TWA predicts mortality in patients with normal ejection fraction

An increased TWA (T-wave alternans) is a significant indicator of all-cause and cardiovascular mortality, as well as of sudden cardiac death in patients with mostly normal ejection fraction, according to a recently published study by researchers led by Dr Tuomo Nieminen.

Dr Tuomo Nieminen, 
Tampere University Hospital
Dr Tuomo Nieminen,
Tampere University Hospital
Until now, this was only known as an indicator for those patients suffering severe heart diseases predisposing to life-threatening arrhythmias. In an interview with Meike Lerner, of European Hospital, Dr Nieminen explained the advantages of the TWA measurement, study results and the consequences these have for future research.

‘The T-wave represents the electric repolarisation of the heart,’ Dr Nieminen explained. ‘Thus, alternans in the T-wave is a marker of an alternating repolarisation process, which might indicate cellular disturbances during repolarisation. This is important, since pathological repolarisation phase predisposes to ventricular arrhythmias. In general, the TWA measurement could be used for arrhythmic risk stratification, but it is also one of the diagnostic criteria for long QT interval syndrome, another repolarisation abnormality.

TWA can be measured with a regular electrocardiogram; no extra examinations are necessary. The possibility to measure the T-wave alternans is a special feature within normal ECG software.
There are two methods for TWA assessment: time-domain modified moving average (MMA) and spectral methods. Both methods seem to measure the same phenomenom. For our study, we used the GE Healthcare software embedded with the MMA method, which can be applied in routine exercise test protocol without stabilising the heart rate to any specific level.’
Several studies have proved the effectiveness of measuring TWA for prognoses. What makes this study different?

‘Essentially all previous studies included patients with an ejection fraction of less than 50 percent, which is called abnormal. But in our population this only refers to 13 percent of the patients.
In 2001, we launched the Finnish Cardiovascular Study (FINCAVAS), in which we enrol all volunteering patients performing a clinical exercise test at Tampere University Hospital. We use the standard protocols of the bicycle ergometer test, with an increasing load every minute. This TWA analysis aimed to test whether TWA predicts mortality in our study population. The results of the study show that the TWA measurement provides prognostic value also in patients with a normal ejection fraction.’

What consequences do these findings have for patients’ treatment?
‘Our results suggest that TWA identifies patients prone to sudden cardiac death at an earlier stage of cardiac disease than supposed before. It is the first but naturally important step to show that a certain marker is associated with mortality. Another equally important step will be to test whether the patients with such a pathological marker will benefit from treatment options, such as anti-arrhythmogenic pharmaceuticals, or an ICD implant. The results of our study did not answer that latter part, which is a big question for the future - studies are being planned and conducted to reach that goal.

We need to bear in mind that estimating the aggregate risk for sudden cardiac death should be based on several parameters. No single marker will suffice, but TWA seems to be a very good candidate to be involved!’

01.09.2007

More on the subject:

Related articles

Photo

News • From the heart to the mind

'Dorian Gray' to uncover link between CVD and MCI

Around one third of people with cardiovascular disease (CVD) also have mild cognitive impairment (MCI), yet the condition is often undiagnosed. A new project aims to untangle this MCI-CVD connection.

Photo

News • Deep learning tool shows promise

Long-term ECG: AI algorithm checks heart rhythm

Analysing long-term ECG recordings for signs of heart abnormalities such as arrhythmias is a time-consuming process. New research finds that AI is better suited for this task than humans.

Photo

News • Deep learning-based approach

3D body composition analysis for assessing health risks

Using 3D imaging and deep learning AI, researchers have developed a new way to accurately assess body fat and muscle distribution, which are crucial for understanding health risks.

Related products

Subscribe to Newsletter