Araoz, Philip A.

Abstract Background Hypertrophic cardiomyopathy (HCM) is a heritable cardiac disorder characterized by increased left ventricular (LV) wall thickness, often leading to heart failure (HF). The role of cardiovascular magnetic resonance (CMR) imaging in predicting new onset of HF symptoms in patients with HCM remains unknown. This study aimed to identify CMR predictors of new-onset HF symptoms in individuals with HCM. Methods This study was a single-centre retrospective cohort study of HCM patients treated at a tertiary referral centre in the USA who underwent CMR examination between 1998 and 2018, had no HF symptoms at baseline CMR, and at least 1 year of follow-up. Clinical data were collected by review of electronic medical records, and CMR images were analyzed by a blinded expert cardiac radiologis. The primary outcome was new onset of HF symptoms, defined as New York Heart Association (NYHA) class ≥ II at follow-up. Kaplan–Meier analyses and Cox proportional hazard analyses were performed. Results Of 1462 patients diagnosed with HCM who had at least 1 CMR, 276 HCM patients without HF symptoms (average age 52.7 years, 33.3% female),median maximum left ventricular (LV) wall thickness was 19 mm ([IQR] 17–22) with a and median LV ejection fraction of 71% (IQR 66–77). Late gadolinium enhancement was detected in 56.2%) patients (60.7% had mild; 30.7% moderate; 8.6% severe). During a median follow-up period of 6.3 years, 93 patients developed HF symptoms (NYHA class II in 56 (60.2%); class III in 31 (33.3%); and class IV in 6 (6.5%). Multivariable analysis adjusted for age showed that LA enlargement (HR 1.626; 95% CI 1.01–2.62; P = .045) and LV mass index (HR 1.014; 95% CI 1.007–1.022; P≤ .001) and sex (HR 1.7; 95% CI 1.074–2.691; P = .023) were independent predictors of new onset of HF symptoms in patients with HCM. Conclusions Nearly half of the patients with HCM developed HF symptoms within 6.3 years. Left atrial enlargement, LV mass index, and sex were independent predictors of new onset of HF symptoms in HCM patients. These findings emphasize the value of CMR in HF risk stratification.

ABSTRACT Purpose Magnetic resonance elastography (MRE) of the heart has predominantly utilized breath‐held acquisitions with limited anatomic coverage. This work investigates the feasibility of 3D, free‐breathing cardiac MRE. Methods A 3D hybrid radial and EPI acquisition is utilized and combined with retrospective binning of k ‐space via physiologic monitoring, iterative reconstruction, MRE processing and inversions, and post‐processing specialized for cardiac imaging. Results Feasibility of free‐breathing stiffness mapping of the left ventricular myocardium was demonstrated in 11 participants. Myocardial stiffness estimates were obtained throughout the left ventricle for seven temporal states of the cardiac cycle. Stiffness estimates were presented in participant‐specific images, 17‐segment bullseye plots, and stiffness plots throughout the cardiac cycle. Results indicate increased stiffness in systolic states, with localized stiffness heterogeneity observed between participants, illustrating a key benefit of 3D cardiac MRE acquisition. Mean stiffness estimates obtained through 11 subjects were 4.44 ± 0.68 kPa (begin‐systole), 5.74 ± 0.94, 5.42 ± 0.87 (end‐systole), 3.84 ± 0.71 (begin‐diastole), 3.51 ± 0.74, 3.54 ± 0.52, and 3.55 ± 0.59 kPa (end‐diastole). Statistical testing indicated significant differences in stiffness across cardiac states with one‐way, repeated‐measures ANOVA ( p < 0.001) and Friedman's test ( p < 0.001). Experiments in scanning three participants with and without applied motion indicated that stiffness changes were more robustly detected in systole than in diastole, although patterns were subject‐specific. Comparison of free‐breathing stiffness measurements against reference breath‐held measurements yielded similar systolic stiffness estimates. Conclusions This study establishes the feasibility of free‐breathing, 3D cardiac MRE in healthy volunteers. With the current framework, stiffness estimation appears more robust in systole, and the reliability of diastolic estimates is limited and subject‐specific.

BACKGROUND: Pharmacological treatments for fibrocalcific aortic valve stenosis (FCAVS) have been elusive for >50 years. Here, we tested the hypothesis that reactivation of oxidized sGC (soluble guanylate cyclase), the primary receptor for nitric oxide, with ataciguat is a safe and efficacious strategy to slow progression of FCAVS. METHODS: We used quantitative real-time reverse transcription polymerase chain reaction, Western blotting, and immunohistochemistry to characterize sGC signaling and the biological effects of ataciguat on signaling cascades related to nitric oxide, calcification, and fibrosis in excised human aortic valve tissue, aortic valve interstitial cells, and mouse aortic valves. We then conducted randomized, placebo-controlled phase I (14-day safety/tolerance) and phase II (6-month efficacy) trials in patients with moderate aortic valve stenosis. RESULTS: In excised human tissue, we found robust losses in sGC signaling despite upregulation of sGC subunits. In vitro, ataciguat increased sGC signaling and reduced BMP2 (bone morphogenetic protein 2) signaling in aortic valve interstitial cells. In mice with established FCAVS, treatment with ataciguat attenuated BMP signaling and slowed progression of valve calcification and dysfunction. In a phase I, randomized, placebo-controlled trial, treatment with ataciguat for 2 weeks was safe and well tolerated in patients with moderate FCAVS ( https://www.clinicaltrials.gov ; Unique identifier: NCT02049203). In a separate phase II, randomized, placebo-controlled trial, treatment with ataciguat for 6 months slowed the progression of aortic valve calcification and tended to slow the progression of valvular and ventricular dysfunction in patients with moderate FCAVS ( https://www.clinicaltrials.gov ; Unique identifier: NCT02481258). CONCLUSIONS: Collectively, this study highlights the therapeutic potential of the targeted restoration of the diseased/inactive form of sGC for treatment of FCAVS. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02049203. URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02481258.

Purpose: We previously analyzed the 2006 appropriateness criteria for cardiac computed tomography (CT) and found 42% of our patients were not classifiable, suggesting the criteria were difficult to apply clinically. These criteria were revised in 2010. The purpose of our study is to compare the new 2010 appropriate use criteria to the original 2006 criteria to determine if the 2010 criteria were more applicable. Materials and Methods: In a previous study we applied the 2006 appropriateness criteria to 251 patients that underwent cardiac CT from June 1 to December 31, 2007. Each patient was assigned an indication from the 2006 appropriate use criteria by two observers, classifying patients as appropriate, inappropriate, uncertain, or nonclassifiable. A third observer settled disagreements. This process was applied to the same 251 patients using the 2010 criteria. The data from the previous study using the 2006 criteria was compared to the data obtained using the 2010 criteria to determine changes in the number of nonclassifiable, appropriate, inappropriate, and uncertain exams. These results were then analyzed using Bowker's test of symmetry. Observer agreement was calculated using the kappa statistic. Results: 115 patients (46%) were not classifiable using the 2006 criteria. When the 2010 criteria were applied, the number of not classifiable patients decreased to 40 (16%), with the plurality, 41 (16%), of the not classifiable patients moving to inappropriate. In 2006, 69 (27%) were classified as appropriate, 25 (10%) were uncertain, 42 (17%) inappropriate. In 2010, the appropriate increased to 84 (33%), uncertain increased to 40 (16%), and inappropriate increased to 87 (35%). Bowker's test of symmetry demonstrated a statistically significant change with a p<0.001. Agreement between the two observers assigning patients to classification groups also improved with a kappa of 0.55 using the 2010 criteria compared to kappa of 0.31 in 2006. Conclusion: The number of not classifiable patients decreased using the 2010 criteria and the interobserver agreement improved, suggesting they are more applicable and complete than the 2006 criteria. This decrease in not classifiable patients came primarily from an increase in the number of inappropriate indications.

Introduction: Aortic Stenosis (AS) is a common valve disease of the adult, caused by calcification of aortic cusps. The relationship between cusp calcification and severity of AS has been established mostly in men and it is unclear whether the calcification process and its relation to AS severity is similar in women Hypothesis: We hypothesized that the physiologic relation between aortic valve calcification (AVC) load and AS severity is different in men and women and that lower calcification loads may lead to severe AS in women Methods: We prospectively performed high resolution computed tomography (CT) without contrast in 679 patients with AS (Aortic valve area (AVA) <2cm 2 , Mean gradient ≥10 mmHg) at two academic centers (Rochester, MN; Paris, France) to measure AVC scores and compare these to the hemodynamic obstruction measured by Echo-Doppler. Results: Women vs. men were older (76±11 vs 74±11, p<0.0001) with slightly higher ejection fraction (62±12 vs 59±13, p=0.001) but AS severity was similar (AVA/body surface area 0.55±0.2 vs 0.56±0.2 cm 2 /m 2 ; Peak velocity 4±1 vs 3.9±1 m/s; Mean gradient 41±21 vs 38±18 mmHg, all p≥0.1). Yet, AVC load was lower in women vs men, score absolute or normalized for LVOT area (551±383 vs 626±399 all p<0.0001). For those with severe AS (Mean gradient >40 mmHg), AVC scores were 2433±1412 in women vs 3805±1587 in men. Multivariate analysis adjusting for age, BSA, LVOT area and mean gradient, confirmed that severe AS was achieved with lower AVC load in women (all p<0.0001). Conclusions: In this large multicenter AS population, women reached similar AS severity than males for lower calcium loads, even after adjustment for lower body and LVOT size. Hence the relation between valvular calcification process and severity of AS differs in women vs. men. Thus, future studies are needed to determine the differential mechanism of aortic calcification in men and women and diagnostic consideration must be given to gender in the interpretation of AS severity by AVC load.

Introduction: Heart failure with preserved ejection fraction (HFpEF) is an increasingly common clinical syndrome with diagnostic challenges and no effective treatment. The high heterogeneity of HFpEF clinical syndrome demands novel technologies to distinguish from heart failure with reduced ejection Fraction (HFrEF). Currently, invasive hemodynamic exercise testing is performed to evaluate for HFpEF with right heart catheterization. Hypothesis: We hypothesize that a deep learning model can noninvasively detect patients with HFpEF using phonocardiogram (PCG). Methods: Eligible patients undergoing right heart catheterization were recruited after IRB approval. Eko duo stethoscope was used to record simultaneous ECG & PCG at the mitral area on the patients prior to right heart catheterization at baseline. Each recording was 30 seconds in length with a frequency range between 250 - 5000 Hz. R-wave peak detection was performed & beat to beat PCG annotations were performed. The annotated PCG signals were converted into Mel-frequency cepstral coefficients (MFCC) and padded to account for the difference in lengths. Deep Learning model was developed using a Sequential Neural Network with 80% data to train and validate the model and 20% hold out data was used for testing accuracy of the model. Receiver operating curve (ROC) was obtained to estimate area under the curve (AUC) for performance analysis. Results: 42 patients were enrolled with 25 patients diagnosed as HFpEF by right heart catheterization and 17 were reported as controls. 2544 & 1771 individual PCG annotations for HFpEF and controls were used for model training/validation and testing. The model performed reasonably well with accuracy of 0.89, F1 score of 0.88, precision of 0.80 and recall of 0.97 on the test data. ROC was obtained with AUC of 0.94. Conclusions: The results demonstrate the ability of the deep learning model to noninvasively detect HFpEF using PCG. Larger study is needed to validate these findings.