OBJECTIVES: The aim of this study was to establish the relation between QT intervals and a wide range of rest heart rates in men. These data provided the basis of a simple method for adjusting the QT interval for heart rate. BACKGROUND: Earlier correction equations give conflicting results, especially at low and high heart rates. METHODS: The QT intervals were measured in 324 electrocardiograms of healthy young men. The sample was weighted for low and high heart rates. A curve relating QT intervals and heart rates from 40 to 120 beats/min was constructed. The QT interval at 60 beats/min was used as the reference value, and an adjusting nomogram for different heart rates was created. The reliabilities of the nomogram and three earlier QT correction equations were tested in the study group and in 396 middle-aged men. RESULTS: The nomogram method presented (QTNc = QT + correcting number) adjusted the QT interval most accurately over the whole range of heart rates on the basis of smallest mean-squared residual values between measured and predicted QT intervals. The Fridericia formula (QTFc = QT/RR1/3) gave the best correction at low, but failed at high, heart rates. The linear regression equation (QTLc = QT + 0.154[1 - RR], Framingham Study) was reliable at normal, but failed at low and high, heart rates. The Bazett formula (QTc = QT/RR1/2) performed poorest at all heart rates. The relation between QT and RR intervals was determined by three linear regressions expressing the slopes 0.116 for heart rates < 60 beats/min, 0.156 for heart rates from 60 to 100 beats/min and 0.384 for heart rates > 100 beats/min. CONCLUSIONS: The QT-RR relation over a wide range of heart rates does not permit the use of one simple adjustment equation. A nomogram providing, for every heart rate, the number of milliseconds that the QT interval must be corrected gives excellent adjustment.
OBJECTIVES: The aim of this study was to establish the relation between QT intervals and a wide range of rest heart rates in men. These data provided the basis of a simple method for adjusting the QT interval for heart rate. BACKGROUND: Earlier correction equations give conflicting results, especially at low and high heart rates. METHODS: The QT intervals were measured in 324 electrocardiograms of healthy young men. The sample was weighted for low and high heart rates. A curve relating QT intervals and heart rates from 40 to 120 beats/min was constructed. The QT interval at 60 beats/min was used as the reference value, and an adjusting nomogram for different heart rates was created. The reliabilities of the nomogram and three earlier QT correction equations were tested in the study group and in 396 middle-aged men. RESULTS: The nomogram method presented (QTNc = QT + correcting number) adjusted the QT interval most accurately over the whole range of heart rates on the basis of smallest mean-squared residual values between measured and predicted QT intervals. The Fridericia formula (QTFc = QT/RR1/3) gave the best correction at low, but failed at high, heart rates. The linear regression equation (QTLc = QT + 0.154[1 - RR], Framingham Study) was reliable at normal, but failed at low and high, heart rates. The Bazett formula (QTc = QT/RR1/2) performed poorest at all heart rates. The relation between QT and RR intervals was determined by three linear regressions expressing the slopes 0.116 for heart rates < 60 beats/min, 0.156 for heart rates from 60 to 100 beats/min and 0.384 for heart rates > 100 beats/min. CONCLUSIONS: The QT-RR relation over a wide range of heart rates does not permit the use of one simple adjustment equation. A nomogram providing, for every heart rate, the number of milliseconds that the QT interval must be corrected gives excellent adjustment.
Authors: Vassilios P Vassilikos; Labros A Karagounis; Apostolos Psichogios; Themistoclis Maounis; John Iakovou; Antonis S Manolis; Dennis V Cokkinos Journal: Ann Noninvasive Electrocardiol Date: 2002-01 Impact factor: 1.468
Authors: Fabrice Extramiana; Pierre Maison-Blanche; René Tavernier; Luc Jordaens; Antoine Leenhardt; Philippe Coumel Journal: Ann Noninvasive Electrocardiol Date: 2002-10 Impact factor: 1.468
Authors: Peter A Noseworthy; Aki S Havulinna; Kimmo Porthan; Annukka M Lahtinen; Antti Jula; Pekka J Karhunen; Markus Perola; Lasse Oikarinen; Kimmo K Kontula; Veikko Salomaa; Christopher Newton-Cheh Journal: Circ Cardiovasc Genet Date: 2011-04-21
Authors: Emmanuel M Kanoupakis; George E Kochiadakis; Emmanuel G Manios; Nikolaos E Igoumenidis; Hercules E Mavrakis; Panos E Vardas Journal: J Interv Card Electrophysiol Date: 2003-02 Impact factor: 1.900
Authors: Robert R Fenichel; Marek Malik; Charles Antzelevitch; Michael Sanguinetti; Dan M Roden; Silvia G Priori; Jeremy N Ruskin; Raymond J Lipicky; Louis R Cantilena Journal: J Cardiovasc Electrophysiol Date: 2004-04