Apical hypertrophic cardiomyopathy (AHCM) is a rare form of hypertrophic cardiomyopathy, occasionally resulting in severe complications. The paper covers the etiology and pathogenesis of AHCM, different imaging methods and characteristic appearance of the disease in each of them. Echocardiography and cardiovascular magnetic resonance imaging (CMR) are known to be the most valuable imaging methods. Moreover, this review presents medical and surgical treatment, as well as the clinical course and prognosis. Despite possible morbid events the overall cardiovascular mortality rate of AHCM patients is low, and the prognosis is relatively optimistic.
Left ventricular non-compaction (LVNC) describes the phenotypical phenomena characterized by the presence of excessive trabeculation of the left ventricle which forms a deep recess filled with blood. Considering the lack of a uniform definition of LVNC as well as the “golden standard” it is difficult to estimate the actual incidence of the disease, however, seems to be overdiagnosed, due to unspecific diagnostic criteria. The non-compacted myocardium may appear both as a disease representation or variant of the norm or as an adaptive phenomenon. This article covers different approaches to incidence, pathogenesis, diagnostics, and treatment of LVNC as well as recommendations for patients during follow-up.
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.
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