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Polynomial analysis of ambulatory blood pressure measurements
Background:
In normotensive subjects blood pressures follow a circadian rhythm. A circadian rhythm in hypertensive patients is less well established, and may be clinically important, particularly with rigorous treatments of daytime blood pressures.
Methods:
Polynomial analysis of ambulatory blood pressure monitoring (ABPM) data were used to identify and study circadian rhythms in ten mildly hypertensive patients both untreated and after single-dose treatment with different categories of antihypertensive agents. ABPM monitoring was performed using validated Space Lab Medical Inc portable equipment, polynomial regression analyses of the systolic blood pressures using Harvard Graphics 3 as well as SPSS Statistical Software. Polynomial curves were compared with the observed data as measured.
Results:
Fourth order polynomial curves provided the best fit not only for individual data but also for group means. A clear circadian rhythm could be identified, with a day-by-day reproducibility as measured by intra-class correlation, as high as 0.64 versus 0.46 for the observed data, and a goodness of fit as measured by level of correlation between observed and modelled data as high as 0.912. The 4th order polynomial curves provided trough-peak ratios with a median value of 0.85 versus 0.65 for the observed data. Also, the approach enabled comparison of the patterns of reaction to single-dose treatment with different categories of antihypertensive drugs. Reproducibility of polynomial analysis of ABPM data is fundamentally better than that of observed data, and this is so not only with means of populations but also with individual data.
Conclusions:
We assume that the difference in reproducibility is due to the potential of polynomial analysis to remove exogenic components from the data and thus visualise the true endogenic circadian rhythm of blood pressures. The method enables us to study circadian rhythms both in treated and untreated patients with mild hypertension, and could be used to predict night-time blood pressure from observed daytime values.
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