Screening for hypertrophic cardiomyopathy

Screening for Hypertrophic Obstructive Cardiomyopathy in Patients With Panic Disorder: A Case Report

Hypertrophic obstructive cardiomyopathy (HOCM) is a cardiovascular disease that is widely recognized as an important cause of various cardiovascular pathologies. Passed through an autosomal dominant inheritance pattern, mutations can result in cardiac dysfunction that can manifest in dyspnea, exercise intolerance, and sudden death. Panic disorder can present similarly to HOCM; however, precautions and treatment differ significantly. Here, we present a case of a 56-year-old male with a history of panic disorder who presented to the emergency department with recurrent episodes of palpitations, lightheadedness, and dyspnea, and who was subsequently hospitalized due to new ventricular tachyarrhythmia and diagnosed with HOCM. This case highlights the importance of detailed history taking, follow-up of chronic symptoms, and consideration of genetic screening for HOCM in patients with panic disorder.

Subtypes and Mechanisms of Hypertrophic Cardiomyopathy Proposed by Machine Learning Algorithms

Hypertrophic cardiomyopathy (HCM) is a relatively common inherited cardiac disease that results in left ventricular hypertrophy. Machine learning uses algorithms to study patterns in data and develop models able to make predictions. The aim of this study is to identify HCM subtypes and examine the mechanisms of HCM using machine learning algorithms. Clinical and laboratory findings of 143 adult patients with a confirmed diagnosis of nonobstructive HCM are analyzed; HCM subtypes are determined by clustering, while the presence of different HCM features is predicted in classification machine learning tasks. Four clusters are determined as the optimal number of clusters for this dataset. Models that can predict the presence of particular HCM features from other genotypic and phenotypic information are generated, and subsets of features sufficient to predict the presence of other features of HCM are determined. This research proposes four subtypes of HCM assessed by machine learning algorithms and based on the overall phenotypic expression of the participants of the study. The identified subsets of features sufficient to determine the presence of particular HCM aspects could provide deeper insights into the mechanisms of HCM.