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Electrocardiographic risk stratification in families with congenital long QT syndrome.
Authors:Gerold M?nnig  Lars Eckardt  Horst Wedekind  Wilhelm Haverkamp  Joachim Gerss  Peter Milberg  Kristina Wasmer  Paulus Kirchhof  Gerd Assmann  Günter Breithardt  Eric Schulze-Bahr
Affiliation:Department of Cardiology and Angiology, Medizinische Klinik und Poliklinik C, University Hospital Münster, Albert-Schweitzer-Str. 33, D-48149 Münster, Germany. moennig@uni-muenster.de
Abstract:AIMS: The QT interval in the surface ECG is one of the most often used risk stratifiers in families with congenital long QT syndrome (LQTS). The best ECG lead for clinical management of LQTS families remains unclear. METHODS AND RESULTS: The predictive power of the QTc interval in all ECG leads was studied in 200 consecutive genotyped LQTS family members to identify mutation carriers (n = 103; age: 35+/-19 years) and high-risk LQTS patients (n = 16 with survived sudden cardiac arrest) using receiver operating curve (ROC) analysis (ROC = area under curve). Additionally, the risk for events (syncope and sudden cardiac arrest) was calculated for QTc decile in all individuals. The predictive power was highest in lead II and lead V5 for identifying carriers in LQTS families. These ECG leads were optimal for risk stratification (ROC range 0.83-0.87). In these leads, positive predictive value (PPV) and negative predictive value (NPV) were highest for suggested QTc cut-offs (440 and 500 ms) for identification of LQTS mutation carriers and high-risk patients (PPV between 78-81 and 73-80%, respectively). The risk for events in QTc deciles increased exponentially from 10 to 80% and was 40% for QTc > 500 ms. CONCLUSION: On the basis of these data, QTc is the best diagnostic and prognostic ECG parameter in LQTS families. A single measurement should be obtained in lead II if measurable and then in left precordial leads (preferably V5) as a second choice.
Keywords:Long QT syndrome    Risk stratification    ECG leads    Sudden cardiac death    Receiver operating characteristic analysis
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