Mortality After Alcohol Septal Ablation vs. Septal Myectomy in Patients With Obstructive Hypertrophic Cardiomyopathy.

Background: Alcohol septal ablation (ASA) and septal myectomy (SM) are 2 options for septal reduction therapy (SRT) to treat medication-resistant symptomatic obstructive hypertrophic cardiomyopathy (HCM). Because differences in mortality rates after these different SRT methods have not been extensively investigated in real-world settings, in this study compared the 1-year mortality rates after ASA and SM using population-based database. Methods and Results: Utilizing New York Statewide Planning and Research Cooperative System (SPARCS) data from 2005 to 2016, we performed a comparative effectiveness study of ASA vs. SM in patients with HCM. The outcome was all-cause death up to 360 days after SRT. We constructed a multivariable logistic regression model and performed sensitivity analysis with propensity score (PS)-matching and inverse probability of treatment weighting (IPTW) methods. We identified 755 patients with HCM who underwent SRT: 348 with ASA and 407 with SM. The multivariable analysis showed that all-cause deaths were significantly fewer in the ASA group at 360 days after SRT (adjusted odds ratio=0.34; 95% confidence interval [CI] 0.13-0.84; P=0.02). The PS-matching and IPTW methods also supported a lower mortality rate in the ASA group at 360 days post-SRT. Conclusions: In this population-based study of patients with HCM who underwent SRT in a real-world setting, the 1-year all-cause mortality rate was significantly lower in patients who underwent ASA compared with SM.

Efficacy and Safety of Aficamten in Symptomatic Nonobstructive Hypertrophic Cardiomyopathy: Results From the REDWOOD-HCM Trial, Cohort 4.

BACKGROUND: This open-label phase 2 trial evaluated the safety and efficacy of aficamten in patients with nonobstructive hypertrophic cardiomyopathy (nHCM). METHODS: Patients with symptomatic nHCM (left ventricular outflow tract obstruction gradient ≤ 30 mmHg, left ventricular ejection fraction [LVEF] ≥ 60%, N-terminal pro-B-type natriuretic peptide [NT-proBNP] > 300 pg/mL) received aficamten 5-15 mg once daily (doses adjusted according to echocardiographic LVEF) for 10 weeks. RESULTS: We enrolled 41 patients (mean ± SD age 56 ± 16 years; 59% female). At Week 10, 22 (55%) patients experienced an improvement of ≥ 1 New York Heart Association class; 11 (29%) became asymptomatic. Clinically relevant improvements in Kansas City Cardiomyopathy Questionnaire Clinical Summary Scores occurred in 22 (55%) patients. Symptom relief was paralleled by reductions in NT-proBNP levels (56%; P < 0.001) and high-sensitivity cardiac troponin I (22%; P < 0.005). Modest reductions in LVEF (mean ± SD) of -5.4% ± 10 to 64.6% ± 9.1 were observed. Three (8%) patients had asymptomatic reduction in LVEF < 50% (range: 41%-48%), all returning to normal after 2 weeks of washout. One patient with prior history of aborted sudden cardiac death experienced a fatal arrhythmia during the study. CONCLUSIONS: Aficamten administration for symptomatic nHCM was generally safe and was associated with improvements in heart failure symptoms and cardiac biomarkers. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04219826.

Prediction of cardiac death in patients with hypertrophic cardiomyopathy using plasma adipokine levels.

BACKGROUNDS AND AIMS: Hypertrophic cardiomyopathy (HCM) causes cardiac death through both sudden cardiac death (SCD) and death due to heart failure (HF). Although adipokines lead to adverse cardiac remodeling in HCM, the prognostic value of plasma adipokines in HCM remains unknown. We aimed to predict cardiac death in patients with HCM using plasma adipokines. METHODS AND RESULTS: We performed a multicenter prospective cohort study of patients with HCM. The outcome was cardiac death including heart transplant, death due to HF, and SCD. With data from 1 institution (training set), a prediction model was developed using random forest classification algorithm based on 10 plasma adipokines. The performance of the prediction model adjusted for 8 clinical parameters was examined in samples from another institution (test set). Time-to-event analysis was performed in the test set to compare the rate of outcome events between the low-risk and high-risk groups determined by the prediction model. In total, 389 (267 in the training set; 122 in the test set) patients with HCM were included. During the median follow-up of 2.7 years, 21 patients experienced the outcome event. The area under the covariates-adjusted receiver-operating characteristics curve was 0.89 (95 % confidence interval [CI] 0.71-0.99) in the test set. revealed the high-risk group had a significantly higher risk of cardiac death (hazard ratio 17.8, 95 % CI 2.1-148.3, P = 0.008). CONCLUSION: The present multicenter prospective study demonstrated that a panel of plasma adipokines predicts cardiac death in patients with HCM.

Signaling Pathways Associated With Prior Cardiovascular Events in Hypertrophic Cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiomyopathy. A subset of patients experience major adverse cardiovascular events (MACEs), including arrhythmias, strokes and heart failure. However, the molecular mechanisms underlying MACEs in HCM are still not well understood. Therefore, we conducted a multicenter case-control study of patients with HCM, comparing those with and without prior histories of MACEs to identify dysregulated signaling pathways through plasma proteomics profiling. METHODS: We performed plasma proteomics profiling of 4986 proteins. We developed a proteomics-based discrimination model in patients enrolled at 1 institution (training set) and externally validated the model in patients enrolled at another institution (test set). We performed pathway analysis of proteins dysregulated in patients with prior MACEs. RESULTS: A total of 402 patients were included, with 278 in the training set and 124 in the test set. In this cohort, 257 (64%) patients had prior MACEs (172 in the training set and 85 in the test set). Using the proteomics-based model from the training set, the area under the receiver operating characteristic curve was 0.82 (95% confidence interval, 0.75-0.90) in the test set. Patients with prior MACEs demonstrated dysregulation in pathways known to be associated with MACEs (eg, TGF-ß) and novel pathways (eg, Ras-MAPK and associated pathways). CONCLUSIONS: In this multicenter study of 402 patients with HCM, we identified both known and novel pathways dysregulated in a subset of patients with more advanced disease.

Prediction of worsening heart failure in hypertrophic cardiomyopathy using plasma proteomics.

OBJECTIVE: Heart failure (HF) is one of the most common and lifestyle-limiting complications of hypertrophic cardiomyopathy (HCM). Prediction of worsening HF using clinical measures alone remains limited. Moreover, the mechanisms by which patients with HCM develop worsening HF have not been elucidated. Therefore, the aim of this study was to develop a plasma proteomics-based model to predict worsening HF among patients with HCM and to identify signalling pathways that are differentially regulated in those who subsequently develop worsening HF. METHODS: In this multi-centre, prospective cohort study of 389 patients with HCM, plasma proteomics profiling of 4986 proteins was performed at enrolment. A proteomics-based random forest model was developed to predict worsening HF using data from one institution (training set, n=268). This model was externally validated in patients from a different institution (test set, n=121). Pathway analysis of proteins significantly dysregulated in patients who subsequently developed worsening HF compared with those who did not was executed, using a false discovery rate (FDR) threshold of <0.001. RESULTS: Using the 11-protein proteomics-based model derived from the training set, the area under the receiver-operating characteristic curve to predict worsening HF was 0.87 (95% CI: 0.76 to 0.98) in the test set. Pathway analysis revealed that the Ras-MAPK pathway (FDR<0.00001) and related pathways were dysregulated in patients who subsequently developed worsening HF. CONCLUSIONS: The present study with comprehensive plasma proteomics profiling demonstrated a high accuracy to predict worsening HF in patients with HCM and identified the Ras-MAPK and related signalling pathways as potential underlying mechanisms.

Comprehensive Transcriptomics Profiling of MicroRNA Reveals Plasma Circulating Biomarkers of Hypertrophic Cardiomyopathy and Dysregulated Signaling Pathways.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is caused by mutations in genes coding for proteins essential for myocardial contraction. However, it remains unclear through which signaling pathways these gene mutations mediate HCM pathogenesis. Growing evidence indicates that microRNAs (miRNAs) play an important role in the regulation of gene expression. We hypothesized that transcriptomics profiling of plasma miRNAs would reveal circulating biomarkers and dysregulated signaling pathways in HCM. METHODS: We conducted a multicenter case-control study of cases with HCM and controls with hypertensive left ventricular hypertrophy. We performed plasma transcriptomics profiling of miRNAs using RNA sequencing. We developed a transcriptomics-based discrimination model using samples retrieved during the first two-thirds of the study period at one institution (training set). We prospectively tested its discriminative ability in samples collected thereafter from the same institution (prospective test set). We also externally validated the model by applying it to samples collected from the other institutions (external test set). We executed pathway analysis of dysregulated miRNAs with univariable P<0.05. RESULTS: This study included 555 patients (392 cases and 163 controls). One thousand one hundred forty-one miRNAs passed our quality control filters. The area under the receiver operating characteristic curve of the transcriptomics-based model derived from the training set was 0.86 (95% CI, 0.79-0.93) in the prospective test set and 0.94 (95% CI, 0.90-0.97) in the external test set. Pathway analysis revealed dysregulation of the Ras-MAPK (mitogen-activated protein kinase) pathway and pathways related to inflammation in HCM. CONCLUSIONS: This study utilized comprehensive transcriptomics profiling with RNA sequencing in HCM, revealing circulating miRNA biomarkers and dysregulated pathways.

Predictors of cardiovascular implantable electronic device dependence at long-term follow-up after alcohol septal ablation in hypertrophic cardiomyopathy patients.

BACKGROUND: The most common complication of alcohol septal ablation (ASA) is transient periprocedural high-grade AV block (HGAVB). To date, no long-term follow-up of cardiovascular implantable electronic device (CIED) utilization after ASA has been reported. We hypothesized that CIED dependence on long-term follow-up can be predicted by ECG or procedural characteristics. METHODS: We analyzed all patients with HCM who underwent ASA from December 1998 to December 2019 and received their first CIED within 30 days after ASA for HGAVB. All follow-up interrogations were reviewed. CIED dependence was defined as ventricular pacing of ≥ 5%. RESULTS: A total of 138 patients with HCM underwent ASA. Of these, 35 had a prior device and were excluded. Of the remaining 103 patients, 25 patients received a CIED for HGAVB within 30 days after ASA. Average follow-up duration was 10.1 years. On long-term follow-up, 16 patients (64%) were found to be CIED-dependent. Baseline characteristics, including pre- and post-ASA ECG, were not significantly different between dependent and non-dependent patients. The only predictor for CIED dependence was > 1 ml of alcohol injected (OR 6.0, p = 0.031). CONCLUSIONS: CIED implantation after ASA is common. Almost two thirds of patients who received a CIED for post-procedural HGAVB were CIED-dependent on long-term follow-up. CIED dependence can be predicted by the amount of injected alcohol > 1 ml.

Phase 2 Study of Aficamten in Patients With Obstructive Hypertrophic Cardiomyopathy.

BACKGROUND: Left ventricular outflow tract (LVOT) obstruction is a major determinant of heart failure symptoms in obstructive hypertrophic cardiomyopathy (oHCM). Aficamten, a next-in-class cardiac myosin inhibitor, may lower gradients and improve symptoms in these patients. OBJECTIVES: This study aims to evaluate the safety and efficacy of aficamten in patients with oHCM. METHODS: Patients with oHCM and LVOT gradients ≥30 mm Hg at rest or ≥50 mm Hg with Valsalva were randomized 2:1 to receive aficamten (n = 28) or placebo (n = 13) in 2 dose-finding cohorts. Doses were titrated based on gradients and ejection fraction (EF). Safety and changes in gradient, EF, New York Heart Association functional class, and cardiac biomarkers were assessed over a 10-week treatment period and after a 2-week washout. RESULTS: From baseline to 10 weeks, aficamten reduced gradients at rest (mean difference: -40 ± 27 mm Hg, and -43 ± 37 mm Hg in Cohorts 1 and 2, P = 0.0003 and P = 0.0004 vs placebo, respectively) and with Valsalva (-36 ± 27 mm Hg and -53 ± 44 mm Hg, P = 0.001 and <0.0001 vs placebo, respectively). There were modest reductions in EF (-6% ± 7.5% and -12% ± 5.9%, P = 0.007 and P < 0.0001 vs placebo, respectively). Symptomatic improvement in ≥1 New York Heart Association functional class was observed in 31% on placebo, and 43% and 64% on aficamten in Cohorts 1 and 2, respectively (nonsignificant). With aficamten, N-terminal pro-B-type natriuretic peptide was reduced (62% relative to placebo, P = 0.0002). There were no treatment interruptions and adverse events were similar between treatment arms. CONCLUSIONS: Aficamten resulted in substantial reductions in LVOT gradients with most patients experiencing improvement in biomarkers and symptoms. These results highlight the potential of sarcomere-targeted therapy for treatment of oHCM.

Apical Aneurysms and Mid-Left Ventricular Obstruction in Hypertrophic Cardiomyopathy.

BACKGROUND: Apical left ventricular (LV) aneurysms in hypertrophic cardiomyopathy (HCM) are associated with adverse outcomes. The reported frequency of mid-LV obstruction has varied from 36% to 90%. OBJECTIVES: The authors sought to ascertain the frequency of mid-LV obstruction in HCM apical aneurysms. METHODS: The authors analyzed echocardiographic and cardiac magnetic resonance examinations of patients with aneurysms from 3 dedicated programs and compared them with 63 normal controls and 47 controls with apical-mid HCM who did not have aneurysms (22 with increased LV systolic velocities). RESULTS: There were 108 patients with a mean age of 57.4 ± 13.5 years; 40 (37%) were women. A total of 103 aneurysm patients (95%) had mid-LV obstruction with mid-LV complete systolic emptying. Of the patients with obstruction, 84% had a midsystolic Doppler signal void, a marker of complete flow cessation, but only 19% had Doppler systolic gradients ≥30 mm Hg. Five patients (5%) had relative hypokinesia in mid-LV without obstruction. Aneurysm size is not bimodal but appears distributed by power law, with large aneurysms decidedly less common. Comparing mid-LV obstruction aneurysm patients with all control groups, the short-axis (SAX) systolic areas were smaller (P < 0.007), the percent SAX area change was greater (P < 0.005), the papillary muscle (PM) areas were larger (P < 0.003), and the diastolic PM areas/SAX diastolic areas were greater (P < 0.005). Patients with aneurysms had 22% greater SAX PM areas compared with those with elevated LV velocities but no aneurysms (median: 3.00 cm2 [IQR: 2.38-3.70 cm2] vs 2.45 [IQR: 1.81-2.95 cm2]; P = 0.004). Complete emptying occurs circumferentially around central PMs that contribute to obstruction. Late gadolinium enhancement was always brightest and the most transmural apical of, or at the level of, complete emptying. CONCLUSIONS: The great majority (95%) of patients in the continuum of apical aneurysms have associated mid-LV obstruction. Further research to investigate obstruction as a contributing cause to apical aneurysms is warranted.

Prediction of Major Adverse Cardiovascular Events in Patients With Hypertrophic Cardiomyopathy Using Proteomics Profiling.

BACKGROUND: Hypertrophic cardiomyopathy often causes major adverse cardiovascular events (MACE), for example, arrhythmias, stroke, heart failure, and sudden cardiac death. Currently, there are no models available to predict MACE. Furthermore, it remains unclear which signaling pathways mediate MACE. Therefore, we aimed to prospectively determine protein biomarkers that predict MACE in hypertrophic cardiomyopathy and to identify signaling pathways differentially regulated in patients who subsequently develop MACE. METHODS: In this multi-centre prospective cohort study of patients with hypertrophic cardiomyopathy, we conducted plasma proteomics profiling of 4979 proteins upon enrollment. We developed a proteomics-based model to predict MACE using data from one institution (training set). We tested the predictive ability in independent samples from the other institution (test set) and performed time-to-event analysis. Additionally, we executed pathway analysis of predictive proteins using a false discovery rate threshold of <0.001. RESULTS: The study included 245 patients (n=174 in the training set and n=71 in the test set). Using the proteomics-based model to predict MACE derived from the training set, the area under the receiver-operating-characteristic curve was 0.81 (95% CI, 0.68-0.93) in the test set. In the test set, the high-risk group determined by the proteomics-based predictive model had a significantly higher rate of developing MACE (hazard ratio, 13.6 [95% CI, 1.7-107]; P=0.01). The Ras-MAPK (mitogen-activated protein kinase) pathway was upregulated in patients who subsequently developed MACE (false discovery rate<1.0×10-7). Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-ß pathway-were also upregulated. CONCLUSIONS: This study serves as the first to demonstrate the ability of proteomics profiling to predict MACE in hypertrophic cardiomyopathy, exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-ß) pathways differentially regulated in patients who subsequently experience MACE.

Comprehensive Proteomics Profiling Identifies Patients With Late Gadolinium Enhancement on Cardiac Magnetic Resonance Imaging in the Hypertrophic Cardiomyopathy Population.

Introduction: In hypertrophic cardiomyopathy (HCM), late gadolinium enhancement (LGE) on cardiac magnetic resonance imaging (CMR) represents myocardial fibrosis and is associated with sudden cardiac death. However, CMR requires particular expertise and is expensive and time-consuming. Therefore, it is important to specify patients with a high pre-test probability of having LGE as the utility of CMR is higher in such cases. The objective was to determine whether plasma proteomics profiling can distinguish patients with and without LGE on CMR in the HCM population. Materials and Methods: We performed a multicenter case-control (LGE vs. no LGE) study of 147 patients with HCM. We performed plasma proteomics profiling of 4,979 proteins. Using the 17 most discriminant proteins, we performed logistic regression analysis with elastic net regularization to develop a discrimination model with data from one institution (the training set; n = 111) and tested the discriminative ability in independent samples from the other institution (the test set; n = 36). We calculated the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity. Results: Overall, 82 of the 147 patients (56%) had LGE on CMR. The AUC of the 17-protein model was 0.83 (95% confidence interval [CI], 0.75-0.90) in the training set and 0.71 in the independent test set for validation (95% CI, 0.54-0.88). The sensitivity of the training model was 0.72 (95% CI, 0.61-0.83) and the specificity was 0.78 (95% CI, 0.66-0.90). The sensitivity was 0.71 (95% CI, 0.49-0.92) and the specificity was 0.74 (95% CI, 0.54-0.93) in the test set. Based on the discrimination model derived from the training set, patients in the test set who had high probability of having LGE had a significantly higher odds of having LGE compared to those who had low probability (odds ratio 29.6; 95% CI, 1.6-948.5; p = 0.03). Conclusions: In this multi-center case-control study of patients with HCM, comprehensive proteomics profiling of 4,979 proteins demonstrated a high discriminative ability to distinguish patients with and without LGE. By identifying patients with a high pretest probability of having LGE, the present study serves as the first step to establishing a panel of circulating protein biomarkers to better inform clinical decisions regarding CMR utilization.

Proteomics profiling reveals a distinct high-risk molecular subtype of hypertrophic cardiomyopathy.

OBJECTIVE: Hypertrophic cardiomyopathy (HCM) is a heterogeneous disease, likely encompassing several subtypes of disease with distinct biological mechanisms (ie, molecular subtypes). Current models based solely on clinical data have yielded limited accuracy in predicting the risk of major adverse cardiovascular events (MACE). Our aim in this study was to derive molecular subtypes in our multicentre prospective cohort of patients with HCM using proteomics profiling and to examine their longitudinal associations with MACE. METHODS: We applied unsupervised machine learning methods to plasma proteomics profiling data of 1681 proteins from 258 patients with HCM who were prospectively followed for a median of 2.8 years. The primary outcome was MACE, defined as a composite of arrhythmia, heart failure, stroke and sudden cardiac death. RESULTS: We identified four molecular subtypes of HCM. Time-to-event analysis revealed significant differences in MACE-free survival among the four molecular subtypes (plogrank=0.007). Compared with the reference group with the lowest risk of MACE (molecular subtype A), patients in molecular subtype D had a higher risk of subsequently developing MACE, with an HR of 3.41 (95% CI 1.54 to 7.55, p=0.003). Pathway analysis of proteins differentially regulated in molecular subtype D demonstrated an upregulation of the Ras/mitogen-activated protein kinase and associated pathways, as well as pathways related to inflammation and fibrosis (eg, transforming growth factor-ß pathway). CONCLUSIONS: Our prospective plasma proteomics study not only exhibited the presence of HCM molecular subtypes but also identified pathobiological mechanisms associated with a distinct high-risk subtype of HCM.

Exercise Oscillatory Ventilation in Hypertrophic Cardiomyopathy.

To assess the prevalence and clinical correlates of exercise oscillatory ventilation (EOV) in patients with hypertrophic cardiomyopathy (HCM). Retrospective single-center study. Thirty-six consecutive HCM patients who underwent cardiopulmonary exercise testing. Two patients (5.6%) had EOV. Both patients with peak oxygen consumption (VO2) less than or equal to 9.1 ml/kg/min had EOV. Left atrial size was greater in patients with EOV. Of the 2 patients in the study population with an abnormal blood pressure response to exercise, 1 had EOV. Both patients with New York Heart Association Class 3 heart failure had EOV. This is the first report of EOV in HCM. EOV is uncommon in patients with HCM. EOV appears to be a marker of disease severity as evidenced by overt heart failure, left atrial enlargement, and low peak VO2.

Effects of Septal Reduction Therapy on Acute Cardiovascular Events and All-Cause Mortality in Patients with Hypertrophic Cardiomyopathy.

Septal reduction therapy (SRT) -i.e. septal myectomy and alcohol septal ablation-has been performed to treat medically refractory hypertrophic cardiomyopathy (HCM) for decades. However, it is largely unknown whether SRT prevents HCM-related cardiovascular events or death. The objective was to examine the effects of SRT on acute cardiovascular events and all-cause mortality in HCM. We performed a propensity score (PS) -matched study using databases that capture all hospitalizations and outpatient visits in New York state. We identified patients with HCM who underwent SRT between 2007 and 2014 (i.e. the SRT group) and those who had never had SRT but had at least one hospitalization for HCM during the same period (i.e. the control group). We performed PS matching at a 1:1 ratio. The primary outcome was a composite of acute cardiovascular events and all-cause mortality during 0-180 days and 181-360 days. The secondary outcome was 180- and 360-day all-cause mortality. We included 846 patients with HCM (423 PS-matched pairs). Patients who underwent SRT had a lower risk of the primary outcome event (0-180 days: odds ratio [OR], 0.54; 95% confidence intervals (CI), 0.37-0.80; P = 0.002 and 181-360 days: OR, 0.33; 95% CI, 0.22-0.51; P < 0.0001). Furthermore, the risk of all-cause mortality was lower at 180 days (OR, 0.37; 95% CI, 0.22-0.63; P = 0.0003) and 360 days post-SRT (OR, 0.32; 95% CI, 0.20-0.51; P < 0.0001). In conclusion, our PS-matched study using population-based datasets demonstrated that SRT was associated with a reduced risk of a composite of acute cardiovascular events and all-cause mortality in HCM during the first post-SRT year.

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.

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)-ß 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-ß) 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.

Difference in Metabolomic Response to Exercise between Patients with and without Hypertrophic Cardiomyopathy.

It is unclear how hypertrophic cardiomyopathy (HCM) affects cardiac metabolic pathways at rest and with exercise. This case-control study compared 15 cases with HCM to 2 control groups without HCM. Metabolomic profiling of 210 metabolites was carried out at rest and at peak exercise. The 50 most discriminant metabolites differentially regulated during exercise were selected using partial least squares discriminant analysis. Pathway enrichment analysis was also performed. At rest, no significant difference was observed in metabolomic profiling of HCM cases as compared to controls. By contrast, there were significant differences in metabolomic profiling in response to exercise (p < 0.05) in the following metabolic pathways: the aminoacyl-tRNA biosynthesis pathway; the nitrogen metabolism pathway; the glycine, serine, and threonine metabolism pathway; and the arginine and proline metabolism pathway. The present study demonstrates differential regulation of several metabolic pathways in patients with HCM in the setting of exercise stress.