Computation of time-specified tolerance intervals for ambulatorily monitored blood pressure.

Hypertension is an important risk factor for strokes, heart attacks, and other vascular diseases. Pharmacologic treatment of high blood pressure reduces the incidence of these complications and prolongs life, so there is a strong incentive to identify and treat individuals with high blood pressure. The development of automatic instrumentation for noninvasive and ambulatory blood pressure monitoring makes it possible to follow the time course of blood pressure variations continuously in healthy peer groups to be used as a reference standard as well as in potentially hypertensive subjects. For clinical applications, tolerance intervals should be substituted, whenever possible for prediction limits. When only hybrid data (time series of data collected from a group of subjects) are available, such a tolerance interval can be difficult to determine following a parametric approach similar to the procedure used for the computation of prediction intervals when consideration of both within-subject and among-subjects variances is wanted. The authors have developed a nonparametric method for the computation of such tolerance intervals. Because the method is based on bootstrap techniques, it does not require the assumption of normality or symmetry in the data and is thus more appropriate when dealing with small samples. The method was used to establish time-qualified reference limits for series of blood pressure and heart rate values monitored automatically in healthy individuals of both genders. The use of these tolerance intervals may eliminate many false-positive and false-negative diagnoses that might be obtained when relying on time-unspecified single samples. These limits can serve as a reference for comparisons of a given subject's blood pressure series over time, yielding nonparametric measures of extent and timing of any blood pressure excess or deficit. Such indices can then be used for an objective and positive definition of health, for the screening and diagnosis of disease, and for gauging responses to treatment.