Population modelling of blood pressure: assessing clinically important factors for cardiovascular diseases
O. Ackaert (1), P. C. Van Rijn-Bikker (2), N. Snelder (1), R. van Hest (3), B. Ploeger (1), G. van Montfrans (2), R. Koopmans (4), R. Mathot (2)
(1) LAP&P Consultants, Leiden-The Netherlands, (2) Academic Medical Center, Amstersdam, The Netherlands (3) Central Hospital Pharmacy, The Hague-The Netherlands, (4) Academic Medical Center Maastricht, Maastricht-The Netherlands.
Objectives: Blood pressure exhibits diurnal variation with a chronobiological (circadian) rhythm with an increase in the morning and a decrease during the night. Circadian processes are often analyzed with a Fourier approach, resulting in a sum of cosine functions. However these models fail to relate directly the obtained parameters (e.g. amplitude, acrophase) with important features of the chronobiological rythm. These features, such as nocturnal dip, morning surge and nocturnal blood pressure, are believed to be predictive for cardiovascular diseases . The objective of this research is to describe the chronobiological rhythm in systolic blood pressure (SBP) and diastolic blood pressure (DBP) using a model with clinically relevant parameters, which are predictive for cardiovascular events.
Methods: Baseline SBP and DBP were recorded at regular time points during 24h from 192 potentially mildly hypertensive patients, that were screened for inclusion into the ROTATE study . A Fourier analysis for the circadian (24h) and the ultradian harmonic (12, 8, 6,...) cosine rythms was performed using NONMEM v7. The model was reparameterized to describe the BP, using parameters, directly related to the dynamic diurnal variations.
Results: A combination of two cosine functions adequately described the circadian variation in both the SBP and DBP. Reparameterization resulted in a model, in which the 2 amplitudes, describing the circadian and ultradian (12h) harmonic rhythm were replaced by the population parameter “morning surge”, representing the change between night and morning BP and by the parameter “nocturnal blood pressure”, defined as the lowest point of BP during a 24h cycle. The model allows estimating clinically relevant parameters using the full 24h profile, whereas previous methods to calculate these dynamic diurnal variations in BP were based on averaged observations in 2-4h time intervals.
Conclusions: The reparameterized baseline model can be used in clinical practice to assess the morning surge and nocturnal blood pressure, both associated with cardiovascular events, based on a 24h baseline measurement. Moreover this baseline model can form the basis for the development of a PK-PD model to evaluate the drug effect of anti-hypertensive drugs. In addition, with the proposed model as baseline model it could be evaluated if anti-hypertensive drugs besides a decrease in BP also reduce other risk factors for cardiovascular diseases or events.
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