Published in:
01-12-2015 | Original Paper
Heart rate turbulence and deceleration capacity for risk prediction of serious arrhythmic events in Marfan syndrome
Authors:
Benjamin N. Schaeffer, Meike Rybczynski, Sara Sheikhzadeh, Ruken Ö. Akbulak, Julia Moser, Mario Jularic, Doreen Schreiber, Anne Daubmann, Stephan Willems, Yskert von Kodolitsch, Boris A. Hoffmann
Published in:
Clinical Research in Cardiology
|
Issue 12/2015
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Abstract
Objective
Marfan syndrome (MFS) is associated with a substantial risk for ventricular arrhythmia and sudden cardiac death (SCD). We used heart rate turbulence (HRT) and deceleration capacity (DC), to evaluate the risk stratification for these patients.
Methods
We enrolled 102 patients [45 male (44.1 %), age 40.5 ± 14.6 years] with MFS. Blood samples were obtained to determine N-terminal pro-brain natriuretic peptide (NT-proBNP) levels. Transthoracic echocardiography studies were conducted to evaluate heart function parameters and a 24-h holter ECG was performed. An analysis of two HRT parameters, turbulence onset (TO) and turbulence slope (TS), and DC was performed. Therefore, optimal cut-off values were calculated. Primary endpoint was the combination of SCD, ventricular arrhythmia and arrhythmogenic syncope. Secondary endpoint was total mortality.
Results
During a follow-up of 1145 ± 491 days, 12 (11.7 %) patients reached the primary and 8 (7.8 %) patients the secondary endpoint. Patients reaching the primary were significantly older, had a higher burden of premature ventricular complexes and NT-proBNP levels and lower values of LVEF, DC and HRT TS. Multivariate analysis identified NT-proBNP (HR 1.25, 95 % CI 1.01–1.56, p = .04) and the abnormal HRT (abnormal TS and/or TO (HR 7.04, 95 % CI 1.07–46.27, p = .04) as independent risk predictor of arrhythmogenic events.
Conclusion
Patients with Marfan syndrome are at risk for severe ventricular arrhythmias and SCD. Abnormal HRT parameters and NT-proBNP values are independent risk factors for arrhythmogenic events and SCD. The assessment of these tools may help predicting SCD patients with MFS.