Objectives There is increasing evidence that thrombotic events occur in patients with coronavirus disease (COVID-19). We evaluated lung and kidney perfusion abnormalities in patients with COVID-19 by dual-energy computed tomography (DECT) and investigated the role of perfusion abnormalities on disease severity as a sign of microvascular obstruction. Methods Thirty-one patients with COVID-19 who underwent pulmonary DECT angiography and were suspected of having pulmonary thromboembolism were included. Pulmonary and kidney images were reviewed. Patient characteristics and laboratory findings were compared between those with and without lung perfusion deficits (PDs). Results DECT images showed PDs in eight patients (25.8%), which were not overlapping with areas of ground-glass opacity or consolidation. Among these patients, two had pulmonary thromboembolism confirmed by CT angiography. Patients with PDs had a longer hospital stay (p = 0.14), higher intensive care unit admission rates (p = 0.02), and more severe disease (p = 0.01). In the PD group, serum ferritin, aspartate aminotransferase, fibrinogen, D-dimer, C-reactive protein, and troponin levels were significantly higher, whereas albumin level was lower (p < 0.05). D-dimer levels ≥ 0.485 μg/L predicted PD with 100% specificity and 87% sensitivity. Renal iodine maps showed heterogeneous enhancement consistent with perfusion abnormalities in 13 patients (50%) with lower sodium levels (p = 0.03). Conclusions We found that a large proportion of patients with mild-to-moderate COVID-19 had PDs in their lungs and kidneys, which may be suggestive of the presence of systemic microangiopathy with micro-thrombosis. These findings help in understanding the physiology of hypoxemia and may have implications in the management of patients with COVID-19, such as early indications of thromboprophylaxis or anticoagulants and optimizing oxygenation strategies. Key Points • Pulmonary perfusion abnormalities in COVID-19 patients, associated with disease severity, can be detected by pulmonary DECT. • A cutoff value of 0.485 μg/L for D-dimer plasma levels predicted lung perfusion deficits with 100% specificity and 87% sensitivity (AUROC, 0.957). • Perfusion abnormalities in the kidney are suggestive of a subclinical systemic microvascular obstruction in these patients.

Context Nonalcoholic fatty liver disease (NAFLD) is a metabolical disorder and can lead to liver fibrosis. Because it is commonly seen, several noninvasive scores (NS) have been validated to identify high-risk patients. Patients with NAFLD have been shown to have higher serum angiopoietin-like protein-8 (ANGPTL-8) levels. Objective The risk of NAFLD is known insufficiently in acromegaly. Moreover, the utility of the NS and the link between NAFLD and ANGPTL-8 in acromegaly is unknown. Methods Thirty-two patients with acromegaly (n = 15, active [AA] and n = 17, controlled acromegaly [CA]) and 19 healthy controls were included. Magnetic resonance imaging (MRI)-proton density fat fraction (PDFF) was used to evaluate hepatic steatosis, and magnetic resonance elastography to evaluate liver stiffness measurement. ANGPTL-8 levels were measured with ELISA. Results Median liver MRI-PDFF and NAFLD prevalence in AA were lower than in CA (P = .026 and P < .001, respectively). Median magnetic resonance elastography-liver stiffness measurement were similar across groups. Of the NS, visceral adiposity index, fatty liver index, hepatic steatosis index, and triglyceride-glucose index (TyG) all showed positive correlation with the liver MRI-PDFF in the control group. However, only TyG significantly correlated with liver fat in the AA and CA groups. There was no correlation between traditional NAFLD risk factors (body mass index, waist circumference, C-reactive protein, homeostasis model assessment for insulin resistance, visceral adipose tissue) and liver MRI-PDFF in the AA and CA. Patients with acromegaly with NAFLD had lower GH, IGF-1, and ANGPTL-8 levels than in those without NAFLD (P = .025, P = .011, and P = .036, respectively). Conclusion Active acromegaly may protect from NAFLD because of high GH. In patients with acromegaly, NAFLD risk cannot be explained with classical risk factors; hence, additional risk factors must be identified. TyG is the best score to evaluate NAFLD risk. Lower ANGPTL-8 in patients with acromegaly and NAFLD implies this hormone may be raised because of insulin resistance rather than being a cause for NAFLD.

The aim of this study was to evaluate visual and software-based quantitative assessment of parenchymal changes and normal lung parenchyma in patients with coronavirus disease 2019 (COVID-19) pneumonia. The secondary aim of the study was to compare the radiologic findings with clinical and laboratory data. Patients with COVID-19 who underwent chest computed tomography (CT) between March 11, 2020 and April 15, 2020 were retrospectively evaluated. Clinical and laboratory findings of patients with abnormal findings on chest CT and PCR-evidence of COVID-19 infection were recorded. Visual quantitative assessment score (VQAS) was performed according to the extent of lung opacities. Software-based quantitative assessment of the normal lung parenchyma percentage (SQNLP) was automatically quantified by a deep learning software. The presence of consolidation and crazy paving pattern (CPP) was also recorded. Statistical analyses were performed to evaluate the correlation between quantitative radiologic assessments, and clinical and laboratory findings, as well as to determine the predictive utility of radiologic findings for estimating severe pneumonia and admission to intensive care unit (ICU). A total of 90 patients were enrolled. Both VQAS and SQNLP were significantly correlated with multiple clinical parameters. While VQAS >8.5 (sensitivity, 84.2%; specificity, 80.3%) and SQNLP 9.5 (sensitivity, 93.3%; specificity, 86.5%) and SQNLP <81.1% (sensitivity, 86.5%; specificity, 86.7%) were predictive of ICU admission. Both consolidation and CPP were more commonly seen in patients with severe pneumonia than patients with nonsevere pneumonia (P = 0.197 for consolidation; P < 0.001 for CPP). Moreover, the presence of CPP showed high specificity (97.2%) for severe pneumonia. Both SQNLP and VQAS were significantly related to the clinical findings, highlighting their clinical utility in predicting severe pneumonia, ICU admission, length of hospital stay, and management of the disease. On the other hand, presence of CPP has high specificity for severe COVID-19 pneumonia.