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Prognostic factors of worse outcome for hospitalized COVID-19 patients, with emphasis on chest computed tomography data: a retrospective study

ABSTRACT

Objective:

To evaluate anthropometric and clinical data, muscle mass, subcutaneous fat, spine bone mineral density, extent of acute pulmonary disease related to COVID-19, quantification of pulmonary emphysema, coronary calcium, and hepatic steatosis using chest computed tomography of hospitalized patients with confirmed diagnosis of COVID-19 pneumonia and verify its association with disease severity.

Methods:

A total of 123 adults hospitalized due to COVID-19 pneumonia were enrolled in the present study, which evaluated the anthropometric, clinical and chest computed tomography data (pectoral and paravertebral muscle area and density, subcutaneous fat, thoracic vertebral bodies density, degree of pulmonary involvement by disease, coronary calcium quantification, liver attenuation measurement) and their association with poorer prognosis characterized through a combined outcome of intubation and mechanical ventilation, need of intensive care unit, and death.

Results:

Age (p=0.013), body mass index (p=0.009), lymphopenia (p=0.034), and degree of pulmonary involvement of COVID-19 pneumonia (p<0.001) were associated with poor prognosis. Extent of pulmonary involvement by COVID-19 pneumonia had an odds ratio of 1,329 for a poor prognosis and a cutoff value of 6.5 for increased risk, with a sensitivity of 64.9% and specificity of 67.1%.

Conclusion:

The present study found an association of high body mass index, older age, extent of pulmonary involvement by COVID-19, and lymphopenia with severity of COVID-19 pneumonia in hospitalized patients.

Keywords:
Coronavirus infections; COVID-19; Pneumonia; Obesity; Multidetector computed tomography; Tomography; X-ray computed; Prognosis

INTRODUCTION

Since December 2019, the world has been experiencing the COVID-19 pandemic.(11 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; China Novel Coronavirus Investigating and Research Team. A Novel Coronavirus from Patients with pneumonia in China, 2019. N Engl J Med. 2020;382(8):727-33.) Imaging findings of COVID-19 pneumonia involve ground glass opacities, crazy paving and consolidations - usually bilateral, with subpleural and peripheral distribution, most often affecting the pulmonary bases.(22 Li K, Wu J, Wu F, Guo D, Chen L, Fang Z, et al. The clinical and chest CT features associated with severe and critical COVID-19 pneumonia. Invest Radiol. 2020;55(6):327-31.)

Clinical and laboratory factors associated with COVID-19 pneumonia severity, such as advanced age, comorbidities, including obesity, elevated D-dimer levels, and lymphopenia are already known.(22 Li K, Wu J, Wu F, Guo D, Chen L, Fang Z, et al. The clinical and chest CT features associated with severe and critical COVID-19 pneumonia. Invest Radiol. 2020;55(6):327-31.,33 Hussain A, Mahawar K, Xia Z, Yang W, EL-Hasani S. Obesity and mortality of COVID-19. Meta-analysis. Obes Res Clin Pract. 2020;14(4):295-300. Retraction in: Obes Res Clin Pract. 2021;15(1):100. Review.) Imaging findings related to the most severe forms of the disease include greater extent of lung involvement and increased rate of lung opacities.(22 Li K, Wu J, Wu F, Guo D, Chen L, Fang Z, et al. The clinical and chest CT features associated with severe and critical COVID-19 pneumonia. Invest Radiol. 2020;55(6):327-31.)

In other clinical scenarios, many factors measurable on chest computed tomography (CT) are associated with worse outcomes, such as muscle mass,(44 Diaz AA, Martinez CH, Harmouche R, Young TP, McDonald ML, Ross JC, et al. Pectoralis muscle area and mortality in smokers without airflow obstruction. Respir Res. 2018;19(1):62.) coronary calcium,(55 Shemesh J, Henschke CI, Shaham D, Yip R, Farooqi AO, Cham MD, 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. Erratum in: Radiology. 2011;259(2):617.) chronic obstructive pulmonary disease,(66 Martinez FJ, Foster G, Curtis JL, Criner G, Weinmann G, Fishman A, DeCamp MM, Benditt J, Sciurba F, Make B, Mohsenifar Z, Diaz P, Hoffman E, Wise R; NETT Research Group. Predictors of mortality in patients with emphysema and severe airflow obstruction. Am J Respir Crit Care Med. 2006;173(12):1326-34.) osteoporosis, vertebral fractures,(77 Teng GG, Curtis JR, Saag KG. Mortality and osteoporotic fractures: Is the link causal, and is it modifiable? Clin Exp Rheumatol. 2008;26(5 Suppl 51):S125-37. Review.

8 Leboime A, Confavreux CB, Mehsen N, Paccou J, David C, Roux C. Osteoporosis and mortality. Joint Bone Spine. 2010;77(Suppl 2):S107-12. Review.
-99 Marinova M, Edon B, Wolter K, Katsimbari B, Schild HH, Strunk HM. Use of routine thoracic and abdominal computed tomography scans for assessing bone mineral density and detecting osteoporosis. Curr Med Res Opin. 2015;31(10):1871-81.) and hepatic steatosis.(1010 Cotter TG, Rinella M. nonalcoholic fatty liver disease 2020: the state of the disease. Gastroenterology. 2020;158(7):1851-64. Review.)

So far, there are few studies that explored the analysis of data obtained through a chest CT in the context of pneumonia by COVID-19 and no Brazilian or Latin American experience with this study design, which is the contribution of the present study.

OBJECTIVE

To evaluate the association of anthropometric, clinical, laboratory, and some potentially useful measurable factors on chest computed tomography (pectorals and paravertebral muscle mass, subcutaneous fat, spine bone mineral density, extent of acute pulmonary disease of COVID-19, coronary calcium, pulmonary emphysema and hepatic steatosis) with worse outcome (reflected by the need of mechanical ventilation or intensive care, or death) in a group of hospitalized patients with COVID-19 pneumonia.

METHODS

Patients

This study was approved by the Research Ethics Committee of the Hospital Israelita Albert Einstein (HIAE), # 4.445.142, CAAE: 30810420.4.0000.0071. Informed consent was waived due to its retrospective nature.

The participants of the present study were patients aged over 18 years, who underwent hospital admission for pneumonia caused by COVID-19, confirmed by the real-time reverse transcription polymerase chain reaction (RT-PCR) method,(1111 Corman VM, Landt O, Kaiser M, Molenkamp R, Meijer A, Chu DK, et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro Surveill. 2020;25(3):2000045. Erratum in: Euro Surveill. 2020;25(14): Erratum in: Euro Surveill. 2020;25(30): Erratum in: Euro Surveill. 2021;26(5).) and who also underwent a chest CT scan during the first day of hospital admission between March 1st and April 31th, 2020, totaling 123 patients.

Anthropometric, clinical, and laboratory data

The anthropometric, clinical, and laboratory data evaluated at hospital admission were sex, age, body mass index (BMI), referred days since symptoms onset and admission to the emergency room, oxygen saturation (SpO2), days of hospital stay, blood count, C-reactive protein, creatinine, lactic acid dehydrogenase (LDH), D-dimer, interleukin-6, ferritin, corticotherapy use, presence of deep vein thrombosis, pulmonary embolism, and lymphopenia.

Computed tomography

Computed tomography scans were acquired using multidetector CT scanners with 40, 80 or 320 detector rows (Biograph mCT, Siemens Healthcare, Erlangen, Germany or Aquilion Prime or Aquilion ONE, Canon Medical Systems, Tochigi, Japan). Regarding the acquisition parameters, the scans were obtained according to institutional protocol, in the supine position during end-inspiration, with or without intravenous contrast material, reconstructed slice thickness of 1mm, voltage of 80-120kVp, and automatic milliampere setting ranging from 10 to 440mA. All the CT scans were performed during the first day of hospital admission.

The following analyses were performed, as previously described in the literature: pectoral and paravertebral muscle mass and subcutaneous fat,(44 Diaz AA, Martinez CH, Harmouche R, Young TP, McDonald ML, Ross JC, et al. Pectoralis muscle area and mortality in smokers without airflow obstruction. Respir Res. 2018;19(1):62.) bone mineral density,(99 Marinova M, Edon B, Wolter K, Katsimbari B, Schild HH, Strunk HM. Use of routine thoracic and abdominal computed tomography scans for assessing bone mineral density and detecting osteoporosis. Curr Med Res Opin. 2015;31(10):1871-81.) presence of vertebral body fractures,(1212 Genant HK, Jergas M, Palermo L, Nevitt M, Valentin RS, Black D, et al. comparison of semiquantitative visual and quantitative morphometric assessment of prevalent and incident vertebral fractures in osteoporosis the study of Osteoporotic Fractures Research Group. J Bone Miner Res. 1996;11(7):984-96.,1313 Genant HK, Wu CY, van Kuijk C, Nevitt MC. Vertebral fracture assessment using a semiquantitative technique. J Bone Miner Res. 1993;8(9):1137-48.) degree of pulmonary involvement by COVID-19 pneumonia,(1414 Ooi GC, Khong PL, Müller NL, Yiu WC, Zhou LJ, Ho JC, et al. Severe acute respiratory syndrome: temporal lung changes at thin-section CT in 30 patients. Radiology. 2004;230(3):836-44.) quantification of pulmonary emphysema,(1515 Lynch DA, Austin JH, Hogg JC, Grenier PA, Kauczor HU, Bankier AA, et al. CT-definable subtypes of chronic obstructive pulmonary disease: a Statement of the Fleishner Society. Radiology. 2015;277(1):192-205.) coronary artery calcium quantification,(55 Shemesh J, Henschke CI, Shaham D, Yip R, Farooqi AO, Cham MD, 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. Erratum in: Radiology. 2011;259(2):617.) and hepatic steatosis.(1010 Cotter TG, Rinella M. nonalcoholic fatty liver disease 2020: the state of the disease. Gastroenterology. 2020;158(7):1851-64. Review.) These measurements are described in more detail in the supplementary material.

Anthropometric measurement

The patients’ weight (kg) was determined using a calibrated scale. They were barefoot and wearing light clothing. Their height (m) was measured with the use of a stadiometer with patient standing barefoot, feet together, arms outstretched beside the body, and straight back.

Body mass index was calculated with weight (kg) divided by the squared height (m), and used to classify nutritional status of the adult patients as: <16kg/m2: malnutrition grade III; 16-16.9kg/m2: malnutrition grade II; 17-18.4kg/m2: malnutrition grade I; 18.5-24.9kg/m2: normal; 25-29.9kg/m2: overweight; 30-34.9kg/m2: obesity class I; 35-39.9kg/m2: obesity class II; ≥40kg/m2: obesity class III.(1616 Executive summary of the clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults. Arch Intern Med. 1998;158(17):1855-67. Review.)

Statistical analysis

Patients’ age was described using summary measures (mean, standard deviation, median, minimum, and maximum).

Qualitative characteristics were described according to worse outcome (need of mechanical ventilation or intensive care, or death). Absolute and relative frequencies and the association with the use of χ2 or exact tests (Fisher’s exact test or likelihood-ratio test) were used. Quantitative characteristics were described according to the same outcome. Summary measures were used and compared between the outcome, using the Mann-Whitney test.

The multiple logistic regression model was adjusted for worse outcome, inserting the statistically significant and clinically relevant variables in the model, and using the stepwise backward method (with 5% entry and exit criteria as the selection criterion).

Interreader agreement was determined by calculating intraclass correlation coefficient (ICC). Interreader agreement was considered to be poor (0.20), fair (0.21-0.40), moderate (0.41-0.60), good (0.61-0.80), or excellent (0.81-1.00).

IBM-SPSS for Windows version 22.0 software was used to perform the analyzes and Microsoft Excel 2010 software was used to tabulate the data. The tests were performed with a significance level of 5%.

RESULTS

A total of 123 patients were included in this study. The mean (SD) age was 57.4 (+16.5) years, 79 (64.2%) were men, and the mean BMI was 27.9 (+ 5.1) kg/m2.

The median days of hospital stay was 11, ranging from 1 to 117 days. The median days from symptoms onset to CT imaging was 6, ranging from 1 to 21 days. Thirty-eight (30.9%) patients were exclusively in a ward bed, while 47 (38.2%) needed a semi-intensive care unit bed, and 38 (30.9%) needed an intensive care unit bed. Thirty-five patients (28.5%) needed orotracheal intubation with mechanical ventilation (MV), with an average length of 6.3 days (+14 days). A total of 6 patients (8.6%) died. The baseline characteristics are summarized in table 1.

Table 1
Baseline clinical and laboratorial characteristics of the patients included in the study

Regarding CT parameters, interreader agreement was excellent for all measures (0.81-1.00), except for the mean density of the pectoral muscle, which was good (0.61-0.80). The CT parameters are summarized in table 2.

Table 2
Computed tomography characteristics of the patients included in the study

After logistic regression, age, BMI, degree of pulmonary involvement, and lymphopenia were identified as factors independently associated with worse outcome (Table 3).

Table 3
Variables associated with worse outcome of the patients included in the study

More extensive pulmonary involvement by COVID-19 pneumonia was associated with worse outcome (MV/ICU/death) with the best cutoff point for the visual score of disease extent of 6.5, with a sensitivity of 64.9% and specificity of 67.1%, which can be observed in figure 1.

Figure 1
Receiver operating characteristic curve of visual score of disease extent as an indicator of poorer prognosis (mechanical ventilation/intensive care unit/death) due to COVID-19 pneumonia. The area under the receiver operating characteristic curve was 0.764 (95%CI: 0.66-0.86)

DISCUSSION

The outcomes of patients hospitalized for COVID-19 pneumonia were assessed through measurement of a combined outcome of intubation and MV, need of ICU, and death. The data analyzed were those commonly requested and available in patients diagnosed with COVID-19 giving emphasis to chest CT.

In a meta-analysis, Hussain et al. demonstrated higher mortality due to COVID-19 pneumonia in patients older than 70 years, BMI >25kg/m2 and advanced respiratory support need.(33 Hussain A, Mahawar K, Xia Z, Yang W, EL-Hasani S. Obesity and mortality of COVID-19. Meta-analysis. Obes Res Clin Pract. 2020;14(4):295-300. Retraction in: Obes Res Clin Pract. 2021;15(1):100. Review.) The present study also showed greater severity of the disease in relation to age and higher BMI. Hendren et al. observed worse prognosis for patients with obesity (death and MV), especially in the youngest (under 50 years old). One of the possible explanations was a weaker association of BMI in older individuals and the presence of other causes of morbidities and fragility.(1717 Hendren NS, de Lemos JA, Ayers C, Das SR, Rao A, Carter S, et al. Association of body mass index and age with morbidity and mortality in patients hospitalized with COVID-19: results from the American Heart Association COVID-19 Cardiovascular Disease Registry. Circulation. 2021;143(2):135-44.)

When assessing patients’ obesity, it is particularly important to remember that this is not usually an isolated condition, but a manifestation of a complex metabolic syndrome, in which the patient is in a pro-inflammatory state, related to multiple negative outcomes. Liver steatosis is one of the most important conditions within the systemic metabolic disorders triggered by obesity and is highly related to an increase in BMI and visceral fat. Although the present study did not find an association between steatosis and a worse outcome of COVID-19 pneumonia, Medeiros et al. showed a higher frequency of hepatic steatosis in patients diagnosed with COVID-19 pneumonia, but they did not access its relationship with the severity of the disease.(1818 Medeiros AK, Barbisan CC, Cruz IR, de Araújo EM, Libânio BB, Albuquerque KS, et al. Higher frequency of hepatic steatosis at CT among COVID-19-positive patients. Abdom Radiol (NY). 2020;45(9):2748-54.)

The use of bone density data in routine chest CT scans has been evidenced in the literature, having a good correlation with bone mineral density measured by dual-energy X-ray absorptiometry (DEXA), the gold standard for that evaluation.(1919 Romme EA, Murchison JT, Phang KF, Jansen FH, Rutten EP, Wouters EF, et al. Bone attenuation on routine chest CT correlates with bone mineral density on DXA in patients with COPD. J Bone Miner Res. 2012;27(11):2338-43.,2020 Kim YW, Kim JH, Yoon SH, Lee JH, Lee CH, Shin CS, et al. Vertebral bone attenuation on low-dose chest CT: quantitative volumetric analysis for bone fragility assessment. Osteoporos Int. 2017;28(1):329-38.) Although this study did not find an association between bone mineral density and worse outcomes, the access to bone mineral density in chest CT can provide relevant information for the clinician as for the medical state that can be used to predict a worse prognosis. This information was not explored extensively in COVID-19 patients, one exception being the study of Ji et al. which demonstrated an association of the diagnosis of COVID-19 with osteoporosis, but was not associated with the severity of the disease.(2121 Ji W, Huh K, Kang M, Hong J, Bae GH, Lee R, et al. Effect of underlying comorbidities on the infection and severity of COVID-19 in Korea: a nationwide case-control study. J Korean Med Sci. 2020;35(25):e237.)

This study did not find an association between pulmonary emphysema and worse prognosis. These findings differ from the study by Zhang et al. who observed an association of deaths from COVID-19 with pulmonary emphysema.(2222 Zhang N, Xu X, Zhou LY, Chen G, Li Y, Yin H, et al. Clinical characteristics and chest CT imaging features of critically ill COVID-19 patients. Eur Radiol. 2020;30(11):6151-60.) This can be explained perhaps by a selection bias of this sample (analysis of hospitalized patients only, that is, more complex disease configuring worse prognosis by definition) or even by the low number of deaths.

Coronary artery calcium is a factor known to be associated with cardiovascular risk in asymptomatic adults and with overall mortality. Regarding the severity of patients with COVID-19, the present study did not demonstrate an association of coronary calcium with worse prognosis. Ferrante et al. evaluated risk factors for myocardial injury and death in patients with COVID-19, coronary calcium was also not shown to be an independent risk factor.(2323 Ferrante G, Fazzari F, Cozzi O, Maurina M, Bragato R, D’Orazio F, et al. Risk factors for myocardial injury and death in patients with COVID-19: Insights from a cohort study with chest computed tomography. Cardiovasc Res. 2020;116(14):2239-46.)

Regarding the pulmonary findings of COVID-19 pneumonia, studies indicate some imaging characteristics that may be related to worse prognosis, as in the study by Li et al., who observed an association of consolidation, linear opacities, crazy-paving pattern, bronchial wall thickening, high CT scores, and extrapulmonary lesions being features of severe/critical COVID-19 pneumonia.(22 Li K, Wu J, Wu F, Guo D, Chen L, Fang Z, et al. The clinical and chest CT features associated with severe and critical COVID-19 pneumonia. Invest Radiol. 2020;55(6):327-31.) In the study by Tabatabaei et al. severity score was the only statistically significant CT predictor of mortality in patients.(2424 Tabatabaei SM, Rahimi H, Moghaddas F, Rajebi H. Predictive value of CT in the short-term mortality of coronavirus disease 2019 (COVID-19) pneumonia in nonelderly patients: a case-control study. Eur J Radiol. 2020;132:109298.) These findings are in agreement with the present results, because this study also observed an association of the degree of pulmonary involvement with disease severity (need for intubation and MV and the total days of intubation). This study found an optimal cutoff value of a CT score of 6.5 (with a sensitivity of 64.9% and specificity of 67.1%) to predict the need for intubation and MV. Yuan et al. already suggested an optimal cutoff of 24.5 (sensitivity of 85.6% and specificity of 84.5%) for the prediction of mortality considering the lung parenchyma CT score with a different methodology, with values ranging from 0 to 72.(2525 Yuan M, Yin W, Tao Z, Tan W, Hu Y. Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in Wuhan, China. PLoS One. 2020;15(3):e0230548.)

Schiaffino et al. also studied chest CT-derived muscle status of patients hospitalized for pneumonia due to COVID-19 and differently from this study, they found an independent association of low muscle mass with ICU admission and mortality.(2626 Schiaffino S, Albano D, Cozzi A, Messina C, Arioli R, Bnà C, et al. CT-derived chest muscle metrics for outcome prediction in patients with COVID-19. Radiology. 2021;300(2):E328-36.) Kim et al. also investigated the association of sarcopenia with the outcomes of patients hospitalized for pneumonia due to COVID-19 using chest CT data, with a single-center sample, similar in size to the present study,(2727 Kim JW, Yoon JS, Kim EJ, Hong HL, Kwon HH, Jung CY, et al. Prognostic implication of baseline sarcopenia for length of hospital stay and survival in patients with coronavirus disease 2019. J Gerontol A Biol Sci Med Sci. 2021;76(8):e110-6.) and they stratified patients according to muscle mass and found an association between low muscle mass and longer hospital stay, but, as in this study, low muscle mass was not an independent risk factor for death.(2727 Kim JW, Yoon JS, Kim EJ, Hong HL, Kwon HH, Jung CY, et al. Prognostic implication of baseline sarcopenia for length of hospital stay and survival in patients with coronavirus disease 2019. J Gerontol A Biol Sci Med Sci. 2021;76(8):e110-6.) These data show the importance of a larger sample and multicentric based data to identify the association of muscle mass with worse outcomes, demonstrated in the study by Schiaffino et al.(2626 Schiaffino S, Albano D, Cozzi A, Messina C, Arioli R, Bnà C, et al. CT-derived chest muscle metrics for outcome prediction in patients with COVID-19. Radiology. 2021;300(2):E328-36.)

The present study also identified an association of lymphopenia with worse outcome of the disease. These data are in agreement with the meta-analysis by Zhao et al. which also demonstrated an increased risk of severe disease, with OR=2.99, 95%CI: 1.31-6.82.(2828 Zhao Q, Meng M, Kumar R, Wu Y, Huang J. Lymphopenia is associated with severe coronavirus disease 2019 ( COVID-19 ) infections : a systemic review and meta-analysis. Int J Infect Dis. 2020;96:131-5.) The mechanisms for this reduction in lymphocyte count is not fully elucidated, but as discussed by Tan et al. it may involve factors such as direct viral infection, destruction of lymphatic organs, production of inflammatory cytokines, and inhibition of production by metabolic disorders such as hyperlactic acidemia.(2929 Tan L, Wang Q, Zhang D, Ding J, Huang Q, Tang YQ, et al. Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study. Signal Transduct Target Ther. 2020;5(1):33. Erratum in: Signal Transduct Target Ther. 2020;5(1):61.) Although this study did not perform a dynamic analysis of the variation of this lymphopenia throughout the research, this information, which is extremely available in the emergency room, can make the clinician alert in cases of newly admitted patients with lymphopenia.

Some limitations of the present study are its retrospective design and the evaluation of more severe patients, that is, those in which a therapeutic decision of hospitalization has already been made. Also, although CT was performed on the day of hospital admission, the time between symptom onset among patients was different. Other limitations and the main ones are the low number of patients, single-center basis, and low number of deaths. Also, DEXA, which is considered the gold standard for body quantification, was not used. Besides that, visceral and parietal fat was not analyzed as the study did not have abdominal CT data.

The present study demonstrated that data easily obtained, such as older age, higher BMI, lymphopenia, and high pulmonary involvement, may be associated with worse outcome in patients hospitalized for COVID-19 pneumonia. This can potentially be included in clinical scores and eventually help to predict which patients should be monitored for worse progression or even more aggressive therapeutic decisions.

CONCLUSION

The present study found an association of higher body mass index, older age, extent of pulmonary involvement by COVID-19, and lymphopenia with the severity of COVID-19 pneumonia in hospitalized patients.

REFERENCES

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    Li K, Wu J, Wu F, Guo D, Chen L, Fang Z, et al. The clinical and chest CT features associated with severe and critical COVID-19 pneumonia. Invest Radiol. 2020;55(6):327-31.
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    Hussain A, Mahawar K, Xia Z, Yang W, EL-Hasani S. Obesity and mortality of COVID-19. Meta-analysis. Obes Res Clin Pract. 2020;14(4):295-300. Retraction in: Obes Res Clin Pract. 2021;15(1):100. Review.
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    Diaz AA, Martinez CH, Harmouche R, Young TP, McDonald ML, Ross JC, et al. Pectoralis muscle area and mortality in smokers without airflow obstruction. Respir Res. 2018;19(1):62.
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    Shemesh J, Henschke CI, Shaham D, Yip R, Farooqi AO, Cham MD, 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. Erratum in: Radiology. 2011;259(2):617.
  • 6
    Martinez FJ, Foster G, Curtis JL, Criner G, Weinmann G, Fishman A, DeCamp MM, Benditt J, Sciurba F, Make B, Mohsenifar Z, Diaz P, Hoffman E, Wise R; NETT Research Group. Predictors of mortality in patients with emphysema and severe airflow obstruction. Am J Respir Crit Care Med. 2006;173(12):1326-34.
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    Teng GG, Curtis JR, Saag KG. Mortality and osteoporotic fractures: Is the link causal, and is it modifiable? Clin Exp Rheumatol. 2008;26(5 Suppl 51):S125-37. Review.
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    Leboime A, Confavreux CB, Mehsen N, Paccou J, David C, Roux C. Osteoporosis and mortality. Joint Bone Spine. 2010;77(Suppl 2):S107-12. Review.
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    Marinova M, Edon B, Wolter K, Katsimbari B, Schild HH, Strunk HM. Use of routine thoracic and abdominal computed tomography scans for assessing bone mineral density and detecting osteoporosis. Curr Med Res Opin. 2015;31(10):1871-81.
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    Cotter TG, Rinella M. nonalcoholic fatty liver disease 2020: the state of the disease. Gastroenterology. 2020;158(7):1851-64. Review.
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    Corman VM, Landt O, Kaiser M, Molenkamp R, Meijer A, Chu DK, et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro Surveill. 2020;25(3):2000045. Erratum in: Euro Surveill. 2020;25(14): Erratum in: Euro Surveill. 2020;25(30): Erratum in: Euro Surveill. 2021;26(5).
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    Genant HK, Jergas M, Palermo L, Nevitt M, Valentin RS, Black D, et al. comparison of semiquantitative visual and quantitative morphometric assessment of prevalent and incident vertebral fractures in osteoporosis the study of Osteoporotic Fractures Research Group. J Bone Miner Res. 1996;11(7):984-96.
  • 13
    Genant HK, Wu CY, van Kuijk C, Nevitt MC. Vertebral fracture assessment using a semiquantitative technique. J Bone Miner Res. 1993;8(9):1137-48.
  • 14
    Ooi GC, Khong PL, Müller NL, Yiu WC, Zhou LJ, Ho JC, et al. Severe acute respiratory syndrome: temporal lung changes at thin-section CT in 30 patients. Radiology. 2004;230(3):836-44.
  • 15
    Lynch DA, Austin JH, Hogg JC, Grenier PA, Kauczor HU, Bankier AA, et al. CT-definable subtypes of chronic obstructive pulmonary disease: a Statement of the Fleishner Society. Radiology. 2015;277(1):192-205.
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    Executive summary of the clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults. Arch Intern Med. 1998;158(17):1855-67. Review.
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    Hendren NS, de Lemos JA, Ayers C, Das SR, Rao A, Carter S, et al. Association of body mass index and age with morbidity and mortality in patients hospitalized with COVID-19: results from the American Heart Association COVID-19 Cardiovascular Disease Registry. Circulation. 2021;143(2):135-44.
  • 18
    Medeiros AK, Barbisan CC, Cruz IR, de Araújo EM, Libânio BB, Albuquerque KS, et al. Higher frequency of hepatic steatosis at CT among COVID-19-positive patients. Abdom Radiol (NY). 2020;45(9):2748-54.
  • 19
    Romme EA, Murchison JT, Phang KF, Jansen FH, Rutten EP, Wouters EF, et al. Bone attenuation on routine chest CT correlates with bone mineral density on DXA in patients with COPD. J Bone Miner Res. 2012;27(11):2338-43.
  • 20
    Kim YW, Kim JH, Yoon SH, Lee JH, Lee CH, Shin CS, et al. Vertebral bone attenuation on low-dose chest CT: quantitative volumetric analysis for bone fragility assessment. Osteoporos Int. 2017;28(1):329-38.
  • 21
    Ji W, Huh K, Kang M, Hong J, Bae GH, Lee R, et al. Effect of underlying comorbidities on the infection and severity of COVID-19 in Korea: a nationwide case-control study. J Korean Med Sci. 2020;35(25):e237.
  • 22
    Zhang N, Xu X, Zhou LY, Chen G, Li Y, Yin H, et al. Clinical characteristics and chest CT imaging features of critically ill COVID-19 patients. Eur Radiol. 2020;30(11):6151-60.
  • 23
    Ferrante G, Fazzari F, Cozzi O, Maurina M, Bragato R, D’Orazio F, et al. Risk factors for myocardial injury and death in patients with COVID-19: Insights from a cohort study with chest computed tomography. Cardiovasc Res. 2020;116(14):2239-46.
  • 24
    Tabatabaei SM, Rahimi H, Moghaddas F, Rajebi H. Predictive value of CT in the short-term mortality of coronavirus disease 2019 (COVID-19) pneumonia in nonelderly patients: a case-control study. Eur J Radiol. 2020;132:109298.
  • 25
    Yuan M, Yin W, Tao Z, Tan W, Hu Y. Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in Wuhan, China. PLoS One. 2020;15(3):e0230548.
  • 26
    Schiaffino S, Albano D, Cozzi A, Messina C, Arioli R, Bnà C, et al. CT-derived chest muscle metrics for outcome prediction in patients with COVID-19. Radiology. 2021;300(2):E328-36.
  • 27
    Kim JW, Yoon JS, Kim EJ, Hong HL, Kwon HH, Jung CY, et al. Prognostic implication of baseline sarcopenia for length of hospital stay and survival in patients with coronavirus disease 2019. J Gerontol A Biol Sci Med Sci. 2021;76(8):e110-6.
  • 28
    Zhao Q, Meng M, Kumar R, Wu Y, Huang J. Lymphopenia is associated with severe coronavirus disease 2019 ( COVID-19 ) infections : a systemic review and meta-analysis. Int J Infect Dis. 2020;96:131-5.
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Publication Dates

  • Publication in this collection
    30 May 2022
  • Date of issue
    2022

History

  • Received
    30 July 2021
  • Accepted
    16 Nov 2021
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