Cardiac Risk Is Not Associated With Hypertension Treatment Intensification | Page 2

This study shows how cardiovascular prevention would be much more efficient if risk were used in treatment decisions, but that currently it plays no role.
Published Online: August 23, 2012
Jeremy B. Sussman, MD, MS; Donna M. Zulman, MD, MS; Rodney Hayward, MD; Timothy P. Hofer, MD, MS; and Eve A. Kerr, MD, MPH
We also estimated the clinical implications of failing to guide treatment by overall CV risk. To do this we created an estimate of the clinical benefits of observed practice compared with the possible benefits of risk-based decision making. To estimate intensification’s benefit we used data from a large meta-analysis with meta-regression.13,14 The meta-regression calculated the decrease in coronary heart disease risk associated with adding a new, normal-dose BP medication. The study found larger relative risk reductions (RRRs) from treating patients with higher SBP and older age. For example, a 55-year-old man with an SBP of 160 would have a 29% RRR of having a CV event. If his BP were 150, the RRR would be 26%. For a 65-year-old with an SBP of 160, the reduction would also be 26%.

We then used these estimates to examine the clinical implications of failing to make treatment decisions based on risk. We used the estimate just described to compare the likely benefit from treatment intensification among the 55% of observed patients who actually received TI in the ABAT e study to the likely benefit if those 55% of persons in the ABAT e study with the highest CV risk had instead been treated. This compares the number of CV events that were likely prevented by treatment in the observed population with the number that would have been prevented if the highest risk patients had been treated. This comparison demonstrates the potential benefit of basing intensification decisions on overall risk as opposed to usual practice. We used only primary prevention patients because the UKPDS Risk Engine was developed exclusively in primary prevention patients.


There were 856 eligible participants: 159 (19%) were low/ medium CV risk primary prevention patients, 324 (38%) were high-risk primary prevention, and 373 (44%) had a history of MI or CHF. Average index visit SBP was 155 (+/- 15 standard deviation) mm Hg and DBP 79 (+/- 12). The 10th percentile had a 10% 10-year estimated cardiac event risk by UKPDS, and the 90th percentile had a 65% risk. More clinical information is provided in Table 1.

There were no significant associations between CV risk and the probability of TI, either before or after adjusting for potential confounders (Table 2). In the final model, the OR of TI was 1.19 (95% confidence interval [CI] 0.77-1.84; P = .43) for high-risk primary prevention versus low/medium-risk and 1.18 (0.76-1.83; P = .46) for history of MI/CHF versus low-medium risk. This analysis did not have substantial collinearity in any variable.

Of the 430 primary prevention patients with complete data, the average 10-year predicted event rate for the 235 intensified patients (55%) was 34.1% versus 30.6% for the

195 (45%) not intensified. In contrast, if the same number of primary prevention patients had received TI but care had been prioritized by overall CV risk, then the intensified group would have had an estimated pre-intensification 10-year risk of 47.5% versus only 14.3% in the non-intensified group. Among the 55% of primary prevention patients who would be intensified, this would eliminate an estimated 10.8 events per 100 treatment years, as opposed to 8.0 with the observed practice, resulting in 35% more CV events averted overall with no increase in overall treatment use or costs (Figure).

While clinicians did not seem to account for overall CV risk, the degree and consistency of systolic hypertension, which is the individual risk factor that is the explicit target of the treatment and the focus of most guidelines, was strongly associated with TI decisions (Table 3). Even though all patients in the study had a measured SBP >140 mm Hg and diabetes, higher systolic BP at the initial visit (OR 1.17; 95% CI 1.02-1.3; P = .01 per 10 mm Hg), mean SBP from the prior year (OR 1.18; 95% CI 1.05-1.3; P = .007 for 10 mm Hg), and patient-reported home BPs being at goal (OR 0.25; 95% CI 0.13-0.43; P <.001) were all independently associated with likelihood of TI. The patient’s A1C was also associated with intensification (OR 1.18; 95% CI 1.04-1.3; P = .01 for 10 mm Hg). The reliability of this analysis may be limited by collinearity, with a variance inflation factor of 7.0. Adding use of angiotensin-converting enzyme inhibitors and angiotensin receptor blockers to this model did not change the results of the model.

However, patient comorbidities, number of overall medications, and the patient already being on 4 or more antihypertension medications were not significantly associated with TI.


Although it may seem intuitive that patient BP level should guide antihypertensive treatment, overall CV risk is likely a better guide. In fact, CV risk predicts benefit from CV preventive treatments much more strongly than any individual cardiac risk factor.3,6,15 Our study is the first to examine the relationship of provider medical decision making to patients’ CV risk. In keeping with our hypothesis that physician treatment decisions are driven by single risk factors and their corresponding treatments, we found that TI was not significantly more likely to occur in those at higher CV risk, but did find evidence that physicians are more likely to advance therapy in people with higher and more consistently elevated BPs. We also demonstrated how care could be dramatically more effective if clinicians did use CV risk to guide their treatment decisions.

Treatment strategies that focus on single risk factors ignore the fact that even people with identical BPs or lipid values and diabetes can have tremendously varied cardiac risk and equally large variation in treatment benefit.3,6,15 Indeed, the potential benefit of hypertension treatment among people with diabetes and identical SBP often varies by orders of magnitude.4,6 The primary determinant of these differences in estimated benefit is overall CV risk. Even in our high-risk population of patients with diabetes and high BP, there was a large range in CV risk.

Although we did not find evidence that clinicians use CV risk in treatment intensification decisions, our study showed that real-world clinical care is more nuanced than the dichotomous recommendations of many guidelines. If the guidelines were followed automatically, every patient in our cohort would have had TI. In reality, treatment is much more likely to be intensified in patients with higher systolic BP and less likely in patients with lower BP measurements at home. This is further evidence that failure to intensify treatment is often a clinical decision, not necessarily “clinical inertia”5 or a mental lapse. If failure to intensify were purely a mental lapse, there would be no relationship between TI and SBP or home BP readings.

Progress in using overall risk assessment for treatment decisions has important implications for clinical management, efficiency, and quality. Indeed, as we have shown, accounting for CV risk would result in more efficient care, saving resources and time, and improve population outcomes. Moreover, it would result in more patient-centered care, because only those patients likely to benefit would be subjected to increasing doses and numbers of medications. Most important, clinical information systems that allow capture of relevant variables and automated display of calculated risk information, with recommendations for treatment, could make assessing and acting upon overall CV risk information dramatically simpler for clinicians. These systems would be fairly easy for clinical organizations to create. The ability to harness the power of information systems and routinely use such data and decision tools in personalized clinical care is not only within our grasp technically, it is within our responsibility as healthcare providers and managers. The focus in the Affordable Care Act on clinical efficiency and value-based health purchasing in accountable care organizations will make identifying patients by event risk and potential benefit from treatment even more important in coming years.16,17

We also found that those with a higher A1C were more likely to be intensified. The most likely reason for this is the observed phenomenon that patients with multiple comorbidities, especially “concordant comorbidities,” receive more aggressive care.18 When related problems have concordant solutions (such as diabetes and high BP), patient care seems to be better for all of the conditions.9,18

The primary strength of our study is the clinical detail of the data. The ABAT e study has information from surveys of clinicians and patients, chart review, and information from the EHR about many facets of the clinical encounter.5 This enabled us to test various factors that might influence TI, including the importance of clinical uncertainty of hypertension and uncertainty of the benefit of treatment.

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