Prediction of normal QT intervals in children

• Verfasst von: Eberle, Tilman [VerfasserIn]
• Verfasst von: Hessling, Gabriele [VerfasserIn]
• Verfasst von: Ulmer, Herbert E. [VerfasserIn]
• Verfasst von: Brockmeier, Konrad [VerfasserIn]
• Titel: Prediction of normal QT intervals in children
• Verf.angabe: Tilman Eberle, Gabriele Hessling, Herbert E Ulmer, and Konrad Brockmeier
• Jahr: 1998
• Umfang: 5 S.
• Teil: volume:31
• Teil: year:1998
• Teil: pages:121-125
• Teil: extent:5
• Fussnoten: Elektronische Reproduktion der Druck-Ausgabe ; Available online 3 August 2004 ; Gesehen am 13.01.2021
• Titel Quelle: Enthalten in: Journal of electrocardiology
• Ort Quelle: New York, NY : Elsevier, 1968
• Jahr Quelle: 1998
• Band/Heft Quelle: 31(1998), Seite 121-125
• ISSN Quelle: 1532-8430
• DOI: doi:10.1016/S0022-0736(98)90303-X
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: children
• Sach-SW: ECG
• Sach-SW: heart rate
• Sach-SW: LQTS
• Sach-SW: normal values
• Sach-SW: QT interval
• K10plus-PPN: 1744519048

Abstract

Measuring QT intervals in individual children is of great importance, particularly in view of increasing evidence linking long QT syndrome to subsequent risk for sudden death. Three hundred seventy-three healthy subjects, 185 women and 188 men, aged 5.2 to 16.5 years, were investigated with a 12-lead-standard electrocardiogram (ECG). Values for predicted QTp50 and QTp95 (percentiles) were calculated by using the cycle length (RR interval [RRI]) and the measured QT interval. We used multiple regression analysis to test the influence of possible important variables and the resulting data were used to generate tables. Additionally, predicted QTp values were compared to QTc values after Bazett's correction. RRI, body height, age, and sex turned out to influence the QTp values most. For clinical use, data are presented in tabular form by RRI and age for both genders. The tables are of great clinical value in predicting the upper limits of normal QTp95 for individual children. Bazett's correction tends to underestimate the values found in our data when heart rate increases.