International Practice Patterns in the Detection and Management of Arrhythmias in Patients With Hypertrophic Cardiomyopathy
Bibliographic record
Abstract
trial fibrillation (AF) occurs in 22% to 32% of patients with hypertrophic cardiomyopathy (HCM), 1,2 with ≈50% experiencing subclinical/ asymptomatic AF on cardiac rhythm monitoring devices, including implantable loop recorders, implantable cardioverter-defibrillators (ICDs), and permanent pacemakers.3 Nonsustained ventricular tachycardia (NSVT) is associated with a higher risk of sudden cardiac death in patients with HCM. 4 Faster, longer, and repetitive episodes of NSVT may confer greater risk.5 Current guidelines do not directly address the role of different arrhythmia screening strategies (screening frequency, role of extended monitoring, wearable devices) and optimal management of detected arrhythmias.We conducted an international multicenter survey to evaluate practice patterns and expert opinions regarding AF and NSVT screening and management at HCM comprehensive care centers. 1 Survey questions were developed by experts with established practices in cardiac electrophysiology (ZL and AWS) and HCM (AWS, KO, and JG).The current study was approved by the University of British Columbia (UBC) research ethics board.This survey was generated and distributed by the Canadian-hosted Qualtrics UBC Survey tool.This survey was anonymously and individually distributed to a predetermined list of 34 clinical experts working at HCM comprehensive care centers in North America, Australia, and Europe in May 2021.Informed consent was obtained at the beginning of the survey.Anonymized data underwent descriptive statistics and visualization using the survey tool.The data that support the findings of this study are available from the corresponding author on reasonable request.We received 23 full and 1 partial response (Table ), of 34, from experts in Canada (n=5), the United States (n=10), Germany (n=1), Denmark (n=1), Italy (n=1), Australia (n=1), Spain (n=1), Switzerland (n=1), and the United Kingdom (n=2).Most respondents (65.2%) had >10 years of practice experience.Each center evaluates a mean of 265 (SD, 165) new patients with HCM annually.Routine screening was performed by 87% of respondents.Most experts (78%) considered left atrial dilatation an important factor when considering screening.Consumer wearable devices were the third most used screening tool (56%) and 91% would
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".