Body Surface Mapping of T-wave Alternans Depends on the Distribution of Myocardial Scarring
Bibliographic record
Abstract
T-Wave alternans (TWA) testing using 12-lead electrocardiogram/Frank leads is emerging as an important non-invasive biomarker to identify patients at high risk of Sudden Cardiac Death (SCD). Cardiac scarring is very common among cardiomyopathy patients; however, its influence on the body surface distribution of TWA has not yet been defined. Our objective was to perform a simulation study in order to determine whether cardiac scarring affects the distribution of TWA on thorax such that the standard leads fail to detect TWA in some of cardiomyopathy patients; thereby producing a false-negative test. Developing such a novel lead configuration could improve TWA quantification and potentially optimize electrocardiogram (ECG) lead configuration and risk stratification of SCD in cardiomyopathy patients. The simulation was performed in a 1500-node heart model using ECGSIM. TWA was mimicked by simulating action potential duration alternans in the ventricles. Cardiac scarring with different sizes were simulated by manipulating the apparent velocity, transmembrane potential and transition zone at varied locations along the left ventricular posterior wall. Our simulation study showed that the location of maximum TWA depends on the location and size of the myocardium scarring in patients with cardiomyopathy, which can give rise to false-negative TWA signal detection using standard clinical leads. The TWA amplitude generally increased with the increment of scar size (P<0.00001). We found one specific location (a non-standard lead) that consistently appeared as the top five maximum TWA leads and could be considered as an additional lead to improve the outcome of the TWA testing in cardiomyopathy patients.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".