Molecular Dynamic Simulations for Biopolymers with Biomedical Applications.

Computational modeling (CM) is a versatile scientific methodology used to examine the properties and behavior of complex systems, such as polymeric materials for biomedical bioengineering. CM has emerged as a primary tool for predicting, setting up, and interpreting experimental results. Integrating in silico and in vitro experiments accelerates scientific advancements, yielding quicker results at a reduced cost. While CM is a mature discipline, its use in biomedical engineering for biopolymer materials has only recently gained prominence. In biopolymer biomedical engineering, CM focuses on three key research areas: (A) Computer-aided design (CAD/CAM) utilizes specialized software to design and model biopolymers for various biomedical applications. This technology allows researchers to create precise three-dimensional models of biopolymers, taking into account their chemical, structural, and functional properties. These models can be used to enhance the structure of biopolymers and improve their effectiveness in specific medical applications. (B) Finite element analysis, a computational technique used to analyze and solve problems in engineering and physics. This approach divides the physical domain into small finite elements with simple geometric shapes. This computational technique enables the study and understanding of the mechanical and structural behavior of biopolymers in biomedical environments. (C) Molecular dynamics (MD) simulations involve using advanced computational techniques to study the behavior of biopolymers at the molecular and atomic levels. These simulations are fundamental for better understanding biological processes at the molecular level. Studying the wide-ranging uses of MD simulations in biopolymers involves examining the structural, functional, and evolutionary aspects of biomolecular systems over time. MD simulations solve Newton's equations of motion for all-atom systems, producing spatial trajectories for each atom. This provides valuable insights into properties such as water absorption on biopolymer surfaces and interactions with solid surfaces, which are crucial for assessing biomaterials. This review provides a comprehensive overview of the various applications of MD simulations in biopolymers. Additionally, it highlights the flexibility, robustness, and synergistic relationship between in silico and experimental techniques.

Inequities in Atrial Fibrillation Trials: An Analysis of Participant Race, Ethnicity, and Sex Over Time.

BACKGROUND: Despite the importance of racial and ethnic representation in clinical trials, limited data exist regarding the enrollment trends of these groups in atrial fibrillation (AF) trials over time. OBJECTIVES: The purpose of this study is to examine the characteristics of contemporary AF clinical trials and evaluate their association with race and ethnicity over time. METHODS: We performed a systematic search of all completed AF trials registered in ClinicalTrials.gov between conception to December 31, 2023 and manually extracted composition of race/ethnicity. We stratified trials by study characteristics, including impact factor, publication status, funding source, and location. We calculated the participation prevalence ratio (PPR) by dividing the percentage of non-White participants by the percentage of non-White participants among the disease population (PPR 0.8-1.2 suggests proportional representation) over time. RESULTS: We identified 277 completed AF trials encompassing a total of 1,933,441 adults, with a median proportion of non-White at 12% (IQR: 6-27), 121 (43.7%) device-focused, and 184 (66.4%) funded by industry. Only 36.1% of trials reported comprehensive race information. Overall, non-White participants were underrepresented (PPR = 0.511; P < 0.001), including Black (PPR = 0.263) and Hispanic (PPR = 0.337) participants. The proportion of non-White participants did not change significantly between 2000 and 2023 (11% vs 9%; P = 0.343). CONCLUSION: Despite greater awareness, race/ethnicity reporting and representation of non-White groups in AF clinical trials are poor and have not improved significantly over time. These findings demand additional recruitment efforts and novel recruitment policies to ensure adequate representation of these demographic subgroups in future AF clinical trials.

Considering radial basis function neural network for effective solution generation in metaheuristic algorithms.

In many engineering optimization problems, the number of function evaluations is severely limited by the time or cost constraints. These limitations present a significant challenge in the field of global optimization, because existing metaheuristic methods typically require a substantial number of function evaluations to find optimal solutions. This paper presents a new metaheuristic optimization algorithm that considers the information obtained by a radial basis function neural network (RBFNN) in terms of the objective function for guiding the search process. Initially, the algorithm uses the maximum design approach to strategically distribute a set of solutions across the entire search space. It then enters a cycle in which the RBFNN models the objective function values from the current solutions. The algorithm identifies and uses key neurons in the hidden layer that correspond to the highest objective function values to generate new solutions. The centroids and standard deviations of these neurons guide the sampling process, which continues until the desired number of solutions is reached. By focusing on the areas of the search space that yield high objective function values, the algorithm avoids exhaustive solution evaluations and significantly reduces the number of function evaluations. The effectiveness of the method is demonstrated through a comparison with popular metaheuristic algorithms across several test functions, where it consistently outperforms existing techniques, delivers higher-quality solutions, and improves convergence rates.

Generation of induced pluripotent stem cell lines from patients with LQT1 caused by heterozygous mutations in the KCNQ1 gene.

Long QT Syndrome (LQTS) is a genetic heart disorder that can induce cardiac arrhythmias. The most prevalent subtype, LQT1, stems from rare variants in the KCNQ1 gene. Utilizing induced pluripotent stem cells (iPSCs) enables detailed cellular studies and personalized medicine approaches for this life-threatening condition. We generated two LQT1 iPSC lines with single nucleotide nonsense mutations, c.1031 C > T and c.1121 T > A in KCNQ1. Both lines exhibited typical iPSC morphology, expressed high levels of pluripotent markers, maintained normal karyotype, and possessed the capability to differentiate into three germ layers. These cell lines serve as important tools for investigating the biological mechanisms underlying LQT1 due to mutations in the KCNQ1 gene.

Image segmentation with Cellular Automata.

Image segmentation is a computer vision technique that involves dividing an image into distinct and meaningful regions or segments. The objective was to partition the image into areas that share similar visual characteristics. Noise and undesirable artifacts introduce inconsistencies and irregularities in image data. These inconsistencies severely affect the ability of most segmentation algorithms to distinguish between true image features, leading to less reliable and lower-quality results. Cellular Automata (CA) is a computational concept that consists of a grid of cells, each of which can be in a finite number of states. These cells evolve over discrete time steps based on a set of predefined rules that dictate how a cell's state changes according to its own state and the states of its neighboring cells. In this paper, a new segmentation approach based on the CA model was introduced. The proposed approach consisted of three phases. In the initial two phases of the process, the primary objective was to eliminate noise and undesirable artifacts that can interfere with the identification of regions exhibiting similar visual characteristics. To achieve this, a set of rules is designed to modify the state value of each cell or pixel based on the states of its neighboring elements. In the third phase, each element is assigned a state that is chosen from a set of predefined states. These states directly represent the final segmentation values for the corresponding elements. The proposed method was evaluated using different images, considering important quality indices. The experimental results indicated that the proposed approach produces better-segmented images in terms of quality and robustness.

Artificial Intelligence in Cardiovascular Care-Part 2: Applications: JACC Review Topic of the Week.

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in effective diagnosis, treatment, and outcomes. More than 600 U.S. Food and Drug Administration-approved clinical AI algorithms now exist, with 10% focusing on cardiovascular applications, highlighting the growing opportunities for AI to augment care. This review discusses the latest advancements in the field of AI, with a particular focus on the utilization of multimodal inputs and the field of generative AI. Further discussions in this review involve an approach to understanding the larger context in which AI-augmented care may exist, and include a discussion of the need for rigorous evaluation, appropriate infrastructure for deployment, ethics and equity assessments, regulatory oversight, and viable business cases for deployment. Embracing this rapidly evolving technology while setting an appropriately high evaluation benchmark with careful and patient-centered implementation will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week.

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Biomineralization of Polyelectrolyte-Functionalized Electrospun Fibers: Optimization and In Vitro Validation for Bone Applications.

In recent years, polyelectrolytes have been successfully used as an alternative to non-collagenous proteins to promote interfibrillar biomineralization, to reproduce the spatial intercalation of mineral phases among collagen fibrils, and to design bioinspired scaffolds for hard tissue regeneration. Herein, hybrid nanofibers were fabricated via electrospinning, by using a mixture of Poly ɛ-caprolactone (PCL) and cationic cellulose derivatives, i.e., cellulose-bearing imidazolium tosylate (CIMD). The obtained fibers were self-assembled with Sodium Alginate (SA) by polyelectrolyte interactions with CIMD onto the fiber surface and, then, treated with simulated body fluid (SBF) to promote the precipitation of calcium phosphate (CaP) deposits. FTIR analysis confirmed the presence of SA and CaP, while SEM equipped with EDX analysis mapped the calcium phosphate constituent elements, estimating an average Ca/P ratio of about 1.33-falling in the range of biological apatites. Moreover, in vitro studies have confirmed the good response of mesenchymal cells (hMSCs) on biomineralized samples, since day 3, with a significant improvement in the presence of SA, due to the interaction of SA with CaP deposits. More interestingly, after a decay of metabolic activity on day 7, a relevant increase in cell proliferation can be recognized, in agreement with the beginning of the differentiation phase, confirmed by ALP results. Antibacterial tests performed by using different bacteria populations confirmed that nanofibers with an SA-CIMD complex show an optimal inhibitory response against S. mutans, S. aureus, and E. coli, with no significant decay due to the effect of CaP, in comparison with non-biomineralized controls. All these data suggest a promising use of these biomineralized fibers as bioinspired membranes with efficient antimicrobial and osteoconductive cues suitable to support bone healing/regeneration.

Physical and structural characterization of bis-acryl composite resin.

During the preparation of fixed prosthesis (including individual bridges and crowns) it is important to select the materials that have the best features and properties to predict a successful clinical treatment. The objective of this study was to determine if the chemical and structural characteristics could cause to increase the fracture resistance, we used four bis-acryl resins Luxatemp, Protemp, Structur and Telio. Three-points bending by Flexural test were performed in ten bars and they were carried out to compare with Anova test. In addition, the bis-acryl resins were analyzed by scanning electron microscopy, to analyze microstructure and morphology and the molecular structure were performed by Infrared Spectroscopy through Attenuated Total Reflectance. A higher flexural strength was found in Luxatemp and Structur with, no significant differences between this study groups. Regarding Protemp and Telio, these study groups showed a lower flexural strength when were compared with Luxatemp and Structur. These results corroborate SEM and ATR analysis because Luxatemp sample showed a regular size particle on the surface and chemically presents a long cross-linkage polymer chain. The presence of CO3, SiO2 and N-H groups as a fillers particle interacting with OH groups cause a higher flexural strength compared with another groups.

Characteristics of contemporary atrial fibrillation clinical trials and their association with industry sponsorship.

BACKGROUND: Industry sponsorship is an important source of funding for atrial fibrillation (AF) clinical trials, the implications of which have not been analyzed. OBJECTIVE: The purpose of this study was to determine the characteristics of contemporary AF clinical trials and to evaluate their association with funding source. METHODS: We systematically assessed all completed AF trials registered in the ClinicalTrials.gov database between conception to October 31, 2023, and extracted publicly available information including funding source, trial size, demographic distribution, intervention, location, and publication status. Trial characteristics were compared using the Wilcoxon rank-sum test and Fisher exact test for continuous and categorical variables, respectively. RESULTS: Of the 253 clinical trials assessed, 171 (68%) reported industry funding. Industry funding was associated with a greater median number of patients enrolled (172 vs 80; P <.001), publication rate (56.7% vs 42.7%; P = .04), probability of being product-focused (48.0% vs 24.4%; P <.001), and multicontinental recruitment location (25.2% vs 2.4%; P <.001) when compared to nonindustry-funded trials. However, industry funding was not associated with a significant difference in median impact factor (7.7 vs 7.7; P = .723). The overall proportion of industry-funded trials did not change over time (P = 1). CONCLUSION: Industry-funded clinical trials in AF often are larger, more frequently published, multicontinental, and product-focused. Industry funding was found to be associated with significant differences in study enrollment and publication metrics.

Expedited loading with intravenous sotalol is safe and feasible-primary results of the Prospective Evaluation Analysis and Kinetics of IV Sotalol (PEAKS) Registry.

BACKGROUND: Loading of oral sotalol for atrial fibrillation requires 3 days, frequently in the hospital, to achieve steady state. The Food and Drug Administration approved loading with intravenous (IV) sotalol through model-informed development, without patient data. OBJECTIVE: We present results of the first multicenter evaluation of this recent labeling for IV sotalol. METHODS: The Prospective Evaluation Analysis and Kinetics of IV Sotalol (PEAKS) Registry was a multicenter observational registry of patients undergoing elective IV sotalol load for atrial arrhythmias. Outcomes, measured from hospital admission until first outpatient follow-up, included adverse arrhythmia events, efficacy, and length of stay. RESULTS: Of 167 consecutively enrolled patients, 23% were female; the median age was 68 (interquartile range, 61-74) years, and the median CHA2DS2-VASc score was 3 (interquartile range, 2-4). Overall, 99% were admitted for sotalol initiation (1% for dose escalation), with a target oral sotalol dose of either 80 mg twice daily (85 [51%]) or 120 mg twice daily (78 [47%]); 62 patients (37%) had an estimated creatinine clearance ≤90 mL/min. On presentation, 40% of patients were in sinus rhythm, whereas 26% underwent cardioversion before sotalol infusion. In 2 patients, sotalol infusion was stopped for bradycardia or hypotension. In 6 patients, sotalol was discontinued before discharge because of QTc prolongation (3), bradycardia (1), or recurrent atrial arrhythmia (2). The mean length of stay was 1.1 days, and 95% (n = 159) were discharged within 1 night. CONCLUSION: IV sotalol loading is safe and feasible for atrial arrhythmias, with low rates of adverse events, and yields shorter hospitalizations. More data are needed on the minimal duration required for monitoring in the hospital.

Prevalence of frequent premature ventricular contractions and nonsustained ventricular tachycardia in older women screened for atrial fibrillation in the Women's Health Initiative.

BACKGROUND: Frequent premature ventricular contractions (PVCs) and nonsustained ventricular tachycardia (NSVT) have been associated with cardiovascular disease and mortality. Their prevalence, especially in ambulatory populations, is understudied and limited by few female participants and the use of short-duration (24- to 48-hour) monitoring. OBJECTIVE: The objective of this study was to report the prevalence of frequent PVCs and NSVT in a community-based population of women likely to undergo electrocardiogram (ECG) screening by sequential patch monitoring. METHODS: Participants from the Women's Health Initiative Strong and Healthy (WHISH) trial with no history of atrial fibrillation (AF) but 5-year predicted risk of incident AF ≥5% by CHARGE-AF score were randomly selected to undergo screening with 7-day ECG patch monitors at baseline, 6 months, and 12 months. Recordings were reviewed for PVCs and NSVT (>5 beats); data were analyzed with multivariate regression models. RESULTS: There were 1067 participants who underwent ECG screening at baseline, 866 at 6 months, and 777 at 12 months. Frequent PVCs were found on at least 1 patch from 4.3% of participants, and 1 or more episodes of NSVT were found in 12 (1.1%) women. PVC frequency directly correlated with CHARGE-AF score and NSVT on any patch. Detection of frequent PVCs increased with sequential monitoring. CONCLUSION: In postmenopausal women at high risk for AF, frequent PVCs were relatively common (4.3%) and correlated with higher CHARGE-AF score. As strategies for AF screening continue to evolve, particularly in those individuals at high risk of AF, the prevalence of incidental ventricular arrhythmias is an important benchmark to guide clinical decision-making.

Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study.

BACKGROUND: Preoperative risk assessments used in clinical practice are insufficient in their ability to identify risk for postoperative mortality. Deep-learning analysis of electrocardiography can identify hidden risk markers that can help to prognosticate postoperative mortality. We aimed to develop a prognostic model that accurately predicts postoperative mortality in patients undergoing medical procedures and who had received preoperative electrocardiographic diagnostic testing. METHODS: In a derivation cohort of preoperative patients with available electrocardiograms (ECGs) from Cedars-Sinai Medical Center (Los Angeles, CA, USA) between Jan 1, 2015 and Dec 31, 2019, a deep-learning algorithm was developed to leverage waveform signals to discriminate postoperative mortality. We randomly split patients (8:1:1) into subsets for training, internal validation, and final algorithm test analyses. Model performance was assessed using area under the receiver operating characteristic curve (AUC) values in the hold-out test dataset and in two external hospital cohorts and compared with the established Revised Cardiac Risk Index (RCRI) score. The primary outcome was post-procedural mortality across three health-care systems. FINDINGS: 45 969 patients had a complete ECG waveform image available for at least one 12-lead ECG performed within the 30 days before the procedure date (59 975 inpatient procedures and 112 794 ECGs): 36 839 patients in the training dataset, 4549 in the internal validation dataset, and 4581 in the internal test dataset. In the held-out internal test cohort, the algorithm discriminates mortality with an AUC value of 0·83 (95% CI 0·79-0·87), surpassing the discrimination of the RCRI score with an AUC of 0·67 (0·61-0·72). The algorithm similarly discriminated risk for mortality in two independent US health-care systems, with AUCs of 0·79 (0·75-0·83) and 0·75 (0·74-0·76), respectively. Patients determined to be high risk by the deep-learning model had an unadjusted odds ratio (OR) of 8·83 (5·57-13·20) for postoperative mortality compared with an unadjusted OR of 2·08 (0·77-3·50) for postoperative mortality for RCRI scores of more than 2. The deep-learning algorithm performed similarly for patients undergoing cardiac surgery (AUC 0·85 [0·77-0·92]), non-cardiac surgery (AUC 0·83 [0·79-0·88]), and catheterisation or endoscopy suite procedures (AUC 0·76 [0·72-0·81]). INTERPRETATION: A deep-learning algorithm interpreting preoperative ECGs can improve discrimination of postoperative mortality. The deep-learning algorithm worked equally well for risk stratification of cardiac surgeries, non-cardiac surgeries, and catheterisation laboratory procedures, and was validated in three independent health-care systems. This algorithm can provide additional information to clinicians making the decision to perform medical procedures and stratify the risk of future complications. FUNDING: National Heart, Lung, and Blood Institute.

2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines.

AIM: The "2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Patients With Atrial Fibrillation" provides recommendations to guide clinicians in the treatment of patients with atrial fibrillation. METHODS: A comprehensive literature search was conducted from May 12, 2022, to November 3, 2022, encompassing studies, reviews, and other evidence conducted on human subjects that were published in English from PubMed, EMBASE, the Cochrane Library, the Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. Additional relevant studies, published through November 2022, during the guideline writing process, were also considered by the writing committee and added to the evidence tables, where appropriate. STRUCTURE: Atrial fibrillation is the most sustained common arrhythmia, and its incidence and prevalence are increasing in the United States and globally. Recommendations from the "2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" and the "2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" have been updated with new evidence to guide clinicians. In addition, new recommendations addressing atrial fibrillation and thromboembolic risk assessment, anticoagulation, left atrial appendage occlusion, atrial fibrillation catheter or surgical ablation, and risk factor modification and atrial fibrillation prevention have been developed.

2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines.

AIM: The "2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation" provides recommendations to guide clinicians in the treatment of patients with atrial fibrillation. METHODS: A comprehensive literature search was conducted from May 12, 2022, to November 3, 2022, encompassing studies, reviews, and other evidence conducted on human subjects that were published in English from PubMed, EMBASE, the Cochrane Library, the Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. Additional relevant studies, published through November 2022, during the guideline writing process, were also considered by the writing committee and added to the evidence tables, where appropriate. STRUCTURE: Atrial fibrillation is the most sustained common arrhythmia, and its incidence and prevalence are increasing in the United States and globally. Recommendations from the "2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" and the "2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" have been updated with new evidence to guide clinicians. In addition, new recommendations addressing atrial fibrillation and thromboembolic risk assessment, anticoagulation, left atrial appendage occlusion, atrial fibrillation catheter or surgical ablation, and risk factor modification and atrial fibrillation prevention have been developed.

Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure.

BACKGROUND: Deep learning models may combat widening racial disparities in heart failure outcomes through early identification of individuals at high risk. However, demographic biases in the performance of these models have not been well-studied. METHODS: This retrospective analysis used 12-lead ECGs taken between 2008 and 2018 from 326 518 patient encounters referred for standard clinical indications to Stanford Hospital. The primary model was a convolutional neural network model trained to predict incident heart failure within 5 years. Biases were evaluated on the testing set (160 312 ECGs) using the area under the receiver operating characteristic curve, stratified across the protected attributes of race, ethnicity, age, and sex. RESULTS: There were 59 817 cases of incident heart failure observed within 5 years of ECG collection. The performance of the primary model declined with age. There were no significant differences observed between racial groups overall. However, the primary model performed significantly worse in Black patients aged 0 to 40 years compared with all other racial groups in this age group, with differences most pronounced among young Black women. Disparities in model performance did not improve with the integration of race, ethnicity, sex, and age into model architecture, by training separate models for each racial group, or by providing the model with a data set of equal racial representation. Using probability thresholds individualized for race, age, and sex offered substantial improvements in F1 scores. CONCLUSIONS: The biases found in this study warrant caution against perpetuating disparities through the development of machine learning tools for the prognosis and management of heart failure. Customizing the application of these models by using probability thresholds individualized by race, ethnicity, age, and sex may offer an avenue to mitigate existing algorithmic disparities.

Clinical Management of Brugada Syndrome: Commentary From the Experts.

Although there is consensus on the management of patients with Brugada Syndrome with high risk for sudden cardiac arrest, asymptomatic or intermediate-risk patients present clinical management challenges. This document explores the management opinions of experts throughout the world for patients with Brugada Syndrome who do not fit guideline recommendations. Four real-world clinical scenarios were presented with commentary from small expert groups for each case. All authors voted on case-specific questions to evaluate the level of consensus among the entire group in nuanced diagnostic and management decisions relevant to each case. Points of agreement, points of controversy, and gaps in knowledge are highlighted.

Utility of smart watches for identifying arrhythmias in children.

BACKGROUND: Arrhythmia symptoms are frequent complaints in children and often require a pediatric cardiology evaluation. Data regarding the clinical utility of wearable technologies are limited in children. We hypothesize that an Apple Watch can capture arrhythmias in children. METHODS: We present an analysis of patients ≤18 years-of-age who had signs of an arrhythmia documented by an Apple Watch. We include patients evaluated at our center over a 4-year-period and highlight those receiving a formal arrhythmia diagnosis. We evaluate the role of the Apple Watch in arrhythmia diagnosis, the results of other ambulatory cardiac monitoring studies, and findings of any EP studies. RESULTS: We identify 145 electronic-medical-record identifications of Apple Watch, and find arrhythmias confirmed in 41 patients (28%) [mean age 13.8 ± 3.2 years]. The arrythmias include: 36 SVT (88%), 3 VT (7%), 1 heart block (2.5%) and wide 1 complex tachycardia (2.5%). We show that invasive EP study confirmed diagnosis in 34 of the 36 patients (94%) with SVT (2 non-inducible). We find that the Apple Watch helped prompt a workup resulting in a new arrhythmia diagnosis for 29 patients (71%). We note traditional ambulatory cardiac monitors were worn by 35 patients (85%), which did not detect arrhythmias in 10 patients (29%). In 73 patients who used an Apple Watch for recreational or self-directed heart rate monitoring, 18 (25%) sought care due to device findings without any arrhythmias identified. CONCLUSION: We demonstrate that the Apple Watch can record arrhythmia events in children, including events not identified on traditionally used ambulatory monitors.

Wearable devices, such as smart watches, have become popular for the monitoring of health, particularly for people with heart conditions. Wearable devices have been well-studied in adults, however there is less information available on their effectiveness in monitoring children's health. We reviewed the heart electrical recordings of a group of children who submitted recordings obtained from their Apple Watches during moments when they felt as though their heart's rhythm was abnormal. The Apple Watches captured rhythm abnormalities that matched the diagnoses obtained using heart monitors used clinically. This study shows that use of Apple Watches can enable clinicians to identify abnormalities that many traditional at-home monitoring devices do not detect. Thus, wearable devices, such as the Apple Watch, could be used to help identify heart rhythm disorders in children.