Summary It has been suggested that the lower prevalence of cardiovascular disease in women before menopause in comparison with men may be explained by differences in body fat distribution, plasma lipoprotein levels and indices of plasma glucose-insulin homeostasis. Thus, gender differences in visceral adipose tissue accumulation measured by computed tomography and metabolic variables were studied in 80 men and 69 pre-menopausal women, aged 23-50 years. Despite the fact that women had higher levels of total body fat (p < 0.0001), they displayed lower areas of abdominal visceral adipose tissue (p < 0.06) and a lower ratio of abdominal visceral to mid-thigh adipose tissue areas than men (p < 0.0001). After adjustment for body fat mass, women generally displayed a more favourable risk profile than men which included higher plasma HDL2-cholesterol and lower plasma insulin, apolipoprotein B and triglyceride levels (p < 0.01). Metabolic variables adjusted for body fat mass were then cornpared between genders after control for differences in abdominal visceral adipose tissue area. After such controls, variables related to plasma glucose-insulin homeostasis were no longer significantly different between men and women. Gender differences for plasma concentrations of triglyceride, apolipoprotein B and the ratio of HDL2-cholesterol/HDL3-cholesterol also disappeared, whereas plasma concentrations of HDL-cholesterol, HDL2-cholesterol as well as the ratio of HDL-cholesterol/total cholesterol remained significantly higher in women than in men (p < 0.01 ). These results suggest that abdominalvisceral adipose tissue is an important correlate of gender differences in cardiovascular disease risk. However, additional factors are likely to be involved in gender differences in plasma HDLcholesterollevels. [Diabetologia (1994) 37: 757-764]

No abstract

Background - Decision scores and ethically mindful algorithms are being established to adjudicate mechanical ventilation in the context of potential resources shortage due to the current onslaught of COVID-19 cases. There is a need for a reproducible and objective method to provide quantitative information for those scores. Purpose - Towards this goal, we present a retrospective study testing the ability of a deep learning algorithm at extracting features from chest x-rays (CXR) to track and predict radiological evolution. Materials and Methods - We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from two open-source datasets (last accessed on April 9, 2020)(Italian Society for Medical and Interventional Radiology and MILA). Data collected form 60 pairs of sequential CXRs from 40 COVID patients (mean age +/- standard deviation: 56 +/- 13 years; 23 men, 10 women, seven not reported) and were categorized in three categories: Worse, Stable, or Improved on the basis of radiological evolution ascertained from images and reports. Receiver operating characteristic analyses, Mann-Whitney tests were performed. Results - On patients from the CheXnet dataset, the area under ROC curves ranged from 0.71 to 0.93 for seven imaging features and one diagnosis. Deep learning features between Worse and Improved outcome categories were significantly different for three radiological signs and one diagnostic (Consolidation, Lung Lesion, Pleural Effusion and Pneumonia; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between Worse and Improved cases with 82.7% accuracy. Conclusion - CXR deep learning features show promise for classifying the disease trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions.

Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from current CXR would be highly desirable. We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from an open-source dataset (COVID-19 image data collection) and from a multi-institutional local ICU dataset. The data was grouped into pairs of sequential CXRs and were categorized into three categories: ‘Worse’, ‘Stable’, or ‘Improved’ on the basis of radiological evolution ascertained from images and reports. Classical machine-learning algorithms were trained on the deep learning extracted features to perform immediate severity evaluation and prediction of future radiological trajectory. Receiver operating characteristic analyses and Mann-Whitney tests were performed. Deep learning predictions between “Worse” and “Improved” outcome categories and for severity stratification were significantly different for three radiological signs and one diagnostic (‘Consolidation’, ‘Lung Lesion’, ‘Pleural effusion’ and ‘Pneumonia’; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between ‘Worse’ and ‘Improved’ cases with a 0.81 (0.74–0.83 95% CI) AUC in the open-access dataset and with a 0.66 (0.67–0.64 95% CI) AUC in the ICU dataset. Features extracted from the CXR could predict disease severity with a 52.3% accuracy in a 4-way classification. Severity evaluation trained on the COVID-19 image data collection had good out-of-distribution generalization when testing on the local dataset, with 81.6% of intubated ICU patients being classified as critically ill, and the predicted severity was correlated with the clinical outcome with a 0.639 AUC. CXR deep learning features show promise for classifying disease severity and trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions.

No abstract

The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk factors for COVID-19. It is proposed that image data, specifically bedside computed X-ray (CXR), provide additional predictive information on mortality following mechanical ventilation that can be incorporated in the guidelines. Deep transfer learning was used to extract convolutional features from a systematically collected, multi-institutional dataset of COVID-19 ICU patients. A model predicting outcome of mechanical ventilation (remission or mortality) was trained on the extracted features and compared to a model based on known, aggregated risk factors. The model reached a 0.702 area under the curve (95% CI 0.707-0.694) at predicting mechanical ventilation outcome from pre-intubation CXRs, higher than the risk factor model. Combining imaging data and risk factors increased model performance to 0.743 AUC (95% CI 0.746-0.732). Additionally, a post-hoc analysis showed an increase performance on high-quality than low-quality CXRs, suggesting that using only high-quality images would result in an even stronger model.

Background Recently, the use of Yttrium-90 transarterial radioembolization in non-surgical hepatocellular carcinoma was suggested but the evidence supporting its use is unclear. Methods We searched Medline, Embase, Web of Science and Cochrane CENTRAL from inception up to April 14, 2020 for randomized controlled trials comparing Y90-TARE to standard of care in non-surgical HCC patients. Our primary outcome was overall survival (OS). Our secondary outcomes were progression-free survival, time to progression, disease control rate, grade ≥3 adverse events and rates of gastro-intestinal ulcers. Hazard ratios (HR) and risk ratios (RR) with random-effects model were used for our analyses. The risk of bias of the included studies was assessed using Cochrane’s RoB 2 tool. Results Of 1,604 citations identified, eight studies (1,439 patients) were included in our analysis. No improvement in overall survival were noted when Yttrium-90 transarterial radioembolization was compared to standard treatments (HR 0.99 [95% CI 0.81–1.21], 6 studies, I2 = 77.6%). However, Yttrium-90 transarterial radioembolization was associated with fewer grade ≥3 adverse events (RR 0.64 [95% CI 0.45–0.92], 7 studies, I2 = 66%). No difference was observed on other secondary outcomes. Discussion In non-surgical HCC patients, Yttrium-90 transarterial radioembolization was not associated with significant effect on survival, progression-free survival, time to progression, disease control rate and the incidence of gastro-intestinal ulcers but was however associated with significantly lower rates of grade ≥3 adverse events. Further randomized controlled trials are warranted to better delineate optimal treatment.

No abstract