Sex-specific cardiac phenotype and clinical outcomes in patients with hypertrophic cardiomyopathy.

BACKGROUND: It is unknown whether sex-specific differences in mortality observed in HCM are due to older age of women at presentation, or whether women have greater degree of LV myopathy than men. METHODS: We retrospectively compared clinical/imaging characteristics and outcomes between women and men in our overall cohort composed of 728 HCM patients, and in an age-matched subgroup comprised of 400 age-matched patients. We examined sex-specific differences in LV myopathy, and dissected the influence of age and sex on outcomes. LV myopathy was assessed by measuring LV mass, LVEF, global peak longitudinal systolic strain (LV-GLS), diastolic function (E/A, E/e'), late gadolinium enhancement (LV-LGE) and myocardial blood flow (MBF) at rest/stress. The primary endpoint was a composite outcome, comprising heart failure (HF), atrial fibrillation (AFib), ventricular tachycardia/fibrillation (VT/VF) and death; individual outcomes were defined as the secondary endpoint. RESULTS: Women in the overall cohort were older by 6 years. Women were more symptomatic and more likely to have obstructive HCM. Women had smaller LV cavity size, stroke volume and LV mass, higher indexed maximum wall thickness (IMWT), more hyperdynamic LVEF and higher/similar LV-GLS. Women had similar LV-LGE and E/A, but higher E/e' and rest/stress MBF. Female sex was independently associated with the composite outcome in the overall cohort, and with HF in the overall cohort and age-matched subgroup after adjusting for obstructive HCM, LA diameter, LV-GLS. CONCLUSIONS: Our results suggest that sex-specific differences in LV geometry, hyper-contractility and diastolic function, not greater degree of LV myopathy, contribute to a higher, age-independent risk of diastolic HF in women with HCM.

Identifying Ventricular Arrhythmias and Their Predictors by Applying Machine Learning Methods to Electronic Health Records in Patients With Hypertrophic Cardiomyopathy (HCM-VAr-Risk Model).

Clinical risk stratification for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HC) employs rules derived from American College of Cardiology Foundation/American Heart Association (ACCF/AHA) guidelines or the HCM Risk-SCD model (C-index ∼0.69), which utilize a few clinical variables. We assessed whether data-driven machine learning methods that consider a wider range of variables can effectively identify HC patients with ventricular arrhythmias (VAr) that lead to SCD. We scanned the electronic health records of 711 HC patients for sustained ventricular tachycardia or ventricular fibrillation. Patients with ventricular tachycardia or ventricular fibrillation (n = 61) were tagged as VAr cases and the remaining (n = 650) as non-VAr. The 2-sample ttest and information gain criterion were used to identify the most informative clinical variables that distinguish VAr from non-VAr; patient records were reduced to include only these variables. Data imbalance stemming from low number of VAr cases was addressed by applying a combination of over- and undersampling strategies. We trained and tested multiple classifiers under this sampling approach, showing effective classification. We evaluated 93 clinical variables, of which 22 proved predictive of VAr. The ensemble of logistic regression and naïve Bayes classifiers, trained based on these 22 variables and corrected for data imbalance, was most effective in separating VAr from non-VAr cases (sensitivity = 0.73, specificity = 0.76, C-index = 0.83). Our method (HCM-VAr-Risk Model) identified 12 new predictors of VAr, in addition to 10 established SCD predictors. In conclusion, this is the first application of machine learning for identifying HC patients with VAr, using clinical attributes. Our model demonstrates good performance (C-index) compared with currently employed SCD prediction algorithms, while addressing imbalance inherent in clinical data.

Hypertrophic Cardiomyopathy Patients With Paroxysmal Atrial Fibrillation Have a High Burden of Left Atrial Fibrosis by Cardiac Magnetic Resonance Imaging.

OBJECTIVES: This study hypothesized that paroxysmal atrial fibrillation (PAF) reflects the presence of a more severe cardiac hypertrophic cardiomyopathy (HCM) phenotype. BACKGROUND: HCM is characterized by myocyte hypertrophy, fibrosis, and a high prevalence of PAF. It is currently unresolved whether atrial fibrillation (AF) is a marker or a mediator of adverse outcomes in HCM. METHODS: This study retrospectively examined 45 HCM patients who underwent cardiovascular magnetic resonance (CMR) imaging in sinus rhythm. The function of all 4 cardiac chambers was assessed, as well as late gadolinium enhancement (LGE) in the left atrium (LA) and left ventricle (LV), as indicators of fibrosis. A fat-saturated, 3-dimensional inversion recovery-prepared, fast-spoiled, gradient-recalled echo sequence, and the image intensity ratio method were used to measure LA-LGE; LGE in the LV was quantified using a semi-automated threshold technique. RESULTS: HCM patients (n = 45) were divided into 2 groups (PAF, no AF) based on history of PAF. All HCM patients had LGE in the LA posterior wall. The PAF group (n = 18) had higher LA volume, a lower LA ejection fraction, a lower global peak longitudinal LA strain (PLAS), and a higher amount of LA-LGE compared with the no AF group (n = 27). A modest inverse association was noted between the LA ejection fraction, PLAS, and LA-LGE; a positive association was present between LV-LGE and LA-LGE. The PAF group had lower ejection fractions in the LV, right atrium, and right ventricle compared with those in the no AF group. CONCLUSIONS: PAF is associated with a greater degree of structural LA remodeling and global myopathy, which suggests a more severe cardiac HCM phenotype.