QT Correction: Using an Observed Regression Factor Applicable to a Population Subset.
A QT interval correction to standardized heart rate (QTc) is essential to compare drug effect or to mitigate cardiac risk in clinical practice. Numerous empirical formulas for QTc have been proposed. However, an effective and readily comprehensible method has been elusive. As QTc is dependent on demographics, concomitant drugs, health status, autonomic and diurnal variation, the applicability of these methods hinge on the characteristics of a population that is assessed. An individual QTc is ideal, but it requires substantial baseline ECG data and is beyond the scope for initial evaluation. As a compromise, an approach for a 'discontinuous' population subset is suggested. In this article, we outline the challenges of QTc, and select a power function [QTc = QT/{(RR)a}] in which a regression factor a relevant to a particular population subset is used. The formula is similar to the one used in the Bazett's (a=1/2) or Fridericia's (a=1/3) method. The use of this approach is illustrated with two small population subsets separated by age and out- or in-patient status. This QTc approach is relatively simple to implement in drug development or by a busy practitioner within his/her institution. Nevertheless, in view of the limitations of the illustrative sample size and confounding factors of this proposal, additional studies will be necessary for further evaluation of QTc methods.
SunLife Biopharma, Morris Plains, NJ, USA.
Charles
C
Oo
Suraj S
SS
Kalbag
D004562:Electrocardiography; D005260:Female; D006339:Heart Rate; D006801:Humans; D008133:Long QT Syndrome; D008297:Male; D012044:Regression Analysis; D018401:Sample Size