Association of Vitamin D Status With Hospital Morbidity and Mortality in Adult Hospitalized Patients With COVID-19.

OBJECTIVE: To determine the association between vitamin D status and morbidity and mortality in adult hospitalized coronavirus disease 2019 (COVID-19) patients METHODS: We performed a retrospective chart review study in COVID-19 patients aged ≥18 year hospitalized at Boston University Medical Center between March 1 and August 4, 2020. All studied patients tested positive for COVID-19 and had serum levels of 25-hydroxyvitamin D (25[OH]D) results measured within 1 year prior to the date of positive tests. Medical information was retrieved from the electronic medical record and was analyzed to determine the association between vitamin D status and hospital morbidity and mortality. RESULTS: Among the 287 patients, 100 (36%) were vitamin D sufficient (25[OH]D >30 ng/mL) and 41 (14%) died during hospitalization. Multivariate analysis in patients aged ≥65 years revealed that vitamin D sufficiency (25[OH]D ≥30 ng/mL) was statistically significantly associated with decreased odds of death (adjusted OR 0.33, 95% CI, 0.12-0.94), acute respiratory distress syndrome (adjusted OR 0.22, 95% CI, 0.05-0.96), and severe sepsis/septic shock (adjusted OR 0.26, 95% CI, 0.08-0.88), after adjustment for potential confounders. Among patients with body mass index <30 kg/m2, vitamin D sufficiency was statistically significantly associated with a decreased odds of death (adjusted OR 0.18, 95% CI, 0.04-0.84). No significant association was found in the subgroups of patients aged <65 years or with body mass index ≥30 kg/m2. CONCLUSION: We revealed an independent association between vitamin D sufficiency defined by serum 25(OH)D ≥30 ng/mL and decreased risk of mortality from COVID-19 in elderly patients and patients without obesity.

Pharmacotherapy of type 2 diabetes: An update and future directions.

Type 2 diabetes (T2D) is a widely prevalent disease with substantial economic and social impact for which multiple conventional and novel pharmacotherapies are currently available; however, the landscape of T2D treatment is constantly changing as new therapies emerge and the understanding of currently available agents deepens. This review aims to provide an updated summary of the pharmacotherapeutic approach to T2D. Each class of agents is presented by mechanism of action, details of administration, side effect profile, cost, and use in certain populations including heart failure, non-alcoholic fatty liver disease, obesity, chronic kidney disease, and older individuals. We also review targets of novel therapeutic T2D agent development. Finally, we outline an up-to-date treatment approach that starts with identification of an individualized goal for glycemic control then selection, initiation, and further intensification of a personalized therapeutic plan for T2D.

An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT.

PURPOSE: Distinguishing between benign granulmoas and adenocarcinomas is confounded by their similar visual appearance on routine CT scans. Unfortunately, owing to the inability to discriminate these lesions radigraphically, many patients with benign granulomas are subjected to unnecessary surgical wedge resections and biopsies for pathologic confirmation of cancer presence or absence. This suggests the need for improved computerized characterization of these nodules in order to distinguish between these two classes of lesions on CT scans. While there has been substantial interest in the use of textural analysis for radiomic characterization of lung nodules, relatively less work has been done in shape based characterization of lung nodules, particularly with respect to granulmoas and adenocarcinomas. The primary goal of this study is to evaluate the role of 3D shape features for discrimination of benign granulomas from malignant adenocarcinomas on lung CT images. Towards this end we present an integrated framework for segmentation, feature characterization and classification of these nodules on CT. METHODS: The nodule segmentation method starts with separation of lung regions from the surrounding lung anatomy. Next, the lung CT scans are projected into and represented in a three dimensional spectral embedding (SE) space, allowing for better determination of the boundaries of the nodule. This then enables the application of a gradient vector flow active contour (SEGvAC) model for nodule boundary extraction. A set of 24 shape features from both 2D slices and 3D surface of the segmented nodules are extracted, including features pertaining to the angularity, spiculation, elongation and nodule compactness. A feature selection scheme, PCA-VIP, is employed to identify the most discriminating set of features to distinguish granulmoas from adenocarcinomas within a learning set of 82 patients. The features thus identified were then combined with a support vector machine classifier and independently validated on a distinct test set comprising 67 patients. The performance of the classifier for both of the training and validation cohorts was evaluated by the area under receiver characteristic curve (ROC). RESULTS: We used 82 and 67 studies from two different institutions respectively for training and independent validation of the model and the shape features. The Dice coefficient between automatically segmented nodules by SEGvAC and the manual delineations by expert radiologists (readers) was 0.84± 0.04 whereas inter-reader segmentation agreement was 0.79± 0.12. We also identified a set of consistent features (Roughness, Convexity and Spherecity) that were found to be strongly correlated across both manual and automated nodule segmentations (R > 0.80, p < 0.0001) and capture the marginal smoothness and 3D compactness of the nodules. On the independent validation set of 67 studies our classifier yielded a ROC AUC of 0.72 and 0.64 for manually- and automatically segmented nodules respectively. On a subset of 20 studies, the AUCs for the two expert radiologists and 1 pulmonologist were found to be 0.82, 0.68 and 0.58 respectively. CONCLUSIONS: The major finding of this study was that certain shape features appear to differentially express between granulomas and adenocarcinomas and thus computer extracted shape cues could be used to distinguish these radiographically similar pathologies.