Correlation of ECG and cardiac MRI for assessment of ventricular hypertrophy and dilatation in adults with repaired tetralogy of Fallot
Bibliographic record
Abstract
Background: Surgically repaired Tetralogy of Fallot (rTOF) is associated with progressive right ventricular hypertrophy (RVH) and dilation (RVD). Accurate estimation of RVH/RVD is vital for the ongoing management of this patient population. The utility of the ECG in evaluating patients with rTOF with pre-existing right bundle branch block (RBBB) has not been studied. We aimed to determine the sensitivity/specificity of currently established ECG criteria in detecting RVH/RVD in this patient population. Methods: We included consecutive patients diagnosed with rTOF who underwent CMR performed at our regional referral centre between January 2012 and December 2019. Each CMR was assessed for LVH, LVD, RVH and or RVD. The ECG corresponding to the CMR was then used to determine RVH/LVH for specificity and sensitivity analysis. Results: Our study included 163 consecutive rTOF patients. The specificity for ECG-based criteria for LVH was 100.00% (95% C.I. (87.75, 100.00)), and the sensitivity was 7.19% (95% C.I. (3.15, 12.83)). When RBBB was present, specificity for RVH was 100.00% (95% C.I. (84.56, 100.00)), and sensitivity was 7.69% (95% C.I. (3.75, 13.69)). When RBBB was absent, specificity for RVH was 100.00% (95% C.I. (15.81, 100.00)), and sensitivity was 0.00% (95% C.I. (0.00, 33.63)). A regression model with the entire group of 163 ToF patients, based on the Sokolow-Lyon criterion (sum of R in V1 + S in V5/V6), produced a new suggested criterion for the diagnosis of RVH in patients with rTOF, which was a sum of R in V1 + S in V5/V6 greater than 13.25 mm. This model's sensitivity for RVH detection was 69.1%, and specificity was 36.8%. Conclusions: Standard ECG voltage criteria have poor sensitivity for detecting right and left ventricular chamber hypertrophy and dilatation in patients with rTOF, so current ECG criteria should not be used to monitor RVH/RVD in this patient population.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".