Relationship between aetiology and left ventricular systolic dysfunction in hypertrophic cardiomyopathy

Background: Hypertrophic cardiomyopathy (HCM) is a common cardiac disease caused by a range of genetic and acquired disorders. The most common cause is genetic variation in sarcomeric proteins genes. Current ESC guidelines suggest that particular clinical features (‘red flags’) assist in differential diagnosis. Aims: To test the hypothesis that left ventricular (LV) systolic dysfunction in the presence of increased wall thickness is an age-specific ‘red flag’ for aetiological diagnosis and to determine long-term outcomes in adult patients with various types of HCM. Methods: A cohort of 1697 adult patients with HCM followed at two European referral centres were studied. Aetiological diagnosis was based on clinical examination, cardiac imaging and targeted genetic and biochemical testing. Main outcomes were: all-cause mortality or heart transplantation (HTx) and heart failure (HF) related-death. All-cause mortality included sudden cardiac death or equivalents, HF and stroke-related death and non-cardiovascular death. Results: Prevalence of different aetiologies was as follows: sarcomeric HCM 1288 (76%); AL amyloidosis 115 (7%), hereditary TTR amyloidosis 86 (5%), Anderson-Fabry disease 85 (5%), wild-type TTR amyloidosis 48 (3%), Noonan syndrome 15 (0.9%), mitochondrial disease 23 (1%), Friedreich’s ataxia 11 (0.6%), glycogen storage disease 16 (0.9%), LEOPARD syndrome 7 (0.4%), FHL1 2 (0.1%) and CPT II deficiency 1 (0.1%). Systolic dysfunction at first evaluation was significantly more frequent in phenocopies than sarcomeric HCM [105/409 (26%) versus 40/1288 (3%), (p<0.0001)]. All-cause mortality/HTx and HF-related death were higher in phenocopies compared to sarcomeric HCM (p<0.001, respectively). When considering specific aetiologies, all-cause mortality and HF-related death were higher in cardiac amyloidosis (p<0.001, respectively). Conclusion: Systolic dysfunction at first evaluation is more common in phenocopies compared to sarcomeric HCM representing an age-specific ‘red flag’ for differential diagnosis. Long-term prognosis was more severe in phenocopies compared to sarcomeric HCM and when comparing specific aetiologies, cardiac amyloidosis showed the worse outcomes

Hypertrophic cardiomyopathy: insights from extracellular volume mapping

Hypertrophic cardiomyopathy (HCM) is a genetic cardiac disease characterized by myocardial hypertrophy and fibrosis. The phenotypic expression ranges from asymptomatic patients to heart failure and sudden death.1 Disease progression and relationship between hypertrophy and fibrosis are not well understood. Extracellular volume fraction (ECV) mapping on cardiovascular magnetic resonance (CMR) can demonstrate pixel-by-pixel ECV elevation (focal or diffuse fibrosis) or reduction (cellular hypertrophy).2 Furthermore, it has been shown that physical training induces remodelling of both heart and vasculature.3,4 In particular, it has been shown that hypertrophied myocardium in athletes has lower ECV, suggesting that cardiac athletic adaptation is an adaptive one caused predominantly by cellular rather than interstitial expansion.4 Hypothesizing that ECV mapping can reveal both differential responses of left ventricular hypertrophy (LVH), we explored the distribution of ECV in HCM

Usefulness of Electrocardiographic Patterns at Presentation to Predict Long-term Risk of Cardiac Death in Patients With Hypertrophic Cardiomyopathy

The objective of this study was to investigate the prognostic significance of 12-lead electrocardiogram (ECG) patterns in a large multicenter cohort of patients with hypertrophic cardiomyopathy; 1,004 consecutive patients with hypertrophic cardiomyopathy and a recorded standard ECG (64% men, mean age 50 ± 16 years) were evaluated at 4 Italian centers. The study end points were sudden cardiac death (SCD) or surrogates, including appropriate implanted cardiac defibrillator discharge and resuscitated cardiac arrest and major cardiovascular events (including SCD or surrogates and death due to heart failure, cardioembolic stroke, or heart transplantation). Prevalence of baseline electrocardiographic characteristics was: normal ECG 4%, ST-segment depression 56%, pseudonecrosis waves 33%, "pseudo-ST-segment elevation myocardial infarction (STEMI)" pattern 17%, QRS duration ≥120 ms 17%, giant inverted T waves 6%, and low QRS voltages 3%. During a mean follow-up of 7.4 ± 6.8 years, 77 patients experienced SCD or surrogates and 154 patients experienced major cardiovascular events. Independent predictors of SCD or surrogates were unexplained syncope (hazard ratio [HR] 2.5, 95% confidence interval [CI] 1.4 to 4.5, p = 0.003), left ventricular ejection fraction &lt;50% (HR 3.5, 95% CI 1.9 to 6.7, p = 0.0001), nonsustained ventricular tachycardia (HR 1.7, 95% CI 1.1 to 2.6, p = 0.027), pseudo-STEMI pattern (HR 2.3, 95% CI 1.4 to 3.8, p = 0.001), QRS duration ≥120 ms (HR 1.8, 95% CI 1.1 to 3.0, p = 0.033), and low QRS voltages (HR 2.3, 95% CI 1.01 to 5.1, p = 0.048). Independent predictors of major cardiovascular events were age (HR 1.02, 95% CI 1.01 to 1.03, p = 0.0001), LV ejection fraction &lt;50% (HR 3.73, 95% CI 2.39 to 5.83, p = 0.0001), pseudo-STEMI pattern (HR 1.66, 95% CI 1.13 to 2.45, p = 0.010), QRS duration ≥120 ms (HR 1.69, 95% CI 1.16 to 2.47, p = 0.007), and prolonged QTc interval (HR 1.68, 95% CI 1.21 to 2.34, p = 0.002). In conclusion, a detailed qualitative and quantitative electrocardiographic analyses provide independent predictors of prognosis that could be integrated with the available score systems to improve the power of the current model

Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning

BACKGROUND: Measurement of cardiac structure and function from images (e.g. volumes, mass and derived parameters such as left ventricular (LV) ejection fraction [LVEF]) guides care for millions. This is best assessed using cardiovascular magnetic resonance (CMR), but image analysis is currently performed by individual clinicians, which introduces error. We sought to develop a machine learning algorithm for volumetric analysis of CMR images with demonstrably better precision than human analysis. METHODS: A fully automated machine learning algorithm was trained on 1923 scans (10 scanner models, 13 institutions, 9 clinical conditions, 60,000 contours) and used to segment the LV blood volume and myocardium. Performance was quantified by measuring precision on an independent multi-site validation dataset with multiple pathologies with n = 109 patients, scanned twice. This dataset was augmented with a further 1277 patients scanned as part of routine clinical care to allow qualitative assessment of generalization ability by identifying mis-segmentations. Machine learning algorithm ('machine') performance was compared to three clinicians ('human') and a commercial tool (cvi42, Circle Cardiovascular Imaging). FINDINGS: Machine analysis was quicker (20 s per patient) than human (13 min). Overall machine mis-segmentation rate was 1 in 479 images for the combined dataset, occurring mostly in rare pathologies not encountered in training. Without correcting these mis-segmentations, machine analysis had superior precision to three clinicians (e.g. scan-rescan coefficients of variation of human vs machine: LVEF 6.0% vs 4.2%, LV mass 4.8% vs. 3.6%; both P < 0.05), translating to a 46% reduction in required trial sample size using an LVEF endpoint. CONCLUSION: We present a fully automated algorithm for measuring LV structure and global systolic function that betters human performance for speed and precision