated radiologic algorithm for COVID-19 pandemic. J Thorac Imaging 35:228–233. https://doi.org/10.1097/RTI.0000000000000516CrossRefPubMed

17.

Ichikado K, Muranaka H, Gushima Y, Kotani T, Nader HM, Fujimoto K, Johkoh T, Iwamoto N, Kawamura K, Nagano J, Fukuda K, Hirata N, Yoshinaga T, Ichiyasu H, Tsumura S, Kohrogi H, Kawaguchi A, Yoshioka M, Sakuma T, Suga M (2012) Fibroproliferative changes on high-resolution CT in the acute respiratory distress syndrome predict mortality and ventilator dependency: a prospective observational cohort study. BMJ Open 2:1–11. https://doi.org/10.1136/bmjopen-2011-000545CrossRef

18.

Edey AJ, Devaraj AA, Barker RP, Nicholson AG, Wells AU, Hansell DM (2011) Fibrotic idiopathic interstitial pneumonias: HRCT findings that predict mortality. Eur Radiol 21:1586–1593. https://doi.org/10.1007/s00330-011-2098-2CrossRefPubMed

19.

Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R (2012) 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30:1323–1341. https://doi.org/10.1016/j.mri.2012.05.001CrossRefPubMedPubMedCentral

20.

Cohen J (1968) Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychol Bull 70:213–220. https://doi.org/10.1037/h0026256CrossRefPubMed

21.

Koo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15:155–163. https://doi.org/10.1016/j.jcm.2016.02.012CrossRefPubMedPubMedCentral

22.

McHugh ML (2012) Interrater reliability: the kappa statistic. Biochem Med (Zagreb) 22(3):276–282CrossRef

23.

World Health Orginization (2020) International guidelines for certification and classification (coding) of COVID-19 as cause of death: based on ICD. Int Stat Classif Dis 3

24.

DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRefPubMed

25.

Borghesi A, Zigliani A, Golemi S, Carapella N, Maculotti P, Farina D, Maroldi R (2020) Chest X-ray severity index as a predictor of in-hospital mortality in coronavirus disease 2019: a study of 302 patients from Italy. Int J Infect Dis 96:291–293. https://doi.org/10.1016/j.ijid.2020.05.021CrossRefPubMedPubMedCentral

26.

Lichter Y, Topilsky Y, Taieb P, Banai A, Hochstadt A, Merdler I, Gal Oz A, Vine J, Goren O, Cohen B, Sapir O, Granot Y, Mann T, Friedman S, Angel Y, Adi N, Laufer-Perl M, Ingbir M, Arbel Y, Matot I, Szekely Y (2020) Lung ultrasound predicts clinical course and outcomes in COVID-19 patients. Intensive Care Med 46:1873–1883. https://doi.org/10.1007/s00134-020-06212-1CrossRefPubMedPubMedCentral

27.

Colombi D, Petrini M, Maffi G, Villani GD, Bodini FC, Morelli N, Milanese G, Silva M, Sverzellati N, Michieletti E (2020) Comparison of admission chest computed tomography and lung ultrasound performance for diagnosis of COVID-19 pneumonia in populations with different disease prevalence. Eur J Radiol 133:109344. https://doi.org/10.1016/j.ejrad.2020.109344CrossRefPubMedPubMedCentral

28.

Guillo E, Bedmar Gomez I, Dangeard S, Bennani S, Saab I, Tordjman M, Jilet L, Chassagnon G, Revel MP (2020) COVID-19 pneumonia: diagnostic and prognostic role of CT based on a retrospective analysis of 214 consecutive patients from Paris, France. Eur J Radiol 131:109209. https://doi.org/10.1016/j.ejrad.2020.109209CrossRefPubMed

29.

Matsuoka S, Yamashiro T, Matsushita S, Kotoku A, Fujikawa A, Yagihashi K, Nakajima Y (2015) Quantitative CT evaluation in patients with combined pulmonary fibrosis and emphysema: correlation with pulmonary function. Acad Radiol 22:626–631. https://doi.org/10.1016/j.acra.2015.01.008CrossRefPubMed

30.

Yin X, Min X, Nan Y, Feng Z, Li B, Cai W, Xi X, Wang L (2020) Assessment of the severity of coronavirus disease: quantitative computed tomography parameters versus semiquantitative visual score. Korean J Radiol 21:1–9. https://doi.org/10.3348/kjr.2020.0423CrossRef

31.

Chen FZMLYZYZKSDMXZY (2020) The characteristics and outcomes of 681 severe cases with COVID-19 in China. J Crit Care 60:32–37. https://doi.org/10.1016/j.jcrc.2020.07.003CrossRef

32.

Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, Guan L, Wei Y, Li H, Wu X, Xu J, Tu S, Zhang Y, Chen H, Cao B (2020) Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395:1054–1062. https://doi.org/10.1016/S0140-6736(20)30566-3CrossRefPubMedPubMedCentral

33.

Calcaterra G, Bassareo PP, Barilla F, Sergi D, Chiocchi M, Romeo F, Mehta JL (2020) The Deadly Quartet (Covid-19, old age, lung disease, and heart failure) explain why coronavirus-related mortality in northern Italy was so high. Curr Cardiol Rev. https://doi.org/10.2174/1573403X16666200731162614

34.

Puntmann VO, Carerj ML, Wieters I, Fahim M, Arendt C, Hoffmann J, Shchendrygina A, Escher F, Vasa-Nicotera M, Zeiher AM, Vehreschild M, Nagel E (2019) Outcomes of cardiovascular magnetic resonance imaging in patients recently recovered from coronavirus disease 2019 (COVID-19). JAMA Cardiol 2020:1–9. https://doi.org/10.1001/jamacardio.2020.3557CrossRef

35.

Puntmann VO, Carr-White G, Jabbour A, Yu CY, Gebker R, Kelle S, Hinojar R, Doltra A, Varma N, Child N, Rogers T, Suna G, Arroyo Ucar E, Goodman B, Khan S, Dabir D, Herrmann E, Zeiher AM, Nagel E (2016) T1-mapping and outcome in nonischemic cardiomyopathy all-cause mortality and heart failure. JACC Cardiovasc Imaging 9:40–50. https://doi.org/10.1016/j.jcmg.2015.12.001CrossRefPubMed

36.

Li Y, Yang Z, Ai T, Wu S (2019) Xia L (2020) Association of “initial CT” findings with mortality in older patients with coronavirus disease 2019 (COVID-19). Eur Radiol 30:6186–6193. https://doi.org/10.1007/s00330-020-06969-5CrossRef

37.

Ferrante G, Fazzari F, Cozzi O, Maurina M, Bragato R, D’Orazio F, Torrisi C, Lanza E, Indolfi E, Donghi V, Mantovani R, Liccardo G, Voza A, Azzolini E, Balzarini L, Reimers B, Stefanini GG, Condorelli G, Monti L (2020) Risk factors for myocardial injury and death in patients with COVID-19: insights from a cohort study with chest computed tomography. Cardiovasc Res. https://doi.org/10.1093/cvr/cvaa193

Title: Qualitative and quantitative chest CT parameters as predictors of specific mortality in COVID-19 patients
Authors: Davide Colombi
Gabriele D. Villani
Gabriele Maffi
Camilla Risoli
Flavio C. Bodini
Marcello Petrini
Nicola Morelli
Pietro Anselmi
Gianluca Milanese
Mario Silva
Nicola Sverzellati
Emanuele Michieletti
Publication date: 01-12-2020
Publisher: Springer International Publishing
Keywords: Computed Tomography
Computed Tomography
Pneumonia
COVID-19
Published in: Emergency Radiology / Issue 6/2020
Print ISSN: 1070-3004
Electronic ISSN: 1438-1435
DOI: https://doi.org/10.1007/s10140-020-01867-1

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Springer Medicine

Qualitative and quantitative chest CT parameters as predictors of specific mortality in COVID-19 patients

Abstract

Purpose

Methods

Results

Conclusions

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Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

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