Cause Of Left Ventricular Hypertrophy Research Articles

A phenomap of TTR amyloidosis to aid diagnostic screening.

Cardiac amyloidosis due to transthyretin (ATTR) remains an underdiagnosed cause of cardiomyopathy. As awareness of the disease grows and referrals for ATTR increase, clinicians are likely to encounter more atypical forms of the condition in clinical practice. Therefore, physicians and treating cardiologists should be aware of the full phenotypic spectrum of ATTR. The phenotypic manifestation of ATTR varies depending on the stage of the disease, the presence and type of TTR mutation and the patient's comorbidities. ATTR findings can be grouped into four major categories: clinical profile and cardiac phenotype, extra-cardiac findings, electrocardiogram and imaging findings, which cumulatively form the full phenomap of ATTR. Results from any diagnostic test for ATTR should be interpreted in light of the pre-test probability for the disease. Findings that suggest negative markers for ATTR can point towards other forms of amyloidosis (such as AL amyloidosis) or alternate causes of left ventricular hypertrophy, including hypertrophic cardiomyopathy or Fabry disease. The rising number of referrals for ATTR cardiomyopathy presents a challenge in daily clinical practice. To prevent an increase in false-positive diagnostic test results, an ATTR phenomap can serve as a valuable tool for guiding diagnostic assessments, interpreting test outcomes and prioritizing appropriate referrals for ATTR screening.

Development and validation of a smartphone based app to guide the differential diagnosis in patients with primary left ventricular hypertrophy: the Thick Heart App

Abstract Introduction Patients presenting with primary Left Ventricular Hypertrophy (LVH) suffers from a diagnostic delay quantified in years. They are often misdiagnosed because of both physician-related and disease-related reasons including fragmented knowledge among different specialties and rarity of the conditions. Purpose We developed and validate a free digital support tool in the form of an App to provide support and to guide the physician in the differential diagnostic process of patients presenting with primary LVH. Methods We studied 712 patients with definitive diagnosis of sarcomeric HCM or one of its genocopies (414 (58%) males, 48±24 years) referred to four tertiary centers in Europe. Pre-specified RF were categorized into five domains: family history; signs/symptoms; electrocardiography, echocardiographic and laboratory. Each patient’s characteristics were inserted by two independent and blind investigators on the App of the digital support tool. The possible diagnostic outcome was categorized as follows: (a) most likely diagnosis (age range for the condition, at least 1 extremely specific RFs or >1 specific RF; (b) possible diagnosis (age range, 1 specific RF); (c) less likely diagnosis (outside age range, no RF selected). Results Ris were common in patients presenting with a genotyped HCM (2568 RFs, 3.5 per patient). RF on physical examination was associated with non sarcomeric aetiology (318/319 physical examination RF identified in non-sarcomeric HCM), whereas 68% of RF associated with sarcomeric HCM were derived from accurate ECG and echocardiography analysis. Sarcomeric HCM, TTR-amyloidosis, Danon and PRKAG2 cardiomyopathy relied extensively on ECG and echocardiographic systematic analyisis (68%, 69%, 67% and 69% respectively). In contrast, AFD, Friederich’s ataxia, Noonan syndrome with multiple lentiges (NSML), mitochondrial, Pompe and Noonan cardiomyopathy presented red flags potentially identifiable in the general practioner setting by anamnesis, physical examination and routine laboratory (57%, 59%, 52%, 57%, 57% and 58% respectively) in the vast majority of case. A total of 697/712 (97.8%) were correctly categorized in the most likely and possible diagnosis section. The App proved to be sensible for sarcomeric HCM, FD (0.99 and 0.91, NPV 0.98 and 0.83) and extremely capable in the identification of rare causes of LVH (Danon, Friederich’s ataxia, LEOPARD, Pompe, Noonan and PRKAG2 diseases). By design, specificity was not high, in particular for sarcomeric HCM and FD. Conclusions The present validation of an App based free digital support tool in the form of an App proved to be extremely sensible in providing the support to the screening of different causes of primary LVH in patients presenting with a HCM phenotype. Further external and multicenter validation is warranted.

Diagnostic and prognostic value of ECG-predicted hypertension mediated left ventricular hypertrophy using machine learning

Abstract Background Hypertension is the commonest cause of left ventricular hypertrophy (LVH). Using cardiac magnetic resonance (CMR) imaging, four distinct hypertension mediated LVH phenotypes have been reported: normal LV, LV remodelling, eccentric LVH and concentric LVH. Early detection of hypertensives with LVH can enable timely initiation of therapy however a CMR based screening strategy is costly, particularly in low resource settings. The electrocardiogram (ECG) is an inexpensive screening tool and can detect LVH but its capacity to discriminate between the 4 phenotypes is unknown. Purpose To classify hypertension mediated LVH phenotypes from the ECG using machine learning (ML) and test for associations of ECG predicted LVH phenotypes with incident cardiovascular outcomes. Methods ECG markers with a known physiological association with LVH (including QRS markers, Sokolow-Lyon, and Cornell) were extracted from the 12-lead ECG of 20,439 hypertensive participants in UK Biobank. Three classification models, logistic regression, support vector machine (SVM) and random forest, were trained in 80% of the participants using extracted ECG markers and clinical variables. The remaining 20% individuals were included in the test set for performance measurement. External validation was sought in 877 hypertensives from the Study of Health in Pomerania (SHIP). Subsequently, we tested for associations between the four ECG-predicted LVH phenotypes and incident major adverse cardiovascular events (MACE) and heart failure using Cox proportional hazard regression in the UK Biobank test set with median follow-up of 4.2 years. Results Among UK Biobank participants (mean age ± standard deviation 65 ± 7.4 years), 23,190 had normal LV, 894 LV remodelling, 218 eccentric and 103 concentric LVH. Classification performance of the three models was comparable, but SVM showed consistent results (accuracy 0.79, sensitivity 0.59, specificity 0.87, AUC 0.69) and superior prediction of eccentric LVH (AUC 0.86). In SHIP (55 ± 12.7 years), 704 had normal LV, 134 LV remodelling, 12 eccentric and 24 concentric LVH. In the external validation performed in SHIP, SVM had accuracy of 0.75 (sensitivity 0.51, specificity 0.85, AUC 0.65), with superior prediction of eccentric LVH (AUC 0.76), as shown in Figure 1. Ventricular rate and QRS amplitude (P<0.001) were the features most strongly associated with LVH. In the test set of UK Biobank, setting normal LV as the reference group, the ECG predicted eccentric LVH group was associated with heart failure (Hazard Ratio 3.42, confidence interval 1.06-9.86), there were no associations with MACE (Figure 2). Conclusions ECG-based ML classifiers were able to discriminate the four hypertension mediated LVH phenotypes with external validation demonstrating robustness. This automated approach enhances capabilities of non-specialists and potentially represents an accessible screening strategy for the early detection of hypertensives with LVH.

Artificial intelligence to assist the echocardiographic identification of transthyretin cardiac amyloidosis

Abstract Introduction Transthyretin cardiac amyloidosis (ATTR-CM) is a severe disease, and treatments are most effective when initiated at early stage. Echocardiography is the main tool for screening patients with suspected ATTR-CM or populations at high risk of ATTR-CM. Suspicion of ATTR-CM triggers a dedicated confirmatory evaluation. Echocardiographic diagnosis is however difficult to implement in a non-expert cardiologist workflow. An algorithm to assist the identification of potential cases would be helpful to assist clinicians. Objective To develop an algorithm using artificial intelligence (AI) to assist the identification of potential ATTR-CM cases using routine transthoracic echocardiography. Methods In this non-interventional, monocentric case-control study, a dedicated database, comprising confirmed ATTR-CM patients and a control group, was built. We trained and validated a deep learning model based on Convolutional Neural Network from transthoracic echocardiography video loops. Results A total of 4394 patients were included in the database: 208 in the ATTR-CM group (76% males, mean age 69±10 years old, 90% hereditary ATTR-CM with NAC I score in 77%) and 4186 in the control group (59% male, mean age 62±16 years). After training the model to identify potential ATTR-CM, the ROC curve AUC was 0.97, accuracy was 94.7%, precision was 87.3%, and recall 84.4%. After selecting a subgroup of 130 pairs of matched ATTR-CM/controls based on age, sex, and wall thickness, the algorithm performance remained robust (AUC 0.99, accuracy 99.4%, precision 100%, recall 98.3%). Conclusion and Perspectives: Diagnosis of ATTR-CM by echocardiography can be assisted by an algorithm using AI. The algorithm remains effective in distinguishing ATTR-CM from other causes of left ventricular hypertrophy. External validation of this tool is underway.ROC curveAssisted classification

Distinguishing hypertensive cardiomyopathy from cardiac amyloidosis in hypertensive patients with heart failure: a CMR study with histological confirmation.

Differentiation of the cause of left ventricular hypertrophy (LVH) is challenging in cases with co-existing hypertension. CMR offers assessment of diffuse myocardial abnormalities via T1 mapping with extracellular volume fraction (ECV) and macroscopic fibrosis via late gadolinium enhancement imaging (LGE). The goal of the study was to understand if CMR parameters can differentiate hypertensive cardiomyopathy (HC) from cardiac amyloidosis (CA) in patients with hypertension and heart failure, using endomyocardial biopsy (EMB) as the gold standard. We retrospectively analyzed patients with hypertension, LVH, and heart failure undergoing EMB due to uncertain diagnosis. CMR parameters including cine, LGE characteristics, T1 mapping, and ECV were analyzed. A total of 34 patients were included (mean age 66.5 ± 10.7 years, 79.4% male). The final EMB-based diagnosis was HC (10, 29%), light chain (AL) CA (7, 21%), and transthyretin (ATTR) CA (17, 50%). There was a significant difference in subendocardial LGE (p = 0.03) and number of AHA segments with subendocardial LGE (p = 0.005). The subendocardial LGE pattern was most common in AL-CA (85.7%) and African American with HC (80%). ECV elevation (≥ 29%) was present in all patients with CA (AL-CA: 57.6 ± 5.2%, ATTR-CA: 59.1 ± 15.3%) and HC (37.3 ± 4.5%). Extensive subendocardial LGE pattern is not pathognomonic for CA but might also be present in African American patients with longstanding or poorly controlled HTN. The ECV elevation in HC with HF might be more significant than previously reported with an overlap of ECV values in HC and CA, particularly in younger African American patients.

Hypertrophic cardiomyopathy – contribution of magnetic resonance imaging

Hypertrophic cardiomyopathy (HCM) is one of the most common form of cardiomyopathies. It is characterized by primary hypertrophy, disorganization, and fibrosis of the myocardium. These morphological characteristics determine the applicability and significance of magnetic resonance imaging in the evaluation of these patients. Cardiac magnetic resonance helps the diagnostic process by identifying hypertrophic segments, which are less accessible by echocardiography ensures more precise measurements of wall thickness and differentiates HCM from other causes of left ventricular hypertrophy. The method allows for not only qualitative but also quantitive evaluation of focal and diffuse fibrosis which plays an important role in evaluation of patients’ prognosis and follow up. The aim of this article is to review the role of cardiac magnetic resonance in the diagnosis of HCM, demonstrating different forms and their typical morphological features, as well as to demonstrate the role of the method in the differential diagnosis of myocardial hypertrophy.

Ongoing assertion of two-dimensional measurements on differentiation type of left ventricular hypertrophy: Focus on inferior vena cava.

Left ventricular hypertrophy (LVH), including hypertensive LVH, hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis (CA), is a commonly encountered condition in cardiology practice, presenting challenges in differential diagnosis. In this study, we aimed to investigate the importance of echocardiographic evaluation of the inferior vena cava (IVC) in distinguishing LVH subtypes including hypertensive LVH, HCM, and CA. In this retrospective study, patients with common causes of LVH including hypertensive LVH, HCM, and CA were included. The role of echocardiographic evaluation of IVC diameter and collapsibility in distinguishing these causes of LVH was assessed in conjunction with other echocardiographic, clinical, and imaging methods. A total of 211 patients (45% HCM, 43% hypertensive heart disease, and 12% CA) were included in our study. Their mean age was 56.6 years and 62% of them were male. While mean IVC diameter was significantly dilated in CA patients (13.4mm in hypertensive LVH, 16.0mm in HCM, and 21.1mm in CA, p<.001), its collapsibility was reduced (IVC collapsible in 95% of hypertensive patients, 72% of HCM patients, and 12% of CA patients, p<.001). In the analysis of diagnostic probabilities, the presence of both hypovoltage and IVC dilation is significant for CA patients. Although it is not statistically significant, the presence of IVC dilation along with atrial fibrillation supports the diagnosis of HCM. In conclusion, although advances in imaging techniques facilitate the diagnosis of LVH, simple echocardiographic methods should never be overlooked. Our study supports the notion that IVC assessment could play an important role in the differential diagnosis of LVH.

Parametric mapping using cardiovascular magnetic resonance for the differentiation of light chain amyloidosis and transthyretin-related amyloidosis.

To evaluate different cardiovascular magnetic resonance (CMR) parameters for the differentiation of light chain amyloidosis (AL) and transthyretin-related amyloidosis (ATTR). In total, 75 patients, 53 with cardiac amyloidosis (20 patients with AL (66±12 years, 14 males [70%]) and 33 patients with ATTR (78±5 years, 28 males [88%])) were retrospectively analyzed regarding CMR parameters such as T1 and T2 mapping, extracellular volume (ECV), and late gadolinium enhancement (LGE) distribution patterns, and myocardial strain, and compared to a control cohort with other causes of left ventricular hypertrophy (LVH; 22 patients (53±16 years, 17 males [85%])). One way-ANOVA and receiver operating characteristic analysis were used for statistical analysis. ECV was the single best parameter to differentiate between cardiac amyloidosis and controls (area under the curve [AUC]: 0.97, 95% confidence intervals [CI]: 0.89-0.99, p<.0001, cutoff: >30%). T2 mapping was the best single parameter to differentiate between AL and ATTR amyloidosis (AL: 63±4 ms, ATTR: 58±2 ms, p<.001, AUC: 0.86, 95% CI: 0.74-0.94, cutoff: >61 ms). Subendocardial LGE was predominantly observed in AL patients (10/20 [50%] vs. 5/33 [15%]; p=.002). Transmural LGE was predominantly observed in ATTR patients (23/33 [70%] vs. 2/20 [10%]; p<.001). The diagnostic performance of T2 mapping to differentiate between AL and ATTR amyloidosis was further increased with the inclusion of LGE patterns (AUC: 0.96, 95% CI: 0.86-0.99]; p=.05). ECV differentiates cardiac amyloidosis from other causes of LVH. T2 mapping combined with LGE differentiates AL from ATTR amyloidosis with high accuracy on a patient level.

Unveiling Cardiovascular Connections between Familial Hypercholesterolemia (FH) and Left Ventricular Hypertrophy (LVH)

Left ventricular hypertrophy (LVH), a complex cardiac condition characterized by the enlargement and thickening of the left ventricle, is primarily associated with hypertension and valvular heart disease. Recent studies have identified familial hypercholesterolemia (FH) as a secondary cause of LVH. It is characterized by high low-density lipoprotein cholesterol (LDL-C) in blood. FH is an inherited disorder which involves genetic variations associated with abnormal metabolism of LDL-C. This review article aims to provide a comprehensive overview of the relationship between FH and LVH. It summarizes the current understanding of the pathophysiological mechanisms underlying this association and discusses its implications for clinical practice. Elevated LDL-C levels in FH patients lead to accelerated atherosclerosis and an increased risk of premature cardiovascular events. Animal models and clinical observations provide insights into the mechanistic links between elevated LDL-C levels, oxidative stress, inflammation, and LVH development. Early diagnosis of FH would certainly play a critical role in preventing or delaying the development of LVH and subsequent cardiovascular complications. Preemptive measures emphasize the identification of at risk individuals, in-depth clinical evaluations, and implementation of effective treatments including lifestyle modifications, statins, and adjunctive therapies, such as PCSK9 inhibitors or lipoprotein apheresis. By increasing the awareness of FH as a secondary cause of LVH, healthcare professionals can improve early detection and implement appropriate management strategies to mitigate the cardiovascular burden associated with this inherited disorder.

Fabry Disease: More than a Phenocopy of Hypertrophic Cardiomyopathy.

Fabry disease (FD) is a genetic lysosomal storage disease with frequent cardiovascular involvement, whose presence is a major determinant of adverse clinical outcomes. As a potentially treatable cause of left ventricular hypertrophy (LVH) and heart failure with preserved ejection fraction, the early recognition of FD is crucial to initiate enzyme replacement therapy and improve long-term prognosis. Multimodality imaging plays a central role in the evaluation of patients with FD and helps in the differential diagnosis of other conditions presenting with LVH. In the present review, we explore the current applications of multimodality cardiac imaging, in particular echocardiography and cardiovascular magnetic resonance, in the diagnosis, prognostic assessment, and follow-up of patients with FD.

Dissociation of LA volume and deformation is a hallmark of ATTR-CA

Abstract Background Transthyretin Cardiac amyloidosis (ATTR-CA) is associated with an infiltrative left atrial (LA) cardiomyopathy. Purpose We sought to identify unique characteristics of LA cardiomyopathy in CA compared to non-amyloid states of left ventricular hypertrophy (LVH). Methods In suspected patients, a diagnosis of ATTR-CA was made by a positive Tc-99m PYP in conjunction with negative serum/urine studies for a clonal plasma cell dyscrasia. Endomyocardial biopsy was performed when clinically indicated. All patients underwent complete transthoracic echocardiography (TTE) with speckle tracking strain imaging. Left ventricular (LV) strain, LA reservoir strain (LASr) and LA volume index (LAVI) were measured. LA stiffness index was estimated by the ratio (E/e’)/LASr. ATTR-CA patients were compared to patient with non- CA LVH (interventricular septal thickness &amp;gt; 1.2 cm). Atrial fibrillation (AF) during the TTE was noted. Logistic regression and Cox-regression analyses were performed to find the independent determinant of ATTR-CA and outcome predictors, respectively. Results We studied 98 patients with ATTR-CA (44 with AF) and 62 patients with non-CA LVH (35 with AF). ATTR-CA patients had a higher prevalence of relative apical sparing (RAS) (79% vs 20%, p&amp;lt;0.0001), reduced LASr (9.8±4.8% vs 14.0±7.2%; p&amp;lt;0.001) and high LA stiffness (2.5±0.2 vs 1.4±0.9; p&amp;lt;0.001) irrespective of AF. Surprisingly, LAVI of ATTR-CA patients was significantly lower than non- CA LVH patients (44±15 ml/m2 vs 51±24 ml/m2; p=0.046). Consequently, LAVI correlated strongly with LASr in non-amyloid LVH group (r= -0.52, p&amp;lt;0.001) whereas no correlation was observed in ATTR-CA (p&amp;lt;0.05) (Figure 1). Patients with ATTR-CA had the lowest LASr and highest LA stiffness regardless of AF (Figure 2). Importantly, LASr determined ATTR-CA among all study subjects independently of RAS (p&amp;lt;0.001) and emerged as a predictor of composite endpoint including all-cause mortality, hospitalization from heart failure and stroke in ATTR-CA patients (p=0.037). Conclusion ATTR-CA is characterized by severely reduced LASr and increased LA stiffness, yet LA dilatation is relatively less severe when compared to other causes of LVH. LASr seems promising to make the differential diagnosis of ATTR-CA and is of prognostic importance.Figure 1Figure 2

Abstract 17053: Impact of Sodium-Glucose Cotransporter-2 Inhibitors on Mortality in Hypertrophic Cardiomyopathy: A Retrospective Study

Background: Sodium-glucose cotransporter-2 inhibitors (SGLT2i) have been demonstrated to improve outcomes in congestive heart failure (HF). However, their effect in hypertrophic cardiomyopathy (HCM) is unknown. Research Question: Can SGLT2i improve clinical outcomes in HCM patients? Methods: Using TriNetX Global Research Network from March 2013 to February 2021, patients diagnosed with HCM (ICD-10 codes I42.1, I42.2) were identified and categorized into 2 groups: those on SGLT2i vs. those who are not. To avoid inclusion of other causes of left ventricular hypertrophy, patients with aortic stenosis or systemic hypertension were excluded. Propensity score matching (PSM) was used to limit confounders. Primary outcome was all-cause mortality and secondary outcomes were HF exacerbation, all-cause hospitalization, and documented cardiovascular symptoms (chest pain, dyspnea, palpitations, or leg edema) over a 2-year follow-up period. Results: A total of 27,561 HCM patients were identified; 408 (1.5%) on SGLT2i and 27,153 (98.5%) were not. Patients on SGLT2i were older (55 ± 15 vs. 48 ± 19, p&lt;0.01) and had more comorbidities, including a nearly 6-fold higher prevalence of diabetes mellitus (44% vs. 7%, p&lt;0.01) and congestive HF (65% vs. 10%, p&lt;0.01). After PSM, each group consisted of 355 patients. HCM patients on SGLT2i had lower rates of all-cause mortality (OR 0.22 [95% CI: 0.12, 0.39]; p&lt;0.01), hospitalizations (OR 0.72 [95% CI: 0.53, 0.98]; p=0.04), and cardiovascular symptoms (OR 0.71 [95% CI: 0.52, 0.97]; p=0.029) when compared to patients not on SGLT2i. There was no statistical difference in the rate of HF exacerbation (OR 0.82 [95% CI: 0.54, 1.24]; p=0.3). Conclusion: In this large dataset, SGLT2i use in a select subset of HCM patients was associated with improved survival, less hospitalizations, and fewer cardiovascular symptoms. These data support future randomized trials to evaluate the efficacy and safety of SGLT2i in more diverse HCM patient populations.

A pragmatic clinical trial assessing the effect of a targeted notification and clinical support pathway on the diagnostic evaluation and treatment of individuals with left ventricular hypertrophy (NOTIFY-LVH)

Electronic health records contain vast amounts of cardiovascular data, including potential clues suggesting unrecognized conditions. One important example is the identification of left ventricular hypertrophy (LVH) on echocardiography. If the underlying causes are untreated, individuals are at increased risk of developing clinically significant pathology. As the most common cause of LVH, hypertension accounts for more cardiovascular deaths than any other modifiable risk factor. Contemporary healthcare systems have suboptimal mechanisms for detecting and effectively implementing hypertension treatment before downstream consequences develop. Thus, there is an urgent need to validate alternative intervention strategies for individuals with preexisting-but potentially unrecognized-LVH. Through a randomized pragmatic trial within a large integrated healthcare system, we will study the impact of a centralized clinical support pathway on the diagnosis and treatment of hypertension and other LVH-associated diseases in individuals with echocardiographic evidence of concentric LVH. Approximately 600 individuals who are not treated for hypertension and who do not have a known cardiomyopathy will be randomized. The intervention will be directed by population health coordinators who will notify longitudinal clinicians and offer to assist with the diagnostic evaluation of LVH. Our hypothesis is that an intervention that alerts clinicians to the presence of LVH will increase the detection and treatment of hypertension and the diagnosis of alternative causes of thickened myocardium. The primary outcome is the initiation of an antihypertensive medication. Secondary outcomes include new hypertension diagnoses and new cardiomyopathy diagnoses. The trial began in March 2023 and outcomes will be assessed 12 months from the start of follow-up. The NOTIFY-LVH trial will assess the efficacy of a centralized intervention to improve the detection and treatment of hypertension and LVH-associated diseases. Additionally, it will serve as a proof-of-concept for how to effectively utilize previously collected electronic health data to improve the recognition and management of a broad range of chronic cardiovascular conditions. NCT05713916.

Classifying hypertension mediated left ventricular hypertrophy phenotypes from the 12-lead electrocardiogram using machine learning

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): British Heart Foundation Pat Merriman Clinical Research Training Fellowship Background Hypertension is the commonest cause of left ventricular hypertrophy (LVH), an established independent predictor of cardiovascular morbidity and mortality. Four distinct LVH phenotypes with varying prognostic implications have been reported using cardiac magnetic resonance (CMR) LV mass to volume ratio: normal LV, LV remodelling, eccentric LVH and concentric LVH. Current electrocardiogram (ECG) criteria can detect LVH but their ability to differentiate between hypertension mediated LVH phenotypes is unknown. Purpose The aim of this study was to classify hypertension mediated LVH phenotypes using 12-lead electrocardiogram (ECG) and clinical data for cost-effective patient stratification. Methods 24,535 hypertensive participants from UK Biobank imaging study were categorised into the CMR-defined LVH phenotypes. ECG biomarkers with a physiological association with LVH (including QRS biomarkers, Sokolow-Lyon and Cornell voltage) were extracted from the 12-lead ECG of each participant using in-house algorithms. Vascular risk factors known to be associated with LVH were also ascertained from the clinical data of each participant. Classification models integrating ECG biomarkers and clinical data were built using logistic regression, support vector machine (SVM) and random forest (RF). The dataset was split into 80% train and 20% test sets for performance measurement. Ten-fold cross validation was applied and the training set was downsampled to the minority class. Results Among the participants (mean age ± standard deviation 65.3 ± 7.4 years), 23,312 (95.0%) had normal LV, 900 (3.7%) LV remodelling, 220 (0.8%) eccentric LVH and 103 (0.4%) concentric LVH. R wave amplitude V5, S wave amplitude V3 and QT duration V6 (P&amp;lt;0.001) were the top ECG biomarkers and systolic blood pressure (p&amp;lt;0.001) the top clinical predictor of LVH phenotypes (Figure 1). Classification with logistic regression had an accuracy of 77% (sensitivity 55%, specificity 85%, Area under the receiver operator curve [AUC] 0.76), SVM 80% accuracy (sensitivity 61%, specificity 87%, AUC 0.75) and RF 75% accuracy (sensitivity 49%, specificity 83%, AUC 0.75). ECG biomarkers enhanced model performance of all classifiers, compared to using clinical data alone. Conclusion A combination of automatically extracted ECG biomarkers and clinical data were able to discriminate the hypertension mediated LVH phenotypes. An advanced classification approach improves selection of hypertensive patients with LVH, thereby adding prognostic value to traditional risk models. We are currently validating our findings in an external population and using deep learning for comparison.

Multi-channel deep learning model-based myocardial spatial–temporal morphology feature on cardiac MRI cine images diagnoses the cause of LVH

BackgroundTo develop a fully automatic framework for the diagnosis of cause for left ventricular hypertrophy (LVH) via cardiac cine images.MethodsA total of 302 LVH patients with cine MRI images were recruited as the primary cohort. Another 53 LVH patients prospectively collected or from multi-centers were used as the external test dataset. Different models based on the cardiac regions (Model 1), segmented ventricle (Model 2) and ventricle mask (Model 3) were constructed. The diagnostic performance was accessed by the confusion matrix with respect to overall accuracy. The capability of the predictive models for binary classification of cardiac amyloidosis (CA), hypertrophic cardiomyopathy (HCM) or hypertensive heart disease (HHD) were also evaluated. Additionally, the diagnostic performance of best Model was compared with that of 7 radiologists/cardiologists.ResultsModel 3 showed the best performance with an overall classification accuracy up to 77.4% in the external test datasets. On the subtasks for identifying CA, HCM or HHD only, Model 3 also achieved the best performance with AUCs yielding 0.895–0.980, 0.879–0.984 and 0.848–0.983 in the validation, internal test and external test datasets, respectively. The deep learning model showed non-inferior diagnostic capability to the cardiovascular imaging expert and outperformed other radiologists/cardiologists.ConclusionThe combined model based on the mask of left ventricular segmented from multi-sequences cine MR images shows favorable and robust performance in diagnosing the cause of left ventricular hypertrophy, which could be served as a noninvasive tool and help clinical decision.

Mitochondrial Cardiomyopathy: Distinctive Cardiac Phenotype Detected with Cardiovascular MRI

Left ventricular hypertrophy (LVH) has a broad differential diagnosis. Pathogenic variants of mitochondrial DNA are a rare cause of LVH, and cardiac MRI is a powerful technique that may aid in differentiating such rare causes. This case report presents three siblings with a pathogenic variant of the mitochondrially encoded tRNA isoleucine (MT-TI) gene. A distinctive cardiac phenotype was detected with cardiac MRI. Extensive LVH and dilatation and decreased ejection fraction were observed with a pattern of increased T2 signal and extensive late gadolinium enhancement, which was remarkably consistent among all three siblings. Keywords: Cardiomyopathies, MR Imaging, Hypertrophic Cardiomyopathy, Mitochondrial, Inherited Cardiomyopathy, Left Ventricular Hypertrophy, Cardiovascular MRI, Late Gadolinium Enhancement Supplemental material is available for this article. © RSNA, 2023.

Effect of the structural modification of Candesartan with Zinc on hypertension and left ventricular hypertrophy

Hypertension is the most common cause of left ventricular hypertrophy, contributing to heart failure progression. Candesartan (Cand) is an angiotensin receptor antagonist widely used for hypertension treatment. Structural modifications were previously performed by our group using Zinc (ZnCand) as a strategy for improving its pharmacological properties. The measurements showed that ZnCand exerts a stronger interaction with the angiotensin II receptor, type 1 (AT1 receptor), reducing oxidative stress and intracellular calcium flux, a mechanism implied in cell contraction. These results were accompanied by the reduction of the contractile capacity of mesangial cells. In vivo experiments showed that the complex causes a significant decrease in systolic blood pressure after 8 weeks of treatment in spontaneously hypertensive rats (SHR). The reduction of heart hypertrophy was evidenced by echocardiography, the histologic cross-sectional area of cardiomyocytes, collagen content, the B-type natriuretic peptide (BNP) marker and connective tissue growth factor (CTGF) and the matrix metalloproteinase 2 (MMP-2) expression. Besides, the complex restored the redox status. In this study, we demonstrated that the complexation with Zn(II) improves the antihypertensive and cardiac effects of the parental drug.

Current and emerging perspectives on pathophysiology, diagnosis, and management of hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is the most common genetically inherited cardiomyopathy with an autosomal dominant inheritance pattern. A disease-causing gene is found between 34% and >60% of the times and the two most frequently mutated genes, which encode sarcomeric proteins, are MYBPC3 and MYH7. HCM is a diagnosis of exclusion since secondary causes of left ventricular hypertrophy should first be ruled out. These include hypertension, aortic stenosis, infiltrative disease, metabolic and endocrine disorders, mitochondrial cardiomyopathies, neuromuscular disorders, malformation syndromes and some chronic drug use. The disease is characterized by great heterogeneity of its clinical manifestations, however diastolic dysfunction and increased ventricular arrhythmogenesis are commonly seen. Current HCM therapies focus on symptom management and prevention of sudden cardiac death. Symptom management includes the use of pharmacological agents, elimination of medication promoting outflow track obstruction, control of comorbid conditions and invasive procedures, whereas in the prevention of sudden cardiac death, implantable cardiac defibrillators and antiarrhythmic drugs are used. A targeted therapy for LVOTO represented by allosteric cardiac myosin inhibitors has been developed. In terms of sport participation, a more liberal approach is recently recommended, after careful evaluation and common-shared decision. The application of the current therapies has lowered HCM mortality rates to <1.0%/year, however it appears to have shifted focus to heart failure and atrial fibrillation, as the predominant causes of disease-related morbidity and mortality and, therefore, unmet treatment need. With improved understanding of the genetic and molecular basis of HCM, the present decade will witness novel treatments for disease prevention and modification.

Abstract P475: Left Ventricular Hypertrophy Prevalence by Electrocardiogram in Patients Presenting With Cerebrovascular Accident

Background: A strong association has been previously suggested between left ventricular hypertrophy (LVH) and stroke. The prevalence of LVH and hypertension, however, differs depending on demographic variables. In light of the most recent hypertension guidelines, more aggressive blood pressure control is called for to achieve the lower goals set forth. This will hopefully translate into lower incidence of LVH and stroke. Objectives: To examine the prevalence of left ventricular hypertrophy in patients receiving an electrocardiogram (EKG) for a diagnosis of stroke, transient ischemic attack, or cerebrovascular accident; all of which will be referred to as CVA. Methods: An EKG database from a university cardiology noninvasive lab was examined for the diagnosis of CVA over a one year period. The EKGs were evaluated for the presence of LVH or atrial fibrillation/flutter. The results were analyzed using Chi square test. Results: There was a total of 14,798 EKGs performed over one year. There were 272 EKGs with a diagnosis of CVA (1.7%); 2364 EKGs (16%) with diagnosis of LVH, and 481 (3.2%) with diagnosis of atrial fibrillation/flutter. Out of the 272 EKGs in patients with CVA diagnosis, there were 174 EKGs in sinus rhythm without LVH (64%), 78 EKGs in sinus rhythm with LVH (29%, P &lt; 0.05), and 20 EKGs in atrial fibrillation or atrial flutter (7.4%, P &lt; 0.05). Discussion: Stroke is a major health problem associated with increased mortality and morbidity. While atrial fibrillation and atrial flutter are well-established causes of stroke and were nearly twice as frequent in CVA patients as the general population in our cohort, LVH by EKG, in the absence of atrial fibrillation/flutter, was also nearly twice as prevalent, underscoring its importance as a potential marker for CVA. Hypertension is a well established risk factor for stroke in addition to cardiovascular events, and is a major cause of LVH on EKG. In our cohort nearly one third of patients with stroke were in sinus rhythm, but had left ventricular hypertrophy by EKG, a prevalence that was nearly four times that of atrial fibrillation/flutter. Since hypertension is the most common cause of left ventricular hypertrophy in our population, this places further significance on hypertension as a major modifiable risk factor and predictor of CVA and cardiovascular disease in general. In light of the most recent hypertension guidelines, aggressive blood pressure management to achieve the newly defined lower limits for a diagnosis of hypertension, with monitoring for signs of left ventricular hypertrophy by EKG, should be emphasized in order to decrease the incidence of stroke and other cardiovascular events.

Rapid progression of left ventricular hypertrophy caused by leukaemic infiltration of myocardium.

A man in his 40s was hospitalized due to recurrence of acute myeloid leukaemia. Echocardiography was performed because of negative T waves in the electrocardiogram. Severe and global left ventricular hypertrophy (LVH) was observed (Panels B and E, see Supplementary data online, Video S2), which was not found 15 months ago (Panels A and D, Supplementary data online, Video S1). Computed tomography scan also showed severe LVH (Panel H), which was not found 3 months ago (Panel G). Thus, it was confirmed that the rapid hypertrophic change occurred within 3 months. Low-dose Ara-C (cytosine arabinoside) and VP-16 (etoposide) therapy was administered for leukaemia. Echocardiography, after two weeks from the start of treatment, showed reduced LVH (Panels C and F). Therefore, the myocardial infiltration of leukaemia cells was considered as the cause of LVH. Unfortunately, 16 weeks later, the patient died by multiple organ failure. Autopsy showed that the cardiac wall thickened and numerous numbers of leukaemia cells infiltrated in the myocardium (Panels J and K). Distribution of the myocardial fibre was disarrayed but necrosis was not found. Infiltration of leukaemia cells was observed in multiple organs.