Citations

Introduction: Body fat (BF) content better predicts adiposity-related cardiovascular risk than the body mass index (BMI). Accurate and accepted methods to assess BF are complex, expensive, and accessible only in clinical settings. Multi-sensor 3D body volume (3D-BV) measurement technology has been shown to accurately estimate BF. We assessed the hypothesis that 3D models generated from biplane imaging (i.e., front and side facing photographs) using mobile devices (App), could be used to predict body volume measurements and derive BF. Methods: We prospectively enrolled 196 subjects, who underwent 3D-BV (gold standard for body volume) within 24 hours, dual-energy X-ray Absorptiometry (iDEXA) (gold standard for BF) and photographs taken with an Ipad® App at a predefined distance and pose. These photos were post-processed with a computer-assisted algorithm to estimate body length, girth, and volume. These were used to calculate body density (BD) using the bicompartmental principles of body composition (BD=Body Mass/Volume) and derive BF% using the Siri equation [(4.95/BD-4.50) X 100]. Correlation indexes and residual plots were created to compare 3D-BV and iDEXA with the App. Mean differences were compared using a paired t-test. Results: Mean ±SD age was 31.9±9.15 years, 53% were women, weight 72.8±14.1 kg, BMI was 25.5±4.5 kg/m 2 .The App volume correlated with 3D-BV (R 2 =0.95,95%CI 0.90,0.95, p<.0001, Figure 1-A ) ; average difference between App volume vs 3D-BV volume was -1.2 Liters, 95%CI,0.03, 2.42, p=0.06, ( Figure 1-B ). App BF% correlated with iDEXA BF% (R 2 =0.92, 95%CI 0.90,0.94, p<.0001, Figure 1-C ) ; while average difference between App BF% vs iDEXA BF% was -0.13 %, 95%CI -0.6,0.4, p=0.6, ( Figure 1-D ). Conclusion: BF can be estimated using volume measurements obtained by biplane imaging from mobile devices and could serve as a home-based, portable, scalable, cost-effective, and convenient measure to assess BF and track changes in body composition over time.

Introduction: Spontaneous coronary artery dissection (SCAD) is an important cause of myocardial infarction in otherwise healthy and most times presumably fit young women. In the absence of evidence for benefit or harm, exercise prescription (EXP) after SCAD remains arbitrary. We assessed EXP patterns among participants in a large multinational SCAD registry. Methods: Participants with SCAD as confirmed by coronary angiography completed surveys detailing exercise (EX) habits prior to SCAD (initial survey), and EXP from their provider on initial and subsequent visits after SCAD (follow-up survey). Demographic and clinical characteristics were collected by electronic health record review by trained nurse abstractors. EXP was categorized according to EX components that include: type, intensity, frequency, time/session, and external conditions. Results: Among 950 participants, mean ± age was 46.8 ± 9.5 years, 96% were women and 77% reported attending ≥1 Cardiopulmonary Rehabilitation session. Prior to SCAD, 49% were aerobically exercising ≥ 3times per week, and only 32% performed strength-building EX regularly. Initial and subsequent post-SCAD visit details in 299/950 participants showed that most (93%) patients received some form of EX (Figure 1-A) counseling, namely: EXP, unspecific advice or were discouraged from any EX (Figure 1-B) . Limits regarding EX type and intensity were the most common advice received among EXP group (Figure 1-C) . Conclusions: Insights from our study show that only 40% of patients were achieving current exercise recommendations for adults prior to SCAD (vs 53% adult U.S population; CDC, 2018) which suggests that SCAD patients may not be as active as previously described. Most patients receive some form of EX counseling after SCAD; however, a structured EXP is needed to avoid overly cautious approaches that could lead to uncertainty, deconditioning, impaired quality of life, or increase risks associated with a sedentary lifestyle.

Objective To describe the process and outcome of creating a patient cohort in the early stages of the COVID-19 pandemic in order to better understand the process of and predict the outcomes of COVID-19. Patients and Methods A total of 1169 adults aged 18 years of age or older who tested positive in Mayo Clinic Rochester or the Mayo Clinic Midwest Health System between January 1 and May 23 of 2020. Results Patients were on average 43.9 years of age and 50.7% were female. Most patients were white (69.0%), and Blacks (23.4%) and Asians (5.8%) were also represented in larger numbers. Hispanics represented 16.3% of the sample. Just under half of patients were married (48.4%). Common comorbid conditions included: cardiovascular diseases (25.1%), dyslipidemia (16.0%), diabetes mellitus (11.2%), chronic obstructive pulmonary disease (6.6%), asthma (7.5%), and cancer (5.1%). All other comorbid conditions were less the 5% in prevalence. Data on 3 comorbidity indices are also available including the: DHHS multi-morbidity score, Charlson Comorbidity Index, and Mayo Clinic COVID-19 Risk Factor Score. Conclusion In addition to managing the ever raging pandemic and growing death rates, it is equally important that we develop adequate resources for the investigation and understanding of COVID-19-related predictors and outcomes.