Resection of hypertrophic papillary muscles and mitral valve replacement in a patient with midventricular hypertrophic obstructive cardiomyopathy

Midventricular hypertrophic obstructive cardiomyopathy (HOCM) is characterized by hypertrophy of the interventricular septum (IVS) and - in rare cases - of the papillary muscles (PM), which subsequently can cause dynamic left ventricular outflow tract obstruction (LVOTO) and severe heart failure symptoms. We report on a rare case of a 44-year-old patient suffering from midventricular HOCM with hypertrophic anterolateral PM and an additional chorda between the PM and the IVS. We describe a new surgical approach via right anterolateral thoracotomy in 3-dimensional (3D) video-assisted minimal-invasive technique with resection of hypertrophic PMs as well as the entire mitral valve-apparatus and valve replacement avoiding septal myectomy and potentially associated complications. After an uneventful procedure clinical symptoms improved from NYHA III-IV at baseline to NYHA 0-I postoperatively and remained stable over a follow-up period of 24 months. Therefore, the presented technique may be considered as a new and alternative approach in patients with hypertrophic PMs and hypertrophic IVS as subtype of midventricular HOCM

CMR-based right ventricular strain analysis in cardiac amyloidosis and its potential as a supportive diagnostic feature

$\it Background:$ Right ventricular (RV) strain has provided valuable prognostic information for patients with $\underline {cardiac amyloidosis}$ (CA). However, the extent to which RV strain and strain rate can differentiate CA is not yet clinically established. CA underdiagnosis delays treatment strategies and exacerbates patient prognosis. $\it Aims:$ Evaluation of $\underline {cardiac magnetic resonance}$ (CMR) quantified RV global and regional strain of CA and $\underline {HCM}$ patients along with CA subtypes. $\it Methods:$ CMR feature tracking attained longitudinal, radial and circumferential global and regional strain in 47 control subjects (CTRL), 43 CA-, 20 hypertrophic cardiomyopathy- (HCM) patients. CA patients were subdivided in 21 transthyretin-related amyloidosis (ATTR) and 20 acquired immunoglobulin light chain (AL) patients. Strain data and baseline clinical parameters were statistically analysed with respect to $\underline {diagnostic performance}$ and discriminatory power between the different clinical entities. $\it Results:$ Effective differentiation of CA from HCM patients was achieved utilizing global longitudinal (GLS: 16.5 $\pm$ 3.9% vs. −21.3 $\pm$ 6.7%, p = 0.032), radial (GRS: 11.7 $\pm$ 5.3% vs. 16.5 $\pm$ 7.1%, p < 0.001) and circumferential (GCS: -7.6 $\pm$ 4.0% vs. −9.4 $\pm$ 4.4%, p = 0.015) right ventricular strain. Highest strain-based hypertrophic phenotype differentiation was attained using GRS (AUC = 0.86). Binomial regression found right $\underline {ventricular ejection fraction}$ (RV-EF) (p = 0.017) to be a significant predictor of CA-HCM differentiation. CA subtypes had comparable cardiac strains. $\it Conclusion:$ CMR-derived RV global strains and various regional longitudinal strains provide discriminative radiological features for CA-HCM differentiation. However, in terms of feasibility, cine-derived RV-EF quantification may suffice for efficient differential diagnostic support

Machine-learning-based diagnostics of cardiac sarcoidosis using multi-chamber wall motion analyses

Background: Hindered by its unspecific clinical and phenotypical presentation, cardiac sarcoidosis (CS) remains a challenging diagnosis. Objective: Utilizing cardiac magnetic resonance imaging (CMR), we acquired multi-chamber volumetrics and strain feature tracking for a support vector machine learning (SVM)-based diagnostic approach to CS. Method: Forty-five CMR-negative (CMR(−), 56.5(53.0;63.0)years), eighteen CMR-positive (CMR(+), 64.0(57.8;67.0)years) sarcoidosis patients and forty-four controls (CTRL, 56.5(53.0;63.0)years)) underwent CMR examination. Cardiac parameters were processed using the classifiers of logistic regression, KNN(K-nearest-neighbor), DT (decision tree), RF (random forest), SVM, GBoost, XGBoost, Voting and feature selection. Results: In a three-cluster analysis of CTRL versus vs. CMR(+) vs. CMR(−), RF and Voting classifier yielded the highest prediction rates (81.82%). The two-cluster analysis of CTRL vs. all sarcoidosis (All Sarc.) yielded high prediction rates with the classifiers logistic regression, RF and SVM (96.97%), and low prediction rates for the analysis of CMR(+) vs. CMR(−), which were augmented using feature selection with logistic regression (89.47%). Conclusion: Multi-chamber cardiac function and strain-based supervised machine learning provides a non-contrast approach to accurately differentiate between healthy individuals and sarcoidosis patients. Feature selection overcomes the algorithmically challenging discrimination between CMR(+) and CMR(−) patients, yielding high accuracy predictions. The study findings imply higher prevalence of cardiac involvement than previously anticipated, which may impact clinical disease management

A machine learning challenge

Background: This study challenges state-of-the-art cardiac amyloidosis (CA) diagnostics by feeding multi-chamber strain and cardiac function into supervised machine (SVM) learning algorithms. Methods: Forty-three CA (32 males; 79 years (IQR 71; 85)), 20 patients with hypertrophic cardiomyopathy (HCM, 10 males; 63.9 years ($\pm$7.4)) and 44 healthy controls (CTRL, 23 males; 56.3 years (IQR 52.5; 62.9)) received cardiovascular magnetic resonance imaging. Left atrial, right atrial and right ventricular strain parameters and cardiac function generated a 41-feature matrix for decision tree (DT), k-nearest neighbor (KNN), SVM linear and SVM radial basis function (RBF) kernel algorithm processing. A 10-feature principal component analysis (PCA) was conducted using SVM linear and RBF. Results: Forty-one features resulted in diagnostic accuracies of 87.9% (AUC = 0.960) for SVM linear, 90.9% (0.996; Precision = 94%; Sensitivity = 100%; F1-Score = 97%) using RBF kernel, 84.9% (0.970) for KNN, and 78.8% (0.787) for DT. The 10-feature PCA achieved 78.9% (0.962) via linear SVM and 81.8% (0.996) via RBF SVM. Explained variance presented bi-atrial longitudinal strain and left and right atrial ejection fraction as valuable CA predictors. Conclusion: SVM RBF kernel achieved competitive diagnostic accuracies under supervised conditions. Machine learning of multi-chamber cardiac strain and function may offer novel perspectives for non-contrast clinical decision-support systems in CA diagnostics

Multi-parametric analyses to investigate dependencies of normal left atrial strain by cardiovascular magnetic resonance feature tracking

Left-atrial (LA) strain is the result of complex hemodynamics, which may be better characterized using a multiparametric approach. Cardiovascular magnetic resonance (CMR) feature tracking was used to perform a comprehensive LA strain assessment of 183 enrolled healthy volunteers (11–70 years, 97 females, median 32.9 $\pm$ 28.3 years). Novel strain dependencies were assessed using multi-parametric regression (MPR) analyses. LA volumetric data, left ventricular strain, transmitral and pulmonary venous blood flow parameters were utilized to create clusters for MPR of all subjects and a heart rate controlled subgroup (pulse: 60–75/min, N = 106). The LA reservoir(r) and conduit(c) strains of the total cohort were significantly elevated (p $\leq$ 0.001) in women (r: 49.7 $\pm$ 12.9%, c: 32.0 $\pm$ 11.0%) compared to men (r: 42.9 $\pm$ 11.4%, c: 26.1 IQ 10.5%). In contrast, there were no gender-specific differences (p > 0.05) for subgroup LA reservoir, conduit and booster(b) strains (all, r: 47.3 $\pm$ 12.7%; c: 29.0 IQ 15.5%; b: 17.6 $\pm$ 5.4%) and strain rates (all, 2.1 IQ 1.0 $s^{−1}$; − 2.9 IQ 1.5 $s^{−1}$; − 2.3 IQ 1.0 $s^{−1}$). MPR found large effect sizes (|$R^{2}$|$\geq$ 0.26) for correlations between strain and various cardiac functional parameters. Largest effect size was found for the association between LA conduit strain and LA indexed booster volume, LA total ejection fraction, left ventricular global radial strain and E-wave (|$R^{2}$|= 0.437). In addition to providing normal values for sex-dependent LA strain and strain rate, no gender differences were found with modified heart rate. MPR analyses of LA strain/strain rate and various cardiac functional parameters revealed that heart rate control improved goodness-of-fit for the overall model

Assessment of pulmonary artery stiffness by multiparametric cardiac magnetic resonance-surrogate for right heart catheterization

$\bf Background:$ Cardiac magnetic resonance (CMR) imaging allows for multiparametric assessment of healthy pulmonary artery (PA) hemodynamics. Gender- and aging-associated PA stiffness and pressure alterations have remained clinically unestablished, however may demonstrate epidemiological differences in disease development. The aim of this study is to evaluate the role of CMR as a surrogate for catheter examinations by providing a comprehensive CMR assessment of sex- and age-related reference values for PA stiffness, flow, and pressure. $\textbf {Methods and Results:}$ PA hemodynamics were studied between gender and age groups (>/50 years) exhibited reduced PA elasticity (41.7% [31.0; 52.9] vs. 66.4% [47.7; 83.0]; $\it P$ < 0.001), reduced PA compliance (15.4 $mm^{2}$/mmHg [12.3; 20.7] vs. 21.3 $\pm$ 6.8 $mm^{2}$/mmHg; $\it P$ < 0.001), higher pulse wave velocity (2.59 m/s [1.57; 3.59] vs. 1.76 m/s [1.24; 2.34]; $\it P$ < 0.001) and a reduced FWHM (218 $\pm$ 29 ms vs. 231 $\pm$ 21 ms; $\it P$ < 0.001) than younger subjects. $\bf Conclusions:$ Velocity-time profiles are dependent on age and gender. PA stiffness indices deteriorate with age. CMR has potential to serve as a surrogate for right heart catheterization

Impact of left atrial appendage fibrosis on atrial fibrillation in patients following coronary bypass surgery

$\bf Objectives:$ We aimed to assess the relationship of left atrial appendage (LAA) fibrosis with atrial fibrillation (AF) and postoperative events in patients receiving coronary artery bypass graft surgery (CABG). $\bf Background:$ Increased atrial fibrosis has been associated with AF and worse outcome following catheter ablation. Only limited data exists focusing on the impact of LAA fibrosis on AF after CABG. $\bf Methods:$ LAA tissue from 164 CABG-patients was stained with Masson-Goldner trichrome. The histological landscape was scanned and segmented into superpixels for software analysis (QuPath). A classification algorithm was extensively trained to detect fibrotic superpixels for quantification. In 43 propensity score matched pairs with AF or sinus rhythm (SR), LAA fibrosis was compared. Moreover, subgroups of mitral valve regurgitation (MR) were analyzed as follows: SR, SR + MR, AF and AF + MR. The predictive value of LAA fibrosis postoperative stroke, postoperative AF and mortality was assessed. $\bf Results:$ Fibrotic remodeling (%) showed no significant difference for the total cohort between the SR and AF group (SR: 30.8 $\pm$ 11.4% and AF: 33.8 $\pm$ 16.0%, respectively, $\it p$ = .32). However, significant fibrotic remodeling was observed for SR and AF subgroups (SR: 27.2 $\pm$ 12.2% vs. AF: 35.3 $\pm$ 13.7%; respectively, $\it p$ = .049) and between SR and SR + MR subgroups (SR: 27.2 $\pm$ 12.2% vs. SR + MR: 34.9 $\pm$ 9.1%, respectively, $\it p$ = .027). LAA fibrosis was not significantly associated with postoperative stroke, postoperative AF or overall mortality (all $\it p$ > .05). $\bf Conclusion:$ LAA fibrosis may contribute to an individual arrhythmia substrate for AF in patients with AF but also in those with SR and coincidence of MR. LAA fibrosis was not found to be predictive for clinical events in patients after CABG

Cardiovascular magnetic resonance imaging-based right atrial strain analysis of cardiac amyloidosis

$\bf Background:$ Cardiac amyloidosis (CA) manifests in a hypertrophic phenotype with a poor prognosis, making differentiation from hypertrophic cardiomyopathy (HCM) challenging and delaying early treatment. The extent to which magnetic resonance imaging (MRI) quantifies the right atrial strain (RAS) and strain rate (RASR), providing valuable diagnostic information, is not yet clinically established. $\bf Aims:$ This study assesses diagnostic differences in the longitudinal RAS and RASR between CA and HCM patients, control subjects (CTRL) and CA subtypes in addition to the impact of atrial fibrillation (AF) on the right atrial function in CA patients. The RAS and RASR of tricuspid regurgitation (TR) patients are used to assess the potential for diagnostic overlap. $\bf Methods:$ RAS and RASR quantification was conducted via MRI feature-tracking for biopsy-confirmed CA patients with subtypes identified. Strain parameters were compared for CTRL, HCM and TR patients. Post hoc testing identified intergroup differences. $\bf Results:$ In total, 41 CA patients were compared to 47 CTRL, 20 HCM and 31 TR patients. Reservoir (R), conduit and booster RAS and RASRs allow for significant differentiation ($\it p$ 0.8). CA patients with AF, in contrast to sinus rhythm, demonstrated a significantly impaired reservoir RAS and RASR and booster RASR. The discriminative power of RAS for CA vs. TR was insufficient (R: 10.6% $\pm$ 14.3% vs. 7.0% $\pm$ 6.0%, $\it p$ = 0.069). Differentiation between 21 transthyretin and 20 light-chain amyloidosis subtypes was not achievable (R: 0.7% $\pm$ 1.0% vs. 0.7% $\pm$ 1.0%, $\it p$ = 0.827). $\bf Conclusion:$ The MRI-derived RAS and RASR are impaired in CA patients and may support noninvasive differentiation between CA, HCM and CTRL