Masri, Ahmad

Introduction: Deep-learning artificial intelligence (AI) algorithms can identify hypertrophic cardiomyopathy (HCM) from the electrocardiogram (ECG). Preliminary data indicate that AI-ECG may also track treatment response in HCM patients (pts) receiving mavacamten (Mava). In this placebo-controlled analysis, we investigated the performance of AI-ECG in HCM detection and its changes during Mava treatment compared to placebo in the phase 3, EXPLORER-HCM trial. Methods: Pts with non-paced ECGs enrolled in EXPLORER-HCM, evaluating Mava over 30 weeks of treatment, were included. All pts had a 12-lead ECG pre-treatment, at each study visit, and 8 weeks after treatment end. Two independently developed AI-ECG convolutional neural network algorithms from the Mayo Clinic and the University of California, San Francisco (UCSF) were applied to all ECGs suitable for analysis. Serial AI-ECG-predicted scores, which correlate with HCM likelihood (range 0-1), were examined between Mava and placebo groups. Results: A total of 101 Mava and 110 placebo pts were included (mean age 58.6, female 40%). At baseline, the mean Mayo and UCSF AI-ECG scores for HCM were 0.82 and 0.65, with 94% and 88% of pts exceeding the respective diagnostic thresholds of 0.11 (Mayo) and 0.4 (UCSF). During 30 weeks of follow-up in the Mava group, the algorithms showed a mean AI-ECG score reduction of 0.32 (Mayo) and 0.17 (UCSF) in all pts between start and end of treatment. In comparison, AI-ECG scores decreased by 0.05 (Mayo) and increased by 0.02 (UCSF) in the placebo group (Mava vs placebo, t-test p-values <0.001 for both algorithms; Figure ) Conclusion: In the EXPLORER-HCM trial population, two independent deep-learning AI-ECG algorithms correctly identified most HCM pts and demonstrated significant reductions in predicted HCM scores after treatment with Mava when compared to placebo. This relative normalization of the ECG suggests disease-modifying properties of Mava, deserving further investigation.

Importance Whether vigorous intensity exercise is associated with an increase in risk of ventricular arrhythmias in individuals with hypertrophic cardiomyopathy (HCM) is unknown. Objective To determine whether engagement in vigorous exercise is associated with increased risk for ventricular arrhythmias and/or mortality in individuals with HCM. The a priori hypothesis was that participants engaging in vigorous activity were not more likely to have an arrhythmic event or die than those who reported nonvigorous activity. Design, Setting, and Participants This was an investigator-initiated, prospective cohort study. Participants were enrolled from May 18, 2015, to April 25, 2019, with completion in February 28, 2022. Participants were categorized according to self-reported levels of physical activity: sedentary, moderate, or vigorous-intensity exercise. This was a multicenter, observational registry with recruitment at 42 high-volume HCM centers in the US and internationally; patients could also self-enroll through the central site. Individuals aged 8 to 60 years diagnosed with HCM or genotype positive without left ventricular hypertrophy (phenotype negative) without conditions precluding exercise were enrolled. Exposures Amount and intensity of physical activity. Main Outcomes and Measures The primary prespecified composite end point included death, resuscitated sudden cardiac arrest, arrhythmic syncope, and appropriate shock from an implantable cardioverter defibrillator. All outcome events were adjudicated by an events committee blinded to the patient’s exercise category. Results Among the 1660 total participants (mean [SD] age, 39 [15] years; 996 male [60%]), 252 (15%) were classified as sedentary, and 709 (43%) participated in moderate exercise. Among the 699 individuals (42%) who participated in vigorous-intensity exercise, 259 (37%) participated competitively. A total of 77 individuals (4.6%) reached the composite end point. These individuals included 44 (4.6%) of those classified as nonvigorous and 33 (4.7%) of those classified as vigorous, with corresponding rates of 15.3 and 15.9 per 1000 person-years, respectively. In multivariate Cox regression analysis of the primary composite end point, individuals engaging in vigorous exercise did not experience a higher rate of events compared with the nonvigorous group with an adjusted hazard ratio of 1.01. The upper 95% 1-sided confidence level was 1.48, which was below the prespecified boundary of 1.5 for noninferiority. Conclusions and Relevance Results of this cohort study suggest that among individuals with HCM or those who are genotype positive/phenotype negative and are treated in experienced centers, those exercising vigorously did not experience a higher rate of death or life-threatening arrhythmias than those exercising moderately or those who were sedentary. These data may inform discussion between the patient and their expert clinician around exercise participation.

AbstractTransthyretin‐mediated amyloidosis (ATTR) is a rare, under‐recognized, progressively debilitating, fatal disease caused by the aggregation and extracellular deposition of amyloid transthyretin (TTR) fibrils in multiple organs and tissues throughout the body. TTR is predominantly synthesized by the liver and normally circulates as a homotetramer, while misfolded monomers aggregate to form amyloid fibrils. One strategy to treat ATTR amyloidosis is to reduce the amount of TTR produced by the liver using drugs that directly target the TTR mRNA or gene. This narrative review focuses on how TTR gene silencing tools act to reduce TTR production, describing strategies for improved targeted delivery of these agents to hepatocytes where TTR is preferentially expressed. Antisense oligonucleotides (ASOs) and small interfering RNAs (siRNAs), termed RNA silencers, cause selective degradation of TTR mRNA, while a TTR gene editing tool reduces TTR expression by introducing nonsense mutations into the TTR gene. Two strategies to facilitate tissue‐specific delivery of these nucleic acid‐based drugs employ endogenous receptors expressed by hepatocytes. Lipid nanoparticles (LNPs) that recruit apolipoprotein E support low‐density lipoprotein receptor‐mediated uptake of unconjugated siRNA and are now used for CRISPR gene editing tools. Additionally, conjugating N‐acetylgalactosamine (GalNAc) moieties to ASOs or siRNAs facilitates receptor‐mediated uptake by the asialoglycoprotein receptor. In summary, ATTR is a progressive disease with various clinical manifestations due to TTR aggregation, deposition, and amyloid formation. Receptor‐targeted ligands (eg, GalNAc) and nanoparticle encapsulation (eg, LNPs) are technologies to deliver ASOs, siRNAs, and gene editing tools to hepatocytes, the primary location of TTR synthesis.