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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».