Long-term Monitoring to Detect Risk of Sudden Cardiac Death in Inherited Arrhythmia Patients
Bibliographic record
Abstract
Background: Risk stratification in inherited arrhythmia syndromes is challenging. Implantable cardioverter defibrillators (ICDs) are effective in the prevention of sudden cardiac death but are associated with significant complications. We aimed to determine the value of long-term implantable loop recorder (ILR) monitoring to determine risk factors for arrhythmias in inherited arrhythmia patients. Methods: We conducted a prospective multicentre study between 2015 and 2020 recruiting inherited arrhythmia probands and family members at intermediate arrhythmic risk, with no class 1 indication for ICD implantation. The primary endpoint was the detection by ILR of nonsustained ventricular tachycardia over ≥ 10 consecutive beats. Secondary endpoints included ICD insertion during follow-up, all-cause mortality, and ILR complication rates. Results: A total of 45 individuals (30 female participants) were enrolled in the study. The most common diagnoses were long-QT syndrome (28%), Brugada syndrome (26%), and arrhythmogenic cardiomyopathy (11%). Following ILR insertion (mean follow-up 633 days; range, 387-969), cardiac symptoms occurred in 19 of 45 patients (42%), 5 of whom had nonsustained ventricular tachycardias (11%), which were symptomatic in 3 individuals. This situation led to ICD implantation based on ILR in 5 of 45 patients (11%). Fifty percent of symptomatic events occurred in ARVC patients. The median time from ILR insertion to ICD implantation was 152 days (interquartile range (25th, 75th percentiles) 55 of 209). No patient experienced sudden cardiac death. Conclusions: ILRs enable the detection of high-risk arrhythmic features and facilitate selection of ICD candidates in inherited arrhythmia patients with borderline indications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".