Association of mediastinal lymphadenopathy with COVID-19 prognosis

Sardanelli, Francesco; Cozzi, Andrea; Monfardini, Lorenzo; Bnà, Claudio; Foà, Riccardo; Spinazzola, A; Tresoldi, Silvia; Cariati, Maurizio; Secchi, Francesco; Schiaffino, Simone

doi:10.1016/s1473-3099(20)30521-1

Cited by 29 publications

(44 citation statements)

References 5 publications

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“…In COVID-19 patients, advanced age and various pre-existing comorbidities have been associated with higher risk of death ( 1 , 2 , 21 , 27 , 28 ). While the same has been documented for pulmonary parenchymal damage and associated pathologic features assessed on chest CT ( 19 , 22 , 29 ), few articles have investigated whether sarcopenia and lower muscle mass are negative predictors for severe COVID-19 ( 12 – 14 ). Nevertheless, impaired muscle status has long been associated with higher mortality risk in critical care: sarcopenia and lower muscle mass – detected by CT – are primary predictors of worse outcome in mechanically ventilated patients ( 30 – 32 ).…”

Section: Discussionmentioning

confidence: 93%

“…Disease extent was classified as proposed by Bernheim et al ( 17 ): 0% (absent, 0); 1–25% (minimal, 1); 26–50% (mild, 2); 51–75% (moderate, 3); over 75% (severe, 4). Presence of crazy-paving pattern, mediastinal lymphadenopathy (i.e., the presence of at least one lymph node with short axis > 10 mm) ( 22 ), and pleural effusion was also recorded.…”

Section: Methodsmentioning

confidence: 99%

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CT-derived Chest Muscle Metrics for Outcome Prediction in Patients with COVID-19

Schiaffino¹,

Albano²,

Cozzi³

et al. 2021

Radiology

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Background Lower muscle mass is a known predictor of unfavorable outcome, but its prognostic impact on COVID-19 patients is unknown. Purpose To investigate the contribution of CT-derived muscle status in predicting clinical outcomes in COVID-19 patients. Materials and Methods Clinical/laboratory data and outcomes (intensive care unit [ICU] admission and death) were retrospectively retrieved for patients with reverse transcriptase polymerase chain reaction-confirmed COVID-19, who underwent chest CT on admission in four hospitals in Northern Italy from February 21 to April 30, 2020. Extent and type of pulmonary involvement, mediastinal lymphadenopathy, and pleural effusion were assessed. Cross-sectional areas and attenuation of paravertebral muscles were measured on axial CT images at T5 and T12 vertebral level. Multivariable linear and binary logistic regression, including calculation odds ratios (OR) with 95% confidence intervals (CIs), were used to build four models to predict ICU admission and death, tested and compared using receiver operating characteristic curve (ROC) analysis. Results A total 552 patients (364 men; median age 65 years, interquartile range 54–75) were included. In a CT-based model, lower-than-median T5 paravertebral muscle area showed the highest ORs for ICU admission (OR 4.8, 95% CI 2.7–8.5; P <.001) and death (OR 2.3, 95% CI 1.0–2.9; P =.027). When clinical variables were included in the model, lower-than-median T5 paravertebral muscle area still showed the highest ORs both for ICU admission (OR 4.3; 95% CI 2.5–7.7; P <.001) and death (OR 2.3, 95% CI 1.3–3.7; P =.001). At ROC analysis, the CT-based model and the model including clinical variables showed the same area under the curve (AUC) for ICU admission prediction (AUC 0.83, P =.380) and were not different in predicting death (AUC 0.86 versus AUC 0.87, respectively, P =.282). Conclusion In hospitalized patients with COVID-19, lower muscle mass on CT was independently associated with ICU admission and hospital mortality.

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Section: Discussionmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 99%

CT-derived Chest Muscle Metrics for Outcome Prediction in Patients with COVID-19

Schiaffino¹,

Albano²,

Cozzi³

et al. 2021

Radiology

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“…This may suggest that lymphadenopathy could be an independent imaging biomarker of the disease, similar to what demonstrated for other infections (Chadburn et al 1989). Indeed, based on limited data, lymphadenopathy has been suggested as a prognostic marker of COVID-19 infection severity (Valette et al 2020) and a predictor of a worse outcome (Sardanelli et al 2020). In view of the grave clinical outcome of our patient, there may be an argument in support of a further prognostic role of higher SUVmax values of FDG positive nodal disease, as a biomarker which could herald a more severe case of the infection with the virus.…”

Section: Discussionmentioning

confidence: 61%

A twisted tale-radiological imaging features of COVID-19 on 18F-FDG PET/CT

O’Neill

Doran

Fraioli

et al. 2020

European J Hybrid Imaging

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The COVID-19 pandemic has had a major impact on health care systems across the globe in a short period of time. There is a growing body of evidence surrounding the findings on hybrid imaging with FDG-PET/CT, and this case highlights the importance of molecular imaging in better understanding of the biomarkers of the disease which ultimately determine the success in building a model to predict the disease severity and monitoring the response to treatment.

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“…The stratification of COVID-19 patients according to their risk of developing severe complications and of being subsequent admitted to ICU remains a priority, several clinical and radiological cofactors having been investigated to this purpose [ 11 , 12 ]. In this study, we assessed the role of visceral and subcutaneous adipose tissue in predicting disease severity of hospitalised COVID-19 patients, highlighting how patients needing ICU admission showed significantly higher CT-derived metrics of increased adipose tissue.…”

Section: Discussionmentioning

confidence: 99%

“…Chest computed tomography (CT) has been up to now widely used for suspected COVID-19 patients, albeit with oscillating specificity and a high organizational burden on ED workflow [ 8 ]. Even if chest x-ray showed interesting diagnostic performance [ 9 , 10 ], chest CT was extensively used for patients’ staging and monitoring, since it allows to evaluate disease severity as well as pulmonary, extra-pulmonary, and vascular features [ 8 , 11 , 12 ]. CT-derived additional data to stratify patients’ risk could be provided by quantification and distribution analysis of abdominal fat, both obtainable with post-processing software including automatic and semiautomatic segmentation tools [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%