Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association.

BACKGROUND: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS: The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2021 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors related to cardiovascular disease. RESULTS: Each of the 27 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS: The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery.

BACKGROUND: The ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, machine learning algorithms, and availability of large-scale data could substantially expand the clinical inferences derived from the ECG while at the same time preserving interpretability for medical decision-making. METHODS AND RESULTS: We identified 36 186 ECGs from the University of California, San Francisco database that would enable training of models for estimation of cardiac structure or function or detection of disease. We segmented the ECG into standard component waveforms and intervals using a novel combination of convolutional neural networks and hidden Markov models and evaluated this segmentation by comparing resulting electrical intervals against 141 864 measurements produced during the clinical workflow. We then built a patient-level ECG profile, a 725-element feature vector and used this profile to train and interpret machine learning models for examples of cardiac structure (left ventricular mass, left atrial volume, and mitral annulus e-prime) and disease (pulmonary arterial hypertension, hypertrophic cardiomyopathy, cardiac amyloid, and mitral valve prolapse). ECG measurements derived from the convolutional neural network-hidden Markov model segmentation agreed with clinical estimates, with median absolute deviations as a fraction of observed value of 0.6% for heart rate and 4% for QT interval. Models trained using patient-level ECG profiles enabled surprising quantitative estimates of left ventricular mass and mitral annulus e' velocity (median absolute deviation of 16% and 19%, respectively) with good discrimination for left ventricular hypertrophy and diastolic dysfunction as binary traits. Model performance using our approach for disease detection demonstrated areas under the receiver operating characteristic curve of 0.94 for pulmonary arterial hypertension, 0.91 for hypertrophic cardiomyopathy, 0.86 for cardiac amyloid, and 0.77 for mitral valve prolapse. CONCLUSIONS: Modern machine learning methods can extend the 12-lead ECG to quantitative applications well beyond its current uses while preserving the transparency that is so fundamental to clinical care.

Does cannabis legalisation change healthcare utilisation? A population-based study using the healthcare cost and utilisation project in Colorado, USA.

OBJECTIVE: To assess the effect of cannabis legalisation on health effects and healthcare utilisation in Colorado (CO), the first state to legalise recreational cannabis, when compared with two control states, New York (NY) and Oklahoma (OK). DESIGN: We used the 2010 to 2014 Healthcare Cost and Utilisation Project (HCUP) inpatient databases to compare changes in rates of healthcare utilisation and diagnoses in CO versus NY and OK. SETTING: Population-based, inpatient. PARTICIPANTS: HCUP state-wide data comprising over 28 million individuals and over 16 million hospitalisations across three states. MAIN OUTCOME MEASURES: We used International Classification of Diseases-Ninth Edition codes to assess changes in healthcare utilisation specific to various medical diagnoses potentially treated by or exacerbated by cannabis. Diagnoses were classified based on weight of evidence from the National Academy of Science (NAS). Negative binomial models were used to compare rates of admissions between states. RESULTS: In CO compared with NY and OK, respectively, cannabis abuse hospitalisations increased (risk ratio (RR) 1.27, 95% CI 1.26 to 1.28 and RR 1.16, 95% CI 1.15 to 1.17; both p<0.0005) post-legalisation. In CO, there was a reduction in total admissions but only when compared with OK (RR 0.97, 95% CI 0.96 to 0.98, p<0.0005). Length of stay and costs did not change significantly in CO compared with NY or OK. Post-legalisation changes most consistent with NAS included an increase in motor vehicle accidents, alcohol abuse, overdose injury and a reduction in chronic pain admissions (all p<0.05 compared with each control state). CONCLUSIONS: Recreational cannabis legalisation is associated with neutral effects on healthcare utilisation. In line with previous evidence, cannabis liberalisation is linked to an increase in motor vehicle accidents, alcohol abuse, overdose injuries and a decrease in chronic pain admissions. Such population-level effects may help guide future decisions regarding cannabis use, prescription and policy.

Assessment of mitral valve disease: a review of imaging modalities.

OPINION STATEMENT: Mitral valve disease (MVD) related to mitral valve prolapse (MVP), coronary artery disease (CAD), and calcific mitral stenosis, is increasing in prevalence across the USA and Europe in the context of a longer life expectancy and aging population. In developing countries, rheumatic heart disease remains a major cause of MVD. Echocardiography represents the primary diagnostic modality for assessment of the mitral valve (MV). With the implementation of three-dimensional imaging, echocardiography has become an indispensable tool to evaluate the morphology, geometry, and function of the MV apparatus in the pre-operative setting. However, recognition of its limitations and advances in newer technologies have led to a growing interest in other imaging modalities such as cardiac magnetic resonance (CMR). Although still not widely available, CMR is an essential complement to echocardiography, especially when poor image quality, significant variability in flow diameter measurements, and geometric assumptions of flow orifice preclude accurate quantification of mitral regurgitation on echocardiographic images. In addition, CMR can reliably provide quantitative determination of ventricular volumes and function, hence facilitating surgical decision-making when serial linear echocardiographic measurements are discrepant. Finally, CMR assessment of fibrosis using late gadolinium enhancement allows better understanding of the interactions between MVD and the myocardium in both MVP and MVD related to CAD or other myopathy. In this review, we summarize the role of the available imaging modalities in understanding valvular pathology and determining severity of regurgitation or stenosis. Recently published valvular guidelines highlight the importance of monitoring MVD progression and the shift to intervention earlier in the course of disease. In this context, we also discuss the potential role of echocardiography and CMR in identifying early stages of MVD and/or pre-clinical markers of myocardial dysfunction.