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]

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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.