To evaluate and compare QT correction formulas in healthy subjects, we used 24-hour Holter monitoring because it allows the assessment of QT intervals over a large range of rates. Computer-assisted QT-interval measurements were obtained from 21 subjects. QT-RR relations for individuals and the group were fitted by regression analysis to 5 QT prediction formulas: simple Bazett's, modified Bazett's, linear (Framingham), modified Fridericia's, and exponential (Sarma's). There were no significant differences in mean squared residuals between formulas. When using individually calculated regression parameters, each formula gave good or acceptable QT correction over the entire range of RR intervals. Simple Bazett's formula (which uses no regression parameters) was unreliable at high rates. Akaike information criteria rank was: Sarma's, Framingham, modified Bazett's, Fridericia's, and simple Bazett's. When group-based regression parameters were applied to individuals, no formula had a clear advantage over simple Bazett's. We conclude that any formula that invokes regression parameters unique to each individual provides satisfactory QT correction. Determination of these parameters requires long-term recording to obtain an adequate range of rates. Group-based regression parameters give poor correction. When individual parameters cannot be determined, as in a 12-lead electrocardiogram, no formula provides an advantage over me familiar simple Bazett's.
ASJC Scopus subject areas
- Cardiology and Cardiovascular Medicine