Journal of Pharmacokinetics and Pharmacodynamics

, Volume 41, Issue 6, pp 693–704 | Cite as

Predicted impact of various clinical practice strategies on cardiovascular risk for the treatment of hypertension: a clinical trial simulation study

  • Yuyan Jin
  • Robert Bies
  • Marc R. Gastonguay
  • Yaning Wang
  • Norman Stockbridge
  • Jogarao Gobburu
  • Rajanikanth Madabushi
Original Paper

Abstract

Hypertension control rate in the US is low with the current clinical practice (JNC 7) and cardiovascular disease (CVD) remain is the leading cause of morbidity and mortality. A 6-month clinical trial simulation case study testing different virtual clinical practice strategies was performed in an attempt to increase the control rate. The CVD risk was calculated using the Framingham CVD risk model at baseline and 6 months post-treatment. The estimated CVD events for the baseline patient sample without any treatment was 998 (95 % CI: 967–1,026) over 6 months in 100,000 patients. Treating these patients for 6 months with current clinical practice, high dose strategy, high dose with low target BP strategy resulted in a reduction in CVD events of 191(95 % CI: 169–205), 284 (95 % CI: 261–305), and 353 (95 % CI: 331–375), respectively. Hence the two alternative strategies resulted in an increase in treatment effect by 49 % (95 %CI: 44–59 %) and 85 % (95 %CI: 79–99 %), respectively. The increased safety with the current low dose strategy may potentially be offset by increased CVD risk in the time necessary to control hypertension.

Keywords

Clinical trial simulation Hypertension Clinical practice Cardiovascular risk Public health Pharmacometrics 

References

  1. 1.
    Yoon SS, Ostchega Y, Louis T (2010) Recent trends in the prevalence of high blood pressure and its treatment and control, 1999–2008. NCHS Data Brief 48:1–8PubMedGoogle Scholar
  2. 2.
    Lewington S et al (2007) Blood cholesterol and vascular mortality by age, sex, and blood pressure: a meta-analysis of individual data from 61 prospective studies with 55,000 vascular deaths. Lancet 370:1829–1839PubMedCrossRefGoogle Scholar
  3. 3.
    Staessen JA et al (2000) Risks of untreated and treated isolated systolic hypertension in the elderly: meta-analysis of outcome trials. Lancet 355:865–872PubMedCrossRefGoogle Scholar
  4. 4.
    World Health Report 2002 (2002) Reducing risks, promoting healthy life. World Health Organization, GenevaGoogle Scholar
  5. 5.
    Chobanian AV et al (2003) The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA 289:2560–2572PubMedCrossRefGoogle Scholar
  6. 6.
    D’Agostino RB Sr et al (2008) General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 117:743–753PubMedCrossRefGoogle Scholar
  7. 7.
    Panagiotakos DB, Stavrinos V (2006) Methodological issues in cardiovascular epidemiology: the risk of determining absolute risk through statistical models. Vasc Health Risk Manag 2:309–315PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Lewington S, Clarke R, Qizilbash N, Peto R, Collins R (2002) Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet 360:1903–1913PubMedCrossRefGoogle Scholar
  9. 9.
    Hypertension diagnosis and treatment. Institute for Clinical Systems Improvement (ICSI), 2008, BloomingtonGoogle Scholar
  10. 10.
    Black HR et al (2001) Baseline characteristics and early blood pressure control in the CONVINCE trial. Hypertension 37:12–18PubMedCrossRefGoogle Scholar
  11. 11.
    Cifkova R et al (2003) Practice guidelines for primary care physicians: 2003 ESH/ESC hypertension guidelines. J Hypertens 21:1779–1786PubMedCrossRefGoogle Scholar
  12. 12.
    Cushman WC et al (2002) Success and predictors of blood pressure control in diverse North American settings: the antihypertensive and lipid-lowering treatment to prevent heart attack trial (ALLHAT). J Clin Hypertens 4:393–404CrossRefGoogle Scholar
  13. 13.
    Jin Y, Bies R, Gastonguay M, Stockbridge N, Gobburu J, Madabushi R (2012) Misclassification and discordance of measured blood pressure from patient’s true blood pressure in current clinical practice: a clinical trial simulation case study. J Pharmacokinet Pharmacodyn 39:283–294PubMedCrossRefGoogle Scholar
  14. 14.
    Vasan RS et al (2001) Impact of high-normal blood pressure on the risk of cardiovascular disease. N Engl J Med 345:1291–1297PubMedCrossRefGoogle Scholar
  15. 15.
    Assmann G, Cullen P, Schulte H (1998) The Munster Heart Study (PROCAM). Results of follow-up at 8 years. Eur Heart J 19 Suppl A:A2–A11PubMedGoogle Scholar
  16. 16.
    Menotti A, Lanti M, Puddu PE, Kromhout D (2000) Coronary heart disease incidence in Northern and Southern European populations: a reanalysis of the seven countries study for a European coronary risk chart. Heart 84:238–244PubMedCentralPubMedCrossRefGoogle Scholar
  17. 17.
    Panagiotakos DB, Pitsavos C, Chrysohoou C, Stefanadis C, Toutouzas P (2002) Risk stratification of coronary heart disease in Greece: final results from the CARDIO2000 Epidemiological Study. Prev Med 35:548–556PubMedCrossRefGoogle Scholar
  18. 18.
    Tunstall-Pedoe H (1991) The Dundee coronary risk-disk for management of change in risk factors. BMJ 303:744–747PubMedCentralPubMedCrossRefGoogle Scholar
  19. 19.
    Yusuf S et al (2004) Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case–control study. Lancet 364:937–952PubMedCrossRefGoogle Scholar
  20. 20.
    Hempel G, Karlsson MO, de Alwis DP, Toublanc N, McNay J, Schaefer HG (1998) Population pharmacokinetic-pharmacodynamic modeling of moxonidine using 24-h ambulatory blood pressure measurements. Clin Pharmacol Ther 64:622–635PubMedCrossRefGoogle Scholar
  21. 21.
    Leonid Gibiansky (2006) M.R.G. R/NONMEM Toolbox for Simualtion from Posterior Parameter (Uncertainty) Distributions.. the Population Approach Group in Europe, Abstr 958Google Scholar
  22. 22.
    Mondick John T, LG, Marc R Gastonguay, Gareth J Veal, Jeffrey S Barrett (2006) Acknowledging Parameter Uncertainty in the Simulation-Based Design of an Actinomycin-D Pharmacokinetic Study in Pediatric Patients with Wilms’ Tumor or Rhabdomyosarcoma. the Population Approach Group in Europe, Abstr 938 (2006)Google Scholar
  23. 23.
    Zierhut ML et al (2008) Population PK-PD model for Fc-osteoprotegerin in healthy postmenopausal women. J Pharmacokinet Pharmacodyn 35:379–399PubMedCrossRefGoogle Scholar
  24. 24.
    Christie GA, Lucas C, Bateman DN, Waring WS (2006) Redefining the ACE-inhibitor dose–response relationship: substantial blood pressure lowering after massive doses. Eur J Clin Pharmacol 62:989–993PubMedCrossRefGoogle Scholar
  25. 25.
    Johnston GD (1992) Dose–response relationships with antihypertensive drugs. Pharmacol Ther 55:53–93PubMedCrossRefGoogle Scholar
  26. 26.
    Smith DH (2007) Dose–response characteristics of olmesartan medoxomil and other angiotensin receptor antagonists. Am J Cardiovasc Drugs 7:347–356PubMedCrossRefGoogle Scholar
  27. 27.
    Neufeld PD, Johnson DL (1986) Observer error in blood pressure measurement. CMAJ 135:633–637PubMedCentralPubMedGoogle Scholar
  28. 28.
    Rouse A, Marshall T (2001) The extent and implications of sphygmomanometer calibration error in primary care. J Hum Hypertens 15:587–591PubMedCrossRefGoogle Scholar
  29. 29.
    Spranger CB, Ries AJ, Berge CA, Radford NB, Victor RG (2004) Identifying gaps between guidelines and clinical practice in the evaluation and treatment of patients with hypertension. Am J Med 117:14–18PubMedCrossRefGoogle Scholar
  30. 30.
    Turner MJ, Irwig L, Bune AJ, Kam PC, Baker AB (2006) Lack of sphygmomanometer calibration causes over- and under-detection of hypertension: a computer simulation study. J Hypertens 24:1931–1938PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York (outside the USA) 2014

Authors and Affiliations

  • Yuyan Jin
    • 1
  • Robert Bies
    • 2
  • Marc R. Gastonguay
    • 3
  • Yaning Wang
    • 4
  • Norman Stockbridge
    • 4
  • Jogarao Gobburu
    • 5
  • Rajanikanth Madabushi
    • 4
    • 6
  1. 1.Clinical Pharmacology, Roche Innovation Centre ShanghaiF. Hoffmann-La Roche Ltd.ShanghaiChina
  2. 2.Division of Clinical Pharmacology, Indiana Clinical and Translational Sciences InstituteIndiana UniversityIndianapolisUSA
  3. 3.Metrum InstituteTariffvilleUSA
  4. 4.U.S. Food and Drug AdministrationSilver SpringUSA
  5. 5.School of Pharmacy and School of MedicineUniversity of MarylandBaltimoreUSA
  6. 6.WO51, RM2172, CDER, FDASilver SpringUSA

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