Hypertension diagnosis based on automated office blood pressure and the use of confidence intervals

Abstract

Herein we present a new algorithm for hypertension diagnosis based on Office Blood Pressure (OBP) readings which considers the inter-visit and intra-visit variability and provides a confidence interval (CI) of the patient’s BP. We assume the following statistical model for OBP: Pij1⁄4OBP+Xi+Yij Where: Pij: j BP reading on the i visit. OBP: Blood pressure of the patient to be estimated. X and Y: Random variables which respectively measure the inter-visit and intra-visit variability; provided that they are Gaussian, zero mean and independent for every i and j, Pij is also Gaussian. In the herein proposed protocol, the average of a fixed number of OBP readings is calculated in every patient’s visit, so that after a number of visits the set of averages forms a simple random sample of a Gaussian distribution, and a CI for OBP can thus be calculated based on the t-Student distribution. This CI can then be compared with the usual thresholds for hypertension diagnosis. For example, a CI for SBP of (128, 132) and DBP of (75, 85) mmHg would indicate that hypertension is discarded. On the other hand values of (133, 148) mmHg for SBP would be indicative of an unreliable diagnosis and the need for more visits. Because the appropriate use of this model requires the use of stabilized OBP readings, Automated OBP (AOBP) is the preferred measuring technique: it allows more readings without consuming time of the clinical staff and diminishes the white-coat effect. We have found that with a pre-reading resting period of 5 min and subsequent 2.5 min intervals between measurements, AOBP readings become stabilized from the 4 reading onwards. Our recommendation is to get at least 6 AOBP readings on each visit and discard the first three. The proposed OBP model provides an effective way to deal with its high inter-visit and intra-visit variability. Moreover, the calculation of CI allows to determine the uncertainty of the OBP estimations and to evaluate if more visits of the patient are needed in order to get an accurate hypertension diagnosis. Support from the European Regional Development Fund (ERDF) and the Galician Government under the agreement for funding the Atlantic Research Center for Information and Communication Technologies (AtlantTIC) is gratefully acknowledged.