Resting ECG measurements can identify abnormalities in children with catecholaminergic polymorphic ventricular tachycardia
Bibliographic record
Abstract
Abstract Background Catecholaminergic Polymorphic Ventricular Tachycardia (CPVT) is a rare inherited disease characterized by exercise- or emotionally-triggered life-threatening ventricular arrhythmias. The resting ECG is considered to be normal and diagnosis has depended on exercise testing that is difficult acutely post-arrest or in young children. Purpose To identify resting ECG abnormalities in children with CPVT through assessment of measurement matrix parameters compared to a cohort of normal children. Methods A discovery cohort of 65 CPVT patients from the Electrophysiology program were identified. After excluding those on antiarrhythmics or in an arrhythmia at the time of their baseline ECG, 31 cases were matched 3:1 by age and sex with 93 healthy controls. Each individual had baseline ECG parameters and 180 machine-derived ECG amplitudes and durations (measurement matrix) measured. Statistical differences between ECG measures were assessed using Student's T-Test adjusted with a False Detection Rate of 1%. Significant measures were processed in a machine-learning algorithm to derive a Tree Model to differentiate individuals with and without CPVT. Results No significant differences were detected between CPVT patients who had and did not have a cardiac arrest. Comparing the CPVT and control cohorts, significant repolarization differences were seen in the amplitudes of lateral (I, negative aVR, V5, V6) and anterior leads (V2, V3, V4) at the J-point, ST midpoint, ST endpoint, and T-wave. Specifically, individuals with CPVT had significantly lower mean amplitudes in all aforementioned leads except aVR where the mean amplitudes were higher. These data were then used to create a Tree Model of the discovery cohort displayed in Figure 1 with a resulting R2 of 0.74, sensitivity of 0.81, and specificity of 0.98. Conclusions Baseline differences in repolarization are detectable in the left and anterior chest leads using machine-derived measurements of baseline ECGs in patients with CPVT, which may guide clinical suspicion when evaluating the resting ECG. These results warrant further analysis including testing of the Tree Model with a validation cohort. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): Scholar Award from the Ted Rogers Centre for Heart Research, Toronto, Ontario, Canada to Trainee Dr. Taraneh TofighiCanadian Institute for Health Research Figure 1. CPVT Tree Model
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".