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Cardiac Risk Is Not Associated With Hypertension Treatment Intensification

Jeremy B. Sussman, MD, MS; Donna M. Zulman, MD, MS; Rodney Hayward, MD; Timothy P. Hofer, MD, MS; and Eve A. Kerr, MD, MPH
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.
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.

DISCUSSION

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.

Our study does have limitations. Since we know of no risk assessment score applicable to both primary and secondary prevention, we were not able to use a single, continuous variable of CV risk, which would have been considerably more statistically efficient. This resulted in reduced statistical power, and therefore we are not able to rule out that clinicians consider CV risk to a low-to-moderate extent. However, the study did have sufficient power to demonstrate that clinicians put greater importance on individual risk factors that have much less impact on patient benefits than risk. The highest risk patients were found to have a trend-level effect toward greater treatment based on risk (P = .46). Our study had just fewer than 1000 patients. Perhaps with a substantially larger sample size we might have found an effect.

Also, this study was conducted within the VHA, in which most patients are male and at high CV risk, resulting in quite a high cardiovascular risk profile. However, the impact of using CV risk is likely to be greater when there is more heterogeneity in CV risk, as would be expected in other clinical populations. Although it is always possible that insensitivity to patient CV risk is unique to VA clinicians, we cannot think of any reason why this would be true.

This study suggests that clinicians frequently target single risk factors rather than overall CV risk when making hypertension TI decisions. Organizational policies and guidelines that focus clinical decisions on CV risk could guide clinicians toward more efficient and effective prevention of cardiac disease. Currently, this opportunity is missed.

Acknowledgment
A previous version of this work was presented at the Society of General Internal Medicine National Meeting on May 6, 2011.

Author Affiliations: From Veterans Affairs Center for Clinical Management Research (JBS, RH, TPH, EAK), HSR&D Center of Excellence, Ann Arbor, MI; Department of Internal Medicine (JBS, RH, TPH, EAK), University of Michigan, Ann Arbor, MI; Center for Health Care Evaluation (DMZ), Veterans Affairs Palo Alto Health Care System, Palo Alto, CA; Division of General Medical Disciplines (DMZ), Department of Internal Medicine, Stanford University, Palo Alto, CA; The Robert Wood Johnson Foundation Clinical Scholars Program (DMZ, RH), University of Michigan, Ann Arbor, MI; Michigan Diabetes Research & Training Center (RH, EAK), Ann Arbor, MI.

Funding Source: This work was supported by the Robert Wood Johnson Clinical Scholars Program and an associated VA Advanced Fellowship, as well as by research grants from the US Department of Veterans Affairs Health Services Research and Development Service (IIR02-225), the Veterans Affairs Quality Enhancement Research Initiative–Diabetes Mellitus (QUERI DM, DIB 98-001), and the Michigan Diabetes Research and Training Center Grant (P60DK-20572).

Author Disclosures: The authors (JBS, DMZ, RH, TPH, EAK) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject
matter of this article.

Authorship Information: Concept and design (JBS, DMZ, TPH); acquisition of data (TPH, EAK); analysis and interpretation of data (JBS, DMZ, RH, TPH); drafting of the manuscript (JBS, DMZ, TPH); critical revision of the manuscript for important intellectual content (JBS, DMZ, RH, TPH, EAK); statistical analysis (JBS, RH, TPH); obtaining funding (TPH, EAK); administrative, technical, or logistic support (JBS); and supervision (EAK).

Address correspondence to: Jeremy B. Sussman, MD, MS, Section of General Internal Medicine, 300 North Ingalls Bldg, Ann Arbor, MI 48109. E-mail: jeremysu@med.umich.edu.
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