Clinical results of combined amiodarone and mexiletine therapy in refractory ventricular tachycardias

Background: Refractory recurrent ventricular tachycardia is a difficult therapeutic problem. There are implantable cardioverter-defibrillator (ICD) patients with amiodarone-refractory of ventricular arrhythmia (VA) who are not eligible for catheter ablation. The aim of this cohort study was to assess the efficacy of mexiletine in combination with amiodarone in the reduction of VA in this group of patients. Methods: This was a retrospective study of all consecutive ICD patients who were treated by adding mexiletine to amiodarone in refractory electrical storm or frequent VA episodes. The enrolled patients were ineligible for catheter ablation. Results: Thirty-seven patients (32 males; mean age, 57 ± 14 years; range, 26–81 years) were studied. Adding mexiletine to amiodarone had no significant effect on QRS width, QTc interval, and PR interval (all P > 0.05). We observed a significant decrease in the number of total ICD shock and significant increase in appropriate antitachycardia pacing during follow-up after initiating mexiletine. Mexiletine therapy also significantly reduced the amiodarone dose during the follow-up. No mortality was observed in the present cohort during the study period. Conclusions: Mexiletine, when added in case of amiodarone failure, reduces VA episodes and appropriate therapies in patients with an implantable cardioverter defibrillator

Cardiac MRI in a Patient with Coincident Left Ventricular Non-Compaction and Hypertrophic Cardiomyopathy

Left ventricular non-compaction cardiomyopathy is a rare congenital cardiomyopathy that affects both children and adults. Since the clinical manifestations are not sufficient to establish diagnosis, echocardiography is the diagnostic tool that makes it possible to document ventricular non-compaction and establish prognostic factors. We report a 47-year-old woman with a history of dilated cardiomyopathy with unknown etiology. Echocardiography showed mild left ventricular enlargement with severe systolic dysfunction (EF = 20-25%). According to cardiac magnetic resonance imaging findings non-compaction left ventricle with hypertrophic cardiomyopathy was considered, and right ventricular septal biopsy was recommended. Right ventricular endomyocardial biopsy showed moderate hypertrophy of cardiac myocytes with foci of myocytolysis and moderate interstitial fibrosis. No evidence of infiltrative deposition was seen

Unusual Clinical Presentation of a Giant Left Ventricle Hydatid Cyst

A 39-year-old woman was hospitalized in our center due to chest and left shoulder pain. Having a history of tamponade and tuberculosis, she was under treatment for the previous two months. Echocardiography, chest CT and MRI documented intramyocardial and pericardial hydatid cyst which was later confirmed by further pathological studies. Later, the cyst was removed surgically

Myocardial Perfusion SPECT Imaging Radiomic Features and Machine Learning Algorithms for Cardiac Contractile Pattern Recognition

A U-shaped contraction pattern was shown to be associated with a better Cardiac resynchronization therapy (CRT) response. The main goal of this study is to automatically recognize left ventricular contractile patterns using machine learning algorithms trained on conventional quantitative features (ConQuaFea) and radiomic features extracted from Gated single-photon emission computed tomography myocardial perfusion imaging (GSPECT MPI). Among 98 patients with standard resting GSPECT MPI included in this study, 29 received CRT therapy and 69 did not (also had CRT inclusion criteria but did not receive treatment yet at the time of data collection, or refused treatment). A total of 69 non-CRT patients were employed for training, and the 29 were employed for testing. The models were built utilizing features from three distinct feature sets (ConQuaFea, radiomics, and ConQuaFea + radiomics (combined)), which were chosen using Recursive feature elimination (RFE) feature selection (FS), and then trained using seven different machine learning (ML) classifiers. In addition, CRT outcome prediction was assessed by different treatment inclusion criteria as the study’s final phase. The MLP classifier had the highest performance among ConQuaFea models (AUC, SEN, SPE = 0.80, 0.85, 0.76). RF achieved the best performance in terms of AUC, SEN, and SPE with values of 0.65, 0.62, and 0.68, respectively, among radiomic models. GB and RF approaches achieved the best AUC, SEN, and SPE values of 0.78, 0.92, and 0.63 and 0.74, 0.93, and 0.56, respectively, among the combined models. A promising outcome was obtained when using radiomic and ConQuaFea from GSPECT MPI to detect left ventricular contractile patterns by machine learning