This copy is for personal use only. To order printed copies, contact reprints@rsna.org I n P r e s s Summary StatementVisual and software-based quantification of well aerated lung parenchyma on admission chest CT were predictors of intensive care unit (ICU) admission or death in patients with pneumonia. Key Results� Patients with COVID-19 pneumonia at baseline chest CT who had ICU admission or who died had 4 or more lobes of the lung affected compared to patients without ICU admission or death (16% versus 6% of patients, p<.04).� After adjustment for patient demographics and clinical parameters, visually assessed well aerated lung parenchyma on admission on chest CT less than 73% was associated with ICU admission or death (OR 5.4, p<.001); software methods for lung quantification showed similar results. List of abbreviations SARS-CoV-2 = severe acute respiratory syndrome coronavirus 2; COVID-19 = coronavirus disease 19; RT-PCR = reverse-transcription polymerase chain reaction; WOG = worse outcome group; N-WOG = not-worse outcome group; %V-WAL = visual assessment of well aerated lung percentage; %S-WAL = software-based assessment of well aerated lung percentage; VOL-WAL = open-source software assessment of well aerated lung absolute volume; AT = adipose tissue. I n P r e s sAbstract Background: Computed tomography (CT) of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease depicts the extent of lung involvement in COVID-19 pneumonia.Purpose: The aim of the study was to determine the value of quantification of the well-aerated lung obtained at baseline chest CT for determining prognosis in patients with COVID-19 pneumonia. Materials and Methods: Patients who underwent chest CT suspected for COVID-19 pneumonia at the emergency department admission between February 17 to March 10, 2020 were retrospectively analyzed. Patients with negative reverse-transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2 in nasalpharyngeal swabs, negative chest CT, and incomplete clinical data were excluded. CT was analyzed for quantification of well aerated lung visually (%V-WAL) and by open-source software (%S-WAL and absolute volume, VOL-WAL). Clinical parameters included demographics, comorbidities, symptoms and symptom duration, oxygen saturation and laboratory values. Logistic regression was used to evaluate relationship between clinical parameters and CT metrics versus patient outcome (ICU admission/death vs. no ICU admission/ death). The area under the receiver operating characteristic curve (AUC) was calculated to determine model performance.Results: The study included 236 patients (females 59/123, 25%; median age, 68 years). A %V-WAL<73% (OR, 5.4; 95% CI, 2.7-10.8; P<0.001), %S-WAL<71% (OR, 3.8; 95% CI, 1.9-7.5; P<0.001), and VOL-WAL<2.9 L (OR, 2.6; 95% CI, 1.2-5.8; P<0.01) were predictors of ICU admission/death. In comparison with clinical model containing only clinical parameters (AUC, 0.83), all three quantitative models showed higher diagnostic performance (AUC 0.86 for all models). ...
Purpose Chest computed tomography (CT) is considered a reliable imaging tool for COVID-19 pneumonia diagnosis, while lung ultrasound (LUS) has emerged as a potential alternative to characterize lung involvement. The aim of the study was to compare diagnostic performance of admission chest CT and LUS for the diagnosis of COVID-19. Methods We included patients admitted to emergency department between February 21-March 6, 2020 (high prevalence group, HP) and between March 30-April 13, 2020 (moderate prevalence group, MP) undergoing LUS and chest CT within 12 h. Chest CT was considered positive in case of “indeterminate”/“typical” pattern for COVID-19 by RSNA classification system. At LUS, thickened pleural line with ≥ three B-lines at least in one zone of the 12 explored was considered positive. Sensitivity, specificity, PPV, NPV, and AUC were calculated for CT and LUS against real-time reverse transcriptase polymerase chain reaction (RT-PCR) and serology as reference standard. Results The study included 486 patients (males 61 %; median age, 70 years): 247 patients in HP (COVID-19 prevalence 94 %) and 239 patients in MP (COVID-19 prevalence 45 %). In HP and MP respectively, sensitivity, specificity, PPV, and NPV were 90–95 %, 43–69 %, 96−72 %, 20–95 % for CT and 94−93 %, 7–31 %, 94−52 %, 7–83 % for LUS. CT demonstrated better performance than LUS in diagnosis of COVID-19, both in HP (AUC 0.75 vs 0.51; P < 0.001) and MP (AUC 0.85 vs 0.62; P < 0.001). Conclusions Admission chest CT shows better performance than LUS for COVID-19 diagnosis, at varying disease prevalence. LUS is highly sensitive, but not specific for COVID-19.
Osteoporosis and sarcopenia represent two major health problems with an increasing prevalence in the elderly population. The correlation between these diseases has been widely reported, leading to the development of the term "osteosarcopenia" to diagnose those patients suffering from both diseases. Several imaging methods for the diagnosis and management of osteoporosis exist, with dual-energy X-ray absorptiometry (DXA) being the most commonly used for measuring bone mineral density (BMD). Imaging technique other than DXA is represented by conventional radiography, computed tomography (CT) and ultrasound (US). Similarly, the imaging technologies used to detect loss of skeletal muscle mass in sarcopenia include DXA, CT, US and magnetic resonance imaging (MRI). These methods differ in terms of reliability, radiation exposure and costs. CT and MRI represent the gold standard for evaluating body composition (BC), but are costly and time-consuming. DXA remains the most often used technology for studying BC, being quick, widely available and with low radiation exposure.
Purpose To test the association between death and both qualitative and quantitative CT parameters obtained visually and by software in coronavirus disease (COVID-19) early outbreak. Methods The study analyzed retrospectively patients underwent chest CT at hospital admission for COVID-19 pneumonia suspicion, between February 21 and March 6, 2020. CT was performed in case of hypoxemia or moderate-to-severe dyspnea. CT scans were analyzed for quantitative and qualitative features obtained visually and by software. Cox proportional hazards regression analysis examined the association between variables and overall survival (OS). Three models were built for stratification of mortality risk: clinical, clinical/visual CT evaluation, and clinical/software-based CT assessment. AUC for each model was used to assess performance in predicting death. Results The study included 248 patients (70% males, median age 68 years). Death occurred in 78/248 (32%) patients. Visual pneumonia extent > 40% (HR 2.15, 95% CI 1.2–3.85, P = 0.01), %high attenuation area – 700 HU > 35% (HR 2.17, 95% CI 1.2–3.94, P = 0.01), exudative consolidations (HR 2.85–2.93, 95% CI 1.61–5.05/1.66–5.16, P < 0.001), visual CAC score > 1 (HR 2.76–3.32, 95% CI 1.4–5.45/1.71–6.46, P < 0.01/ P < 0.001), and CT classified as COVID-19 and other disease (HR 1.92–2.03, 95% CI 1.01–3.67/1.06–3.9, P = 0.04/ P = 0.03) were significantly associated with shorter OS. Models including CT parameters (AUC 0.911–0.913, 95% CI 0.873–0.95/0.875–0.952) were better predictors of death as compared to clinical model (AUC 0.869, 95% CI 0.816–0.922; P = 0.04 for both models). Conclusions In COVID-19 patients, qualitative and quantitative chest CT parameters obtained visually or by software are predictors of mortality. Predictive models including CT metrics were better predictors of death in comparison to clinical model. Supplementary Information The online version contains supplementary material available at 10.1007/s10140-020-01867-1.
Background & Aims: Non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (T2DM) are closely associated, and liver fibrosis has been related to macrovascular complications. We examined whether liver fibrosis, diagnosed by FibroScan ® , correlates with chronic vascular complications in a cohort of T2DM. Methods:We recruited 394 outpatients with T2DM attending five Italian diabetes centres who underwent liver ultrasonography (US), FibroScan ® and extensive evaluation of macrovascular and microvascular diabetic complications.Results: Steatosis by US was present in 89%. Almost all patients (96%) were on hypoglycaemic drugs, 58% had at least one chronic vascular complication, 19% a macrovascular complication (prior myocardial infarction and/or ischaemic stroke) and 33% a microvascular one (26% chronic kidney disease [CKD]; 16% retinopathy; 6% neuropathy). In all, 171 (72%) patients had CAP ≥ 248dB/m (ie hepatic steatosis), whereas 83 (21%) patients had LSM ≥ 7.0/6.2 kPa (M/XL probes) (significant liver fibrosis). CAP was not associated with any macro/microvascular complications, whereas LSM ≥ 7.0/6.2 kPa was independently associated with prior cardiovascular disease (adjusted OR 3.3, 95%CI 1.2-8.8; P = .02) and presence of microvascular complications (adjusted OR 4.2, 95%CI 1.5-11.4; P = .005), mainly CKD (adjusted OR 3.6, 95%CI 1.3-10.1; P = .01) and retinopathy (adjusted OR 3.7, CI 95% 1.2-11.9; P = .02). Neither diabetes duration nor haemoglobin A1c differed according to CAP or LSM values.Conclusion: Significant fibrosis, detected by FibroScan ® , is independently associated with increased prevalence of macrovascular and microvascular complications, thus opening a new scenario in the use of this tool for a comprehensive evaluation of hepatic and vascular complications in patients with T2DM.
The coronavirus disease 2019 (COVID-19) lockdown dramatically changed people’s lifestyles. Diet, physical activity, and the PNPLA3 gene are known risk factors for non-alcoholic fatty liver disease (NAFLD). Aim: To evaluate changes in metabolic and hepatic disease in NAFLD patients after the COVID-19 lockdown. Three hundred and fifty seven NAFLD patients were enrolled, all previously instructed to follow a Mediterranean diet (MD). Anthropometric, metabolic, and laboratory data were collected before the COVID-19 lockdown in Italy and 6 months apart, along with ultrasound (US) steatosis grading and information about adherence to MD and physical activity (PA). In 188 patients, PNPLA3 genotyping was performed. After the lockdown, 48% of patients gained weight, while 16% had a worsened steatosis grade. Weight gain was associated with poor adherence to MD (p = 0.005), reduced PA (p = 0.03), and increased prevalence of PNPLA3 GG (p = 0.04). At multivariate analysis (corrected for age, sex, MD, PA, and PNPLA3 GG), only PNPLA3 remained independently associated with weight gain (p = 0.04), which was also associated with worsened glycemia (p = 0.002) and transaminases (p = 0.02). During lockdown, due to a dramatic change in lifestyles, half of our cohort of NAFLD patients gained weight, with a worsening of metabolic and hepatologic features. Interestingly, the PNPLA3 GG genotype nullified the effect of lifestyle and emerged as an independent risk factor for weight gain, opening new perspectives in NAFLD patient care.
In this confirmatory study, we tested if a calculation that included the non-uniformity of dose deposition through a voxel-based dosimetric variable Ψ was able to improve the dose–response agreement with respect to the mean absorbed dose D. We performed dosimetry with 99mTc-MAA SPECT/CT and 90Y-PET/CT in 86 patients treated 8 instead of 4 days after the reference date with 2.8 times more 90Y glass microspheres/GBq than in our previous study. The lesion-by-lesion response was assessed with the mRECIST method and with an experimental densitometric criterion. A total of 106 lesions were studied. Considering Ψ as a prognostic response marker, having no Ψ provided a significantly higher AUC than D. The correlation, t-test, and AUC values were statistically significant only with the densitometric method and only with post-therapy dosimetry. In comparison with our previous study, the dose–response correlation and AUC values were poorer (maximum r = 0.43, R2 = 0.14, maximal AUC = 0.71), and the efficacy at a high dose did not reach 100%. The expected advantages of voxel dosimetry were nullified by the correlation between any Ψ and D due to the limited image spatial resolution. The lower AUC and efficacy may be explained by the mega-clustering effect triggered by the higher number of microspheres/GBq injected on day 8.
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