Evaluation of Prolonged QT Interval: Structural Heart Disease Mimicking Long QT Syndrome
Bibliographic record
Abstract
BACKGROUND: In about 20-25% of patients with congenital long QT syndrome (LQTS) a causative pathogenic mutation is not found. The aim of this study was to explore the prevalence of alternative cardiac diagnoses among patients exhibiting prolongation of QT interval with negative genetic testing for LQTS genes. METHODS: We conducted a retrospective analysis of 239 consecutive patients who were evaluated in the inherited arrhythmia clinic at the Toronto General Hospital between July 2013 and December 2015 for possible LQTS. A detailed review of the patients' charts, electrocardiograms, and imaging was carried out. RESULTS: The analysis included 56 gene-negative patients and 61 gene-positive patients. Of the gene-negative group, 25% had structural heart disease compared to only 1.6% of gene-positive patients (P < 0.001). Structural heart disease was more likely if only one abnormal QTc parameter was found in the course of the evaluation (35.2% vs 9.1%, P = 0.01). The most common structural cardiac pathology was bileaflet mitral valve prolapse (8.9%). No gene-positive patient had episodes of nonsustained ventricular tachycardia, compared to seven of the gene-negative patients (0% vs 12.5%, P = 0.005). CONCLUSIONS: Structural pathology was detected in a quarter of gene-negative patients evaluated for possible LQTS. Hence, cardiac imaging and Holter monitoring should be strongly encouraged to rule out structural heart disease in this 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".