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Background and Aim: The prevalence of depressive symptoms and major depressive disorder is high among adults living with HIV. Depressive symptoms are associated with increased cardiovascular disease risk. This study examined the association between depressive symptoms and echocardiographic indices of left ventricular diastolic dysfunction (LVDD) among men and women living with and without HIV. Methods: Cross-sectional analysis included individuals in the Multicenter AIDS Cohort Study (MACS) and Women’s Interagency HIV Study (WIHS) who participated in transthoracic echocardiogram substudies and completed measures of depressive symptoms at the same visit as, or up to 6 months prior to, the transthoracic echocardiogram visit. Participants had helper T cells (CD4) >350 cells/mm3 and HIV RNA viral load less than 499 copies/mL. The presence of LVDD was defined according to the Characterizing Heart Function on Antiretroviral Therapy (CHART) criteria. Secondary outcomes were continuous values of each component of the CHART criteria: left ventricular ejection fraction >50%, septal e’ velocity, lateral e’ velocity, left atrial volume index, left ventricular mass index, and relative wall thickness. Logistic and linear regression were used to adjust for sociodemographic, behavioural, cardiometabolic, and HIV-related factors. Results: Among 874 men (51% with HIV) and 1,191 women (76% with HIV), in whom the overall prevalence of LVDD was 22.5% and depressive symptoms 30.8%, depressive symptoms were not significantly associated with LVDD. The associations between individual LVDD components and depression were in the small to medium range, though generally not significant. Conclusion: Findings warrant further research regarding the association between LVDD and depressive symptoms in the era of combination antiretroviral therapy.

Background Patients with obstructive hypertrophic cardiomyopathy have increased symptomatic burden. Mavacamten was recently approved for treatment of obstructive hypertrophic cardiomyopathy based on 2 randomized controlled trials. However, its use under real‐world conditions and in diverse populations is under‐studied. Methods and Results This was a prospective observational cohort study of patients seen at the Johns Hopkins HCM center and prescribed mavacamten for obstructive hypertrophic cardiomyopathy between July 7, 2022 and January 6, 2024. Patients were followed longitudinally, with serial echocardiography and clinical evaluation as mandated by the risk evaluation and mitigation strategy program. Sixty‐six patients received mavacamten (mean age 59 years, 47% male, 29% non‐White [Black, Hispanic/Latino, Asian, Native Hawaiian or Pacific Islander], 47% obese). Before treatment, all patients had New York Heart Association class II (51.5%) or III (48.5%) heart failure symptoms. Initial maximum peak left ventricular outflow tract gradient was 107±46 mm Hg. Median treatment duration was 9 months. For patients on mavacamten after ≥6 months (n=43), symptoms improved by ≥1 New York Heart Association class in 72% of patients, and peak left ventricular outflow tract gradient decreased by 80±46 mm Hg, eliminating hemodynamically significant left ventricular outflow tract obstruction in 79.1% of patients. Mavacamten was temporarily discontinued in 3 patients due to left ventricular ejection fraction decrease

Abstract Background Infection with human immunodeficiency virus (HIV) is associated with higher risk for myocardial disease despite modern combination antiretroviral therapy (cART). Factors contributing to this excess risk, however, remain poorly characterized. We aimed to assess cross-sectional relationships between elevations of left atrial volume index (LAVI) and myocardial extracellular volume (ECV) fraction that have been reported in persons living with HIV and levels of circulating biomarkers of inflammation, fibrosis, and myocyte stretch among persons living with and without HIV (PLWH, PLWOH). Methods Participants from three cohorts of PLWH and PLWOH underwent cardiovascular magnetic resonance imaging for measurement of LAVI and ECV. Levels of circulating proteins (IL-6, sCD14, galectin-3, NT-proBNP, GDF-15, TIMP-2, MMP-2, and hsTnI) were measured using immunoassays. Associations were assessed using logistic and linear regression, adjusting for demographics, substance use, and clinical characteristics. Results Among 381 participants with and without HIV, median age (IQR) was 55.1 (51.2, 58.4) years, 28% were female, 69% were Black, and 46% were current smokers. Sixty-two percent were PLWH (n = 235), of whom 88% were receiving cART and 72% were virally suppressed. PLWH had higher levels of sCD14 (p =

Background: Visualizing fibrosis on cardiac magnetic resonance (CMR) imaging with contrast enhancement (late gadolinium enhancement; LGE) is paramount in characterizing disease progression and identifying arrhythmia substrates. Segmentation and fibrosis quantification from LGE-CMR is intensive, manual, and prone to interobserver variability. There is an unmet need for automated LGE-CMR image segmentation that ensures anatomical accuracy and seamless extraction of clinical features. Objective: This study aimed to develop a novel deep learning solution for analysis of contrast-enhanced CMR images that produces anatomically accurate myocardium and scar/fibrosis segmentations and uses these to calculate features of clinical interest. Methods: Data sources were 155 2-dimensional LGE-CMR patient scans (1124 slices) and 246 synthetic “LGE-like” scans (1360 slices) obtained from cine CMR using a novel style-transfer algorithm. We trained and tested a 3-stage neural network that identified the left ventricle (LV) region of interest (ROI), segmented ROI into viable myocardium and regions of enhancement, and postprocessed the segmentation results to enforce conforming to anatomical constraints. The segmentations were used to directly compute clinical features, such as LV volume and scar burden. Results: Predicted LV and scar segmentations achieved 96% and 75% balanced accuracy, respectively, and 0.93 and 0.57 Dice coefficient when compared to trained expert segmentations. The mean scar burden difference between manual and predicted segmentations was 2%. Conclusion: We developed and validated a deep neural network for automatic, anatomically accurate expert-level LGE- CMR myocardium and scar/fibrosis segmentation, allowing direct calculation of clinical measures. Given the training set heterogeneity, our approach could be extended to multiple imaging modalities and patient pathologies.

Background Heart failure (HF) has been increasing in prevalence, and a need exists for biomarkers with improved predictive and prognostic ability. GDF‐15 (growth differentiation factor‐15) is a novel biomarker associated with HF mortality, but no serial studies of GDF‐15 have been conducted. This study aimed to investigate the association between GDF‐15 levels over time and the occurrence of ventricular arrhythmias, HF hospitalizations, and all‐cause mortality. Methods and Results We used a retrospective case–control design to analyze 148 patients with ischemic and nonischemic cardiomyopathies and primary prevention implantable cardioverter‐defibrillator (ICD) from the PROSe‐ICD (Prospective Observational Study of the ICD in Sudden Cardiac Death Prevention) cohort. Patients had blood drawn every 6 months and after each appropriate ICD therapy and were followed for a median follow‐up of 4.6 years, between 2005 to 2019. We compared serum GDF‐15 levels within ±90 days of an event among those with a ventricular tachycardia/fibrillation event requiring ICD therapies and those hospitalized for decompensated HF. A comparator/control group comprised patients with GDF‐15 levels available during 2‐year follow‐up periods without events. Median follow‐up was 4.6 years in the 148 patients studied (mean age 58±12, 27% women). The HF cohort had greater median GDF‐15 values within 90 days (1797 pg/mL) and 30 days (2039 pg/mL) compared with the control group (1062 pg/mL, both P

Abstract Better models to identify individuals at low risk of ventricular arrhythmia (VA) are needed for implantable cardioverter-defibrillator (ICD) candidates to mitigate the risk of ICD-related complications. We designed the CERTAINTY study (CinE caRdiac magneTic resonAnce to predIct veNTricular arrhYthmia) with deep learning for VA risk prediction from cine cardiac magnetic resonance (CMR). Using a training cohort of primary prevention ICD recipients (n = 350, 97 women, median age 59 years, 178 ischemic cardiomyopathy) who underwent CMR immediately prior to ICD implantation, we developed two neural networks: Cine Fingerprint Extractor and Risk Predictor. The former extracts cardiac structure and function features from cine CMR in a form of cine fingerprint in a fully unsupervised fashion, and the latter takes in the cine fingerprint and outputs disease outcomes as a cine risk score. Patients with VA (n = 96) had a significantly higher cine risk score than those without VA. Multivariate analysis showed that the cine risk score was significantly associated with VA after adjusting for clinical characteristics, cardiac structure and function including CMR-derived scar extent. These findings indicate that non-contrast, cine CMR inherently contains features to improve VA risk prediction in primary prevention ICD candidates. We solicit participation from multiple centers for external validation.

BackgroundCardiac arrhythmias are associated with increased maternal morbidity. There are limited data on trends of arrhythmias among women hospitalized for delivery.Materials and methodsWe used the National Inpatient Sample (NIS) database to identify delivery hospitalizations for individuals aged 18–49 years between 2009 to 2019 and utilized coding data from the 9th and 10th editions of the International Classification of Diseases to identify supraventricular tachycardias (SVT), atrial fibrillation (AF), atrial flutter, ventricular tachycardia (VT), and ventricular fibrillation (VF). Arrhythmia trends were analyzed by age, race-ethnicity, hospital setting, and hospital geographic regions. Multivariable logistic regression was used to evaluate the association of demographic, clinical, and socioeconomic characteristics with arrhythmias.ResultsAmong 41,576,442 delivery hospitalizations, the most common arrhythmia was SVT (53%), followed by AF (31%) and VT (13%). The prevalence of arrhythmia among delivery hospitalizations increased between 2009 and 2019. Age > 35 years and Black race were associated with a higher arrhythmia burden. Factors associated with an increased risk of arrhythmias included valvular disease (OR: 12.77; 95% C1:1.98–13.61), heart failure (OR:7.13; 95% CI: 6.49–7.83), prior myocardial infarction (OR: 5.41, 95% CI: 4.01–7.30), peripheral vascular disease (OR: 3.19, 95% CI: 2.51–4.06), hypertension (OR: 2.18; 95% CI: 2.07–2.28), and obesity (OR 1.69; 95% CI: 1.63–1.76). Delivery hospitalizations complicated by arrhythmias compared with those with no arrhythmias had a higher proportion of all-cause in-hospital mortality (0.95% vs. 0.01%), cardiogenic shock (0.48% vs. 0.00%), preeclampsia (6.96% vs. 3.58%), and preterm labor (2.95% vs. 2.41%) (all p < 0.0001).ConclusionPregnant individuals with age > 35 years, obesity, hypertension, valvular heart disease, or severe pulmonary disease are more likely to have an arrhythmia history or an arrhythmia during a delivery hospitalization. Delivery hospitalizations with a history of arrhythmia are more likely to be complicated by all-cause in-hospital mortality, cardiovascular, and adverse pregnancy outcomes (APOs). These data highlight the increased risk associated with pregnancies among individuals with arrhythmias.

Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of “virtual hearts” using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.

BACKGROUND Sudden cardiac death (SCD) remains a worldwide public health problem in need of better noninvasive predictive tools. Current guidelines for primary preventive SCD therapies, such as implantable cardioverter defibrillators (ICDs), are based on left ventricular ejection fraction (LVEF), but these guidelines are imprecise: fewer than 5% of ICDs deliver lifesaving therapy per year. Impaired cardiac metabolism and ATP depletion cause arrhythmias in experimental models, but to our knowledge a link between arrhythmias and cardiac energetic abnormalities in people has not been explored, nor has the potential for metabolically predicting clinical SCD risk.METHODS We prospectively measured myocardial energy metabolism noninvasively with phosphorus magnetic resonance spectroscopy in patients with no history of significant arrhythmias prior to scheduled ICD implantation for primary prevention in the setting of reduced LVEF (≤35%).RESULTS By 2 different analyses, low myocardial ATP significantly predicted the composite of subsequent appropriate ICD firings for life-threatening arrhythmias and cardiac death over approximately 10 years. Life-threatening arrhythmia risk was approximately 3-fold higher in patients with low ATP and independent of established risk factors, including LVEF. In patients with normal ATP, rates of appropriate ICD firings were several-fold lower than reported rates of ICD complications and inappropriate firings.CONCLUSION To the best of our knowledge, these are the first data linking in vivo myocardial ATP depletion and subsequent significant arrhythmic events in people, suggesting an energetic component to clinical life-threatening ventricular arrhythmogenesis. The findings support investigation of metabolic strategies that limit ATP loss to treat or prevent life-threatening cardiac arrhythmias and herald noninvasive metabolic imaging as a complementary SCD risk stratification tool.TRIAL REGISTRATION ClinicalTrials.gov NCT00181233.FUNDING This work was supported by the DW Reynolds Foundation, the NIH (grants HL61912, HL056882, HL103812, HL132181, HL140034), and Russell H. Morgan and Clarence Doodeman endowments at Johns Hopkins.

Aortic insufficiency is commonly observed in rheumatologic diseases such as ankylosing spondylitis, systemic lupus erythematosus, antiphospholipid syndrome, Behçet's disease, granulomatosis with polyangiitis, and Takayasu arteritis. Aortic insufficiency with an underlying rheumatologic disease may be caused by a primary valve pathology (leaflet destruction, prolapse or restriction), annular dilatation due to associated aortitis or a combination of both. Early recognition of characteristic valve and aorta morphology on cardiac imaging has both diagnostic and prognostic importance. Currently, echocardiography remains the primary diagnostic tool for aortic insufficiency. Complementary use of computed tomography, cardiac magnetic resonance imaging and positron emission tomography in these systemic conditions may augment the assessment of underlying mechanism, disease severity and identification of relevant non-valvular/extracardiac pathology. We aim to review common rheumatologic diseases associated with aortic insufficiency and describe their imaging findings that have been reported in the literature.

Background The prevalence and extent of subclinical large vessel vasculopathy is not well defined among people living with HIV. We aimed to evaluate associations between aortic root and ascending aortic sizes measured by 2‐dimensional transthoracic echocardiography and HIV serostatus, and to identify risk factors for larger aortic sizes among men with HIV, including levels of circulating inflammatory markers. Methods and Results Using clinical and echocardiographic data from the MACS (Multicenter AIDS Cohort Study), adjusted multivariable linear and logistic regression was performed. Four segments of the proximal aorta were measured: aortic annulus, aortic root at the sinuses of Valsalva, sinotubular junction, and ascending aorta. HIV infection was associated with significantly larger aortic root (0.03 cm [95% CI, 0.002–0.06 cm]) and ascending aorta (0.04 cm [95% CI, 0.01–0.06 cm]) diameters. Higher standardized nadir CD4 (cluster of differentiation 4) T‐cell count was significantly associated with smaller aortic root (−0.03 cm [95% CI, −0.05 to −0.01 cm]), sinotubular junction (−0.03 cm [95% CI, −0.05 to −0.01 cm]), and ascending aorta (−0.03 cm [95% CI, −0.05 to −0.004 cm]) diameters. Higher levels of standardized TNF‐α (tumor necrosis factor‐α) were associated with larger diameters of the aortic annulus (0.02 cm [95% CI, 0.003–0.04 cm]) and sinotubular junction (0.02 cm [95% CI, 0.002–0.04 cm]). There were no other cardiovascular or HIV disease severity–related risk factors associated with the aortic dimensions. Conclusions HIV infection is an independent risk factor for greater ascending aortic sizes. Lower nadir CD4 T‐cell count and higher TNF‐α levels are associated with larger aortic sizes in men with HIV. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT00046280.

Abstract Myocardial infarction with nonobstructive coronary arteries (MINOCA) is a heterogeneous clinical entity, encompassing multiple different causes, and a cause of substantial morbidity and mortality. Current guidelines suggest a multimodality imaging approach in establishing the underlying cause for MINOCA, which is considered a working diagnosis. Recent studies have suggested that an initial workup consisting of cardiac magnetic resonance and invasive coronary imaging can yield the diagnosis in most patients. Cardiac magnetic resonance is particularly helpful in excluding nonischemic causes that can mimic MINOCA including myocarditis and Takotsubo cardiomyopathy, as well as for long‐term prognostication. Additionally, intracoronary imaging with intravascular ultrasound or optical coherence tomography may be warranted to evaluate plaque composition, or evaluate for plaque disruption or spontaneous coronary dissection. The role of noninvasive imaging modalities such as coronary computed tomography angiography is currently being investigated in the diagnostic approach and follow‐up of MINOCA and may be appropriate in lieu of invasive coronary angiography in select patients. In recent years, many strides have been made in the workup of MINOCA; however, significant knowledge gaps remain in the field, particularly in terms of treatment strategies. In this review, we summarize recent society guideline recommendations and consensus statements on the initial evaluation of MINOCA, review contemporary multimodality imaging approaches, and discuss treatment strategies including an ongoing clinical trial.

Abstract Background Clinical research and medical practice can be advanced through the prediction of an individual’s health state, trajectory, and responses to treatments. However, the majority of current clinical risk prediction models are based on regression approaches or machine learning algorithms that are static, rather than dynamic. To benefit from the increasing emergence of large, heterogeneous data sets, such as electronic health records (EHRs), novel tools to support improved clinical decision making through methods for individual-level risk prediction that can handle multiple variables, their interactions, and time-varying values are necessary. Methods We introduce a novel dynamic approach to clinical risk prediction for survival, longitudinal, and multivariate (SLAM) outcomes, called random forest for SLAM data analysis (RF-SLAM). RF-SLAM is a continuous-time, random forest method for survival analysis that combines the strengths of existing statistical and machine learning methods to produce individualized Bayes estimates of piecewise-constant hazard rates. We also present a method-agnostic approach for time-varying evaluation of model performance. Results We derive and illustrate the method by predicting sudden cardiac arrest (SCA) in the Left Ventricular Structural (LV) Predictors of Sudden Cardiac Death (SCD) Registry. We demonstrate superior performance relative to standard random forest methods for survival data. We illustrate the importance of the number of preceding heart failure hospitalizations as a time-dependent predictor in SCA risk assessment. Conclusions RF-SLAM is a novel statistical and machine learning method that improves risk prediction by incorporating time-varying information and accommodating a large number of predictors, their interactions, and missing values. RF-SLAM is designed to easily extend to simultaneous predictions of multiple, possibly competing, events and/or repeated measurements of discrete or continuous variables over time.Trial registration: LV Structural Predictors of SCD Registry (clinicaltrials.gov, NCT01076660), retrospectively registered 25 February 2010

Probabilistic estimation of cardiac electrophysiological model parameters serves an important step toward model personalization and uncertain quantification. The expensive computation associated with these model simulations, however, makes direct Markov Chain Monte Carlo (MCMC) sampling of the posterior probability density function (pdf) of model parameters computationally intensive. Approximated posterior pdfs resulting from replacing the simulation model with a computationally efficient surrogate, on the other hand, have seen limited accuracy. In this study, we present a Bayesian active learning method to directly approximate the posterior pdf function of cardiac model parameters, in which we intelligently select training points to query the simulation model in order to learn the posterior pdf using a small number of samples. We integrate a generative model into Bayesian active learning to allow approximating posterior pdf of high-dimensional model parameters at the resolution of the cardiac mesh. We further introduce new acquisition functions to focus the selection of training points on better approximating the shape rather than the modes of the posterior pdf of interest. We evaluated the presented method in estimating tissue excitability in a 3D cardiac electrophysiological model in a range of synthetic and real-data experiments. We demonstrated its improved accuracy in approximating the posterior pdf compared to Bayesian active learning using regular acquisition functions, and substantially reduced computational cost in comparison to existing standard or accelerated MCMC sampling.

In recent years, improvement in outcomes related to cardiovascular disease is in part due to the prioritization and progress of primary and secondary prevention efforts. The Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease expanded ‘ABC's approach is used to highlight key findings in Preventive Cardiology from 2020 and further emphasize the importance of cardiovascular prevention. This simplified approach helps clinicians focus on the most relevant and up to date recommendations for optimizing cardiovascular disease risk through accurate risk assessment and appropriate implementation of lifestyle, behavioral and pharmacologic interventions. While 2020 not only provided practice changing updates by way of clinical guidelines and randomized controlled trials on topics related to antithrombotic and lipid lowering therapy, diabetes management and risk assessment, it also provided promising data on how to improve dietary and exercise adherence and manage genetic risk. By providing clinicians with a systematic approach to cardiovascular prevention and key highlights from the prior year, the goal of significantly reducing the burden of cardiovascular disease worldwide can be achieved.

Background We aimed to investigate whether there are differences in cardiac structure and systolic and diastolic function evaluated by 2‐dimensional echocardiography among men living with versus without HIV in the era of combination antiretroviral therapy. Methods and Results We performed a cross‐sectional analysis of 1195 men from MACS (Multicenter AIDS Cohort Study) who completed a transthoracic echocardiogram examination between 2017 and 2019. Associations between HIV serostatus and echocardiographic indices were assessed by multivariable regression analyses, adjusting for demographics and cardiovascular risk factors. Among men who are HIV+, associations between HIV disease severity markers and echocardiographic parameters were also investigated. Average age was 57.1±11.9 years; 29% of the participants were Black, and 55% were HIV+. Most men who were HIV+ (77%) were virally suppressed; 92% received combination antiretroviral therapy. Prevalent left ventricular (LV) systolic dysfunction (ejection fraction

Background Current approaches fail to separate patients at high versus low risk for ventricular arrhythmias owing to overreliance on a snapshot left ventricular ejection fraction measure. We used statistical machine learning to identify important cardiac imaging and time‐varying risk predictors. Methods and Results Three hundred eighty‐two cardiomyopathy patients (left ventricular ejection fraction ≤35%) underwent cardiac magnetic resonance before primary prevention implantable cardioverter defibrillator insertion. The primary end point was appropriate implantable cardioverter defibrillator discharge or sudden death. Patient characteristics; serum biomarkers of inflammation, neurohormonal status, and injury; and cardiac magnetic resonance‐measured left ventricle and left atrial indices and myocardial scar burden were assessed at baseline. Time‐varying covariates comprised interval heart failure hospitalizations and left ventricular ejection fractions. A random forest statistical method for survival, longitudinal, and multivariable outcomes incorporating baseline and time‐varying variables was compared with (1) Seattle Heart Failure model scores and (2) random forest survival and Cox regression models incorporating baseline characteristics with and without imaging variables. Age averaged 57±13 years with 28% women, 66% white, 51% ischemic, and follow‐up time of 5.9±2.3 years. The primary end point (n=75) occurred at 3.3±2.4 years. Random forest statistical method for survival, longitudinal, and multivariable outcomes with baseline and time‐varying predictors had the highest area under the receiver operating curve, median 0.88 (95% CI, 0.75‐0.96). Top predictors comprised heart failure hospitalization, left ventricle scar, left ventricle and left atrial volumes, left atrial function, and interleukin‐6 level; heart failure accounted for 67% of the variation explained by the prediction, imaging 27%, and interleukin‐6 2%. Serial left ventricular ejection fraction was not a significant predictor. Conclusions Hospitalization for heart failure and baseline cardiac metrics substantially improve ventricular arrhythmic risk prediction.

Background Prevention of adverse remodeling after myocardial infarction (MI) is an important goal of stem cell therapy. Clinical trial results vary, however, and poor cell retention and survival after delivery likely limit the opportunity to exert beneficial effects. To overcome these limitations, we built an implantable intravascular bioreactor (IBR) designed to protect contained cells from washout, dilution, and immune attack while allowing sustained release of beneficial paracrine factors. Methods and Results IBRs were constructed using semipermeable membrane adhered to a clinical‐grade catheter shaft. Mesenchymal stem cell (MSC) viability in and paracrine factor release from IBRs were assessed in vitro and IBR biocompatibility and immune protection confirmed in vivo. In a porcine anterior MI model, IBRs containing 25 million allogeneic MSCs (IBR‐MSCs) were compared with IBRs containing media alone (IBR‐Placebo; n=8 per group) with adverse remodeling assessed by magnetic resonance imaging. Four weeks after MI, IBR‐MSCs had no significant change in end‐diastolic volume (+0.33±4.32 mL; P=0.89), end‐systolic volume (+2.14±4.13 mL; P=0.21), and left ventricular ejection fraction (−2.27±2.94; P=0.33) while IBR‐Placebo had significant increases in end‐diastolic volume (+10.37±3.84 mL; P=0.01) and ESV (+11.35±2.88 mL; P=0.01), and a significant decrease in left ventricular ejection fraction (−5.78±1.70; P=0.025). Eight weeks after MI, adherent pericarditis was present in 0 of 8 IBR‐MSCs versus 4 of 8 IBR‐Placebo (P=0.02), suggesting an anti‐inflammatory effect. In a separate study, 25 million allogeneic pig MSCs directly injected in the peri‐infarct zone 3 days after MI (n=6) showed no significant benefit in adverse remodeling at 4 weeks compared with IBR‐MSCs. Conclusions MSCs deployed inside an implantable, removable, and potentially rechargeable bioreactor in a large animal model remain viable, are immunoprotected, and attenuate adverse remodeling 4 weeks after MI.

Sudden arrhythmic death is a leading cause of mortality, however approaches to identify at-risk patients are of low sensitivity and specificity. Here, the authors develop a personalized approach to assess arrhythmia risk in post-infarction patients based on cardiac imaging and computational modelling that significantly outperforms existing clinical metrics.