AIM: The conventional method for analyzing 24-h ambulatory blood pressure monitoring (24-h ABPM) is insufficient to deal with the large amount of data collected. The aim of this study was to develop a novel cyclic fluctuation model for 24-h ABPM in Chinese patients with mild to moderate hypertension. METHODS: The data were obtained from 4 independent antihypertensive drug clinical trials in Chinese patients with mild to moderate hypertension. The measurements of 24-h ABPM at the end of the placebo run-in period in study 1 were used to develop the cyclic fluctuation model. After evaluated, the structural model was used to analyze the measurements in the other 3 studies. Models were fitted using NONMEM software. RESULTS: The cyclic fluctuation model, which consisted of 2 cosine functions with fixed-effect parameters for rhythm-adjusted 24-h mean blood pressure, amplitude and phase shift, successfully described the blood pressure measurements of study 1. Model robustness was validated by the bootstrap method. The measurements in the other 3 studies were well described by the same structural model. Moreover, the parameters from all the 4 studies were very similar. Visual predictive checks demonstrated that the cyclic fluctuation model could predict the blood pressure fluctuations in the 4 studies. CONCLUSION: The cyclic fluctuation model for 24-h ABPM deepens our understanding of blood pressure variability, which will be beneficial for drug development and individual therapy in hypertensive patients.
AIM: The conventional method for analyzing 24-h ambulatory blood pressure monitoring (24-h ABPM) is insufficient to deal with the large amount of data collected. The aim of this study was to develop a novel cyclic fluctuation model for 24-h ABPM in Chinese patients with mild to moderate hypertension. METHODS: The data were obtained from 4 independent antihypertensive drug clinical trials in Chinese patients with mild to moderate hypertension. The measurements of 24-h ABPM at the end of the placebo run-in period in study 1 were used to develop the cyclic fluctuation model. After evaluated, the structural model was used to analyze the measurements in the other 3 studies. Models were fitted using NONMEM software. RESULTS: The cyclic fluctuation model, which consisted of 2 cosine functions with fixed-effect parameters for rhythm-adjusted 24-h mean blood pressure, amplitude and phase shift, successfully described the blood pressure measurements of study 1. Model robustness was validated by the bootstrap method. The measurements in the other 3 studies were well described by the same structural model. Moreover, the parameters from all the 4 studies were very similar. Visual predictive checks demonstrated that the cyclic fluctuation model could predict the blood pressure fluctuations in the 4 studies. CONCLUSION: The cyclic fluctuation model for 24-h ABPM deepens our understanding of blood pressure variability, which will be beneficial for drug development and individual therapy in hypertensivepatients.
Authors: R L Lalonde; K G Kowalski; M M Hutmacher; W Ewy; D J Nichols; P A Milligan; B W Corrigan; P A Lockwood; S A Marshall; L J Benincosa; T G Tensfeldt; K Parivar; M Amantea; P Glue; H Koide; R Miller Journal: Clin Pharmacol Ther Date: 2007-05-23 Impact factor: 6.875
Authors: Giuseppe Mancia; Guy De Backer; Anna Dominiczak; Renata Cifkova; Robert Fagard; Giuseppe Germano; Guido Grassi; Anthony M Heagerty; Sverre E Kjeldsen; Stephane Laurent; Krzysztof Narkiewicz; Luis Ruilope; Andrzej Rynkiewicz; Roland E Schmieder; Harry A J Struijker Boudier; Alberto Zanchetti; Alec Vahanian; John Camm; Raffaele De Caterina; Veronica Dean; Kenneth Dickstein; Gerasimos Filippatos; Christian Funck-Brentano; Irene Hellemans; Steen Dalby Kristensen; Keith McGregor; Udo Sechtem; Sigmund Silber; Michal Tendera; Petr Widimsky; José Luis Zamorano; Serap Erdine; Wolfgang Kiowski; Enrico Agabiti-Rosei; Ettore Ambrosioni; Lars H Lindholm; Margus Viigimaa; Stamatis Adamopoulos; Enrico Agabiti-Rosei; Ettore Ambrosioni; Vicente Bertomeu; Denis Clement; Serap Erdine; Csaba Farsang; Dan Gaita; Gregory Lip; Jean-Michel Mallion; Athanasios J Manolis; Peter M Nilsson; Eoin O'Brien; Piotr Ponikowski; Josep Redon; Frank Ruschitzka; Juan Tamargo; Pieter van Zwieten; Bernard Waeber; Bryan Williams Journal: J Hypertens Date: 2007-06 Impact factor: 4.844
Authors: Tanika N Kelly; Treva K Rice; Dongfeng Gu; James E Hixson; Jing Chen; Depei Liu; Cashell E Jaquish; Lydia A Bazzano; Dongsheng Hu; Jixiang Ma; C Charles Gu; Jianfeng Huang; L Lee Hamm; Jiang He Journal: Am J Hypertens Date: 2009-07-02 Impact factor: 2.689