Literature DB >> 20625081

Monitoring initial response to Angiotensin-converting enzyme inhibitor-based regimens: an individual patient data meta-analysis from randomized, placebo-controlled trials.

Katy J L Bell1, Andrew Hayen, Petra Macaskill, Jonathan C Craig, Bruce C Neal, Kim M Fox, Willem J Remme, Folkert W Asselbergs, Wiek H van Gilst, Stephen Macmahon, Giuseppe Remuzzi, Piero Ruggenenti, Koon K Teo, Les Irwig.   

Abstract

Most clinicians monitor blood pressure to estimate a patient's response to blood pressure-lowering therapy. However, the apparent change may not actually reflect the effect of the treatment, because a person's blood pressure varies considerably even without the administration of drug therapy. We estimated random background within-person variation, apparent between-person variation, and true between-person variation in blood pressure response to angiotensin-converting enzyme inhibitors after 3 months. We used meta-analytic mixed models to analyze individual patient data from 28 281 participants in 7 randomized, controlled trials from the Blood Pressure Lowering Trialists Collaboration. The apparent between-person variation in response was large, with SDs for change in systolic blood pressure/diastolic blood pressure of 15.2/8.5 mm Hg. Within-person variation was also large, with SDs for change in systolic blood pressure/diastolic blood pressure of 14.9/8.45 mm Hg. The true between-person variation in response was small, with SDs for change in systolic blood pressure/diastolic blood pressure of 2.6/1.0 mm Hg. The proportion of the apparent between-person variation in response that was attributed to true between-person variation was only 3% for systolic blood pressure and 1% for diastolic blood pressure. In conclusion, most of the apparent variation in response is not because of true variation but is a consequence of background within-person fluctuation in day-to-day blood pressure levels. Instead of monitoring an individual's blood pressure response, a better approach may be to simply assume the mean treatment effect.

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Year:  2010        PMID: 20625081     DOI: 10.1161/HYPERTENSIONAHA.110.152421

Source DB:  PubMed          Journal:  Hypertension        ISSN: 0194-911X            Impact factor:   10.190


  4 in total

1.  Variability in response to albuminuria-lowering drugs: true or random?

Authors:  Sergei I Petrykiv; Dick de Zeeuw; Frederik Persson; Peter Rossing; Ron T Gansevoort; Gozewijn D Laverman; Hiddo J L Heerspink
Journal:  Br J Clin Pharmacol       Date:  2017-02-01       Impact factor: 4.335

2.  Can we identify response markers to antihypertensive drugs? First results from the IDEAL Trial.

Authors:  F Gueyffier; F Subtil; T Bejan-Angoulvant; Y Zerbib; J P Baguet; J M Boivin; A Mercier; G Leftheriotis; J P Gagnol; J P Fauvel; C Giraud; G Bricca; D Maucort-Boulch; S Erpeldinger
Journal:  J Hum Hypertens       Date:  2014-04-17       Impact factor: 3.012

3.  Power to identify a genetic predictor of antihypertensive drug response using different methods to measure blood pressure response.

Authors:  Stephen T Turner; Gary L Schwartz; Arlene B Chapman; Amber L Beitelshees; John G Gums; Rhonda M Cooper-Dehoff; Eric Boerwinkle; Julie A Johnson; Kent R Bailey
Journal:  J Transl Med       Date:  2012-03-13       Impact factor: 5.531

4.  The potential for overdiagnosis and underdiagnosis because of blood pressure variability: a comparison of the 2017 ACC/AHA, 2018 ESC/ESH and 2019 NICE hypertension guidelines.

Authors:  Katy Bell; Jenny Doust; Kevin McGeechan; Andrea Rita Horvath; Alexandra Barratt; Andrew Hayen; Christopher Semsarian; Les Irwig
Journal:  J Hypertens       Date:  2021-02-01       Impact factor: 4.776

  4 in total

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