Currently Viewing:
The American Journal of Managed Care August 2012
Racial Disparities in African Americans With Diabetes: Process and Outcome Mismatch
John B. Bulger, DO; Jay H. Shubrook, DO; and Richard Snow, DO, MPH
Currently Reading
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
Primary Nonadherence to Medications in an Integrated Healthcare Setting
Janet Shin, PharmD; Jeffrey S. McCombs, PhD; Robert J. Sanchez, RPh, PhD; Margarita Udall, MPH; Michael C. Deminski, MS, RPh; and T. Craig Cheetham, PharmD, MS
Slowing Medicare Spending Growth: Reaching for Common Ground
Michael E. Chernew, PhD; Richard G. Frank, PhD; and Stephen T. Parente, PhD
Patient Medical Group Continuity and Healthcare Utilization
Louise H. Anderson, PhD; Thomas J. Flottemesch, PhD; Patricia Fontaine, MD, MS; Leif I. Solberg, MD; and Stephen E. Asche, MA
Cost-Sharing and Initiation of Disease-Modifying Therapy for Multiple Sclerosis
John Romley, PhD; Dana Goldman, PhD; Michael Eber, BSE; Homa Dastani, PhD; Edward Kim, MD, MBA; and Swetha Raparla, BPharm, MS
Which Components of Health Information Technology Will Drive Financial Value?
Lisa M. Kern, MD, MPH; Adam Wilcox, PhD; Jason Shapiro, MD; Rina V. Dhopeshwarkar, MPH; and Rainu Kaushal, MD, MPH
Preventable Hospitalizations and Medicare Managed Care: A Small Area Analysis
Jayasree Basu, PhD
Home Care Program for Patients at High Risk of Hospitalization
Stuart Levine, MD, MHA; Bernard A. Steinman, PhD; Karol Attaway, MHA; Tyler Jung, MD; and Susan Enguidanos, PhD
Questioning the Widely Publicized Savings Reported for North Carolina Medicaid
Al Lewis, JD
Measuring Migraine-Related Quality of Care Across 10 Health Plans
Valerie P. Pracilio, MPH; Stephen Silberstein, MD; Joseph Couto, PharmD, MBA; Jon Bumbaugh, MA; Mary Hopkins, RN; Daisy Ng-Mak, PhD; Cary Sennett, MD, PhD; and Neil I. Goldfarb, BA
Impact of Point-of-Care Case Management on Readmissions and Costs
Andrew Kolbasovsky, PsyD, MBA; Joseph Zeitlin, MD; and William Gillespie, MD
Impact of a Program Encouraging the Use of Generic Antipsychotics
Eric D. A. Hermes, MD; Michael Sernyak, MD; and Robert Rosenheck, MD

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.
Objectives: Considering cardiovascular (CV) risk could make clinical care more efficient and individualized, but most practice guidelines focus on single risk factors. We sought to determine if hypertension treatment intensification (TI) is more likely in patients with elevated CV risk.


Study Design: Prospective cohort study of 856 US veterans with diabetes and elevated blood pressure (BP).


Methods: We used multilevel logistic regression to compare TI across 3 CV risk groups: those with history of heart disease, a high-risk primary prevention group (10-year event risk >20% but no history of heart disease), and those with low/ medium CV risk (10-year event risk <20%).


Results: There were no significant differences in TI rates across risk groups, with adjusted odds ratios (ORs) of 1.19 (95% confidence interval 0.77-1.84) and 1.18 (0.76-1.83) for high-risk patients and those with a history of CVD, respectively, compared with those of low/medium risk. Several individual risk factors were associated with higher rates of TI: systolic BP, mean BP in the prior year, and higher glycated hemoglobin. Self-reported home BP <140/90 mm Hg was associated with lower rates of TI. Incorporating CV risk into TI decision algorithms could prevent an estimated 38% more cardiac events without increasing the number of treated patients.


Conclusions: While an individual’s BP alters clinical decisions about TI, overall CV risk does not appear to play a role in clinical decision making. Adoption of TI decision algorithms that incorporate CV risk could substantially enhance the efficiency and clinical utility of CV preventive care.


(Am J Manag Care. 2012;18(8):414-420)
  • Cardiac risk is easy to calculate and the major predictor of benefit for blood pressure (BP) medications.

  •  Among patients with high BP and diabetes, those with higher BP are more likely to have treatment changes.

  •  Those with higher cardiac risk are not more likely to have treatment changes.

  •  A focus on cardiac risk could make BP treatment much more efficient and effective.
Preventing cardiovascular disease efficiently and effectively should be a primary goal of healthcare organizations, but clinical focus on non–patient-centered end points can limit efficiency. Clinical decision making and organizational guidance for prevention of cardiovascular (CV) disease has often focused on reduction of individual risk factors, such as hyperlipidemia and hypertension. Care could be more efficient and effective if decision making focused more on processes that reduce overall CV risk, which can be measured by the UK Prospective Diabetes Study (UKPDS)1 Risk Engine or Framingham Heart Score.2 Overall risk is a better indicator of treatment benefit because those with a higher likelihood of having an event have a higher absolute benefit from treatment.3,4 Even among patients with diabetes there exist large variations of potential benefit.3-5 For example, a patient with diabetes and hypertension in the lowest decile of risk has one-eighth the benefit from a treatment to reduce CV events as a patient in the highest decile of risk.6 However, given the current focus of guidelines on discrete risk factors, clinicians may be less likely to take CV risk into account when making decisions about modifying individual CV risk factors.

One way to assess how clinicians prioritize overall CV risk in patients with a known CV risk factor is by assessing hypertension treatment intensification (TI) decisions in those with elevated blood pressure (BP). While failure of TI has been considered an indicator of poor clinical quality,7 more recent research5,8 has shown that it often occurs due to clinical circumstances that make the potential benefits of TI less clear, such as clinical uncertainty about the validity of a BP measurement or the presence of comorbidities. Given the variation in benefit as a function of CV risk, if clinicians think about CV risk in decision making, patients at higher CV risk should have more consistent, reliable TI than those of lower CV risk and treatment benefit. This strategy of individually tailored care would maximize benefits and minimize risks for patients.

In this study we examined if clinicians account for overall CV risk when making TI decisions in response to an elevated BP. Using data from a study of veterans with diabetes and an elevated measured BP, we assessed whether patients with higher CV risk are more likely to have TI than those with lower CV risk. We also examined whether individual clinical risk factors predict clinical action. We then developed a decision analysis to estimate the potential benefit of making treatment more risk focused.

METHODS

Setting and Participants


The Addressing Barriers to Treatment for Hypertension (ABAT e) study was a prospective cohort study of patients with diabetes from 9 Veterans Health Administration (VHA) facilities in 3 Midwestern states. The study conducted a detailed examination of the factors that influence BP management. As has been described elsewhere,5,9 the study enrolled 1169 US veterans with diabetes who were found to have elevated triage BP (>140 mm Hg systolic or >90 mm Hg diastolic) before an index primary care visit. Participants were patients of 96 attending-level primary care providers with at least 2 half-days per week of clinic at the involved sites. 87% of all approached and eligible patients and 83% of approached and eligible clinicians participated. Data were obtained from baseline provider and patient surveys, brief post-visit provider and patient surveys, an electronic medical record review, and data from automated VHA data sources. Because of the very small number of women in the population, they were excluded from analysis. We have removed from our analyses patients whose measurements were <140/90 mm Hg when repeated by the clinician that day, because of the clinical uncertainty about the appropriate clinical action in these circumstances. The original study examined patients with diabetes because of their greater cardiac risk and benefit from hypertension treatment. The VA electronic health record (EHR) does not have an automated Framingham or UKPDS risk calculator, although they are available on many websites.

Institutional review boards of all participating facilities approved the study protocol, and all patients and providers gave written informed consent before participating.

Outcome Variable: Intensification of Hypertension Treatment

Our dependent variable was whether a provider intensified a patient’s BP medication within 3 months after the index visit in response to the elevated measured BP. We considered a treatment intensified if the dosage was increased on any antihypertensive medication or if any antihypertensive medications were started or switched. We included any actions taken within 3 months after the initial visit to allow time for laboratory work and BP reassessments.

CV Risk Variable

We defined 3 mutually exclusive categories of CV risk: 1) the highest risk group, which consisted of those in need of secondary prevention (individuals with a history of myocardial infarction [MI] or congestive heart failure [CHF]); 2) the high-risk primary prevention group, which consisted of those with a UKPDS1 10- year event risk of >20% but without a history of MI or CHF; and 3) the low/medium risk primary prevention group, which included those with a UKPDS 10-year event risk of <20% and no history of MI or CHF. We could not estimate an overall risk for secondary prevention patients or create an overall continuous risk score because we know of no validated risk predictor that integrates primary and secondary prevention. We also examined a continuous measure of CV risk in a secondary analysis. For this analysis, we excluded patients with a history of MI or CHF. The UKPDS risk score has better discrimination than the Framingham scores for patients with diabetes.10,11

Covariates for the Primary Model

Following a previously developed conceptual model,5 we built sequential models based on 4 categories of potential confounders. The first category, “baseline BP,” includes systolic BP (SBP) at study entry and the mean SBP in the year prior to entry. As in clinical practice, we used the clinic measurement of BP for this value. The second category, “clinical factors,” includes comorbidity count12 and number of hypertension medication classes. Comorbidity count was measured with a method developed by the Veterans Affairs Health Economics Resource Center using visit codes from the International Classification of Diseases, Ninth Revision, Clinical Modification.12 The third category, “clinical uncertainty,” includes information that might make a provider question the patient’s elevated BP, specifically patient-reported lower home BP. The final category, “uncertain benefit,” includes clinical characteristics that are associated with decreased benefit from TI, specifically being on 4 or more classes of antihypertensive medication at baseline.

Data Analysis

We were principally interested in the association of patient CV risk with rates of BP treatment intensification. We created 4 categories of variables apart from cardiovascular risk that might influence treatment intensification decisions and sequentially added these categories as covariates. These were BP (visit SBP and mean SBP in the year before the study), clinical factors (comorbidity scale12 and number of medications), clinical uncertainty (self-report of good home BP), and uncertain benefit (use of 4 BP medications). We included BP as a covariate even though it is a component of cardiovascular risk, as we wanted to look for evidence of any effect of CV risk status on intensification beyond that represented directly by the BP.

To account for treatment differences between physicians and patient clustering within physicians, we used a multilevel logistic regression model with physician as a random effect. We started by estimating the relationship between CV risk level and intensification, and then sequentially added the covariates to the CV risk level: first “baseline BP” variables, then “clinical factors,” then “clinical uncertainty” variables, and finally variables indicating “uncertain benefit.”

In a secondary analysis we assessed if clinicians use any specific facets of CV risk in decision making. To do this, we split the aggregate CV risk variable into its component predictors (patient age, race, glycated hemoglobin [A1C], duration of diabetes, the presence of atrial fibrillation, ratio of total cholesterol to high-density lipoprotein [HDL] cholesterol, smoking status, and number of hypertension medication classes) and used each of them in a model containing the same covariates described above.

Estimation of Clinical Impact

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.

RESULTS

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.

 
Copyright AJMC 2006-2017 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
x
Welcome the the new and improved AJMC.com, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up
×

Sign In

Not a member? Sign up now!