Search

Background With improving survival of patients with single ventricle physiology who underwent Fontan palliation, there is also an increase in the prevalence of overweight and obesity in these patients. This tertiary care single‐center study aims to determine the association of body mass index (BMI) with the clinical characteristics and outcomes in adults with Fontan. Methods and Results Adult patients (aged ≥18 years) with Fontan who were managed at a single tertiary care center between January 1, 2000, and July 1, 2019, and had BMI data available were identified via retrospective review of medical records. Univariate and multivariable (after adjusting for age, sex, functional class, and type of Fontan) linear and logistic regression, as appropriate, were utilized to evaluate associations between BMI and diagnostic testing and clinical outcomes. A total of 163 adult patients with Fontan were included (mean age, 29.9±9.08 years), with a mean BMI of 24.2±5.21 kg/m2 (37.4% of patients had BMI ≥25 kg/m2). Echocardiography data were available for 95.7% of patients, exercise testing for 39.3% of patients, and catheterization for 53.7% of patients. Each SD increase in BMI was significantly associated with decreased peak oxygen consumption (P=0.010) on univariate analysis and with increased Fontan pressure (P=0.035) and pulmonary capillary wedge pressure (P=0.037) on multivariable analysis. In addition, BMI ≥25 kg/m2 was independently associated with heart failure hospitalization (adjusted odds ratio [AOR], 10.2; 95% CI, 2.79–37.1 [P

Background Clinicians vary markedly in their ability to detect murmurs during cardiac auscultation and identify the underlying pathological features. Deep learning approaches have shown promise in medicine by transforming collected data into clinically significant information. The objective of this research is to assess the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform. Methods and Results Using >34 hours of previously acquired and annotated heart sound recordings, we trained a deep neural network to detect murmurs. To test the algorithm, we enrolled 962 patients in a clinical study and collected recordings at the 4 primary auscultation locations. Ground truth was established using patient echocardiograms and annotations by 3 expert cardiologists. Algorithm performance for detecting murmurs has sensitivity and specificity of 76.3% and 91.4%, respectively. By omitting softer murmurs, those with grade 1 intensity, sensitivity increased to 90.0%. Application of the algorithm at the appropriate anatomic auscultation location detected moderate‐to‐severe or greater aortic stenosis, with sensitivity of 93.2% and specificity of 86.0%, and moderate‐to‐severe or greater mitral regurgitation, with sensitivity of 66.2% and specificity of 94.6%. Conclusions The deep learning algorithm’s ability to detect murmurs and clinically significant aortic stenosis and mitral regurgitation is comparable to expert cardiologists based on the annotated subset of our database. The findings suggest that such algorithms would have utility as front‐line clinical support tools to aid clinicians in screening for cardiac murmurs caused by valvular heart disease. Registration URL: https://clinicaltrials.gov; Unique Identifier: NCT03458806.

Background As patients with congenital heart disease (CHD) are living longer, understanding the comorbidities they develop as they age is increasingly important. However, there are no published population‐based estimates of the comorbidity burden among the US adult patients with CHD. Methods and Results Using the IBM MarketScan commercial claims database from 2010 to 2016, we identified adults aged ≥18 years with CHD and 2 full years of continuous enrollment. These were frequency matched with adults without CHD within categories jointly defined by age, sex, and dates of enrollment in the database. A total of 40 127 patients with CHD met the inclusion criteria (mean [SD] age, 36.8 [14.6] years; and 48.2% were women). Adults with CHD were nearly twice as likely to have any comorbidity than those without CHD (P