Myocardial Contraction Fraction Predicts Cardiovascular Events in Patients With Hypertrophic Cardiomyopathy and Normal Ejection Fraction

Myocardial contraction fraction predicts outcomes in patients enrolled in the TOPCAT trial

BACKGROUND

Myocardial contraction fraction (MCF)-the ratio of left ventricular stroke volume to myocardial volume-is a volumetric measure of myocardial shortening that distinguishes between pathologic and physiologic hypertrophy. In this post-hoc analysis of the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist) trial, we investigated the prognostic value of MCF and its association with heterogeneity of treatment effect in heart failure with preserved ejection fraction (HFpEF).

METHODS

TOPCAT randomized patients with HFpEF to spironolactone or placebo. Patients with echocardiography data allowing for the calculation of MCF were included. The primary outcome was a composite of all-cause mortality, HF hospitalization, myocardial infarction, and stroke.

RESULTS

588 patients (median age 72.0 [63.0-79.3] years; 49.1 % female) were included. Median MCF was 27.0 % (21.8-32.8 %) for the overall group and was not different in the spironolactone and placebo groups. Over a median follow-up of 3.0 (1.9-4.5) years, MCF below median was associated with a worse prognosis (p = 0.003). On multivariable regression analysis (HR, 95 % CI), only New York Heart Association class (1.47, 1.14-1.91, p = 0.003) and MCF (0.76, 0.64-0.90, p = 0.001) were associated with the composite outcome. In this subset, spironolactone as compared to placebo was not associated with improved outcomes, but stratifying by MCF showed differential outcomes to spironolactone therapy (p = 0.010).

CONCLUSIONS

Among patients with HFpEF enrolled in TOPCAT, reduced MCF was independently associated with worse outcomes. Larger prospectively designed studies are needed to further assess the role of MCF in patients with HFpEF.

The predictive value of cardiac MRI strain parameters in hypertrophic cardiomyopathy patients with preserved left ventricular ejection fraction and a low fibrosis burden: a retrospective cohort study

Background

Prompt interventions prevent adverse events (AE) in hypertrophic cardiomyopathy (HCM). We evaluated the pattern and the predictive role of feature tracking (FT)-cardiac magnetic resonance (CMR) imaging parameters in an HCM population with a normal left ventricular ejection fraction (LVEF) and a low fibrosis burden.

Methods

The CMR and clinical data of 170 patients, consisting of 142 HCM (45 ± 15.7 years, 62.7% male) and 28 healthy (42.2 ± 11.26 years, 50% male) subjects, who were enrolled from 2015 to 2020, were evaluated. HCM patients had a normal LVEF with a late gadolinium enhancement (LGE) percentage below 15%. Between-group differences were described, and the potent predictors of AE were determined. A P-value below 0.05 was considered significant.

Results

LV global longitudinal, circumferential, and radial strains (GLS, GCS, and GRS, respectively) and the LV myocardial mass index (MMI) were different between the healthy and HCM cases (all Ps < 0.05). Strains were significantly impaired in the HCM patients with a normal MMI. A progressive decrease in LVGLS and a distinct fall in LVGCS were noted with a rise in MMI. AE were predicted by LVGLS, LVGCS, and the LGE percentage, and LVGCS was the single robust predictor (HR, 1.144; 95% CI, 1.080-1.212; P = 0.001). An LVGCS below 16.2% predicted AE with 77% specificity and 58% sensitivity.

Conclusions

LV strains were impaired in HCM patients with a normal EF and a low fibrosis burden, even in the presence of a normal MMI. CMR parameters, especially FT-CMR values, predicted AE in our HCM patients.

Quantification of Myocardial Contraction Fraction with Three-Dimensional Automated, Machine-Learning-Based Left-Heart-Chamber Metrics: Diagnostic Utility in Hypertrophic Phenotypes and Normal Ejection Fraction

Aims: The differentiation of left ventricular (LV) hypertrophic phenotypes is challenging in patients with normal ejection fraction (EF). The myocardial contraction fraction (MCF) is a simple dimensionless index useful for specifically identifying cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) when calculated by cardiac magnetic resonance. The purpose of this study was to evaluate the value of MCF measured by three-dimensional automated, machine-learning-based LV chamber metrics (dynamic heart model [DHM]) for the discrimination of different forms of hypertrophic phenotypes. Methods and Results: We analyzed the DHM LV metrics of patients with CA (n = 10), hypertrophic cardiomyopathy (HCM, n = 36), isolated hypertension (IH, n = 87), and 54 healthy controls. MCF was calculated by dividing LV stroke volume by LV myocardial volume. Compared with controls (median 61.95%, interquartile range 55.43-67.79%), mean values for MCF were significantly reduced in HCM-48.55% (43.46-54.86% p < 0.001)-and CA-40.92% (36.68-46.84% p < 0.002)-but not in IH-59.35% (53.22-64.93% p < 0.7). MCF showed a weak correlation with EF in the overall cohort (R2 = 0.136) and the four study subgroups (healthy adults, R2 = 0.039 IH, R2 = 0.089; HCM, R2 = 0.225; CA, R2 = 0.102). ROC analyses showed that MCF could differentiate between healthy adults and HCM (sensitivity 75.9%, specificity 77.8%, AUC 0.814) and between healthy adults and CA (sensitivity 87.0%, specificity 100%, AUC 0.959). The best cut-off values were 55.3% and 52.8%. Conclusions: The easily derived quantification of MCF by DHM can refine our echocardiographic discrimination capacity in patients with hypertrophic phenotype and normal EF. It should be added to the diagnostic workup of these patients.

Clinical significance of myocardial contraction fraction in significant primary mitral regurgitation

BACKGROUND

The optimal timing for mitral valve (MV) surgery in asymptomatic patients with primary mitral regurgitation (MR) remains a matter of debate. Myocardial contraction fraction (MCF) - the ratio of the left ventricular (LV) stroke volume to that of the myocardial volume - is a volumetric measure of LV myocardial shortening independent of size or geometry.

AIM

To assess the relationship between MCF and outcome in patients with significant chronic primary MR due to prolapse managed in contemporary practice.

METHODS

Clinical, Doppler-echocardiographic and outcome data prospectively collected in 174 patients (mean age 62 years, 27% women) with significant primary MR and no or mild symptoms were analysed. The impact of MCF< or ≥30% on cardiac events (cardiovascular death, acute heart failure or MV surgery) was studied.

RESULTS

During an estimated median follow-up of 49 (22-77) months, cardiac events occurred in 115 (66%) patients. The 4-year estimates of survival free from cardiac events were 21±5% for patients with MCF <30% and 40±6% for those with ≥30% (P<0.001). MCF <30% was associated with a considerable increased risk of cardiac events after adjustment for established clinical risk factors, MR severity and current recommended class I triggers for MV surgery (adjusted hazard ratio: 2.33, 95% confidence interval: 1.51-3.58; P<0.001). Moreover, MCF<30% improved the predictive performance of models, with better global fit, reclassification and discrimination.

CONCLUSIONS

MCF<30% is strongly associated with occurrence of cardiac events in patients with significant primary MR due to prolapse. Further studies are needed to assess the direct impact of MCF on patient management and outcomes.

Predictive value of cardiac magnetic resonance mechanical parameters for myocardial fibrosis in hypertrophic cardiomyopathy with preserved left ventricular ejection fraction

Objective

Myocardial fibrosis leads to systolic dysfunction in hypertrophic cardiomyopathy (HCM) patients. This study aims to investigate the relationship between cardiac magnetic resonance mechanical parameters for evaluating the left ventricular function in HCM with preserved left ventricular ejection fraction (LVEF ≥50%) and the association between myocardial fibrosis defined by late gadolinium enhancement (LGE).

Methods

This study was a retrospective analysis of CMR images of 93 patients with HCM with preserved ejection fraction (HCMpEF) and 96 controls diagnosed by cardiac magnetic resonance (CMR) at our hospital from July 2019 to January 2022. The myocardial contraction fraction (MCF) was calculated, and myocardial mechanical parameters, including global myocardial longitudinal strain (GLS), circumferential strain (GLS), and myocardial strain (GLS), were obtained by tissue tracking and LGE quantitative modules of dedicated software, respectively. The correlation between myocardial strain and LGE was analyzed, and a multivariate logistic regression model was developed to discuss the risk predictors of LGE.

Results

Compared to the control group, the left ventricular mechanical parameters GLS (-13.90 ± 3.80% versus -18.20 ± 2.10%, p < 0.001), GCS (-16.62 ± 3.50% versus -18.4 ± 2.69%, p < 0.001), GRS (28.99 ± 10.38% versus 33.02 ± 6.25%, p < 0.01), and MCF (64 ± 16% versus 99 ± 18%, p < 0.001) were found significantly lower in HCM group. Moreover, even in LGE-negative HCM patients, GLS (-16.3 ± 3.9%) and MCF (78 ± 19%) were significantly lower compared to the control group. Left ventricular GLS [OR = 1.61, (1.29, 2.02), p = 0.001] and MCF [OR = 0.90, (0.86, 0.94), p = 0.001] independently predicted myocardial late gadolinium enhancement (LGE).

Conclusion

In participants of HCM with preserved ejection fraction, the early onset of reduced left ventricular GLS and MCF in patients with HCMpEF may provide new evidence for evaluating impaired myocardial systolic function. The reduction of myocardial mechanical indexes may reflect the presence and extent of myocardial fibrosis, and the more significant the reduction, the more severe the myocardial fibrosis; GLS and MCF may be ideal predictors for LGE.

Left ventricular strain patterns and their relationships with cardiac biomarkers in hypertrophic cardiomyopathy patients with preserved left ventricular ejection fraction

Aims

This study aims to assess left ventricular (LV) function in hypertrophic cardiomyopathy (HCM) patients with preserved left ventricular ejection fraction (LVEF) by LV strain patterns based on cardiac magnetic resonance feature tracking (CMR-FT) and to explore the relationships between LV strain patterns and cardiac biomarkers in these patients, such as cardiac troponin (cTnT) and N-terminal prohormone of the brain natriuretic peptide (NT-proBNP).

Methods

A total of 64 HCM patients with preserved LVEF and 33 healthy people were included in this study. All subjects underwent contrast-enhanced CMR, and all patients took blood tests for cTnT and NT-proBNP during hospitalization.

Results

Despite the absence of a significant difference in LVEF between HCM patients and healthy controls, almost all global and segmental strains in radial, circumferential, and longitudinal directions in the HCM group deteriorated significantly as compared to controls (p < 0.05). Moreover, some global and segmental strains correlated significantly with NT-proBNP and cTnT in HCM patients, and the best correlations were global radial strain (GRS) (r = -0.553, p < 0.001) and mid-ventricular radial strain (MRS) (r = -0.582, p < 0.001), respectively, with a moderate correlation. The receiver operating characteristic (ROC) results showed that among the LV deformation parameters, GRS [area under the curve (AUC), 0.76; sensitivity, 0.49; specificity, 1.00], MRS (AUC, 0.81; sensitivity, 0.77; specificity, 0.79) demonstrated greater diagnostic accuracy to predict elevated NT-proBNP, and abnormal cTnT, respectively. Their cut-off values were 21.17 and 20.94%, respectively. Finally, all global strains demonstrated moderate, good, and excellent intra- and inter-observer reproducibility.

Conclusion

LV strain patterns can be used to assess the subclinical cardiac function of HCM patients on the merit of being more sensitive than LVEF. In addition, LV strain patterns can detect serious HCM patients and may be helpful to non-invasively predict elevated NT-proBNP and cTnT.

Comparison of Nonclassic and Classic Phenotype of Hypertrophic Cardiomyopathy Focused on Prognostic Cardiac Magnetic Resonance Parameters: A Single-Center Observational Study

Patients with nonclassic phenotypes (NCP)—more advanced stages of hypertrophic cardiomyopathy (HCM)—constitute an intriguing and heterogeneous group that is difficult to diagnose, risk-stratify, and treat, and often neglected in research projects. We aimed to compare cardiac magnetic resonance (CMR) parameters in NCP versus classic phenotypes (CP) of HCM with special emphasis given to the parameters of established and potential prognostic importance, including numerous variables not used in everyday clinical practice. The CMR studies of 88 patients performed from 2011 to 2019 were postprocessed according to the study protocol to obtain standard and non-standard parameters. In NCP, the late gadolinium enhancement extent expressed as percent of left ventricular mass (%LGE) and left ventricular mass index (LVMI) were higher, left atrium emptying fraction (LAEF) was lower, minimal left atrial volume (LAV min) was greater, and myocardial contraction fraction (MCF) and left ventricular global function index (LVGFI) were lower than in CP (p < 0.001 for all). In contrast, HCM risk score and left ventricular maximal thickness (LVMT) were similar in NCP and CP patients. No left ventricular outflow tract obstruction (LVOTO) was observed in the NCP group. Left ventricular outflow tract diameter (LVOT), aortic valve diameter (Ao), and LVOT/Ao ratio were significantly higher and anterior mitral leaflet (AML)/LVOT ratio was lower in the NCP compared to the CP group. In conclusion, significant differences in nonstandard CMR parameters were noted between the nonclassic and classic HCM phenotypes that may contribute to future studies on disease stages and risk stratification in HCM.

Deep Learning Analysis of Echocardiographic Images to Predict Positive Genotype in Patients With Hypertrophic Cardiomyopathy

Genetic testing provides valuable insights into family screening strategies, diagnosis, and prognosis in patients with hypertrophic cardiomyopathy (HCM). On the other hand, genetic testing carries socio-economical and psychological burdens. It is therefore important to identify patients with HCM who are more likely to have positive genotype. However, conventional prediction models based on clinical and echocardiographic parameters offer only modest accuracy and are subject to intra- and inter-observer variability. We therefore hypothesized that deep convolutional neural network (DCNN, a type of deep learning) analysis of echocardiographic images improves the predictive accuracy of positive genotype in patients with HCM. In each case, we obtained parasternal short- and long-axis as well as apical 2-, 3-, 4-, and 5-chamber views. We employed DCNN algorithm to predict positive genotype based on the input echocardiographic images. We performed 5-fold cross-validations. We used 2 reference models—the Mayo HCM Genotype Predictor score (Mayo score) and the Toronto HCM Genotype score (Toronto score). We compared the area under the receiver-operating-characteristic curve (AUC) between a combined model using the reference model plus DCNN-derived probability and the reference model. We calculated the p-value by performing 1,000 bootstrapping. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, we examined the net reclassification improvement. We included 99 adults with HCM who underwent genetic testing. Overall, 45 patients (45%) had positive genotype. The new model combining Mayo score and DCNN-derived probability significantly outperformed Mayo score (AUC 0.86 [95% CI 0.79–0.93] vs. 0.72 [0.61–0.82]; p < 0.001). Similarly, the new model combining Toronto score and DCNN-derived probability exhibited a higher AUC compared to Toronto score alone (AUC 0.84 [0.76–0.92] vs. 0.75 [0.65–0.85]; p = 0.03). An improvement in the sensitivity, specificity, PPV, and NPV was also achieved, along with significant net reclassification improvement. In conclusion, compared to the conventional models, our new model combining the conventional and DCNN-derived models demonstrated superior accuracy to predict positive genotype in patients with HCM.

Spironolactone in Patients With an Echocardiographic HFpEF Phenotype Suggestive of Cardiac Amyloidosis: Results From TOPCAT

OBJECTIVES

This study investigated an enriched cohort of patients with heart failure and preserved ejection fraction (HFpEF) in TOPCAT (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist) with an echocardiographic phenotype of cardiac amyloidosis.

BACKGROUND

There is a high prevalence of increased interventricular septal (IVS) thickness and decreased mitral annular systolic (s') velocity in cardiac amyloidosis. In addition, clinical trials of neurohormonal blockade are missing in this population.

METHODS

TOPCAT randomized patients with HFpEF to spironolactone or placebo therapy with a primary endpoint of cardiovascular death, HF hospitalization, or aborted cardiac arrest. Patients with IVS and s' velocity measurements were included, and adjusted Cox models assessed the effect of echocardiographic variables and spironolactone on the primary endpoint.

RESULTS

Among 590 patients, mean s' velocity was 6.4 ± 2.1 cm/s and IVS thickness was 1.2 ± 0.2 cm. The enriched cohort with characteristics of cardiac amyloidosis (s' velocity ≤6 cm/s and IVS thickness ≥1.2 cm) included 135 patients (23% of the cohort). After a median follow-up of 2.6 years (1.5-3.9 years), these patients had the worst prognosis (adjusted HR: 2.10; 95% CI: 1.26-3.50; P = 0.004). Both s' velocity and IVS thickness were individually associated with the primary endpoint, and abnormalities in these parameters were additive as lower s' velocity was particularly prognostic in those with greater IVS thickness (interaction: P = 0.013). Spironolactone was associated with improved outcomes in the overall cohort (P = 0.024), and patients in the enriched cohort had a benefit similar to that in other groups (interaction: P = 0.382).

CONCLUSIONS

An enriched subset of patients with structural and functional echocardiographic features of cardiac amyloidosis had the worst prognosis in the TOPCAT study, but they benefitted similarly from spironolactone therapy. Future studies of mineralocorticoid receptor antagonists in patients with cardiac amyloidosis are warranted.

Myocardial Contraction Fraction for Risk Stratification in Low-Gradient Aortic Stenosis With Preserved Ejection Fraction

BACKGROUND

Myocardial contraction fraction (MCF) is a volumetric measure of myocardial shortening independent of left ventricular size and geometry. This multicenter study investigates the usefulness of MCF for risk stratification in low-gradient severe aortic stenosis with preserved ejection fraction.

METHODS

We included 643 consecutive patients with low-gradient severe aortic stenosis with preserved ejection fraction in whom MCF was computed at baseline and analyzed mortality during follow-up.

RESULTS

Throughout follow-up with medical and surgical management (34.9 [16.1-65.3] months), lower MCF tertiles had higher mortality than the highest tertile. Eighty-month survival was 56±4% for MCF>41%, 41±4% for MCF 30% to 41%, and 40±4% for MCF<30% (P<0.001). After comprehensive adjustment, mortality risk remained high for MCF 30% to 41% (adjusted hazard ratio, 1.53 [1.08-2.18]) and for MCF<30% (adjusted hazard ratio, 1.82 [1.24-2.66]) versus MCF>41%. The optimal MCF cutoff point for mortality prediction was 41%. Age, body mass index, Charlson index, peak aortic velocity, and ejection fraction were independently associated with mortality. MCF (χ² to improve 10.39; P=0.001), provided greater additional prognostic value over the baseline parameters than stroke volume (SV) index (χ² to improve 5.41; P=0.042), left ventricular mass index (χ² to improve 2.15; P=0.137), or global longitudinal strain (χ² to improve 3.67; P=0.061). MCF outperformed ejection fraction for mortality prediction. When patients were classified by SV index and MCF, mortality risk was low when SV index was ≥30 mL/m² and MCF>41%, higher for patients with SV index ≥30 mL/m² and MCF≤41% (adjusted hazard ratio, 1.47 [1.05-2.07]) and extremely high for patients with SV index <30 mL/m² (adjusted hazard ratio, 2.29 [1.45-3.62]).

CONCLUSIONS

MCF is a valuable marker of risk in low-gradient severe aortic stenosis with preserved ejection fraction and could improve decision-making, especially in normal-flow low-gradient severe aortic stenosis with preserved ejection fraction.

Comprehensive Proteomics Profiling Reveals Circulating Biomarkers of Hypertrophic Cardiomyopathy

BACKGROUND

Hypertrophic cardiomyopathy (HCM) is caused by mutations in the genes coding for proteins essential in normal myocardial contraction. However, it remains unclear through which molecular pathways gene mutations mediate the development of HCM. The objectives were to determine plasma protein biomarkers of HCM and to reveal molecular pathways differentially regulated in HCM.

METHODS

We conducted a multicenter case-control study of cases with HCM and controls with hypertensive left ventricular hypertrophy. We performed plasma proteomics profiling of 1681 proteins. We performed a sparse partial least squares discriminant analysis to develop a proteomics-based discrimination model with data from 1 institution (ie, the training set). We tested the discriminative ability in independent samples from the other institution (ie, the test set). As an exploratory analysis, we executed pathway analysis of significantly dysregulated proteins. Pathways with false discovery rate <0.05 were declared positive.

RESULTS

The study included 266 cases and 167 controls (n=308 in the training set; n=125 in the test set). Using the proteomics-based model derived from the training set, the area under the receiver operating characteristic curve was 0.89 (95% CI, 0.83-0.94) in the test set. Pathway analysis revealed that the Ras-MAPK (mitogen-activated protein kinase) pathway, along with its upstream and downstream pathways, was upregulated in HCM. Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-β pathway-were also upregulated.

CONCLUSIONS

This study serves as the largest-scale investigation with the most comprehensive proteomics profiling in HCM, revealing circulating biomarkers and exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-β) pathways differentially regulated in HCM.

Prediction of Genotype Positivity in Patients With Hypertrophic Cardiomyopathy Using Machine Learning

BACKGROUND

Genetic testing can determine family screening strategies and has prognostic and diagnostic value in hypertrophic cardiomyopathy (HCM). However, it can also pose a significant psychosocial burden. Conventional scoring systems offer modest ability to predict genotype positivity. The aim of our study was to develop a novel prediction model for genotype positivity in patients with HCM by applying machine learning (ML) algorithms.

METHODS

We constructed 3 ML models using readily available clinical and cardiac imaging data of 102 patients from Columbia University with HCM who had undergone genetic testing (the training set). We validated model performance on 76 patients with HCM from Massachusetts General Hospital (the test set). Within the test set, we compared the area under the receiver operating characteristic curves (AUROCs) for the ML models against the AUROCs generated by the Toronto HCM Genotype Score (the Toronto score) and Mayo HCM Genotype Predictor (the Mayo score) using the Delong test and net reclassification improvement.

RESULTS

Overall, 63 of the 178 patients (35%) were genotype positive. The random forest ML model developed in the training set demonstrated an AUROC of 0.92 (95% CI, 0.85-0.99) in predicting genotype positivity in the test set, significantly outperforming the Toronto score (AUROC, 0.77 [95% CI, 0.65-0.90], P=0.004, net reclassification improvement: P<0.001) and the Mayo score (AUROC, 0.79 [95% CI, 0.67-0.92], P=0.01, net reclassification improvement: P=0.001). The gradient boosted decision tree ML model also achieved significant net reclassification improvement over the Toronto score (P<0.001) and the Mayo score (P=0.03), with an AUROC of 0.87 (95% CI, 0.75-0.99). Compared with the Toronto and Mayo scores, all 3 ML models had higher sensitivity, positive predictive value, and negative predictive value.

CONCLUSIONS

Our ML models demonstrated a superior ability to predict genotype positivity in patients with HCM compared with conventional scoring systems in an external validation test set.

Predicting the development of adverse cardiac events in patients with hypertrophic cardiomyopathy using machine learning

BACKGROUND

Only a subset of patients with hypertrophic cardiomyopathy (HCM) develop adverse cardiac events - e.g., end-stage heart failure, cardiovascular death. Current risk stratification methods are imperfect, limiting identification of high-risk patients with HCM. Our aim was to improve the prediction of adverse cardiac events in patients with HCM using machine learning methods.

METHODS

We applied modern machine learning methods to a prospective cohort of adults with HCM. The outcome was a composite of death due to heart failure, heart transplant, and sudden death. As the reference model, we constructed logistic regression model using known predictors. We determined 20 predictive characteristics based on random forest classification and a priori knowledge, and developed 4 machine learning models. Results Of 183 patients in the cohort, the mean age was 53 (SD = 17) years and 45% were female. During the median follow-up of 2.2 years (interquartile range, 0.6-3.8), 33 subjects (18%) developed an outcome event, the majority of which (85%) was heart transplant. The predictive accuracy of the reference model was 73% (sensitivity 76%, specificity 72%) while that of the machine learning model was 85% (e.g., sensitivity 88%, specificity 84% with elastic net regression). All 4 machine learning models significantly outperformed the reference model - e.g., area under the receiver-operating-characteristic curve 0.79 with the reference model vs. 0.93 with elastic net regression (p < 0.001).

CONCLUSIONS

Compared with conventional risk stratification, the machine learning models demonstrated a superior ability to predict adverse cardiac events. These modern machine learning methods may enhance identification of high-risk HCM subpopulations.

Myocardial contraction fraction predicts mortality for patients with hypertrophic cardiomyopathy

The myocardial contraction fraction (MCF: stroke volume to myocardial volume) is a novel volumetric measure of left ventricular myocardial shortening. The purpose of the present study was to assess whether MCF could predict adverse outcomes for HCM patients. A retrospective cohort study of 438 HCM patients was conducted. The primary and secondary endpoints were all-cause mortality and HCM-related mortality. The association between MCF and endpoints was analysed. During a follow-up period of 1738.2 person-year, 76 patients (17.2%) reached primary endpoint and 50 patients (65.8%) reached secondary endpoint. Both all-cause mortality rate and HCM-related mortality rate decreased across MCF tertiles (24.7% vs. 17.9% vs. 9.5%, P trend = 0.003 for all-cause mortality; 16.4% vs. 9.7% vs. 6.1%, P trend = 0.021 for HCM-related mortality). Patients in the third tertile had a significantly lower risk of developing adverse outcomes than patients in the first tertile: all-cause mortality (adjusted HR: 0.26, 95% CI: 0.12–0.56, P = 0.001), HCM-related mortality (adjusted HR: 0.17, 95% CI: 0.07–0.42, P < 0.001). At 1-, 3-, and 5-year of follow-up, areas under curve were 0.699, 0.643, 0.618 for all-cause mortality and 0.749, 0.661, 0.613 for HCM-related mortality (all P value < 0.001), respectively. In HCM patients, MCF could independently predict all-cause mortality and HCM-related mortality, which should be considered for overall risk assessment in clinical practice.

Echocardiographic phenotype and prognosis in transthyretin cardiac amyloidosis

Aims

Transthyretin amyloidosis cardiomyopathy (ATTR-CM) is an increasingly recognized cause of heart failure. We sought to characterize the structural and functional echocardiographic phenotype across the spectrum of wild-type (wtATTR-CM) and hereditary (hATTR-CM) transthyretin cardiomyopathy and the echocardiographic features predicting prognosis.

Methods and results

We studied 1240 patients with ATTR-CM who underwent prospective protocolized evaluations comprising full echocardiographic assessment and survival between 2000 and 2019, comprising 766 with wtATTR-CM and 474 with hATTR-CM, of whom 314 had the V122I variant and 127 the T60A variant. At diagnosis, patients with V122I-hATTR-CM had the most severe degree of systolic and diastolic dysfunction across all echocardiographic parameters and patients with T60AhATTR-CM the least; patients with wtATTR-CM had intermediate features. Stroke volume index, right atrial area index, longitudinal strain, and E/e’ were all independently associated with mortality (P &lt; 0.05 for all). Severe aortic stenosis (AS) was also independently associated with prognosis, conferring a significantly shorter survival (median survival 22 vs. 53 months, P = 0.001).

Conclusion

The three distinct genotypes present with varying degrees of severity. Echocardiography indicates a complex pathophysiology in which both systolic and diastolic function are independently associated with mortality. The presence of severe AS was independently associated with significantly reduced patient survival.