Use of Optimal Medical Therapy in Patients With Cardiovascular Disease Undergoing Cardiac Rehabilitation.

Optimal medical therapy (OMT) in patients with coronary artery disease (CAD) and/or heart failure (HF) is underused despite the established benefits of these medications. Cardiac rehabilitation (CR) may be one place where OMT could be promoted. We sought to describe the prevalence and characteristics of OMT use in patients with CAD or HF undergoing CR. We included patients with CAD (myocardial infarction, percutaneous coronary intervention, coronary artery bypass grafting, angina) and HF enrolled in our CR program. For patients with CAD, we defined OMT to consist of aspirin or other antiplatelets, statins, and beta-blockers (BB). For patients with HF or EF ≤ 40%, OMT included BB, spironolactone, and either Angiotensin Converting Enzyme inhibitors (ACEi)/angiotensin receptor blockers or angiotensin receptor neprilysin inhibitor (ARNI). For CAD patients with normal EF, OMT also included ACEi/ARB/ARNI if they also had diabetes type 2. From January 2015 to December 2019, 828 patients were referred to CR and 743 attended. Among 612 patients (mean age: 65, 23% female) with CAD, 483 (79%) patients were on OMT. Of the 131 HF patients (mean age: 64, 21% female) enrolled in CR, only 23 (18%) met all 3 OMT criteria, whereas most patients were on only 1 (93 %) or 2 (76%) HF specific medications. Spironolactone was the least prescribed (22%) medication. Over the study period, we observed a steady increase in the use of ARNI (2015: 0% vs 2019: 27%, p < 0.01). Among the individuals, 69 patients experienced both CAD and HF, while only 7 patients were under OMT for both CAD and HF. Most patients attending CR with CAD are receiving OMT, but most patients with HF are not. Although OMT has improved over time, there remains room for improvement, particularly among patients with HF.

Physical and Psychological Well-being Effects of Cardiac Rehabilitation on Patients Following Mitral Valve and Aortic Valve Procedures.

PURPOSE: Patients participating in cardiac rehabilitation (CR) following an aortic valve procedure demonstrate improvements in physical capacity and psychological well-being. The primary aim of this study is to evaluate baseline exercise capacity and psychological well-being for mitral valve patients participating in CR and to compare physical and psychological outcomes between mitral valve and aortic valve patients. METHODS: The primary endpoint was improvement in 6-min walk test (6MWT) distance. Secondary endpoints included change in exercise min/wk, depression scores (Patient Health Questionnaire-9 [PHQ-9]), anxiety scores (General Anxiety Disorder-7 [GAD-7]), and overall quality of life (Dartmouth Cooperative Functional Assessment [COOP]) scores. RESULTS: Between January 2015 and December 2019, 94 patients who underwent an aortic valve procedure and 46 patients who underwent mitral valve procedures were enrolled prospectively in CR. At the completion of their CR program, patients had similar improvements in their 6MWT (mitral valve: 173 ft [125, 238] vs aortic valve 197 ft [121, 295], P = .42); exercise min/wk (mitral valve: 90 min [45, 175] vs aortic valve: 80 min [40, 130], P = .44). Changes in anxiety (GAD-7), depression (PHQ-9), and COOP scores were smaller but similar between the two groups. CONCLUSIONS: CR participation resulted in similar improvements in physical activity between patients undergoing mitral valve and aortic valve procedures. Psychological well-being and quality of life scores improved minimally and similarly between the two groups.

Weight Loss and Its Predictors During Participation in Cardiac Rehabilitation.

We aimed to assess the prevalence and magnitude of clinically meaningful weight loss among cardiac rehabilitation (CR) participants who were overweight or obese and identify its predictors. We analyzed subjects with body mass index (BMI) ≥25 who were enrolled in a 12-week CR outpatient program from January 1, 2015, to December 31, 2019, and had paired pre- and post-CR weight data. Patients who lost 3% or more of their body weight by the end of the program were compared with the remaining participants. Multivariable logistic regression was used to determine predictors of weight loss. Overall, 129 of 485 subjects (27%) with overweight or obesity reduced their weight by at least 3% (average percent weight change: -5.0% ± 1.8% vs -0.02% ± 2.2%, average weight change: -10.9 ± 5.0 vs -0.1 ± 4.4 pounds, and average BMI change: -1.7 ± 0.7 vs -0.02 ± 0.7 kg/m2). Compared with the remaining 356 patients, those who achieved the defined weight loss were younger (p = 0.016) and had higher baseline weight (p = 0.002) and BMI (p <0.001). The weight loss group tended to be enrolled more likely for an acute myocardial infarction or percutaneous coronary intervention (p <0.001) and less likely for coronary artery bypass grafting (p = 0.001) or a heart valve procedure (p = 0.05). By the end of the CR program, the weight loss group demonstrated a greater increase in Rate Your Plate - Heart score (7 [3, 11] vs 4 [1, 8]; p <0.001) and a greater decrease in triglycerides (-20 ± 45 vs -7 ± 55 mg/dL; p = 0.026) and glycated hemoglobin (-0.1 [-0.5, 0.1] vs 0.1 [-0.3, 0.4] %; p = 0.05, among patients with diabetes or prediabetes). In a multivariable logistic regression model, baseline predictors of clinically meaningful weight loss included higher BMI and not being enrolled for a surgical CR indication (p = 0.001). In conclusion, throughout 12 weeks of CR participation, 129 of 485 subjects (27%) with BMI ≥25 had a 3% or more reduction in body weight. Patients with higher baseline BMI and participants without a surgical enrollment diagnosis were more likely to achieve the defined weight loss. Efforts to improve CR referral and enrollment for eligible patients with overweight and obesity should be encouraged, and suitable and efficient weight reduction interventions in CR settings need to be further studied.

Statistical Workflow for Feature Selection in Human Metabolomics Data.

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations.

Machine Learning Approaches in Cardiovascular Imaging.

Cardiovascular imaging technologies continue to increase in their capacity to capture and store large quantities of data. Modern computational methods, developed in the field of machine learning, offer new approaches to leveraging the growing volume of imaging data available for analyses. Machine learning methods can now address data-related problems ranging from simple analytic queries of existing measurement data to the more complex challenges involved in analyzing raw images. To date, machine learning has been used in 2 broad and highly interconnected areas: automation of tasks that might otherwise be performed by a human and generation of clinically important new knowledge. Most cardiovascular imaging studies have focused on task-oriented problems, but more studies involving algorithms aimed at generating new clinical insights are emerging. Continued expansion in the size and dimensionality of cardiovascular imaging databases is driving strong interest in applying powerful deep learning methods, in particular, to analyze these data. Overall, the most effective approaches will require an investment in the resources needed to appropriately prepare such large data sets for analyses. Notwithstanding current technical and logistical challenges, machine learning and especially deep learning methods have much to offer and will substantially impact the future practice and science of cardiovascular imaging.