Predictive factors for an effective beta-blocker therapy in chinese patients with congenital long QT syndrome: a multivariate regression analysis

Lexin Wang, Tongguo Wu

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
35 Downloads (Pure)

Abstract

OBJECTIVE: To investigate the factors that may predict the effectiveness of beta-blocker therapy for congenital long QT syndrome (LQTS) in a Chinese patient population. METHODS: Twenty-six LQTS patients were treated with oral propranolol (n=16) or metoprolol tartrate (n=10) for 38 months. Clinical symptoms, heart rate, corrected QT interval (QTc) and left ventricular ejection fraction were assessed before and after the therapy. RESULTS: Cardiac events were reduced by more than 50% in 22 patients with beta-blocker therapy. The average number of syncopes/patient and the average frequency of syncopes/patient/year in the responders were reduced from 16.2±5.1 to 1.1±0.9 (P<0.01) and from 4.5+1.2 to 0.7+0.6 (p<0.01), respectively. The QTc was also reduced from 0.56±0.06 s to 0.50±0.03 s. There was no significant difference in the reduction of syncopes and QTc in patients treated with propranolol and metoprolol tartrate. Multivariate regression analysis showed no correlation between the reduction in syncopal attacks and patients' age, sex, heart rate or left ventricular function (P>0.05). QTc reduction was the only independent predictive factor for syncope control (R=0.81, P<0.001). CONCLUSIONS: Oral beta-blockers are an effective therapy for Chinese patients with LQTS. A significant reduction in QTc is highly indicative of treatment success with beta-blockers.
Original languageEnglish
Pages (from-to)193-195
Number of pages3
JournalExperimental and Clinical Cardiology
Volume9
Issue number3
Publication statusPublished - 2004

Fingerprint

Dive into the research topics of 'Predictive factors for an effective beta-blocker therapy in chinese patients with congenital long QT syndrome: a multivariate regression analysis'. Together they form a unique fingerprint.

Cite this