Sex-differences and utility of treadmill testing in identification and genotype prediction in LQTS: a sub-study of the national LQTS registry and Canadian Hearts in Rhythm registry
Bibliographic record
Abstract
Abstract Background/Purpose Long-QT (LQT) Syndrome is an inherited heart rhythm condition presenting with QT-prolongation and failure to shorten with exercise, leading to life-threatening cardiac events. The prevalent normal-to-borderline phenotype remains a challenge for diagnosis. A three-step algorithm was developed to predict genotype from phenotypic characteristics with exercise testing. Sex-specific cut offs for determining a prolonged corrected QT value are 470ms for males and 480ms for females, serving as step 1 in the algorithm. The purpose of this study is to validate the algorithm using a national cohort that is more representative of the general LQT population, with a milder phenotype and more frequent ambiguity in phenotype. Methods A review of cases in the Canadian National Long-QT Registry, housed in the HiRO Registry was undertaken. Eligible cases from September 2014 to May 2020 were included. Gene-positive patients included 93 probands and 122 first-degree relatives (FDR) with a likely-pathogenic or pathogenic mutation according to ACMG criteria, limited to LQT1/2 subtypes, with 164 and 51 patients, respectively. Controls were composed of 39 gene-negative FDRs. Continuous variables were compared by the Mann-Whitney U test for 2-group comparisons, and Kruskal-Wallis test for multiple group comparisons. The predictive value of exercise ECG characteristics were analysed using ROC analysis and optimal cut-off values for exercise ECG characteristics (supine, standing, peak exercise, 1 and 4-minutes into recovery) were determined for males and females, using a sensitivity of 0.80 for carrier status and 0.75 for subtype. Results The 4-minute recovery QTc had the best predictive value for males, with an AUC of 0.86, and a cut-off point of 442ms given a sensitivity of 0.81 and specificity of 0.86. The 4-minute recovery QTc yielded an AUC of 0.79 for females, with a cut-off of 452ms given a sensitivity of 0.81 and specificity of 0.71. The 1-minute recovery QTc had the best predictive value for females, with an AUC of 0.92 and a cut-off point of 424ms given a sensitivity of 0.82 and specificity of 0.94. In prediction of LQT1, the 1-minute recovery QTc yielded the highest AUC for both males and females, at 0.68 and 0.80, respectively. Males had a cut off of 428ms with a sensitivity of 0.75 and specificity of 0.47, while females had a cut off of 451ms given a sensitivity of 0.76 and specificity of 0.75. Conclusion The current study demonstrates that exercise testing is a valid approach to diagnosing LQTS, with a differential optimal best measurement in males vs. females. Test performance measured by AUC was generally better at all time points in females compared to males. The algorithm is a reliable and simple method for the identification and prediction of genotype for probands and FDR carriers. The algorithm should be sex-stratified at the second step, with the 4-minute recovery QTc used for males and the 1-minute recovery QTc for females. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Canadian Institute of Health Research
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".