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Misclassification and discordance of measured blood pressure from patient’s true blood pressure in current clinical practice: a clinical trial simulation case study


Treatment decisions for hypertension using sphygmomanometer based measurements and the current clinical practice paradigm do not account for the timing of blood pressure (BP) measurement. This study aimed to evaluate the clinical implications of discordance between measured and true BP, to quantify BP misclassification rate at a typical clinical visit in current clinical practice, and to propose a BP calibration system to decrease the impact of timing of BP measurement. A clinical trial simulation case study was performed using an in silico Monte Carlo Simulation approach. The time-courses of BPs with and without an antihypertensive treatment effect were simulated from a baseline BP model combined with an antihypertensive PK/PD model. Virtual subject characteristics were sampled from the FDA internal database. The baseline BP model was qualified using observed 24 h ambulatory BP monitoring (ABPM) data from 225 subjects by a visual predictive check as well as a global sensitivity analysis. First of all, our results showed that the measured cuff BP in current typical clinical practice deviated from the true values. (1) Cuff BP deviated from the true values by more than 5 mmHg in 57 % (95 % CI: 54–61 %) of patients and more than 10 mmHg in 26 % (95 % CI: 22–32 %) of patients respectively. (2) These discordances were reduced to 28 % (deviation ≥5 mmHg, 95 % CI: 18–40 %) and 9 % (deviation ≥10 mmHg, 95 % CI: 4–18%) of patients assuming perfect sphygmomanometer measurement and thus represent the contribution of ignoring the daily circadian rhythm of BP. Secondly, our results showed 23–32 % of patients were misclassified to an incorrect BP category for a casual clinical visit based on JNC 7 guideline. In addition, the accuracy of the measured cuff BP varied by time of clinic visit. Specifically, 11:00 AM to 3:00 PM was identified to be the better time frame, while times before 9:00 AM were the worst time frame. Therefore, clinic visit time may need to be adjusted accordingly. Finally, we proposed an easy BP calibration method for clinic use to adjust for time of day differences due to circadian variability in case that the desirable clinic visit time cannot be tailored for practical reasons.

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  1. 1.

    Egan BM, Zhao Y, Axon RN (2010) US trends in prevalence, awareness, treatment, and control of hypertension 1988–2008. JAMA 303:2043–2050

    PubMed  Article  CAS  Google Scholar 

  2. 2.

    Ong KL, Cheung BM, Man YB, Lau CP, Lam KS (2007) Prevalence, awareness, treatment, and control of hypertension among United States adults 1999–2004. Hypertension 49:69–75

    PubMed  Article  CAS  Google Scholar 

  3. 3.

    D’Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB (2008) General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 117:743–753

    PubMed  Article  Google Scholar 

  4. 4.

    Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, Jones DW, Materson BJ, Oparil S, Wright JT Jr, Roccella EJ (2003) The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. JAMA 289:2560–2572

    PubMed  Article  CAS  Google Scholar 

  5. 5.

    Bennett S (1994) Blood pressure measurement error: its effect on cross-sectional and trend analyses. J Clin Epidemiol 47:293–301

    PubMed  Article  CAS  Google Scholar 

  6. 6.

    Hart CL, Hole DJ, Davey Smith G (2001) Are two really better than one? Empirical examination of repeat blood pressure measurements and stroke risk in the Renfrew/Paisley and collaborative studies. Stroke 32:2697–2699

    PubMed  Article  CAS  Google Scholar 

  7. 7.

    Marshall T (2004) When measurements are misleading: modelling the effects of blood pressure misclassification in the English population. BMJ 328:933

    PubMed  Article  Google Scholar 

  8. 8.

    Marshall T (2006) Misleading measurements: modeling the effects of blood pressure misclassification in a United States population. Med Decis Making 26:624–632

    PubMed  Article  Google Scholar 

  9. 9.

    Marshall T, Rouse A (2001) Blood pressure measurement. Doctors who cannot calibrate sphygmomanometers should stop taking blood pressures. BMJ 323:806

    PubMed  Article  CAS  Google Scholar 

  10. 10.

    Neufeld PD, Johnson DL (1986) Observer error in blood pressure measurement. CMAJ 135:633–637

    PubMed  CAS  Google Scholar 

  11. 11.

    Rouse A, Marshall T (2001) The extent and implications of sphygmomanometer calibration error in primary care. J Hum Hypertens 15:587–591

    PubMed  Article  CAS  Google Scholar 

  12. 12.

    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 117:14–18

    PubMed  Article  Google Scholar 

  13. 13.

    Turner MJ, Baker AB, Kam PC (2004) Effects of systematic errors in blood pressure measurements on the diagnosis of hypertension. Blood Press Monit 9:249–253

    PubMed  Article  Google Scholar 

  14. 14.

    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 24:1931–1938

    PubMed  Article  CAS  Google Scholar 

  15. 15.

    Waugh JJ, Gupta M, Rushbrook J, Halligan A, Shennan AH (2002) Hidden errors of aneroid sphygmomanometers. Blood Press Monit 7:309–312

    PubMed  Article  Google Scholar 

  16. 16.

    Pickering TG, Hall JE, Appel LJ, Falkner BE, Graves J, Hill MN, Jones DW, Kurtz T, Sheps SG, Roccella EJ (2005) Recommendations for blood pressure measurement in humans and experimental animals: part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Circulation 111:697–716

    PubMed  Article  Google Scholar 

  17. 17.

    Pickering TG, Hall JE, Appel LJ, Falkner BE, Graves JW, Hill MN, Jones DH, Kurtz T, Sheps SG, Roccella EJ (2005) Recommendations for blood pressure measurement in humans: an AHA scientific statement from the Council on High Blood Pressure Research Professional and Public Education Subcommittee. J Clin Hypertens (Greenwich) 7:102–109

    Article  Google Scholar 

  18. 18.

    Hempel G, Karlsson MO, de Alwis DP, Toublanc N, McNay J, Schaefer HG (1998) Population pharmacokinetic-pharmacodynamic modeling of moxonidine using 24-hour ambulatory blood pressure measurements. Clin Pharmacol Ther 64:622–635

    PubMed  Article  CAS  Google Scholar 

  19. 19.

    Parati G, Vrijens B, Vincze G (2008) Analysis and interpretation of 24-h blood pressure profiles: appropriate mathematical models may yield deeper understanding. Am J Hypertens 21:123–125 discussion 127–129

    PubMed  Article  Google Scholar 

  20. 20.

    Head GA, Reid CM, Shiel LM, Jennings GL, Lukoshkova EV (2006) Rate of morning increase in blood pressure is elevated in hypertensives. Am J Hypertens 19:1010–1017

    PubMed  Article  Google Scholar 

  21. 21.

    Hui Kimko SBD (2002) Simulation for designing clinical trial: pharmacokinetic and pharmacodynamic modeling perspective. Drugs Pharm Sci 127. Accessed July 2011

  22. 22.

    Gibiansky L, Gastonguay MR (2006) R/NONMEM toolbox for simualtion from posterior parameter (uncertainty) distributions. The Population Approach Group in Europe: Abstract 958

  23. 23.

    Mondick JT, Gibiansky L, Gastonguay MR, Veal GJ, Barrett JS (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: Abstract 938

  24. 24.

    Asmar R, Safar M, Queneau P (2001) Evaluation of the placebo effect and reproducibility of blood pressure measurement in hypertension. Am J Hypertens 14:546–552

    PubMed  Article  CAS  Google Scholar 

  25. 25.

    Jones DW, Appel LJ, Sheps SG, Roccella EJ, Lenfant C (2003) Measuring blood pressure accurately: new and persistent challenges. JAMA 289:1027–1030

    PubMed  Article  Google Scholar 

  26. 26.

    Jones JS, Ramsey W, Hetrick T (1987) Accuracy of prehospital sphygmomanometers. J Emerg Med 5:23–27

    PubMed  Article  CAS  Google Scholar 

  27. 27.

    Idema RN, Gelsema ES, Wenting GJ, Grashuis JL, van den Meiracker AH, Brouwer RM, Man in ‘t Veld AJ (1992) A new model for diurnal blood pressure profiling. Square wave fit compared with conventional methods. Hypertension 19:595–605

    PubMed  CAS  Google Scholar 

  28. 28.

    Head GA, Chatzivlastou K, Lukoshkova EV, Jennings GL, Reid CM (2010) A novel measure of the power of the morning blood pressure surge from ambulatory blood pressure recordings. Am J Hypertens 23:1074–1081

    PubMed  Article  Google Scholar 

  29. 29.

    Head GA, Reid CM, Lukoshkova EV (2005) Nonsymmetrical double logistic analysis of ambulatory blood pressure recordings. J Appl Physiol 98:1511–1518

    PubMed  Article  Google Scholar 

  30. 30.

    Muneta S, Kohara K, Hiwada K (1999) Effects of benidipine hydrochloride on 24-hour blood pressure and blood pressure response to mental stress in elderly patients with essential hypertension. Int J Clin Pharmacol Ther 37:141–147

    PubMed  CAS  Google Scholar 

  31. 31.

    Hanninen MR, Niiranen TJ, Puukka PJ, Mattila AK, Jula AM (2011) Determinants of masked hypertension in the general population: the Finn-Home study. J Hypertens 29:1880–1888

    PubMed  Article  Google Scholar 

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The authors would like to thank Dr. Karen A. Matthews (Distinguished Professor of Psychiatry, Professor of Epidemiology and Psychology, Director, Pittsburgh Mind–Body Center Director, Cardiovascular Behavioral Medicine Research Program, Department of Psychiatry, University of Pittsburgh School of Medicine) for providing ABPM data to evaluate baseline BP model. The project was also supported by Indiana Clinical and Translational Sciences Institute (Dr. Robert Bies). Funded by FDA-Pharmacometrics Critical Path Fellowship.


The views expressed in this article are those of the authors and do not necessarily reflect the official views of the FDA.

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Correspondence to Rajanikanth Madabushi.

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Jin, Y., Bies, R., Gastonguay, M.R. et al. 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, 283–294 (2012).

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  • Hypertension
  • Sphygmomanometer
  • Circadian rhythm
  • Modeling and simulation
  • Blood pressure misclassification
  • Blood pressure calibration
  • Public health