Citations

Background: Hypertrophic cardiomyopathy (HCM) is a genetic cardiomyopathy marked by increased left ventricular wall thickness, leading in some cases to left ventricular outflow tract (LVOT) obstruction, heart failure, and arrhythmias. Mavacamten, a selective allosteric inhibitor of cardiac myosin, has demonstrated benefits in improving hemodynamics and reducing LVOT obstruction. However, its impact on arrhythmic burden remains unclear, with reports of early atrial fibrillation (AF) risk contrasting with long-term reductions in arrhythmias. This study assesses the temporal patterns of Holter-detected arrhythmias in HCM patients treated with mavacamten. Methods: This retrospective study included HCM patients from three Mayo Clinic sites. Baseline demographic, clinical, and echocardiographic data were collected. Holter monitoring was performed at baseline, short-term (<6 months), and long-term (>6 months) follow-up. Arrhythmic events, including premature atrial contractions (PACs), premature ventricular contractions (PVCs), and supraventricular tachycardia (SVT), were analyzed using standardized rates per 24 h. Statistical comparisons utilized the Wilcoxon signed-rank test. Results: Twenty-seven patients (56% female, median age 66 years) were included. PACs, PVCs, and SVT duration transiently but not significantly increased at short-term follow-up but returned to baseline at long-term follow-up. No sustained or high-risk ventricular arrhythmias were observed. Conclusions: Mavacamten is associated with transient arrhythmic fluctuations early in treatment, followed by stabilization. These findings support its long-term electrophysiological safety and underscore the need for early rhythm monitoring. Further research should explore its role in arrhythmic risk stratification in HCM patients.

OBJECTIVE This study aimed to develop an artificial intelligence (AI) model for automatically detecting Hounsfield unit (HU) values at the L1 vertebra in preoperative thoracolumbar CT scans. This model serves as a screening tool for osteoporosis in patients undergoing spine surgery, offering an alternative to traditional bone mineral density measurement methods like dual-energy x-ray absorptiometry. METHODS The authors utilized two CT scan datasets, comprising 501 images, which were split into training, validation, and test subsets. The nnU-Net framework was used for segmentation, followed by an algorithm to calculate HU values from the L1 vertebra. The model’s performance was validated against manual HU calculations by expert raters on 56 CT scans. Statistical measures included the Dice coefficient, Pearson correlation coefficient, intraclass correlation coefficient (ICC), and Bland-Altman plots to assess the agreement between AI and human-derived HU measurements. RESULTS The AI model achieved a high Dice coefficient of 0.91 for vertebral segmentation. The Pearson correlation coefficient between AI-derived HU and human-derived HU values was 0.96, indicating strong agreement. ICC values for interrater reliability were 0.95 and 0.94 for raters 1 and 2, respectively. The mean difference between AI and human HU values was 7.0 HU, with limits of agreement ranging from −21.1 to 35.2 HU. A paired t-test showed no significant difference between AI and human measurements (p = 0.21). CONCLUSIONS The AI model demonstrated strong agreement with human experts in measuring HU values, validating its potential as a reliable tool for automated osteoporosis screening in spine surgery patients. This approach can enhance preoperative risk assessment and perioperative bone health optimization. Future research should focus on external validation and inclusion of diverse patient demographics to ensure broader applicability.

Abstract Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date—the electrocardiogram (ECG). The application of artificial intelligence‐enabled ECG (AI‐ECG) has resulted in the ability to identify electrocardiographic signatures of conventional and unique variables and pathologies, giving way to tremendous clinical potential. However, what these AI‐ECG models are detecting that the human eye is missing remains unclear. In this article, we highlight some of the recent developments in the field and their potential clinical implications, while also attempting to shed light on the physiologic and pathophysiologic features that enable these models to have such high diagnostic yield. © 2022 American Physiological Society. Compr Physiol 12:3417‐3424, 2022.

Abstract The advent of high-throughput sequencing has led to an exponential increase in genomic data, highlighting the need for efficient and accurate models to analyze and interpret this information. Here, we introduce a novel, exomic foundational model that leverages a combination of human reference genome and multispecies data to improve variant detection and interpretation. Our model utilizes a short- range transformer architecture and is trained on a large dataset of human exomic sequences derived from the Tapestry study. Through a series of ablation studies and scaling experiments, we demonstrate the effectiveness of our model in pre- dicting next token accuracy and identifying clinically pathogenic variants. We also show that our model outperforms existing models in a range of downstream tasks, including variant effect prediction and disease state identification. In fact, our largest STRAND variant (1B parameters) surpassed previous benchmarks, demonstrating a mean accuarcy of 0.880 (8.2% improvement over the original NT and 7% improvement over NT-v2). Furthermore, we construct a unique exomic ClinVar dataset to evaluate the model’s performance on pathogenicity and disease states. Our results highlight the potential of this model to improve our understanding of the human exome and its role in disease. The model and its applications have significant implications for genomic based diagnosis and personalized medicine including tailored therapeutic development.

Background and Aims: Among those with primary sclerosing cholangitis (PSC), perihilar cholangiocarcinoma (pCCA) is often diagnosed at a late stage and is a leading source of mortality. Detection of pCCA in PSC when curative action can be taken is challenging. Our aim was to create a deep learning model that analyzed MRI to detect early-stage pCCA and compare its diagnostic performance with expert radiologists. Approach and Results: We conducted a multicenter, international, retrospective cohort study involving adults with large duct PSC who underwent contrast-enhanced MRI. Senior abdominal radiologists reviewed the images. All patients with pCCA had early-stage cancer and were registered for liver transplantation. We trained a 3D DenseNet-121 model, a form of deep learning, using MRI images and assessed its performance in a separate test cohort. The study included 398 patients (training cohort n=150; test cohort n=248). pCCA was present in 230 individuals (training cohort n=64; test cohort n=166). In the test cohort, the respective performances of the model compared to the radiologists were: sensitivity 87.9% versus 50.0%, p <0.001; specificity 84.1% versus 100.0%, p <0.001; area under receiving operating curve 86.0% versus 75.0%, p <0.001. Even when a mass was absent, the model had a higher sensitivity for pCCA than radiologists (91.6% vs. 50.6%, p <0.001) and maintained good specificity (84.1%). Conclusions: The 3D DenseNet-121 MRI model effectively detects early-stage pCCA in PSC patients. Compared to expert radiologists, the model missed fewer cases of cancer.

ABSTRACT Background Mitral annular disjunction (MAD) carries an increased risk of complex ventricular arrhythmias, which can lead to sudden cardiac death. Many of these patients undergo implantable cardioverter defibrillator (ICD) implantation, but their ICD outcomes are not known. Objective The aim of this study was to assess the outcomes of ICD implantation and the predictors of appropriate ICD therapies in patients with MAD. Methods The study included patients with MAD who underwent ICD implantation. Clinical, electrocardiographic, cardiac imaging, and device therapy data were collected. Results Forty‐nine patients with MAD and ICD were included. Median age was 49 (21) years, and 29 (59%) were female. 13 (27%) patients underwent ICD implantation for primary prevention and 36 (73%) patients for secondary prevention. Over a median follow‐up of 27.3 (35.3) months, 23 (47%) patients received ICD therapies. 18 (37%) patients had appropriate ICD therapies, and 5 (10%) patients had inappropriate ICD shocks. Median time to first appropriate therapy was 22.3 (63.3) months. In patients with a secondary prevention ICD indication, the rate of appropriate ICD therapies was 44%, while in patients with a primary prevention ICD indication, it was 15%. Among patients with appropriate ICD therapies, the first therapies were delivered for monomorphic ventricular tachycardia (VT) in 7 (39%) patients and polymorphic VT or ventricular fibrillation (VF) in 11 (61%) patients. Patients with appropriate ICD therapies were more likely to have a history of SCA ( p = 0.003) and/or low left ventricular ejection fraction (LVEF) ( p = 0.022) before ICD implantation as compared to patients without appropriate ICD therapies. Conclusions In our cohort of patients with MAD and ICD, appropriate ICD therapies were common. Most appropriate ICD therapies were delivered for polymorphic VT or VF. Larger studies are needed to elucidate the mechanisms of VAs and refine risk stratification in MAD.