BACKGROUND: Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a primary arrhythmia disorder characterized by syncope or sudden cardiac death and typically caused by a gain-of-function of the Ryanodine Receptor Type 2 (RyR2) mutation. Calmodulin is a calcium-binding protein responsible for many intracellular signalling pathways and disruptions in function or regulation may lead to potentially fatal arrhythmias. We present a case of a young patient with CPVT found to have an unusual, potentially causative, Calmodulin 2-a protein coding gene (CALM2) mutation. CASE SUMMARY: A 21-year-old female with autism was brought to the ED following cardiac arrest. Bidirectional ventricular tachycardia was captured on electrocardiogram. Propranolol was initiated, and patient had no further episodes of ventricular arrhythmia. A subcutaneous implantable cardioverter defibrillator (ICD) was implanted, and further genetics testing was done. Rapid Whole Genome Sequencing (PGnome®-RAPID) resulted heterozygous variant of uncertain significance in CALM2 gene NM_001743.5 for variant c.136G>A. DISCUSSION: To the authors knowledge, this is the third known record of such mutation in accordance with the International Calmodulin Registry (n = 74). Identification of CALM mutations can help advance the understanding of genetic underpinnings of arrhythmias and underscore necessity of genetic screening and personalized treatment strategies. Subcutaneous ICDs offer a promising therapeutic option while minimizing risks associated with traditional transvenous ICDs.

Primary cardiac angiosarcomas are rare malignant tumors with a very poor prognosis. We present a case of a 48-year-old man with no previous cardiac history who developed an incessant focal atrial tachycardia complicated by tachycardia-mediated cardiomyopathy as a consequence of cardiac angiosarcoma. (Level of Difficulty: Beginner.).

Bedside patient monitors are ubiquitous tools in modern critical care units to provide timely patient status. However, current systems suffer from high volume of false alarms leading to alarm fatigue, one of top technical hazards in clinical settings. Many studies are racing to develop improved algorithms towards precision patient monitoring, while little has been done to investigate the aspect of algorithm generalizability across different health institutions. Our group has been developing an evolving framework termed SuperAlarm that extracts multivariate patterns in data streams (monitor alarms, electronic health records and physiologic waveforms) of modern health enterprise to predict patient deterioration and has demonstrated great potential in mitigating alarm fatigue. In this study, we further investigate the generalizability of SuperAlarm by designing a comprehensive approach to achieve performance comparison in predicting in-hospital code blue (CB) events across two health institutions. SuperAlarm model trained with alarm data in one institution is tested on both internal and external test sets. Results show comparable performance with sensitivity up to 80% within one-hour window of events and over 90% in reduction of false alarms in both institutions. Cross-institutional performance agreement can be further improved by predicting a more stringent CB subtype (cardiopulmonary arrest), with internal sensitivity lying within 95% confident interval of external one up to 8-hour before event onset. The cross-institutional performance comparison offers first-hand knowledge on both advantages and challenges in generalizing a prediction algorithm across different institutions, which hold key information to guide the design of model training and deployment strategy.

We show the virtual simulation of the fluoroscopic location of the membranous septum using preprocedural cardiac computed tomographic data sets. Recognizing the risk distance before the procedure can help individualize implantation strategy to reduce the risk of atrioventricular conduction axis damage during transcatheter aortic valve replacement. (Level of Difficulty: Advanced.).

Background

Accurate and timely detection of atrial fibrillation (AF) episodes is important in primarily and secondary prevention of ischemic stroke and heart-related problems. In this work, heart rate regularity of ECG inter-beat intervals was investigated in episodes of AF and other rhythms using a wavelet leader based multifractal analysis. Our aim was to improve the detectability of AF episodes.

Methods

Inter-beat intervals from 25 ECG recordings available in the MIT-BIH atrial fibrillation database were analysed. Four types of annotated rhythms (atrial fibrillation, atrial flutter, AV junctional rhythm, and other rhythms) were available. A wavelet leader based multifractal analysis was applied to 5 min non-overlapping windows of each recording to estimate the multifractal spectrum in each window. The width of the multifractal spectrum was analysed for its discrimination power between rhythm episodes.

Results

In 10 of 25 recordings, the width of multifractal spectrum was significantly lower in episodes of AF than in other rhythms indicating increased regularity during AF. High classification accuracy (95%) of AF episodes was achieved using a combination of features derived from the multifractal analysis and statistical central moment features.

Conclusions

An increase in the regularity of inter-beat intervals was observed during AF episodes by means of multifractal analysis. Multifractal features may be used to improve AF detection accuracy.

Facet tropism has been investigated as a predisposing factor for degenerative changes in the lumbar spine; however, no prior study has evaluated the relationship between disc bulge and facet tropism. In this study, we used kinetic magnetic resonance imaging (kMRI) to investigate the association between degree of facet tropism and amount of disc bulge in the lumbar spine in relation to age. kMRIs in the flexion, neutral, and extension positions were performed on 410 consecutive patients with low back pain. T2-weighted midsagittal and axial mid-disc cuts were analyzed to measure disc bulge and facet angle. Facet asymmetry was calculated and classified as: no facet tropism, <6°; mild facet tropism, 6–11°; or severe facet tropism, ≥11°. Maximal static bulge (MSB), maximal dynamic bulge (MDB), and age in the facet tropism groups were compared by age subpopulations and MDB categories, defined by the positions between which the largest change in disc bulge occurs. We found the severe facet tropism group to be associated with a nearly significant increase in MSB and MDB over the no facet tropism group in the older subpopulation at the L4–L5 level only, and a larger MDB in the L4–L5 MDB category [E–N], where the greatest change in disc bulge occurs between neutral and extension positions (p = 0.013). Our findings suggest that severe facet tropism is associated with increased disc bulge at L4–L5 in only a subset of older age patients, but may in large part be due to biomechanical factors that define the [E–N] category.

Photoplethysmography (PPG) is a ubiquitous physiological measurement that detects beat-to-beat pulsatile blood volume changes and hence has a potential for monitoring cardiovascular conditions, particularly in ambulatory settings. A PPG dataset that is created for a particular use case is often imbalanced, due to a low prevalence of the pathological condition it targets to predict and the paroxysmal nature of the condition as well. To tackle this problem, we propose log-spectral matching GAN (LSM-GAN), a generative model that can be used as a data augmentation technique to alleviate the class imbalance in a PPG dataset to train a classifier. LSM-GAN utilizes a novel generator that generates a synthetic signal without a up-sampling process of input white noises, as well as adds the mismatch between real and synthetic signals in frequency domain to the conventional adversarial loss. In this study, experiments are designed focusing on examining how the influence of LSM-GAN as a data augmentation technique on one specific classification task - atrial fibrillation (AF) detection using PPG. We show that by taking spectral information into consideration, LSM-GAN as a data augmentation solution can generate more realistic PPG signals.

Anomalous left coronary artery to pulmonary artery (ALCAPA) is a leading cause of pediatric myocardial ischemia. This paper presents a case of a young man presenting with ventricular tachycardia storm 18 years after ALCAPA repair. Clinicians should recognize the risk of ventricular tachycardia in this patient population.

Survival from in-hospital cardiac arrest (IHCA) due to pulseless electrical activity/asystole remains poor. We aimed to evaluate whether electrocardiographic changes provide predictive information for risk of IHCA from pulseless electrical activity/asystole. We conducted a retrospective case-control study, utilizing continuous electrocardiographic data from case and control patients. We selected 3 consecutive 3-hour blocks (block 3, 2, and 1 in that order); block 1 immediately preceded cardiac arrest in cases, whereas block 1 was chosen at random in controls. In each block, we measured dominant positive and negative trends in electrocardiographic parameters, evaluated for arrhythmias, and compared these between consecutive blocks. We created random forest and logistic regression models, and tested them on differentiating case versus control patients (case block 1 vs control block 1), and temporal relation to cardiac arrest (case block 2 vs case block 1). Ninety-one cases (age 63.0 ± 17.6, 58% male) and 1,783 control patients (age 63.5 ± 14.8, 67% male) were evaluated. We found significant differences in electrocardiographic trends between case and control block 1, particularly in QRS duration, QTc, RR, and ST. New episodes of atrial fibrillation and bradyarrhythmias were more common before IHCA. The optimal model was the random forest, achieving an area under the curve of 0.829, 63.2% sensitivity, 94.6% specificity at differentiating case versus control block 1 on a validation set, and area under the curve 0.954, 91.2% sensitivity, 83.5% specificity at differentiating case block 1 versus case block 2. In conclusion, trends in electrocardiographic parameters during the 3-hour window immediately preceding IHCA differ significantly from other time periods, and provide robust predictive information.