Neubauer, Stefan

Importance Current risk prediction guidelines for hypertrophic cardiomyopathy predict only sudden cardiac death and are imperfect, leading to avoidable deaths and unnecessary implantable cardioverter defibrillators. Objective To combine prospectively collected clinical history, imaging, genetic, and biomarker data to improve risk prediction of adverse events in hypertrophic cardiomyopathy. Design, Setting, and Participants A total of 2750 patients with hypertrophic cardiomyopathy were prospectively enrolled in the registry-based study from 44 sites in North America and Europe with expertise in hypertrophic cardiomyopathy and cardiac magnetic resonance (CMR) imaging. Participants were enrolled from April 1, 2014, to April 7, 2017. Exposures Patients underwent a health history questionnaire, blood sampling for biomarkers and genotyping, and contrast-enhanced CMR. Patients were followed up yearly by telephone and through records review regarding event documentation. Main Outcomes and Measures The predefined composite adjudicated primary end point was time to first event for hypertrophic cardiomyopathy–related deaths; nonfatal sustained ventricular arrhythmias (VAs) requiring cardioversion or defibrillation; and left ventricular (LV) assist device implant or heart transplant. A secondary end point was a composite of sudden cardiac death and nonfatal VA events. The elastic-net method identified the most important predictors. Cox proportional hazards regression assessed associations with time to the first end point. Results Of the 2750 prospectively enrolled patients, 2698 (98%) had analyzable data after 9 were excluded because they had hypertrophic cardiomyopathy phenocopies and 43 withdrew. Of these remaining patients, 1919 (71%) were male, mean age was 50 years (SD, 11 years), and 423 (16%) were from underrepresented racial and minority groups. The mean follow-up was 6.9 years (SD, 2.1 years). The primary event model in 104 patients included LV scar as a percentage of LV mass by late gadolinium enhancement (LGE%; hazard ratio [HR], 1.86; 95% CI, 1.58-2.20; P < .001), LV mass index (HR, 1.09; 95% CI, 1.01-1.17; P = .03), LV end-systolic volume index (HR, 1.28; 95% CI, 1.12-1.46; P < .001 ), all per 10-unit increase, history of heart failure at study entry (HR, 2.89; 95% CI, 1.75-4.77; P < .001), and log N-terminal pro–B-type natriuretic peptide (NT-proBNP; HR, 1.41; 95% CI, 1.17-1.70; P < .001) level per log unit, (C index for all, 0.77). An LGE percentage of the LV mass of 9% or higher substantially increased the primary composite event rate ( P = .001). The secondary sudden cardiac death and VA risk factor model (in 69 patients) included LGE%, LV mass index, LV ejection fraction, and log(NT-proBNP) (C index, 0.76). Conclusions and Relevance These results provide prospective evidence for incorporating cardiac magnetic resonance and NT-proBNP in the evaluation of patients with hypertrophic cardiomyopathy. Trial Registration ClinicalTrials.gov Identifier: NCT01915615

Abstract Aims Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts. Methods and results A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm’s ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P < 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910–0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case–control ratio 1:2), the AUC was 0.921 (95% CI 0.909–0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%. Conclusion The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation.

Abstract Introduction Cardiac geometry is affected by body composition, with total body adipose volume being related to left ventricular (LV) dilatation (due to increased intravascular volume and cardiac output), and visceral adipose tissue (VAT) to smaller LV cavity size and concentric remodelling (because of insulin resistance) [1]. As such we hypothesised that changes in VAT and total body weight during weight loss would have a differential impact on cardiac geometry. Alongside this, we hypothesised that, as the pericardium is a fixed volume, changes in epicardial adipose tissue (EAT), may have an additional mechanical effect by reducing pericardial restraint. Purpose We sought to investigate long term changes in VAT, total weight and EAT following bariatric surgery and relate them to changes in cardiac geometry. Methods Forty patients underwent cardiac magnetic resonance (CMR) imaging before and after bariatric surgery, including 21 who underwent short-term (median 209 days), 28 medium-term (median 428 days) and 12 long-term (median 1030 days) imaging follow up. Cardiac volumes (left atrial (LA), LV end-diastolic volume (LVEDV) and stroke volume (LVSV)) were assessed using cardiac MRI. VAT was assessed at L5 using a T1 weighted, water suppressed sequence. EAT volumes were calculated by manual contouring in end-ventricular systole on short axis slices from the mitral valve to the apex. Percentage changes in volumes were calculated between scans for each individual. Results Patients on average lost 32kg within the first 428 days following bariatric surgery (54% excess weight loss, p<0.0001), with no significant change in weight at the longer-term time point (Figure 1A). Most VAT loss occurred in the first 209 days (−42%, p<0.0001) with no subsequent change observed at the final two timepoints (Figure 1B). Similarly, EAT loss occurred in the first 209 days (−13%, mean 10ml, p<0.0001) with non-significant changes thereafter (Figure 1C). There was a significant decrease in both LA (13%, mean 12ml, p<0.0001) and LVEDV (4%, mean 8ml, p=0.0249) at 209 days post-surgery. Both LA volume and LVEDV had returned to baseline by the longest term time point of 1030 days (Figure 2). LVSV followed a similar pattern being reduced at 209 days (by 10%, mean 9ml, p=0.0019), then returning to levels similar to those pre-weight loss at 1030 days (p=0.44) (Figure 2C). Conclusions Cardiac volumes show a biphasic response to weight loss, initially becoming smaller and then returning to baseline by 1030 days. We hypothesise that the early drop in LA and LV cavity size is a response to reduced volume from body mass reduction. In contrast, we propose that the increase in LA and LVEDV that follows results from the longer-term effects of reducing VAT, and increased space within the pericardium resulting from EAT loss allowing expansion to occur. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): We acknowledge support from the British Heart Foundation Oxford Center of Research Excellence

Abstract Aims We aimed to determine the effect of increasing body weight upon right ventricular (RV) volumes, energetics, systolic function, and stress responses using cardiovascular magnetic resonance (CMR). Methods and results We first determined the effects of World Health Organization class III obesity [body mass index (BMI) > 40 kg/m2, n = 54] vs. healthy weight (BMI < 25 kg/m2, n = 49) upon RV volumes, energetics and systolic function using CMR. In less severe obesity (BMI 35 ± 5 kg/m2, n = 18) and healthy weight controls (BMI 21 ± 1 kg/m2, n = 9), we next performed CMR before and during dobutamine to evaluate RV stress response. A subgroup undergoing bariatric surgery (n = 37) were rescanned at median 1 year to determine the effects of weight loss. When compared with healthy weight, class III obesity was associated with adverse RV remodelling (17% RV end-diastolic volume increase, P < 0.0001), impaired cardiac energetics (19% phosphocreatine to adenosine triphosphate ratio reduction, P < 0.001), and reduction in RV ejection fraction (by 3%, P = 0.01), which was related to impaired energetics (R = 0.3, P = 0.04). Participants with less severe obesity had impaired RV diastolic filling at rest and blunted RV systolic and diastolic responses to dobutamine compared with healthy weight. Surgical weight loss (34 ± 15 kg weight loss) was associated with improvement in RV end-diastolic volume (by 8%, P = 0.006) and systolic function (by 2%, P = 0.03). Conclusion Increasing body weight is associated with significant alterations in RV volumes, energetic, systolic function, and stress responses. Adverse RV modelling is mitigated with weight loss. Randomized trials are needed to determine whether intentional weight loss improves symptoms and outcomes in patients with obesity and heart failure.

Introduction: The mechanisms linking obesity to increased mortality in patients with coronavirus-disease 2019 (COVID-19) are unclear. Hypothesis: We hypothesised that obesity would be associated with adverse right ventricular (RV) remodelling and function which might be further exacerbated by COVID-19 associated increases in RV afterload/demand. Methods: We used echocardiography and computed tomography to establish links between RV dimensions and function and bodyweight in patients with severe COVID-19. In obese and non-obese participants without COVID-19, we used cardiovascular magnetic resonance to define the effects of obesity upon RV volumes, energetics, systolic function and stress reserve. Results: In patients with severe COVID-19, increasing bodyweight was associated with disproportionate RV dilatation (n=26, echocardiography, R=0.42, P=0.03, CT n=34, R=0.23, P=0.004) and impairment of RV systolic function (fractional area change (FAC) R=-0.45, P=0.04); obesity (BMI >30kg/m2) was associated with a 32% reduction in RV FAC (P=0.03). Similar associations were seen in a validation cohort (n=91, all P<0.05). In participants without COVID-19, class III obesity (n=54, BMI 47±4 kg/m 2 ) was associated with RV remodelling, impaired cardiac energetics (PCr/ATP ratio 23% lower, P<0.001) and reduction in RV systolic function at rest (by 3±1%, P=0.01), which was related to reduced energetics (R=0.3, P=0.04). Participants with class I-II obesity (BMI 35±5 kg/m 2 , n=27) had impaired RV diastolic filling rate at rest (P<0.001) and blunted RV dobutamine stress systolic response (by 68%, P=0.03) and diastolic response (by 78%, P=0.008). Surgical weight loss in patients obesity (n=37, 34±15kg weight loss) was associated with improvement in RV volume (12±26 ml lower, P=0.006) and function (ejection fraction 2±5% higher, P=0.03). Conclusions: Increasing body weight is associated with both significant alterations in RV volumes, systolic function and stress responses which are mitigated with weight loss and also with disproportionate RV dilatation and dysfunction in severe COVID-19. RV dysfunction is a modifiable mechanism which might link obesity to adverse outcomes in patients with COVID-19 and other disorders stressing the right heart.