Abstract 4115: QT Response to Exercise Maneuvers Predicts Genotype in Long QT Syndrome
Bibliographic record
Abstract
Genotyping of patients with suspected LQTS may influence prognosis and response to therapy. It remains expensive and difficult to access in many settings. The resting QT interval is normal or borderline in up to half of genetically affected patients, making expert directed genotyping important in the management of LQTS patients. We examined the use of provocative postural and exercise testing as a tool to diagnose LQTS and predict genotype. Patients with suspected LQTS based on a history of syncope or cardiac arrest, with an affected first-degree relative, or a borderline or prolonged QT interval underwent exercise testing. 117 genotyped patients underwent provocative testing consisting of resting supine and standing ECGs, and exercise testing using a modified Bruce protocol. ECGs were obtained during exercise and at 1-minute intervals during recovery. Medians and IQRs are presented, and compared with Wilcoxon scores. 57 Of the 117 patients had an LQT mutation (LQT+) identified by genetic testing (LQT1=29, LQT2=38). The resting and standing ECGs were most useful in discriminating LQT+ patients from LQT− patients, with a prolonged resting supine QTc that underwent exaggerated prolongation compared to unaffected family members (Table ). Genotype prediction in the LQT+ patients was best achieved using a combination of exercise QT and QTc changes that were most abnormal in LQT1 patients, along with hysteresis that was abnormal in LQT2 patients. Postural QTc changes are useful in identifying mutation positive LQTS patients. In patients with abnormal findings, LQT1 is associated with impaired QT and QTc shortening at peak exercise, and LQT2 patients have exaggerated hysteresis. Exercise testing is a useful simple tool in the diagnosis of LQTS that helps direct genetic testing.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".