Background: Data regarding the phenotypic correlates and prognostic value of albumin in heart failure with preserved ejection fraction (HFpEF) are scarce. The goal of the current study is to determine phenotypic correlates (myocardial hypertrophy, myocardial fibrosis, detailed pulsatile hemodynamics, and skeletal muscle mass) and prognostic implications of serum albumin in HFpEF. Methods and Results: We studied 118 adults with HFpEF. All‐cause death or heart‐failure–related hospitalization was ascertained over a median follow‐up of 57.6 months. We measured left ventricular mass, myocardial extracellular volume, and axial muscle areas using magnetic resonance imaging. Pulsatile arterial hemodynamics were assessed with a combination of arterial tonometry and phase‐contrast magnetic resonance imaging. Subjects with lower serum albumin exhibited a higher body mass index, and a greater proportion of black ethnicity and diabetes mellitus. A low serum albumin was associated with higher myocardial extracellular volume (52.3 versus 57.4 versus 39.3 mL in lowest to highest albumin tertile, respectively; P =0.0023) and greater N‐terminal pro B‐type natriuretic peptide levels, but not with a higher myocardial cellular volume (123 versus 114 versus 102 mL; P =0.13). Lower serum albumin was also associated with an increased forward wave amplitude and markedly increased pulsatile power in the aorta. Serum albumin was a strong predictor of death or heart failure hospitalization even after adjustment for N‐terminal pro B‐type natriuretic peptide levels and the Meta‐Analysis Global Group in Chronic Heart Failure (MAGGIC) risk score (adjusted standardized hazard ratio=0.56; 95% CI=0.37–0.83; P <0.0001). Conclusions: Serum albumin is associated with myocardial fibrosis, adverse pulsatile aortic hemodynamics, and prognosis in HFpEF. This readily available clinical biomarker can enhance risk stratification in HFpEF and identifies a subgroup with specific pathophysiological abnormalities.

The objective was to develop a fully automated algorithm for abdominal fat segmentation and to deploy this method at scale in an academic biobank. We built a fully automated image curation and labeling technique using deep learning and distributive computing to identify subcutaneous and visceral abdominal fat compartments from 52,844 computed tomography scans in 13,502 patients in the Penn Medicine Biobank (PMBB). A classification network identified the inferior and superior borders of the abdomen, and a segmentation network differentiated visceral and subcutaneous fat. Following technical evaluation of our method, we conducted studies to validate known relationships with visceral and subcutaneous fat. When compared with 100 manually annotated cases, the classification network was on average within one 5-mm slice for both the superior (0.4 ± 1.1 slice) and inferior (0.4 ± 0.6 slice) borders. The segmentation network also demonstrated excellent performance with intraclass correlation coefficients of 1.00 (P < 2 × 10^-16) for subcutaneous and 1.00 (P < 2 × 10^-16) for visceral fat on 100 testing cases. We performed integrative analyses of abdominal fat with the phenome extracted from the electronic health record and found highly significant associations with diabetes mellitus, hypertension, and renal failure, among other phenotypes. This work presents a fully automated and highly accurate method for the quantification of abdominal fat that can be applied to routine clinical imaging studies to fuel translational scientific discovery.

Background: Patients with COVID-19 infection are commonly reported to have an increased risk of venous thrombosis. The choice of anti-thrombotic agents and doses are currently being studied in randomized controlled trials and retrospective studies. There exists a need for individualized risk stratification of venous thromboembolism (VTE) to assist clinicians in decision-making on anticoagulation. We sought to identify the risk factors of VTE in COVID-19 patients, which could help physicians in the prevention, early identification, and management of VTE in hospitalized COVID-19 patients and improve clinical outcomes in these patients. Method: This is a multicenter, retrospective database of four main health systems in Southeast Michigan, United States. We compiled comprehensive data for adult COVID-19 patients who were admitted between 1st March 2020 and 31st December 2020. Four models, including the random forest, multiple logistic regression, multilinear regression, and decision trees, were built on the primary outcome of in-hospital acute deep vein thrombosis (DVT) and pulmonary embolism (PE) and tested for performance. The study also reported hospital length of stay (LOS) and intensive care unit (ICU) LOS in the VTE and the non-VTE patients. Four models were assessed using the area under the receiver operating characteristic curve and confusion matrix. Results: The cohort included 3531 admissions, 3526 had discharge diagnoses, and 6.68% of patients developed acute VTE (N = 236). VTE group had a longer hospital and ICU LOS than the non-VTE group (hospital LOS 12.2 days vs. 8.8 days, p < 0.001; ICU LOS 3.8 days vs. 1.9 days, p < 0.001). 9.8% of patients in the VTE group required more advanced oxygen support, compared to 2.7% of patients in the non-VTE group (p < 0.001). Among all four models, the random forest model had the best performance. The model suggested that blood pressure, electrolytes, renal function, hepatic enzymes, and inflammatory markers were predictors for in-hospital VTE in COVID-19 patients. Conclusions: Patients with COVID-19 have a high risk for VTE, and patients who developed VTE had a prolonged hospital and ICU stay. This random forest prediction model for VTE in COVID-19 patients identifies predictors which could aid physicians in making a clinical judgment on empirical dosages of anticoagulation.

Background:The number of IVC filter-related complications has increased with their growing utilization; however, IVC filter perforation of the duodenum is rare. It can manifest with nonspecific abdominal pain, gastrointestinal bleeding, cava-duodenal...