Clinical utility of 12‐lead electrocardiogram in evaluating heart disease in patients with muscular dystrophy: Assessment of left ventricular hypertrophy, conduction disease, and cardiomyopathy
Bibliographic record
Abstract
INTRODUCTION: Heart disease remains a leading cause of mortality in patients with muscular dystrophy (MD), and cardiac assessment by standard imaging modalities is challenging due to the prominence of physical limitations. METHODS: In this prospective cohort study of 169 MD patients and 34 negative control patients, we demonstrate the clinical utility of a 12-lead electrocardiogram (ECG) as an effective modality for the assessment of cardiac status in patients with MD. We assessed the utility of conventional criteria for electrocardiogram-indicated left ventricular hypertrophy (ECG-LVH) as well as ECG morphologies. RESULTS: Cornell voltage, Cornell voltage-duration, Sokolow-Lyon voltage, and Romhilt-Estes point score criteria demonstrated low sensitivity and minimal positive predictive value for ECG-LVH when compared with cardiac imaging. Patients with LBBB had a high probability of a cardiomyopathy (relative risk [RR], 2.75; 95% confidence interval [CI], 2.14-3.53; p < .001), and patients with QRS fragmentation (fQRS) had a high probability of a cardiomyopathy (RR, 1.76; 95% CI, 1.20-2.59; p = .004), requiring cardiac medication and device intervention. We found that an R/S ratio >1 in V1 and V2 is highly specific (specificity, 0.89; negative predictive value [NPV], 0.89 and specificity, 0.82; NPV, 0.89, respectively) for patients with dystrophinopathies compared with other types of MD. CONCLUSION: The identification of LBBB and fQRS was linked to cardiomyopathy in patients with MD, while ECG-LVH was of limited utility. Importantly, these findings can be applied to effectively screen a broad cohort of MD patients for structural heart disease and prompt further evaluation and therapeutic intervention.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".