Arrhythmias & electrophysiology
Software-based analysis of T-wave morphology: identifying the electrocardiogram signature of high-risk long QT syndrome
Main finding · Cardiac eventsResult — go to the resultHazard ratio 1.01 95% CI 1.0–1.01Authors' conclusion
The software-based AnTwM on digital ECGs represented a reliable tool in clinical practice and identified unique ECG T-wave 'fingerprints' that allowed prediction of CEs in a genotype-specific manner.
Study context
- Population
- Genotype-confirmed long QT syndrome (LQTS) patients (LQT1, LQT2, LQT3) with ≥1 digital ECG58% female
- Participants
- 467 patients
- Exposure
- LQT1 vs LQT2 vs LQT3
- LQT1n = 253
- LQT2n = 182
- LQT3n = 32
- Outcome
- Predictors of cardiac events:
- Syncope
- SCD
- Resuscitated cardiac arrest
- Appropriate ICD shock
- Follow-up
- 15 ± 9 years
Methodological detailsAutomated analysis of T-wave morphology (AnTwM) analysis using Glasgow and Bravo algorithms (CalECG software)
- Automated AnTwM analysis using Glasgow and Bravo algorithms (CalECG software)
Main finding
Cardiac events
Hazard ratio
1.01
95% CI 1.0–1.01
P = 0.03
QTc (per 1 ms increase)
HR 1.01 (95% CI 1.0–1.01); P = 0.03
Per 1 ms increase, as printed
As reported in the abstract
Observational data: associations, not effects.
Original publication
Software-based analysis of T-wave morphology: identifying the electrocardiogram signature of high-risk long QT syndrome. Europace. Published 11 September 2025. doi:10.
Summarised from the published abstract; every number is checked against it