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BACKGROUND Many patients undergoing cardiac device implantation are taking direct oral anticoagulation (DOAC). Continuing DOAC during device implantation may increase periprocedural bleeding risk; however, interrupting DOACs may increase thromboembolic risk. OBJECTIVE To compare the incidence of clinically significant pocket hematoma and thromboembolism in patients who have their DOAC continued or interrupted for cardiac device implantation. METHODS We searched MEDLINE, EMBASE, and randomized controlled trial (CENTRAL) until December 2019 and included randomized controlled trials (RCTs) and observational studies that compared outcomes after continuing or interrupting DOAC during cardiac device implantation. Independently and in duplicate, reviewers screened titles, abstracts, and full text of potentially eligible studies. They then evaluated risk of bias and abstracted data. RCT data were pooled using a fixed-effect model. Quality of evidence was assessed using grading of recommendations assessment, development and evaluation (GRADE). RESULTS Two RCTs, representing 763 patients, and three observational studies met eligibility criteria. In RCTs, continuing DOAC for device implantation compared to interrupting DOAC resulted in no significant difference in clinically significant pocket hematoma (2.1% vs 1.8%; RR 1.15; 95% CI 0.44-3.05) or thromboembolism (0.03% vs 0.03%; RR 1.02; 95% CI 0.06-16.21). Quality of evidence for both outcomes was moderate due to imprecision. Observational studies showed similar results. CONCLUSIONS Continuing DOACs for device implantation results in little to no difference in the incidence of clinically significant pocket hematoma or thromboembolism. Given the ease of stopping and restarting DOACs, interrupting DOACs may be the preferred strategy for most patients. However, whenever continuous therapeutic anticoagulation is desired, DOAC continuation should be preferred over bridging with parenteral anticoagulation.

Objectives This study sought to explore the natural clustering of echocardiographic variables used for assessing left ventricular (LV) diastolic dysfunction (DD) in order to isolate high-risk phenotypic patterns and assess their prognostic significance. Background Assessment of LV DD is important in the management and prognosis of cardiovascular diseases. Data-driven approaches such as cluster analysis may be useful in segregating similar cases without the constraint of an a priori algorithm for risk stratification. Methods The study included a convenience sample of 866 consecutive patients referred for myocardial function assessment (age: 65 ± 17 years; 55.3% women; ejection fraction: 60 ± 9%) for whom echocardiographic parameters of DD assessment were obtained per conventional guideline recommendations. Unsupervised, hierarchical cluster analysis of these parameters was conducted using the Ward linkage method. Major adverse cardiovascular events, hospitalization, and mortality were compared between conventional and cluster-based classifications. Results Clustering algorithms for screening the presence of DD in 559 of 866 patients identified 2 distinct groups and revealed modest agreement with conventional classification (kappa = 0.41, p Conclusions An unsupervised assessment of echocardiographic variables for assessing LV DD revealed unique patterns of grouping. These natural patterns of clustering may better identify patient groups who have similar risk, and their incorporation into clinical practice may help eliminate indeterminate results and improve clinical outcome prediction.

Summary Background Angiotensin-converting enzyme 2 (ACE2) is an endogenous counter-regulator of the renin–angiotensin hormonal cascade. We assessed whether plasma ACE2 concentrations were associated with greater risk of death or cardiovascular disease events. Methods We used data from the Prospective Urban Rural Epidemiology (PURE) prospective study to conduct a case-cohort analysis within a subset of PURE participants (from 14 countries across five continents: Africa, Asia, Europe, North America, and South America). We measured plasma concentrations of ACE2 and assessed potential determinants of plasma ACE2 levels as well as the association of ACE2 with cardiovascular events. Findings We included 10 753 PURE participants in our study. Increased concentration of plasma ACE2 was associated with increased risk of total deaths (hazard ratio [HR] 1·35 per 1 SD increase [95% CI 1·29–1·43]) with similar increases in cardiovascular and non-cardiovascular deaths. Plasma ACE2 concentration was also associated with higher risk of incident heart failure (HR 1·27 per 1 SD increase [1·10–1·46]), myocardial infarction (HR 1·23 per 1 SD increase [1·13–1·33]), stroke (HR 1·21 per 1 SD increase [1·10–1·32]) and diabetes (HR 1·44 per 1 SD increase [1·36–1·52]). These findings were independent of age, sex, ancestry, and traditional cardiac risk factors. With the exception of incident heart failure events, the independent relationship of ACE2 with the clinical endpoints, including death, remained robust after adjustment for BNP. The highest-ranked determinants of ACE2 concentrations were sex, geographic ancestry, and body-mass index (BMI). When compared with clinical risk factors (smoking, diabetes, blood pressure, lipids, and BMI), ACE2 was the highest ranked predictor of death, and superseded several risk factors as a predictor of heart failure, stroke, and myocardial infarction. Interpretation Increased plasma ACE2 concentration was associated with increased risk of major cardiovascular events in a global study. Funding Canadian Institute of Health Research, Heart & Stroke Foundation of Canada, and Bayer.

Analyses of medical data have been steered to support and justify orthodox systems and graphical models that reproduce the relatively subjective reasoning of experts. Evidence-based decision tree analyses and risk scores are valid tools for clinical decision-making that are widely used in medicine

Purpose of Review Peripheral artery disease (PAD) affects an estimated 200 million people worldwide and is associated with significant cardiovascular morbidity and mortality. Cardiovascular risk is further increased among individuals with polyvascular disease, where either cerebrovascular or coronary artery disease is present in addition to PAD. In this review, we present common clinical scenarios encountered when managing patients with PAD and provide an evidence-based approach to prescribing optimal antithrombotics in this population. Recent Findings The COMPASS trial recently demonstrated that rivaroxaban 2.5 mg BID + ASA daily significantly reduces major adverse cardiac and limb events in patients with PAD. Despite these advances, morbidity following MALE events remains high. Summary With widespread approval by federal health regulators, the COMPASS regimen should be strongly considered in PAD patients who do not have a high bleeding risk. Implementing the COMPASS regimen in patients with PAD, along with other vascular risk reduction strategies, will have a substantial impact on reducing atherothromboembolic risk in patients with established vascular disease.

Background Machine-learning models may aid cardiac phenotypic recognition by using features of cardiac tissue deformation. Objectives This study investigated the diagnostic value of a machine-learning framework that incorporates speckle-tracking echocardiographic data for automated discrimination of hypertrophic cardiomyopathy (HCM) from physiological hypertrophy seen in athletes (ATH). Methods Expert-annotated speckle-tracking echocardiographic datasets obtained from 77 ATH and 62 HCM patients were used for developing an automated system. An ensemble machine-learning model with 3 different machine-learning algorithms (support vector machines, random forests, and artificial neural networks) was developed and a majority voting method was used for conclusive predictions with further K -fold cross-validation. Results Feature selection using an information gain (IG) algorithm revealed that volume was the best predictor for differentiating between HCM ands. ATH (IG = 0.24) followed by mid-left ventricular segmental (IG = 0.134) and average longitudinal strain (IG = 0.131). The ensemble machine-learning model showed increased sensitivity and specificity compared with early-to-late diastolic transmitral velocity ratio (p 13 mm. In this subgroup analysis, the automated model continued to show equal sensitivity, but increased specificity relative to early-to-late diastolic transmitral velocity ratio, e′, and strain. Conclusions Our results suggested that machine-learning algorithms can assist in the discrimination of physiological versus pathological patterns of hypertrophic remodeling. This effort represents a step toward the development of a real-time, machine-learning–based system for automated interpretation of echocardiographic images, which may help novice readers with limited experience.

We thank Dr. Kampaktsis and colleagues for their interest in our investigation. Although we agree that the 2016 guidelines may have made diastolic dysfunction (DD) more conceptually simple in a clinical approach; there are 2 problems with this apparent simplification. First, the large number of

The estimation of left ventricular (LV) filling pressure from the ratio of transmitral and annular velocities (E/e′) is used commonly for identifying diastolic dysfunction in patients who complain of exertional dyspnea [(1)][1]. We have recently illustrated that LV and left atrial speckle tracking

Objectives The aim of this study was to investigate whether cluster analysis of left atrial and left ventricular (LV) mechanical deformation parameters provide sufficient information for Doppler-independent assessment of LV diastolic function. Background Medical imaging produces substantial phenotyping data, and superior computational analyses could allow automated classification of repetitive patterns into patient groups with similar behavior. Methods The authors performed a cluster analysis and developed a model of LV diastolic function from an initial exploratory cohort of 130 patients that was subsequently tested in a prospective cohort of 44 patients undergoing cardiac catheterization. Patients in both study groups had standard echocardiographic examination with Doppler-derived assessment of diastolic function. Both the left ventricle and the left atrium were tracked simultaneously using speckle-tracking echocardiography (STE) for measuring simultaneous changes in left atrial and ventricular volumes, volume rates, longitudinal strains, and strain rates. Patients in the validation group also underwent invasive measurements of pulmonary capillary wedge pressure and LV end diastolic pressure immediately after echocardiography. The similarity between STE and conventional 2-dimensional and Doppler methods of diastolic function was investigated in both the exploratory and validation cohorts. Results STE demonstrated strong correlations with the conventional indices and independently clustered the patients into 3 groups with conventional measurements verifying increasing severity of diastolic dysfunction and LV filling pressures. A multivariable linear regression model also allowed estimation of E/e′ and pulmonary capillary wedge pressure by STE in the validation cohort. Conclusions Tracking deformation of the left-sided cardiac chambers from routine cardiac ultrasound images provides accurate information for Doppler-independent phenotypic characterization of LV diastolic function and noninvasive assessment of LV filling pressures.

We would like to thank Dr. Krittanawong and colleagues for the encouraging comment about our work on introducing machine learning for artificial intelligence (AI)–aided interpretation of cardiac imaging. The authors make an interesting proposition through introducing the emerging concept of deep

Unsupervised learning methods can find hidden patterns in data and uncover disease subphenotypes. We investigated unsupervised clustering of echocardiographic variables for grading diastolic dysfunction (DD) and test its concordance and prognostic performance in comparison with expert-guided

Medical imaging in the era of precision medicine aims for accurate phenotyping of diseases that can benefit from early targeted therapies. We hypothesize that cardiac biomechanics generate high level of information that can leverage big data analytics and machine-learning frameworks for automated