We sought to examine the association of epicardial adipose tissue (EAT) quantified on chest computed tomography (CT) with the extent of pneumonia and adverse outcomes in patients with coronavirus disease 2019 (COVID-19). Methods: We performed a post-hoc analysis of a prospective international registry comprising 109 consecutive patients (age 64 ± 16 years; 62% male) with laboratory-confirmed COVID-19 and noncontrast chest CT imaging. Using semi-automated software, we quantified the burden (%) of lung abnormalities associated with COVID-19 pneumonia. EAT volume (mL) and attenuation (Hounsfield units) were measured using deep learning software. The primary outcome was clinical deterioration (intensive care unit admission, invasive mechanical ventilation, or vasopressor therapy) or in-hospital death. Results: In multivariable linear regression analysis adjusted for patient comorbidities, the total burden of COVID-19 pneumonia was associated with EAT volume (β = 10.6, p = 0.005) and EAT attenuation (β = 5.2, p = 0.004). EAT volume correlated with serum levels of lactate dehydrogenase (r = 0.361, p = 0.001) and C-reactive protein (r = 0.450, p < 0.001). Clinical deterioration or death occurred in 23 (21.1%) patients at a median of 3 days (IQR 1-13 days) following the chest CT. In multivariable logistic regression analysis, EAT volume (OR 5.1 [95% CI 1.8-14.1] per doubling p = 0.011) and EAT attenuation (OR 3.4 [95% CI 1.5-7.5] per 5 Hounsfield unit increase, p = 0.003) were independent predictors of clinical deterioration or death, as was total pneumonia burden (OR 2.5, 95% CI 1.4-4.6, p = 0.002), chronic lung disease (OR 1.3 [95% CI 1.1-1.7], p = 0.011), and history of heart failure (OR 3.5 [95% 1.1-8.2], p = 0.037). Conclusions: EAT measures quantified from chest CT are independently associated with extent of pneumonia and adverse outcomes in patients with COVID-19, lending support to their use in clinical risk stratification.
C oronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an unprecedented global health crisis with over 29.7 million confirmed cases worldwide as of September 17, 2020 (1). The most critical complication is acute respiratory failure requiring invasive mechanical ventilation, occurring in up to 17% of patients (2,3) which is associated with a high rate of in-hospital mortality (4,5). While the reversetranscription polymerase chain reaction (RT-PCR) assay is considered the reference standard for diagnosing COV-ID-19 infection (6), chest CT has greater sensitivity for early disease detection (7). This is particularly useful in patients in whom initial RT-PCR testing is negative and a high clinical suspicion remains (8). Furthermore, chest CT findings can indicate disease stage (9-11) and predict adverse outcomes (12,13) in COVID-19 pneumonia.Characteristic CT abnormalities are bilateral patchy ground-glass opacities (GGO) with or without consolidation in a peripheral, posterior, and diffuse or lower lung zone distribution (9-11). Increasing lung consolidation is typically observed later in the disease course (10,11) and is associated with critical illness (13,14). Studies have demonstrated that the extent of diseased lungs in COVID-19 pneumonia assessed by visual scoring correlates with
Coronary 18 F-sodium fluoride ( 18 F-NaF) positron emission tomography (PET) and computed tomography (CT) angiography-based quantitative plaque analysis have shown promise in refining risk stratification in patients with coronary artery disease. We combined both of these novel imaging approaches to develop an optimal machine-learning model for the future risk of myocardial infarction in patients with stable coronary disease.
MethodsPatients with known coronary artery disease underwent coronary 18 F-NaF PET and CT angiography on a hybrid PET/CT scanner. Machine-learning by extreme gradient boosting was trained using clinical data, CT quantitative plaque analysis measures and 18 F-NaF PET, and it was tested using repeated 10-fold hold-out testing.
ResultsAmong 293 study participants (65±9 years; 84% male), 22 subjects experienced a myocardial infarction over the 53 [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59] months of follow-up. On univariable receiver-operator-curve analysis, only 18 F-NaF coronary uptake emerged as a predictor of myocardial infarction (cstatistic 0.76, 95% confidence interval (CI) 0.68-0.83). When incorporated into machine-learning models, clinical characteristics showed limited predictive performance (c-statistic 0.64, 95% CI 0.53-0.76;) and were outperformed by a quantitative plaque analysis-based machine-learning model (c-statistic 0.72, 95% CI 0.60-0.84). After inclusion of all available data (clinical, quantitative plaque and 18 F-NaF PET), we achieved a substantial improvement (p=0.008 versus 18 F-NaF PET alone) in the model performance (c-statistic 0.85, 95% CI 0.79-0.91).
ConclusionsBoth 18 F-NaF uptake and quantitative plaque analysis measures are additive and strong predictors of outcome in patients with established coronary artery disease. Optimal risk stratification can be achieved by combining clinical data with these approaches in a machinelearning model.
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