Feasibility and safety of left bundle branch area pacing in patients with septal hypertrophy

INTRODUCTION

Left bundle branch area pacing (LBBAP) aims to provide physiological ventricular activation during pacing. Left ventricular septal hypertrophy (LVSH) might be challenging for LBBAP due to the thickness of the interventricular septum and potential presence of septal scar. This study assesses the feasibility, safety, and outcome of LBBAP in patients with LVSH using primarily stylet-driven leads (SDL).

METHODS

Adult patients with LVSH who underwent LBBAP between March 2019 and November 2022 were enrolled. Baseline patient characteristics, procedural data and postprocedural results were collected. The feasibility of LBBAP in LVSH patients was compared to a cohort of LBBAP patients with normal septal wall thickness (NST).

RESULTS

Seventeen LVSH and 133 NST patients underwent LBBAP with successful implantation achieved in 15 LVSH patients (88%). Mean implant depth was 17.2 ± 1.9 mm, with 53% proven left bundle branch (LBB) capture. Paced QRS duration (146 ± 14 ms) and V6 R-wave peak time (V6 RWPT; 79 ± 20 ms) were comparable between patients with and without septal hypertrophy, although patients with NST had higher rates of proven LBB capture (71% vs. 53%). In LVSH pacing thresholds (0.6 ± 0.3 V at 0.4 ms) and R-wave amplitude (13.9 ± 5.6 mV) were favorable and remained stable at follow-up. At 12 months, 87% of patients had stable or improved left ventricular ejection fraction.

CONCLUSION

The results of the study indicate that LBBAP in patients with LVSH is safe and feasible and no lead-related complications were observed despite a mean implant depth exceeding 15 mm. LBBAP using SDL results in favorable pacing and electrocardiographic characteristics in LVSH patients, comparable to patients with NST.

Left Ventricular Hypertrophy Geometry and Vascular Calcification Co-Modify the Risk of Cardiovascular Mortality in Patients with End-Stage Kidney Disease: A Retrospective Cohort Study

AIM

Patients with end-stage kidney disease (ESKD) have an unparalleled risk of left ventricular hypertrophy (LVH) and vascular calcification (VC), both of which introduce excessive cardiovascular risk. However, it remains unclear whether LVH geometry co-modulates cardiovascular outcomes with VC in this population.

METHODS

A retrospective cohort study was conducted. Patients with ESKD requiring chronic hemodialysis were identified from Shin Kong Wu Ho-Su Memorial Hospital between October and December 2018, with echocardiographic LVH geometry and aortic arch calcification (AoAC) determined. They were divided into four groups according to AoAC severity and eccentric or concentric LVH. We used Kaplan-Meier analysis and Cox proportional hazard regression to analyze their cardiovascular and all-cause mortality after multivariate adjustment.

RESULTS

Overall, 223 patients with ESKD with LVH were analyzed, among whom 29.1%, 23.3%, 25.1%, and 22.4% had non-to-mild AoAC with eccentric and concentric LVH and moderate-to-severe AoAC with eccentric and concentric LVH, respectively. After 3.5 years of follow-up, patients with ESKD with moderate-to-severe AoAC and concentric LVH had a significantly higher risk of cardiovascular mortality than those with non-to-mild AoAC and eccentric LVH (hazard ratio 3.35, p=0.002). However, those with moderate-to-severe AoAC but eccentric LVH did not have higher cardiovascular mortality. Similarly, patients with ESKD with moderate-to-severe AoAC and concentric LVH had a significantly higher all-cause mortality than those with non-to-mild AoAC and eccentric LVH, whereas the other two groups did not have higher risk.

CONCLUSION

LVH geometry could help stratify the risk of patients with ESKD when they had severe VC, and co-existing severe VC and concentric LVH aggravated cardiovascular risk.

Cardiac Morphology, Function, and Left Ventricular Geometric Pattern in Patients with Hypertensive Crisis: A Cardiovascular Magnetic Resonance-Based Study

(1) Background: Altered cardiac morphology and function are associated with increased risks of adverse cardiac events in hypertension. Our study aimed to assess left ventricular (LV) morphology, geometry, and function using cardiovascular magnetic resonance (CMR) imaging in patients with hypertensive crisis. (2) Methods: Patients with hypertensive crisis underwent CMR imaging at 1.5 Tesla to assess cardiac volume, mass, function, and contrasted study. Left ventricular (LV) function and geometry were defined according to the guideline recommendations. Late gadolinium enhancement (LGE) was qualitatively assessed and classified into ischemic and nonischemic patterns. Predictors of LGE was determined using regression analysis. (3) Results: Eighty-two patients with hypertensive crisis (aged 48.5 ± 13.4 years, and 57% males) underwent CMR imaging. Of these patients, seventy-eight percent were hypertensive emergency and twenty-two percent were urgency. Diastolic blood pressure was higher under hypertensive emergency (p = 0.032). Seventy-nine percent (92% of emergency vs. 59% of urgency, respectively; p = 0.003) had left ventricular hypertrophy (LVH). The most prevalent LV geometry was concentric hypertrophy (52%). Asymmetric LVH occurred in 13 (22%) of the participants after excluding ischemic LGE. Impaired systolic function occurred in 46% of patients, and predominantly involved hypertensive emergency. Nonischemic LGE occurred in 75% of contrasted studies (67.2% in emergency versus 44.4% in urgency, respectively; p < 0.001). Creatinine and LV mass were independently associated with nonischemic LGE. (5) Conclusion: LVH, altered geometry, asymmetric LVH, impaired LV systolic function, and LGE are common under hypertensive crisis. LVH and LGE more commonly occurred under hypertensive emergency. Longitudinal studies are required to determine the prognostic implications of asymmetric LVH and LGE in hypertensive crisis.

Cardiac magnetic resonance of hypertrophic heart phenotype: A review

Hypertrophic heart phenotype is characterized by an abnormal left ventricular (LV) thickening. A hypertrophic phenotype can develop as adaptive response in many different conditions such as aortic stenosis, hypertension, athletic training, infiltrative heart muscle diseases, storage disorders and metabolic disorders. Hypertrophic cardiomyopathy (HCM) is the most frequent primary cardiomyopathy (CMP) and a genetical cause of cardiac hypertrophy. It requires the exclusion of any other cause of LV hypertrophy. Cardiac magnetic resonance (CMR) is a comprehensive imaging technique that allows a detailed evaluation of myocardial diseases. It provides reproducible measurements and myocardial tissue characterization. In clinical practice CMR is increasingly used to confirm the presence of ventricular hypertrophy, to detect the underlying cause of the phenotype and more recently as an efficient prognostic tool. This article aims to provide a detailed overview of the applications of CMR in the setting of hypertrophic heart phenotype and its role in the diagnostic workflow of such condition.

Cardiac MRI in Fabry disease

Fabry disease is a rare, progressive X-linked inherited disorder of glycosphingolipid metabolism due to a deficiency of α-galactosidase A enzyme. It leads to the accumulation of globotriaosylceramide within lysosomes of multiple organs, predominantly the vascular, renal, cardiac, and nervous systems. Fabry cardiomyopathy is characterized by increased left ventricular wall thickness/mass, functional abnormalities, valvular heart disease, arrhythmias, and heart failure. Early diagnosis and treatment are critical to avoid cardiac or renal complications that can significantly reduce life expectancy in untreated FD. This review will focus on the role of cardiovascular magnetic resonance imaging in the diagnosis, clinical decision-making, and monitoring of treatment efficacy.

Differential diagnosis of common etiologies of left ventricular hypertrophy using a hybrid CNN-LSTM model

Differential diagnosis of left ventricular hypertrophy (LVH) is often obscure on echocardiography and requires numerous additional tests. We aimed to develop a deep learning algorithm to aid in the differentiation of common etiologies of LVH (i.e. hypertensive heart disease [HHD], hypertrophic cardiomyopathy [HCM], and light-chain cardiac amyloidosis [ALCA]) on echocardiographic images. Echocardiograms in 5 standard views (parasternal long-axis, parasternal short-axis, apical 4-chamber, apical 2-chamber, and apical 3-chamber) were obtained from 930 subjects: 112 with HHD, 191 with HCM, 81 with ALCA and 546 normal subjects. The study population was divided into training (n = 620), validation (n = 155), and test sets (n = 155). A convolutional neural network-long short-term memory (CNN-LSTM) algorithm was constructed to independently classify the 3 diagnoses on each view, and the final diagnosis was made by an aggregate network based on the simultaneously predicted probabilities of HCM, HCM, and ALCA. Diagnostic performance of the algorithm was evaluated by the area under the receiver operating characteristic curve (AUC), and accuracy was evaluated by the confusion matrix. The deep learning algorithm was trained and verified using the training and validation sets, respectively. In the test set, the average AUC across the five standard views was 0.962, 0.982 and 0.996 for HHD, HCM and CA, respectively. The overall diagnostic accuracy was significantly higher for the deep learning algorithm (92.3%) than for echocardiography specialists (80.0% and 80.6%). In the present study, we developed a deep learning algorithm for the differential diagnosis of 3 common LVH etiologies (HHD, HCM and ALCA) by applying a hybrid CNN-LSTM model and aggregate network to standard echocardiographic images. The high diagnostic performance of our deep learning algorithm suggests that the use of deep learning can improve the diagnostic process in patients with LVH.

Deep learning assessment of left ventricular hypertrophy based on electrocardiogram

Background

Current electrocardiogram (ECG) criteria of left ventricular hypertrophy (LVH) have low sensitivity. Deep learning (DL) techniques have been widely used to detect cardiac diseases due to its ability of automatic feature extraction of ECG. However, DL was rarely applied in LVH diagnosis. Our study aimed to construct a DL model for rapid and effective detection of LVH using 12-lead ECG.

Methods

We built a DL model based on convolutional neural network-long short-term memory (CNN-LSTM) to detect LVH using 12-lead ECG. The echocardiogram and ECG of 1,863 patients obtained within 1 week after hospital admission were analyzed. Patients were evenly allocated into 3 sets at 3:1:1 ratio: the training set (n = 1,120), the validation set (n = 371) and the test set 1 (n = 372). In addition, we recruited 453 hospitalized patients into the internal test set 2. Different DL model of each subgroup was developed according to gender and relative wall thickness (RWT).

Results

The LVH was predicted by the CNN-LSTM model with an area under the curve (AUC) of 0.62 (sensitivity 68%, specificity 57%) in the test set 1, which outperformed Cornell voltage criteria (AUC: 0.57, sensitivity 48%, specificity 72%) and Sokolow-Lyon voltage (AUC: 0.51, sensitivity 14%, specificity 96%). In the internal test set 2, the CNN-LSTM model had a stable performance in predicting LVH with an AUC of 0.59 (sensitivity 65%, specificity 57%). In the subgroup analysis, the CNN-LSTM model predicted LVH by 12-lead ECG with an AUC of 0.66 (sensitivity 72%, specificity 60%) for male patients, which performed better than that for female patients (AUC: 0.59, sensitivity 50%, specificity 71%).

Conclusion

Our study established a CNN-LSTM model to diagnose LVH by 12-lead ECG with higher sensitivity than current ECG diagnostic criteria. This CNN-LSTM model may be a simple and effective screening tool of LVH.

The Relationship Between Famine Exposure During Early Life and Left Ventricular Hypertrophy in Adulthood

Background

Although the evidence was still limited, some studies suggested that childhood malnutrition might affect cardiac function and structure in adulthood. To address the knowledge gap, this study investigated if the Great Chinese Famine exposure during early life had affected left ventricular hypertrophy (LVH).

Methods

This research was a cross-sectional study. It included participants who had cardiac ultrasound assessments and were born in Guangdong, China, from 1 October 1952 to 30 September 1964. They were classified according to their exposure period to famine, namely, no exposure, fetal-, early-, mid-, and late childhood. Multivariate logistic regression and subgroup analysis have been conducted to determine the odds ratio (OR) and confidence intervals (CIs) between famine exposure and LVH.

Results

This research included 2,543 participants, 1,612 women, their mean age was 59.07 ± 3.65 years, and 704 participants had LVH. LVH prevalence was 122 (23.6%), 87 (25.1%), 133 (27.3%), 184 (29.2%), and 178 (31.7%), in non-, fetal-, early-, mid-, and late-childhood exposed groups, respectively (p = 0.031), while in the non-exposed group, the ORs for developing carotid plaque as a result of fetal, early-, mid- to late-childhood exposure were 1.08 (95% CI: 0.76, 1.59, p = 0.619), 1.24 (95% CI: 1.03, 1.79, p = 0.031), 1.49 (95% CI: 1.10, 2.01, p = 0.009), and 1.64 (95% CI: 1.25, 2.18, p = 0.001), respectively (p for trend = 0.003). There was no interactive effect between gender, obesity, or hypertension history with how the famine influenced LVH, as the subgroups analyses demonstrated (all p for interaction > 0.05).

Conclusion

This research has demonstrated the potential relationship between Great Chinese Famine exposure during childhood and LVH in adults.

Automated Classification of Left Ventricular Hypertrophy on Cardiac MRI

Left ventricular hypertrophy is an independent predictor of coronary artery disease, stroke, and heart failure. Our aim was to detect LVH cardiac magnetic resonance (CMR) scans with automatic methods. We developed an ensemble model based on a three-dimensional version of ResNet. The input of the network included short-axis and long-axis images. We also introduced a standardization methodology to unify the input images for noise reduction. The output of the network is the decision whether the patient has hypertrophy or not. We included 428 patients (mean age: 49 ± 18 years, 262 males) with LVH (346 hypertrophic cardiomyopathy, 45 cardiac amyloidosis, 11 Anderson–Fabry disease, 16 endomyocardial fibrosis, 10 aortic stenosis). Our control group consisted of 234 healthy subjects (mean age: 35 ± 15 years; 126 males) without any known cardiovascular diseases. The developed machine-learning-based model achieved a 92% F1-score and 97% recall on the hold-out dataset, which is comparable to the medical experts. Experiments showed that the standardization method was able to significantly boost the performance of the algorithm. The algorithm could improve the diagnostic accuracy, and it could open a new door to AI applications in CMR.

The Role of Cardiovascular Magnetic Resonance Imaging in the Evaluation of Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disorder, affecting 1 out of 500 adults globally. It is a widely heterogeneous disorder characterized by a range of phenotypic expressions, and is most often identified by non-invasive imaging that includes echocardiography and cardiovascular magnetic resonance imaging (CMR). Within the last two decades, cardiac magnetic resonance imaging (MRI) has emerged as the defining tool for the characterization and prognostication of cardiomyopathies. With a higher image quality, spatial resolution, and the identification of morphological variants of HCM, CMR has become the gold standard imaging modality in the assessment of HCM. Moreover, it has been crucial in its management, as well as adding prognostic information that clinical history nor other imaging modalities may not provide. This literature review addresses the role and current applications of CMR, its capacity in evaluating HCM, and its limitations.

Cardiovascular Magnetic Resonance Imaging and Heart Failure

Purpose of Review

The purpose of this review is to summarize the application of cardiac magnetic resonance (CMR) in the diagnostic and prognostic evaluation of patients with heart failure (HF).

Recent Findings

CMR is an important non-invasive imaging modality in the assessment of ventricular volumes and function and in the analysis of myocardial tissue characteristics. The information derived from CMR provides a comprehensive evaluation of HF. Its unique ability of tissue characterization not only helps to reveal the underlying etiologies of HF but also offers incremental prognostic information.

Summary

CMR is a useful non-invasive tool for the diagnosis and assessment of prognosis in patients suffering from heart failure.

Cardiovascular Magnetic Resonance for the Differentiation of Left Ventricular Hypertrophy

PURPOSE OF REVIEW

Left ventricular hypertrophy (LVH) is a common presentation encountered in clinical practice with a diverse range of potential aetiologies. Differentiation of pathological from physiological hypertrophy can be challenging but is crucial for further management and prognostication. Cardiovascular magnetic resonance (CMR) with advanced myocardial tissue characterisation is a powerful tool that may help to differentiate these aetiologies in the assessment of LVH.

RECENT FINDINGS

The use of CMR for detailed morphological assessment of LVH is well described. More recently, advanced CMR techniques (late gadolinium enhancement, parametric mapping, diffusion tensor imaging, and myocardial strain) have been used. These techniques are highly promising in helping to differentiate key aetiologies of LVH and provide valuable prognostic information. Recent advancements in CMR tissue characterisation, such as parametric mapping, in combination with detailed morphological assessment and late gadolinium enhancement, provide a powerful resource that may help assess and differentiate important causes of LVH.