The Canadian Hypertension Education Program--a unique Canadian initiative

Abstract

While almost two-thirds of all strokes and one-half of all myocardial infarctions could be prevented if hypertensive individuals had their blood pressures optimally controlled, only a minority of hypertensive individuals (even in publicly funded health care systems with subsidization of medication costs) achieve target blood pressures. Traditional hypertension guidelines have had limited impact on hypertension management and control rates. As a result, the Canadian Hypertension Education Program was developed to address the perceived flaws in the traditional hypertension guideline approach. In the present article, the key features of the Canadian Hypertension Education Program methodology are reviewed, with attention to those factors thought to be critical to the successful translation of recommendations into practice.

Figure 1

Canadian Hypertension Education Program 2005 organizational chart for the 2006 Recommendations Task Force. BP Blood pressure; CVD Cardiovascular disease

Figure 2

Algorithm for assigning evidence grades to therapy recommendations (step 1). (a) Randomized, controlled trial (RCT) with blinded assessment of outcomes, intention-to-treat analysis, adequate follow-up (ie, at least 90%, or losses to follow-up are too few to materially affect the results) and sufficient sample size to detect a clinically important difference with power greater than 80%. (b) Subgroup analysis was a priori, done within an adequate RCT, one of only a few tested, and there was sufficient sample size within the examined subgroup to detect a clinically important difference with power greater than 80%. (c) Systematic review (SR, also known as meta-analysis) in which the comparison arms were derived from head-to-head comparisons within the same RCT. (d) SR in which the comparison arms were derived from different placebo-controlled RCTs, then extrapolations were made across RCTs

Figure 3

Algorithm for assigning evidence grades to recommendations (continued from Figure 2 – for adequate randomized controlled trials [RCTs], systematic reviews [SRs] or subgroup analyses). (e) Adequate power in a negative study implies that 95% CIs exclude a clinically important difference. (f) Effect estimates in each study included in the systematic review were qualitatively similar (ie, in the same direction). (g) ‘Hard’ end points such as death, stroke, myocardial infarction and hospitalization. (h) End points that have been consistently shown to be associated with the clinical end point in multiple studies (observational or RCT), and RCTs consistently demonstrated that improvement in the surrogate translated into a consistent and predictable improvement in the clinical end point

Figure 4

Algorithm for assigning evidence grades to recommendations (continued from Figure 2 – for observational studies). (e) Adequate power in a negative study implies that 95% CI exclude a clinically important difference. (f) Effect estimates in each study included in the systematic review are qualitatively similar (ie, in the same direction). (g) ‘Hard’ end points such as death, stroke, myocardial infarction and hospitalization. (h) End points that have been consistently shown to be associated with the clinical end point in multiple studies (observational or RCT), and RCTs consistently demonstrated that improvement in the surrogate translated into a consistent and predictable improvement in the clinical end point

Figure 5

Algorithm for assigning evidence grades to diagnostic recommendations. (a) The gold standard. This can be either another test which is currently accepted as the gold standard or analysis of a representative cohort of patients who underwent the test of interest and are followed for a sufficient length of time that occurrence of the target outcome is likely if the diagnosis is present (with adjustment for covariates associated with prognosis). (b) Note that if follow-up of a cohort is not sufficiently long or complete enough to rule out diagnostic errors, or if data are not adjusted for covariates, then this category would apply