The Diagnostic Utility of Holter Monitoring in Catecholaminergic Polymorphic Ventricular Tachycardia
Bibliographic record
Abstract
BACKGROUND: Holter monitoring may raise suspicion of an underlying catecholaminergic polymorphic ventricular tachycardia (CPVT) diagnosis. Although not a primary investigation for CPVT, Holter monitoring is ubiquitously used as a diagnostic tool in the heart rhythm clinic. OBJECTIVES: The objective of this study was to explore Holter monitoring in CPVT diagnosis. METHODS: This retrospective cohort study analyzed off-therapy Holter monitoring from 13 ryanodine receptor 2-positive CPVT and 34 healthy patients from the Canadian Hearts in Rhythm Organization national registry. Using the Edwards method, the ratio of ambient-maximum heart rate during Holter monitoring was correlated with exertion level to separate premature ventricular contractions (PVCs) during periods of adrenergic and nonadrenergic stress. A receiver operating characteristic curve analysis determined the optimal threshold for isolating CPVT-induced PVCs during adrenergic states. RESULTS: PVC burden differed between groups (P = 0.001) but was within population norm, suggesting ambient PVCs are uncommon in CPVT. CPVT patients had higher PVC counts than healthy controls (P = 0.002), with a different distribution based on adrenergic state. The optimal threshold for separating PVCs into periods of adrenergic and nonadrenergic stress in CPVT patients was 76% of the maximum heart rate during the monitoring period. Compared with healthy controls, CPVT patients had a higher PVC count, limited to periods of adrenergic stress, defined by >76% maximum heart rate threshold (P = 0.002; area under the receiver operating characteristic curve: 0.84). Below this threshold, there was no significant PVC difference (P = 0.604). CONCLUSIONS: Holter monitor PVC counts alone are inadequate for CPVT diagnosis, owing to the adrenergic nature of the disease. Quantifying PVC prevalence at a heart rate threshold >76% identified CPVT with moderate sensitivity (69%) and high specificity (94%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".