Abstract
Aim: To investigate the performance of a novel radiological-metabolic scoring (RM-S) system to predict mortality and intensive care unit (ICU) requirements among COVID-19 patients and to compare performance with the chest computed-tomography severity-scoring (C-CT-SS). The RMS was created from scoring systems such as visual coronary-artery-calcification scoring (V-CAC-S), hepatic-steatosis scoring (HS-S) and pancreatic-steatosis scoring (PS-S).
Methods: Between May 2021 and January 2022, 397 patients with COVID-19 were included in this retrospective cohort study. All demographic, clinical and laboratory data and chest CT images of patients were retrospectively reviewed. RM-S, V-CAC-S, HS-S, PS-S and C-CT-SS scores were calculated, and their performance in predicting mortality and ICU requirement were evaluated by univariate and multivariable analyses.
Results: A total of 32 (8.1%) patients died, and 77 (19.4%) patients required ICU admission. Mortality and ICU admission were both associated with older age (p < 0.001). Sex distribution was similar in the deceased vs. survivor and ICU vs. non-ICU comparisons (p = 0.974 and p = 0.626, respectively). Multiple logistic regression revealed that mortality was independently associated with having a C-CT-SS score of ≥ 14 (p < 0.001) and severe RM-S category (p = 0.010), while ICU requirement was independently associated with having a C-CT-SS score of ≥ 14 (p < 0.001) and severe V-CAC-S category (p = 0.010).
Conclusion: RM-S, C-CT-SS, and V-CAC-S are useful tools that can be used to predict patients with poor prognoses for COVID-19. Long-term prospective follow-up of patients with high RM-S scores can be useful for predicting long COVID.
[1]
Yu Z, Razzaq A, Rehman A, Shah A, Jameel K, Mor RS. Disruption in global supply chain and socio-economic shocks: A lesson from COVID-19 for sustainable production and consumption. Oper Manag Res 2021; 15: 233-48.
[2]
World Health Organization. Coronavirus (COVID-19) Dashboard 2022. Available at https://covid19.who.int/. Accessed October 5, 2022.
[3]
Lieveld AWE, Azijli K, Teunissen BP, et al. Chest CT in COVID-19 at the ED: Validation of the COVID-19 reporting and data system (CO-RADS) and CT severity score. Chest 2021; 159(3): 1126-35.
[http://dx.doi.org/10.1016/j.chest.2020.11.026] [PMID: 33271157]
[http://dx.doi.org/10.1016/j.chest.2020.11.026] [PMID: 33271157]
[4]
Wu C, Chen X, Cai Y, et al. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 Pneumonia in Wuhan, China. JAMA Intern Med 2020; 180(7): 934-43.
[http://dx.doi.org/10.1001/jamainternmed.2020.0994] [PMID: 32167524]
[http://dx.doi.org/10.1001/jamainternmed.2020.0994] [PMID: 32167524]
[5]
Halmaciu I, Arbănași EM, Kaller R, Mureșan AV, Arbănași EM, Bacalbasa N. Chest CT severity score and systemic inflammatory biomarkers as predictors of the need for invasive mechanical ventilation and of COVID-19 patients' mortality. Diagnostics 2022; 12 (9).
[6]
Wendel Garcia PD, Fumeaux T, Guerci P, et al. Prognostic factors associated with mortality risk and disease progression in 639 critically ill patients with COVID-19 in Europe: Initial report of the international RISC-19-ICU prospective observational cohort. EClinicalMedicine 2020; 25: 100449.
[http://dx.doi.org/10.1016/j.eclinm.2020.100449] [PMID: 32838231]
[http://dx.doi.org/10.1016/j.eclinm.2020.100449] [PMID: 32838231]
[7]
Guneyli S, Dogan H, Esengur OT, Hassoy H. Computed tomography evaluation of pancreatic steatosis: Correlation with COVID-19 prognosis. Future Virol 2022; 17(4): 231-7.
[http://dx.doi.org/10.2217/fvl-2021-0257] [PMID: 35173796]
[http://dx.doi.org/10.2217/fvl-2021-0257] [PMID: 35173796]
[8]
Yousefimoghaddam F, Goudarzi E, Ramandi A, Khaheshi I. Coronary artery calcium score as a prognostic factor of adverse outcomes in patients with COVID-19: A comprehensive review. Curr Probl Cardiol 2022; 101175.
[http://dx.doi.org/10.1016/j.cpcardiol.2022.101175] [PMID: 35339532]
[http://dx.doi.org/10.1016/j.cpcardiol.2022.101175] [PMID: 35339532]
[9]
Trivedi HD, Wilechansky R, Goyes D, et al. Radiographic hepatic steatosis is not associated with key clinical outcomes among patients hospitalized with COVID-19. Gastroenterol Res 2021; 14(3): 179-83.
[http://dx.doi.org/10.14740/gr1389] [PMID: 34267833]
[http://dx.doi.org/10.14740/gr1389] [PMID: 34267833]
[10]
Rollas K, Köse Güldogan I, Pekçevik Y, Gezer NS, Zincircioğlu Ç, Sahar İ, et al. Chest computed tomography severity score in patients admitted to intensive care unit with COVID-19 pneumonia. Eurasian J Pulmonology 2022; 24(1): 40.
[http://dx.doi.org/10.14744/ejop_56_21]
[http://dx.doi.org/10.14744/ejop_56_21]
[11]
Puhr-Westerheide D, Reich J, Sabel BO, Kunz WG, Fabritius MP, Reidler P. Sequential organ failure assessment outperforms quantitative chest CT imaging parameters for mortality prediction in COVID-19 ARDS. Diagnostics 2021; 12 (1)
[12]
Aquino-Matus J, Uribe M, Chavez-Tapia N. COVID-19: Current status in gastrointestinal, hepatic, and pancreatic diseases—a concise review. Trop Med Infect Dis 2022; 7(8): 187.
[http://dx.doi.org/10.3390/tropicalmed7080187] [PMID: 36006279]
[http://dx.doi.org/10.3390/tropicalmed7080187] [PMID: 36006279]
[13]
Scoccia A, Gallone G, Cereda A, et al. Impact of clinical and subclinical coronary artery disease as assessed by coronary artery calcium in COVID-19. Atherosclerosis 2021; 328: 136-43.
[http://dx.doi.org/10.1016/j.atherosclerosis.2021.03.041] [PMID: 33883086]
[http://dx.doi.org/10.1016/j.atherosclerosis.2021.03.041] [PMID: 33883086]
[14]
Luo S, Qiu XM, Zeng XJ, Zhang DY, Wan B, Li X. Coronary artery calcification and risk of mortality and adverse outcomes in patients with COVID-19: A Chinese multicenter retrospective cohort study. Chin J Acad Radiol 2022; 5(1): 20-8.
[15]
Pan L, Huang P, Xie X, Xu J, Guo D, Jiang Y. Metabolic associated fatty liver disease increases the severity of COVID-19: A meta-analysis. Dig Liver Dis 2021; 53(2): 153-7.
[16]
Ji D, Qin E, Xu J, et al. Non-alcoholic fatty liver diseases in patients with COVID-19: A retrospective study. J Hepatol 2020; 73(2): 451-3.
[http://dx.doi.org/10.1016/j.jhep.2020.03.044] [PMID: 32278005]
[http://dx.doi.org/10.1016/j.jhep.2020.03.044] [PMID: 32278005]
[17]
Tahtabasi M, Hosbul T, Karaman E, et al. Frequency of hepatic steatosis and its association with the pneumonia severity score on chest computed tomography in adult COVID-19 patients. World J Crit Care Med 2021; 10(3): 47-57.
[http://dx.doi.org/10.5492/wjccm.v10.i3.47] [PMID: 34046310]
[http://dx.doi.org/10.5492/wjccm.v10.i3.47] [PMID: 34046310]
[18]
Doğan H, Uzer E, Esengür Ö T, Hassoy H, Güneyli S. Relationship between hepatic and pancreatic steatosis and the COVID-19 pneumonia total severity score and prognosis with an emphasis on prognostic strength. Diagn Interv Radiol 2022.
[21]
Kilic AU, Kara F, Alp E, Doganay M. New threat: 2019 novel Coronavirus infection and infection control perspective in Turkey. North Clin Istanb 2020; 7(2): 95-8.
[PMID: 32259028]
[PMID: 32259028]
[22]
Sharma S, Aggarwal A, Sharma RK, Patras E, Singhal A. Correlation of chest CT severity score with clinical parameters in COVID-19 pulmonary disease in a tertiary care hospital in Delhi during the pandemic period. Egypt J Radiol Nucl Med 2022; 53(1): 1-8.
[23]
Shemesh J, Henschke CI, Shaham D, et al. Ordinal scoring of coronary artery calcifications on low-dose CT scans of the chest is predictive of death from cardiovascular disease. Radiology 2010; 257(2): 541-8.
[http://dx.doi.org/10.1148/radiol.10100383] [PMID: 20829542]
[http://dx.doi.org/10.1148/radiol.10100383] [PMID: 20829542]
[24]
Natori T, Katada Y. Effects of pancrelipase on hepatic steatosis after pancreaticoduodenectomy. HPB 2016; 18: e458-9.
[http://dx.doi.org/10.1016/j.hpb.2016.03.207]
[http://dx.doi.org/10.1016/j.hpb.2016.03.207]
[25]
Hoogenboom SA, Bolan CW, Chuprin A, Raimondo MT, van Hooft JE, Wallace MB. Pancreatic steatosis on computed tomography is an early imaging feature of pre-diagnostic pancreatic cancer: A preliminary study in overweight patients. Pancreatology 2021; 21(2): 428-33.
[http://dx.doi.org/10.1016/j.pan.2021.01.003]
[http://dx.doi.org/10.1016/j.pan.2021.01.003]
[26]
Mazhar SM, Shiehmorteza M, Sirlin CB. Noninvasive assessment of hepatic steatosis. Clin Gastroenterol Hepatol 2009; 7(2): 135-40.
[http://dx.doi.org/10.1016/j.cgh.2008.11.023]
[http://dx.doi.org/10.1016/j.cgh.2008.11.023]
[27]
Akdur G, Daş M, Bardakci O, et al. Prediction of mortality in COVID-19 through combing CT severity score with NEWS, qSOFA, or peripheral perfusion index. Am J Emerg Med 2021; 50: 546-52.
[http://dx.doi.org/10.1016/j.ajem.2021.08.079] [PMID: 34547696]
[http://dx.doi.org/10.1016/j.ajem.2021.08.079] [PMID: 34547696]
[28]
Cai Q, Chen F, Wang T, et al. Obesity and COVID-19 severity in a designated Hospital in Shenzhen, China. Diabetes Care 2020; 43(7): 1392-8.
[http://dx.doi.org/10.2337/dc20-0576] [PMID: 32409502]
[http://dx.doi.org/10.2337/dc20-0576] [PMID: 32409502]
[29]
Wu Z, Tang Y, Cheng Q. Diabetes increases the mortality of patients with COVID-19: A meta-analysis. Acta Diabetol 2021; 58(2): 139-44.
[http://dx.doi.org/10.1007/s00592-020-01546-0] [PMID: 32583078]
[http://dx.doi.org/10.1007/s00592-020-01546-0] [PMID: 32583078]
[30]
Hegyi PJ, Váncsa S, Ocskay K, et al. Metabolic associated fatty liver disease is associated with an increased risk of severe COVID-19: A systematic review with meta-analysis. Front Med 2021; 8: 626425.
[http://dx.doi.org/10.3389/fmed.2021.626425] [PMID: 33777974]
[http://dx.doi.org/10.3389/fmed.2021.626425] [PMID: 33777974]
[31]
Zhang W, Li C, Liu B, et al. Pioglitazone upregulates hepatic angiotensin converting enzyme 2 expression in rats with steatohepatitis. Ann Hepatol 2013; 12(6): 892-900.
[http://dx.doi.org/10.1016/S1665-2681(19)31294-3] [PMID: 24114819]
[http://dx.doi.org/10.1016/S1665-2681(19)31294-3] [PMID: 24114819]
[32]
Chiyanika C, Chan DFY, Hui SCN, et al. The relationship between pancreas steatosis and the risk of metabolic syndrome and insulin resistance in Chinese adolescents with concurrent obesity and non-alcoholic fatty liver disease. Pediatr Obes 2020; 15(9): e12653.
[http://dx.doi.org/10.1111/ijpo.12653] [PMID: 32351030]
[http://dx.doi.org/10.1111/ijpo.12653] [PMID: 32351030]
[33]
Öz A, Akçalar S. Increased incidence of pancreatic steatosis detected using computed tomography at initial diagnosis of coronavirus disease 2019. Turk J Gastroenterol 2023; 34(3): 270-7.
[34]
Rubin GD, Ryerson CJ, Haramati LB, et al. The role of chest imaging in patient management during the COVID-19 Pandemic: A multinational consensus statement from the fleischner society. Radiology 2020; 296(1): 172-80.
[http://dx.doi.org/10.1148/radiol.2020201365] [PMID: 32255413]
[http://dx.doi.org/10.1148/radiol.2020201365] [PMID: 32255413]
[35]
Chang YC, Yu CJ, Chang SC, et al. Pulmonary sequelae in convalescent patients after severe acute respiratory syndrome: Evaluation with thin-section CT. Radiology 2005; 236(3): 1067-75.
[http://dx.doi.org/10.1148/radiol.2363040958] [PMID: 16055695]
[http://dx.doi.org/10.1148/radiol.2363040958] [PMID: 16055695]
[36]
Bellos I, Tavernaraki K, Stefanidis K, et al. 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): 436-45.
[http://dx.doi.org/10.1016/j.resinv.2021.02.008] [PMID: 33820751]
[http://dx.doi.org/10.1016/j.resinv.2021.02.008] [PMID: 33820751]
[37]
Li K, Wu J, Wu F, et al. The clinical and chest CT features associated with severe and critical COVID-19 pneumonia. Invest Radiol 2020; 55(6): 327-31.
[http://dx.doi.org/10.1097/RLI.0000000000000672] [PMID: 32118615]
[http://dx.doi.org/10.1097/RLI.0000000000000672] [PMID: 32118615]
[38]
Li K, Fang Y, Li W, et al. CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19). Eur Radiol 2020; 30(8): 4407-16.
[http://dx.doi.org/10.1007/s00330-020-06817-6] [PMID: 32215691]
[http://dx.doi.org/10.1007/s00330-020-06817-6] [PMID: 32215691]
[39]
Nishiga M, Wang DW, Han Y, Lewis DB, Wu JC. COVID-19 and cardiovascular disease: From basic mechanisms to clinical perspectives. Nat Rev Cardiol 2020; 17(9): 543-58.
[http://dx.doi.org/10.1038/s41569-020-0413-9] [PMID: 32690910]
[http://dx.doi.org/10.1038/s41569-020-0413-9] [PMID: 32690910]
[40]
Grasselli G, Zangrillo A, Zanella A, et al. Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the lombardy region, Italy. JAMA 2020; 323(16): 1574-81.
[http://dx.doi.org/10.1001/jama.2020.5394] [PMID: 32250385]
[http://dx.doi.org/10.1001/jama.2020.5394] [PMID: 32250385]
[41]
Dillinger JG, Benmessaoud FA, Pezel T, et al. Coronary artery calcification and complications in patients with COVID-19. JACC Cardiovasc Imaging 2020; 13(11): 2468-70.
[http://dx.doi.org/10.1016/j.jcmg.2020.07.004] [PMID: 33153535]
[http://dx.doi.org/10.1016/j.jcmg.2020.07.004] [PMID: 33153535]
[42]
Nai Fovino L, Cademartiri F, Tarantini G. Subclinical coronary artery disease in COVID-19 patients. Eur Heart J Cardiovasc Imaging 2020; 21(9): 1055-6.
[http://dx.doi.org/10.1093/ehjci/jeaa202] [PMID: 32671381]
[http://dx.doi.org/10.1093/ehjci/jeaa202] [PMID: 32671381]
[43]
Ding S, Wang H, Lu H, Nappi M, Wan S. Two path gland segmentation algorithm of colon pathological image based on local semantic guidance. IEEE J Biomed Health Inform 2023; 27(4): 1701-8.
[http://dx.doi.org/10.1109/JBHI.2022.3207874] [PMID: 36126032]
[http://dx.doi.org/10.1109/JBHI.2022.3207874] [PMID: 36126032]
