A Novel High-Resolution Surface Electrocardiographic Method to Identify and Characterize Myocardial Scar: A Proof-of-Concept Study
Bibliographic record
Abstract
BackgroundThe placement of the left ventricular (LV) lead in an area free of myocardial scar is an important determinant of cardiac resynchronization therapy response. We sought to develop and validate a simple, practical, and novel electrocardiographic (ECG)-based approach to intraoperatively identify the presence of LV scar. We hypothesized that there would be a reduction in the measured amplitude of the LV pacing stimulus on the skin surface using a high-resolution (HR) ECG when pacing from LV regions with scar compared with regions without scar. We term this the ECG Amplitude Signal Evaluation (EASE) method.MethodsConsecutive patients with ischemic LV systolic dysfunction and standard criteria for de novo cardiac resynchronization therapy implantation were prospectively enrolled. All underwent a preimplant contrast-enhanced cardiac magnetic resonance study to assess for scar. The average amplitude of the LV pacing impulse was sampled on HR surface ECG intraprocedurally and then compared with the cardiac magnetic resonance results.ResultsA total of 38 LV pacing sites were assessed among 13 recipients. The median voltage measured on the surface HR ECG in regions with scar was reduced by 41% (interquartile range, 17% to 63%), whereas there was no measurable change in voltage (interquartile range, 0 to 0%) in regions without scar compared with the maximal amplitude (Wilcoxon P < 0.0001).ConclusionThe EASE method appears to be of potential value as a novel intraoperative tool to guide LV lead placement to regions free of scar. Future work is required to validate the utility of this method in a larger patient cohort.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".