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The Role of Clinical Uncertainty in Treatment Decisions for Diabetic Patients with Uncontrolled Blood Pressure FREE

Eve A. Kerr, MD, MPH; Brian J. Zikmund-Fisher, PhD; Mandi L. Klamerus, MPH; Usha Subramanian, MD, MS; Mary M. Hogan, PhD, RN; and Timothy P. Hofer, MD, MS
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From the Center for Clinical Management Research, Veterans Affairs Ann Arbor Healthcare System, and the University of Michigan Department of Internal Medicine, Ann Arbor, Michigan, and Roudebush Veterans Affairs Medical Center and Indiana University, Indianapolis, Indiana.


Acknowledgment: The authors thank recruitment coordinator Claire Robinson; research assistants Stacey Hirth, Susan Jaeger, Madhavi Diwanji, Janice Thompson, Caroline Lynch, and Diana Newman, who worked tirelessly to recruit patients; data manager Jennifer Davis; site principal investigators Drs. David Aron, Martin Bermann, and Ketan Shah, without whom the study could not have been done; and the many providers and patients who participated. They also thank Drs. Rodney Hayward, Michele Heisler, and John Piette for their suggestions on earlier drafts of this manuscript. The authors are particularly grateful to Drs. Jane Forman and Richard Frankel for their insightful contributions to the overall study design.

Grant Support: By a research grant from the U.S. Department of Veterans Affairs Health Services Research and Development Service (IIR 02-225) and in part by the Michigan Diabetes Research and Training Center Grant (P60DK-20572) from the National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health.

Potential Financial Conflicts of Interest: None disclosed.

Reproducible Research Statement:Study protocol: Available by contacting Dr. Kerr (e-mail, ekerr@umich.edu). Statistical code: Available by contacting Dr. Hofer (e-mail, thofer@umich.edu). Data set: Not available.

Requests for Single Reprints: Eve A. Kerr, MD, MPH, Ann Arbor Veteran Affairs Health Services Research and Development Service Center of Excellence, PO Box 130170, Ann Arbor, Michigan 48113; e-mail, ekerr@umich.edu.

Current Author Addresses: Drs. Kerr, Hogan and Hofer, and Ms. Klamerus: Ann Arbor Veteran Affairs Health Services Research and Development Service Center of Excellence, PO Box 130170, Ann Arbor, MI 48113-0170.

Dr. Zikmund-Fisher: University of Michigan Division of General Medicine, 300 North Ingalls, #7C27, Ann Arbor, MI 48109-5429.

Dr. Subramanian: Roudebush Veterans Affairs Medical Center and Indiana University, Diabetes Translation Research Center, IF-122, 250 University Boulevard, Indianapolis, IN 46202.

Author Contributions: Conception and design: E.A. Kerr, B.J. Zikmund-Fisher, M.M. Hogan, T.P. Hofer.

Analysis and interpretation of the data: E.A. Kerr, B.J. Zikmund-Fisher, M.L. Klamerus, U. Subramanian, M.M. Hogan, T.P. Hofer.

Drafting of the article: E.A. Kerr, M.L. Klamerus, T.P. Hofer.

Critical revision of the article for important intellectual content: B.J. Zikmund-Fisher, U. Subramanian, M.M. Hogan, T.P. Hofer.

Final approval of the article: E.A. Kerr, B.J. Zikmund-Fisher, M.L. Klamerus, U. Subramanian, T.P. Hofer.

Provision of study materials or patients: U. Subramanian.

Statistical expertise: T.P. Hofer.

Obtaining of funding: E.A. Kerr, T.P. Hofer.

Administrative, technical, or logistic support: M.L. Klamerus.

Collection and assembly of data: M.L. Klamerus, U. Subramanian.


Ann Intern Med. 2008;148(10):717-727. doi:10.7326/0003-4819-148-10-200805200-00004
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Editors' Notes
Context

  • Why do clinicians fail to intensify antihypertensive therapy when a patient's blood pressure is elevated?

Contribution

  • This study involved 1169 diabetic patients seen by 92 primary care providers at 9 Veterans Affairs facilities. All had elevated triage blood pressures, but only half received antihypertensive treatment intensification by providers. Patient reports of home blood pressures or repeated blood pressures by providers within normal limits and discussion of medication issues decreased the likelihood of antihypertensive intensification at clinic visits.

Implication

  • Uncertainty about true blood pressure values may underlie many reasons why physicians do not intensify antihypertensive therapy.

—The Editors

Despite some recent improvements in blood pressure control, the number of patients with inadequate control remains high and contributes to excess morbidity and mortality, especially among patients at high risk from complications of hypertension (18). Several studies have suggested that “clinical inertia”—the failure by providers to initiate or intensify therapy (medication intensification) in the face of apparent need to do so—is a main contributor to poor control of hypertension (912).

Although the failure to intensify treatment medications for patients with elevated blood pressures at visits has been well documented (56, 1218), factors underlying what seems to be clinical inertia have been studied less systematically. When providers are queried after clinic visits about the lack of medication intensification for elevated blood pressure, they variously report that the patient's “true” blood pressure was lower than the clinic blood pressure reading, that other patient concerns precluded attention on blood pressure management, and that patient adherence should be improved before medication intensification (6, 17). Some studies have examined the role of various clinical and patient factors in intensification decisions (6, 8, 17, 1920), but no study has used a detailed conceptual model to comprehensively examine the relative contribution of a broad array of potential patient, provider, organizational, and visit-specific contributors to a medication intensification decision. In addition, although a frequently cited reason for deferring medication changes is that the clinic blood pressure does not reflect the patient's “true” blood pressure (2122), this clinical uncertainty and its effects have not been explored.

To better understand factors underlying apparent clinical inertia for hypertension, we designed the ABATe (Addressing Barriers to Treatment for Hypertension) study to examine treatment change decisions for diabetic primary care patients with elevated triage blood pressures before a primary care visit. We defined elevated blood pressure for this population to be 140/90 mm Hg, a value well above guideline targets for diabetic patients and one clearly requiring some type of action (4). Our goals were to assess how often patients presenting with an elevated triage blood pressure received medication intensification or were scheduled for close follow-up and the role that clinical uncertainty about blood pressure, competing demands and prioritization, medication-related factors, and care organization play in treatment change decisions.

Conceptual Model

On the basis of theories of patient, provider, and organization behavior (2336), we developed a conceptual model—the hypertension clinical action model—to examine decisions that drive treatment change (medication intensification or prompt blood pressure follow-up) for elevated blood pressure (Figure 1). The model, developed by 2 internists and 3 PhD-level methodologists in conjunction with development of ABATe and before data collection, proposes such treatment change decisions at a visit are based on 4 main conceptual domains: clinical uncertainty (Is the patient's blood pressure truly elevated? Does the clinic blood pressure reflect the “true” blood pressure?), competing demands and prioritization (What other problems need to be addressed at this visit? Is blood pressure management a priority for this particular patient? Does the provider place priority on blood pressure management in general?), medication-related factors (Should adherence be addressed first? Is the medication regimen too complex? Will the patient accept another medication?), and care organization (Is there sufficient time to address hypertension? Are staff available for follow-up?). In addition, as part of grant development, we hypothesized that the following factors would lead to a lower probability of treatment change: uncertainty about whether the patient's visit blood pressure reflected their true blood pressure (“clinical uncertainty”), comorbid conditions unrelated to hypertension and diabetes (37), a lower priority placed by the provider on the importance of treating elevated blood pressure, a higher number of baseline medications, perceived medication adherence problems, shorter clinic visit times, and lack of staff to follow up for blood pressure medication adjustment.

Design
Setting

We conducted a prospective cohort study of patients with scheduled primary care visits at 9 Veterans Affairs facilities located in 3 midwestern states. These facilities varied in size and structure, with 3 large academic-affiliated medical centers, 1 large nonacademic medical center, and 1 large and 4 small community-based outpatient clinics. From 15 February 2005 to 14 February 2006, approximately 33 500 diabetic patients visited primary care providers (including residents) in these facilities (range per facility, 1050 to 9200 diabetic patients). The institutional review boards of all participating facilities approved the study protocol. Both patients and providers gave written informed consent before participating. Providers received a $50 bookstore gift card, and patients received a $10 department store gift card for completing initial surveys. Providers were told that the study was about diabetes and hypertension, with the purpose being to “study challenges in treating patients with diabetes and ways to overcome these challenges so that quality of care can be enhanced.”

Primary Care Providers

We invited all nonresident primary care providers with patient care responsibility at least 2 half-days per week to participate in the study. Of the eligible 126 providers approached, 104 consented to participate, for an overall recruitment rate of 83% (median facility-level recruitment rate, 88%). By the time recruitment started, 12 providers had stopped working at their facility or changed their patient care responsibilities, leaving 92 primary care providers still eligible to participate (range per facility, 2 to 28 providers; median, 8).

Patients

As specified by our institutional review board protocols, potentially eligible patients were referred to study staff by triage personnel. During the enrollment periods at each facility, study staff screened all referred patients who presented for a scheduled visit to participating primary care providers and whose lowest triage systolic blood pressure was 140 mm Hg or greater or whose lowest triage diastolic blood pressure was 90 mm Hg or greater (Figure 2). In each of the facilities, triage staff routinely used an electronic cuff to check the patient's blood pressure before the provider visit. Triage policies specified that a second blood pressure measurement should be obtained if the first blood pressure was elevated. In addition to the triage blood pressure, study staff screened patients for the following inclusion criteria: the participant confirmed a diagnosis of diabetes, the participating provider was the primary provider of diabetes care for the participant, and the participant spoke English. Patients with impaired decision-making ability (for example, dementia and traumatic brain injury) or terminal disease and residents of nursing homes were excluded. Of the 1556 patients approached by study staff, 213 were ineligible (Figure 2) and 1169 provided written informed consent to participate in the study (approached and eligible, 87%; median facility-level recruitment rate, 89%). We enrolled a median of 14 patients per provider (range, 1 to 16 patients) from February 2005 to March 2006. Recruitment time per facility varied from 4 to 12 months.

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Figure 2.
Study flow diagram.

PCP = primary care provider. *Diabetic patients presenting for a primary care visit to 1 of 92 participating providers were referred for eligibility assessment if their lowest triage blood pressure was ≥140/90 mm Hg. *Number of responses varied by individual item.

Grahic Jump Location

Our prespecified sample size calculations stipulated that we needed at least 11 patients from 80 physicians across 8 sites (that is, 880 patients) to detect a moderate difference in treatment change (about 12%) when competing demands were or were not present.

Data Sources

We included data from 5 sources in our analysis (Table 1). First, a baseline survey completed by all providers provided variables assessing provider prioritization to blood pressure management, general provider characteristics, and availability of ancillary support for blood pressure management. Second, providers completed a brief visit survey for each patient after the same clinic session in which they saw the patient (completion rate, 99%). This survey provided information on which issues were discussed during the visit, the provider's blood pressure goal for the patient, and whether medications were changed during the visit. Third, a patient survey conducted at enrollment provided sociodemographic characteristics, self-reported adherence and difficulty with medications, and self-management practices (completion rate, 91%). Fourth, review of electronic medical records documented free text blood pressure values and notes on actions taken at the enrollment visit. Finally, patient age, prescribed medications and their dosages, triage blood pressure values, and comorbid conditions (International Classification of Diseases, Ninth Revision, codes in the previous year) were obtained from Veterans Health Administration automated data sources.

Variables
Dependent Variable: Treatment Change in Response to Blood Pressure Elevation

Our main dependent variable was whether a provider changed a patient's blood pressure treatment at the visit in response to the elevated triage blood pressure. We considered treatment change to have occurred if the provider indicated on the visit survey (or in the medical record if a visit survey was not completed) that he or she added a medication or increased the dose of an existing medication or if the provider documented in the medical record that he or she intended to have the patient return for blood pressure follow-up within 4 weeks. We wanted to capture whether the provider determined that blood pressure was of sufficient priority at the visit to either intensify treatment or to specifically note a need for prompt follow-up. We considered only documented plans for follow-up because medical record documentation is currently the standard for establishing treatment plans and the main way to communicate to other providers that blood pressure at that visit was a problem that needed to be addressed.

Independent Variables and Covariates

Table 1 summarizes the independent variables, categorized according to the dimensions in our conceptual model and their data sources. Covariates related to blood pressure control included the visit systolic and diastolic blood pressures and the mean systolic blood pressure in the previous year (calculated as the mean of all systolic blood pressures in the previous year stored in the automated data).

We assessed competing comorbid demands in 2 ways. First, we categorized all conditions reported by providers to have been discussed during the visit as either related (concordant) or unrelated (discordant) to diabetes and hypertension (37). Concordant conditions included hyperlipidemia, obesity, heart failure, ischemic heart disease, peripheral vascular disease, renal disease, and cerebrovascular disease. All other conditions were considered discordant. Second, we used the method described by the Department of Veterans Affairs Health Economics Resource Center (38) to identify chronic conditions prevalent in our study sample during the year before the visit by using codes from the International Classification of Diseases, Ninth Revision. We similarly classified these conditions as concordant or discordant.

Statistical Analysis

We first examined associations between treatment change and visit systolic blood pressure, visit diastolic blood pressure, and systolic blood pressure in the previous year by using a 3-level logistic regression model; the first level addressed patient variables, the second level addressed the primary care provider, and the third level addressed the site where the provider worked. Multilevel models allow us to explicitly estimate the variability in treatment change attributable to provider and site while appropriately accounting for the clustering of patients within providers and sites. Next, we separately examined the association of each independent variable with treatment change. Because it makes sense clinically that blood pressure should be the principal determinant of treatment change decisions, we controlled for visit systolic blood pressure, visit diastolic blood pressure, and mean systolic blood pressure in the previous year in all of our models (referred to as “blood pressure–adjusted models”).

We then constructed a multilevel model (referred to as the “final multivariate model”) with all the independent variables that were associated with treatment change (P ≤ 0.20) in the analyses above (we also required a monotonic pattern of response if added as a set of ordinal dummy variables). Variables were then eliminated in a backward fashion if they were no longer statistically significant (P < 0.05). We assessed model fit for variables or groups of variables by using a likelihood ratio test between nested models. Estimates of the proportion of variance explained by predictors and variance components are for the latent variable represented by the logistic regression (41).

For clarity of presentation, we calculated a probability of treatment change from both the blood pressure–adjusted and final multivariate models across levels of each independent variable for the modal provider and a patient with visit systolic, diastolic, and mean previous year blood pressures set at the population means. The presented results are from the final multivariate model.

The missing data rates were low for variables not collected by survey, ranging from 0% to 1.6%. The patient survey had a completion rate of 91%, and the variables used from the survey had a missing data rate of 9% to 15.5% (for income). The blood pressure–adjusted model was conducted for the participants with nonmissing data, and the numbers for each variable are reported. In the final multivariate model, no survey variables entered, and only 68 of 1169 patients had any missing values.

All analyses were conducted by using STATA software, version 10.0, with the xtmelogit procedure (Stata, College Station, Texas).

Role of the Funding Source

This study was funded by the U.S. Department of Veterans Affairs Health Services Research and Development Service. The salaries of 2 of the authors were also supported in part by the Michigan Diabetes Research and Training Center Grant (P60DK-20572) from the National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health. The funding sources were not involved in the conduct or analysis of this study.

Patient and Provider Characteristics

The mean age of the 1169 participants was 66 years (SD, 11), and 80% were white. On average, patients reported having had diabetes for 11 years. The mean age of the 92 primary care providers was 48 years (SD, 9). Among the providers, 64 were physicians, 21 were nurse practitioners, and 7 were physician assistants. Tables 2 and 3 list other baseline patient and provider characteristics.

Table Jump PlaceholderTable 2.  Patient and Provider Characteristics
Table Jump PlaceholderTable 3.  Association of Measured Factors with the Predicted Probability of Treatment Change
Treatment Change and Blood Pressure

Overall, 573 of 1169 (49%) patients had a treatment change at the visit. Of those, 511 received an initial dose of medication or had a dose increased and 62 had no medication intensification but had a documented plan to follow up blood pressure within 4 weeks. The mean triage systolic blood pressure at enrollment was 154 mm Hg (SD, 14), and the mean triage diastolic blood pressure was 78 mm Hg(SD, 12). The mean previous year systolic blood pressure was 145 mm Hg (SD, 15). The likelihood of treatment change increased substantially with higher systolic and diastolic blood pressures at triage and higher systolic blood pressure during the previous year (Figure 3). There were no statistically significant differences in treatment change associated with patient age, education, or race or provider age (data not shown).

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Figure 3.
Relationship of systolic and diastolic blood pressures at enrollment and mean previous year systolic blood pressure with probability of treatment change.

Each curve is shown with the other 2 blood pressure components adjusted to their mean value and the intensification rate for the average provider and clinic site. For each blood pressure component, the curve is presented only for a range of values actually seen when the other 2 components are both close to their mean (±10 mm Hg of the mean value).

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Relationship between Main Model Variables and Treatment Change
Clinical Uncertainty

Patient report that they self-monitored blood pressure at home was not associated with treatment change (Table 3); however, when providers recorded at the visit that the patient's blood pressure as measured at home was adequate, providers were less likely to change treatment (18% vs. 52%; P < 0.001). Similarly, as expected, treatment was much less likely to be changed when a provider recorded a repeated blood pressure measurement during the visit as less than 140/90 mm Hg than when repeated measurement was 140/90 mm Hg or greater or when no repeated blood pressure was recorded (13% vs. 61%; P < 0.001).

Competing Demands and Prioritization

When providers reported discussing a condition that was discordant with diabetes and hypertension (for example, chronic pain or preventive care) during the visit, they were less likely to initiate treatment change at that visit (Table 3). However, the total number of discordant chronic conditions in the past year was not associated with treatment change as a continuous variable (although the odds of treatment change were decreased for the small group with ≥5 discordant chronic conditions).

Providers whose systolic blood pressure goal for a given patient was 130 mm Hg or less were more likely than those with higher treatment goals to initiate treatment change (52% vs. 33%; P < 0.002). Providers who reported willingness to wait longer than 4 weeks before following up a modestly elevated blood pressure on the baseline survey were much less likely to initiate treatment change at the visit than those willing to wait 2 weeks or less (41% vs. 58%; P < 0.03), but providers' general propensity to intensify blood pressure medications was not associated with treatment change (Table 3).

Medication Factors

Contrary to our hypotheses, the number of antihypertensive medications, dosing, and the total number of medications were not statistically significantly associated with treatment change (Table 3). Treatment change was much less likely to occur when the provider reported that he or she discussed medication adherence or other medication problems at the visit, compared with providers who did not discuss such issues (23% vs. 52%; P < 0.001). However, patient self-reported medication adherence was not associated with treatment change, nor was patient self-reported difficulty with adding a new blood pressure medication.

Care Organization

The average rate of intensification varied greatly by facility (26% to 65%; P = 0.033 for the site coefficients as fixed effects). However, contrary to our hypotheses, the provider reported that the average number of patients seen in a half-day clinic, the minutes allotted for a return visit, and the available staff to follow-up high blood pressure were not statistically significantly associated with treatment change.

Multivariate Model

In the final 3-level model, the 3 blood pressure covariates (visit systolic blood pressure, visit diastolic blood pressure, and previous year systolic blood pressure) together explained 12% of variance in treatment changes (Appendix Table). The variables reflecting clinical uncertainty at the visit (repeated blood pressure <140 mm Hg and adequate home blood pressure measurements) were strongly associated with decreased odds of treatment change (odds ratio for treatment change, 0.09 and 0.20, respectively) and together increased the variance explained by the predictors from 12% to 29%. For the full model, the independent predictors together explained 35% of the variance in treatment change. In addition, a higher provider systolic blood pressure goal, a threshold for follow-up greater than 4 weeks, and a discussion about medication problems or discordant conditions at the visit were strongly associated with decreased odds of treatment change (odds ratios, 0.27 to 0.66).

Table Jump PlaceholderAppendix Table.  Three-Level Logistic Regression Models Assessing Associations between Patient, Provider, and Visit Factors and Treatment Change

Because the clinical uncertainty variables were so highly predictive of treatment change, we examined the variability in rates of these 2 variables across providers and facilities. Overall, providers recorded a repeated blood pressure value in 529 (45%) visits. The provider rates of repeating blood pressure measurements varied considerably (interquartile range, 11% to 80%). Recording a repeated blood pressure value varied enormously by site, from 16% of encounters to 95% of encounters. Similar provider and site variability was seen in the documentation of adequate home blood pressure measurements, although the overall rate of such documentation was much lower (about 8% of encounters).

For approximately 50% of diabetic patients in our study who presented with a triage blood pressure of 140/90 mm Hg or greater, providers initiated treatment change (that is, intensified medication or planned close follow-up) at the time of the primary care visit. Even after controlling for several other predictors, factors related to clinical uncertainty at the visit about the true blood pressure value were the most prominent predictors of treatment change. Specifically, when primary care providers recorded lower repeated blood pressure assessments and considered patient reports of lower home blood pressure values, they were much less likely to initiate treatment change. Competing demands, in contrast, impeded treatment change more modestly and primarily, as others have found (20), when the comorbid conditions were specifically addressed at the visit. This suggests that comorbid conditions act as competing demands not simply because of their number but rather because of their severity, acuity, or dominance at a given point in time (37, 42).

Two other factors related to blood pressure prioritization were related to treatment change. Specifically, providers with lower blood pressure treatment goals and those with shorter general thresholds for follow-up of an elevated blood pressure reading were more likely to initiate treatment change. This finding is important because such factors are potentially modifiable by educational efforts.

We purposely chose to evaluate repeated blood pressure assessments by providers independent of the triage measurements and categorize them as reflecting “clinical uncertainty.” Given natural variation in blood pressure (43) and differences in techniques to assess blood pressure (44), providers have often been trained to recheck an elevated reading (usually by using an auscultatory method), with the implication that they should trust the value they obtain more than the value obtained at triage (usually obtained by using an electronic cuff). Thus, repeating the blood pressure measurement reflects uncertainty about whether the elevated triage reading accurately reflects the patient's true blood pressure. However, although providers often treat the repeated blood pressure as the gold standard, evidence indicates that provider values are more susceptible to bias than automated office or home blood pressure readings because of terminal digit preference (rounding to the nearest 10), threshold bias (rounding below a treatment threshold), and treatment bias (expecting a lower blood pressure in those receiving treatment) (4547). In addition, other studies have demonstrated statistically significant variation among physicians in the magnitude of differences between triage blood pressures measured by a nurse using an electronic method and those measured by physicians using auscultation (48) and large interobserver variation in blood pressure values using auscultatory methods (49).

Furthermore, in our study, primary care providers' decisions about when to repeat a blood pressure were not systematic—some providers repeated blood pressure readings consistently, whereas others did so only occasionally. Much confusion exists about which blood pressure value or which combination of values would best represent a patient's true blood pressure. Even the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (50) is not clear on this point. Although the complete report (50) suggests that an average of 2 values should be used for decision making, the summary report simply states that 2 measurements should be made but not whether an average or the lower of the 2 values should be used (4). Nonetheless, our results show that when the provider repeated a patient's blood pressure measurement and the result was less than 140/90 mm Hg, providers almost always acted as if this repeated value more accurately reflected the true blood pressure and did not initiate medication intensification or schedule blood pressure follow-up. Providers in our study were also much less likely to initiate treatment change if patients reported “adequate” home blood pressures; there is a similar lack of clarity about incorporating home blood pressure reports in clinical practice (4, 5152).

To our knowledge, ours is the largest study with the most clinically detailed data sources to systematically examine potential reasons for clinical inertia in hypertension management. Although many English-language, peer-reviewed studies from 1995 to present have documented potential clinical or therapeutic inertia for patients with hypertension (12, 1418, 2122, 5355), none has systematically elucidated the role of clinical uncertainty in medication intensification decisions. Nonetheless, some investigators have noted that the reasons providers do not change medications often has to do with the belief that the clinic blood pressure does not reflect the patient's true blood pressure (2122). Safford and colleagues (56) developed a cognitive map of reasons why physicians would not intensify medications for a hypothetical patient with elevated blood pressure. Among the many factors offered, the role of “white coat hypertension” and “good ambulatory blood pressure” was enumerated as contributing to reasons for clinical inaction.

Several limitations of this study should be noted. Medication intensification rates are typically higher in Veterans Affairs than in non–Veterans Affairs settings, and in fact, the intensification rate that we saw was higher than that reported in both Veterans Affairs (5, 21) and non–Veterans Affairs populations (6, 12). This higher rate may stem from our definition of treatment change, secular trends, and the fact that providers knew that they were participating in a study about diabetes and hypertension (although they were not aware of study hypotheses or which patients were enrolled in the study until after the visit). However, a strength of our multisite design is that financial access to medications among Veterans Affairs patients is relatively homogeneous; thus, differences among patients in the ability to pay is unlikely to contribute to site variation in decision making about their blood pressure treatments. Our study was designed to examine cross-sectional treatment change: that is, change measured at the time of 1 visit. The 50% treatment change rate at any 1 visit may translate to high levels of intensification over time, but the focus on a single visit allowed us to better identify patient, provider, organizational, and visit predictors that may be diluted in longitudinal studies. In addition, our treatment change variable incorporated follow-up plans documented in the medical record. Providers may have asked patients to follow up without documenting the plan, and even when follow-up was planned, it may have not always been completed. Therefore, integrating our results with information about factors that serve as barriers to medication intensification over time, including completion of follow-up, is needed.

In conclusion, by using a detailed conceptual model and data reports from providers, patients, and the medical record, we delineated factors related to clinical uncertainty, competing demands, and provider blood pressure prioritization and what influenced treatment change decisions for high-risk patients with elevated blood pressure. Perhaps our most actionable finding is that rather than simply failing to act (inertia), providers are often confronted with the inherent clinical uncertainty about blood pressure values and document actions to incorporate additional information (for example, repeating measurements or eliciting home blood pressure values), which in turn has an enormous effect on decisions to change treatment. Unfortunately, providers are doing so without a systematic approach and possibly placing undue faith in their own repeated measurements or home blood pressure values. Although providers' reliance on these additional measures might be considered a way to justify lack of medication intensification through “soft” reasons or “wishful thinking” (9), it also seems to represent true clinical uncertainty about which values to believe. Regardless of what we call it, it is clear that this ambiguous approach to using blood pressure measurements is fraught with potential bias that could undermine performance improvement initiatives and may be a major obstacle to optimizing management of hypertension and improving outcomes for high-risk populations. We must promote more systematic approaches to the use of clinic and home blood pressure measurements in the treatment of hypertension.

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Bolen SD, Samuels TA, Yeh HC, Marinopoulos SS, McGuire M, Abuid M. et al.  Failure to intensify antihypertensive treatment by primary care providers: a cohort study in adults with diabetes mellitus and hypertension. J Gen Intern Med. 2008. PubMed
 
Cook CB, Ziemer DC, El-Kebbi IM, Gallina DL, Dunbar VG, Ernst KL. et al.  Diabetes in urban African-Americans. XVI. Overcoming clinical inertia improves glycemic control in patients with type 2 diabetes. Diabetes Care. 1999; 22:1494-500. PubMed
 
Parchman ML, Pugh JA, Romero RL, Bowers KW.  Competing demands or clinical inertia: the case of elevated glycosylated hemoglobin. Ann Fam Med. 2007; 5:196-201. PubMed
 
Lin ND, Martins SB, Chan AS, Coleman RW, Bosworth HB, Oddone EZ. et al.  Identifying barriers to hypertension guideline adherence using clinician feedback at the point of care. AMIA Annu Symp Proc. 2006; 494-8. PubMed
 
Ferrari P, Hess L, Pechere-Bertschi A, Muggli F, Burnier M.  Reasons for not intensifying antihypertensive treatment (RIAT): a primary care antihypertensive intervention study. J Hypertens. 2004; 22:1221-9. PubMed
 
Haynes B, Haines A.  Barriers and bridges to evidence based clinical practice. BMJ. 1998; 317:273-6. PubMed
 
Casalino L, Gillies RR, Shortell SM, Schmittdiel JA, Bodenheimer T, Robinson JC. et al.  External incentives, information technology, and organized processes to improve health care quality for patients with chronic diseases. JAMA. 2003; 289:434-41. PubMed
 
Jaén CR, Stange KC, Nutting PA.  Competing demands of primary care: a model for the delivery of clinical preventive services. J Fam Pract. 1994; 38:166-71. PubMed
 
Nutting PA, Rost K, Smith J, Werner JJ, Elliot C.  Competing demands from physical problems: effect on initiating and completing depression care over 6 months. Arch Fam Med. 2000; 9:1059-64. PubMed
 
Stange KC, Fedirko T, Zyzanski SJ, Jaén CR.  How do family physicians prioritize delivery of multiple preventive services? J Fam Pract. 1994; 38:231-7. PubMed
 
Janz NK, Becker MH.  The Health Belief Model: a decade later. Health Educ Q. 1984; 11:1-47. PubMed
 
Green LW, Eriksen MP, Schor EL.  Preventive practices by physicians: behavioral determinants and potential interventions. Am J Prev Med. 1988;4:101-7; discussion 108-10. [PMID: 3079134]
 
Cummings KM, Becker MH, Maile MC.  Bringing the models together: an empirical approach to combining variables used to explain health actions. J Behav Med. 1980; 3:123-45. PubMed
 
Rubenstein LV, Mittman BS, Yano EM, Mulrow CD.  From understanding health care provider behavior to improving health care: the QUERI framework for quality improvement. Quality Enhancement Research Initiative. Med Care. 2000; 38:I129-41. PubMed
 
Wagner EH, Austin BT, Von Korff M.  Organizing care for patients with chronic illness. Milbank Q. 1996; 74:511-44. PubMed
 
Wagner EH, Austin BT, Davis C, Hindmarsh M, Schaefer J, Bonomi A.  Improving chronic illness care: translating evidence into action. Health Aff (Millwood). 2001; 20:64-78. PubMed
 
Bodenheimer T, Wagner EH, Grumbach K.  Improving primary care for patients with chronic illness: the chronic care model, Part 2. JAMA. 2002; 288:1909-14. PubMed
 
Weingarten SR, Henning JM, Badamgarav E, Knight K, Hasselblad V, Gano A Jr. et al.  Interventions used in disease management programmes for patients with chronic illness-which ones work? Meta-analysis of published reports. BMJ. 2002; 325:925. PubMed
 
Cabana MD, Rand CS, Powe NR, Wu AW, Wilson MH, Abboud PA. et al.  Why don't physicians follow clinical practice guidelines? A framework for improvement. JAMA. 1999; 282:1458-65. PubMed
 
Piette JD, Kerr EA.  The impact of comorbid chronic conditions on diabetes care. Diabetes Care. 2006; 29:725-31. PubMed
 
Yu W, Ravelo A, Wagner TH, Phibbs CS, Bhandari A, Chen S. et al.  Prevalence and costs of chronic conditions in the VA health care system. Med Care Res Rev. 2003; 60:146S-167S. PubMed
 
Hofer TP, Klamerus ML, Zikmund-Fisher B, Kerr EA.  Providers vary substantially in their propensity to intensify blood pressure treatment. J Gen Intern Med. 2006; 21:Suppl 4112-113.
 
Horne R, Weinman J, Hankins M.  The beliefs about medicines questionnaire: The development and evaluation of a new method for assessing the cognitive representation of medication. Psychol Health. 1999; 14:1-24.
 
Snijders TA, Bosker RJ.  Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modelings. Thousand Oaks, CA: Sage; 1999.
 
Kerr EA, Heisler M, Krein SL, Kabeto M, Langa KM, Weir D. et al.  Beyond comorbidity counts: how do comorbidity type and severity influence diabetes patients' treatment priorities and self-management? J Gen Intern Med. 2007; 22:1635-40. PubMed
 
Sechrest L.  Validity of measures is no simple matter. Health Serv Res. 2005; 40:1584-604. PubMed
 
Messerli FH, White WB, Staessen JA.  If only cardiologists did properly measure blood pressure. Blood pressure recordings in daily practice and clinical trials. J Am Coll Cardiol. 2002; 40:2201-3. PubMed
 
McManus RJ, Mant J, Hull MR, Hobbs FD.  Does changing from mercury to electronic blood pressure measurement influence recorded blood pressure? An observational study. Br J Gen Pract. 2003; 53:953-6. PubMed
 
Vaur L, Dubroca II, Dutrey-Dupagne C, Genès N, Chatellier G, Bouvier-d'Yvoire M. et al.  Superiority of home blood pressure measurements over office measurements for testing antihypertensive drugs. Blood Press Monit. 1998; 3:107-114. PubMed
 
Sassano P, Chatellier G, Billaud E, Alhenc-Gelas F, Corvol P, Ménard J.  Treatment of mild to moderate hypertension with or without the converting enzyme inhibitor enalapril. Results of a six-month double-blind trial. Am J Med. 1987; 83:227-35. PubMed
 
La Batide-Alanore A, Chatellier G, Bobrie G, Fofol I, Plouin PF.  Comparison of nurse- and physician-determined clinic blood pressure levels in patients referred to a hypertension clinic: implications for subsequent management. J Hypertens. 2000; 18:391-8. PubMed
 
Bruce NG, Shaper AG, Walker M, Wannamethee G.  Observer bias in blood pressure studies. J Hypertens. 1988; 6:375-80. PubMed
 
Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr. et al.  Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension. 2003; 42:1206-52. PubMed
 
Pickering TG, Hall JE, Appel LJ, Falkner BE, Graves J, Hill MN. et al.  Recommendations for blood pressure measurement in humans and experimental animals: part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Circulation. 2005; 111:697-716. PubMed
 
Parati G, Stergiou G.  Self blood pressure measurement at home: how many times? [Editorial]. J Hypertens. 2004; 22:1075-9. PubMed
 
Roumie CL, Elasy TA, Wallston KA, Pratt S, Greevy RA, Liu X. et al.  Clinical inertia: a common barrier to changing provider prescribing behavior. Jt Comm J Qual Patient Saf. 2007; 33:277-85. PubMed
 
Grant RW, Cagliero E, Dubey AK, Gildesgame C, Chueh HC, Barry MJ. et al.  Clinical inertia in the management of type 2 diabetes metabolic risk factors. Diabet Med. 2004; 21:150-5. PubMed
 
Asai Y, Heller R, Kajii E.  Hypertension control and medication increase in primary care. J Hum Hypertens. 2002; 16:313-8. PubMed
 
Safford MM, Shewchuk R, Qu H, Williams JH, Estrada CA, Ovalle F. et al.  Reasons for not intensifying medications: differentiating “clinical inertia” from appropriate care. J Gen Intern Med. 2007; 22:1648-55. PubMed
 

Figures

Grahic Jump Location
Figure 2.
Study flow diagram.

PCP = primary care provider. *Diabetic patients presenting for a primary care visit to 1 of 92 participating providers were referred for eligibility assessment if their lowest triage blood pressure was ≥140/90 mm Hg. *Number of responses varied by individual item.

Grahic Jump Location
Grahic Jump Location
Figure 3.
Relationship of systolic and diastolic blood pressures at enrollment and mean previous year systolic blood pressure with probability of treatment change.

Each curve is shown with the other 2 blood pressure components adjusted to their mean value and the intensification rate for the average provider and clinic site. For each blood pressure component, the curve is presented only for a range of values actually seen when the other 2 components are both close to their mean (±10 mm Hg of the mean value).

Grahic Jump Location

Tables

Table Jump PlaceholderTable 2.  Patient and Provider Characteristics
Table Jump PlaceholderTable 3.  Association of Measured Factors with the Predicted Probability of Treatment Change
Table Jump PlaceholderAppendix Table.  Three-Level Logistic Regression Models Assessing Associations between Patient, Provider, and Visit Factors and Treatment Change

References

Saaddine JB, Cadwell B, Gregg EW, Engelgau MM, Vinicor F, Imperatore G. et al.  Improvements in diabetes processes of care and intermediate outcomes: United States, 1988-2002. Ann Intern Med. 2006; 144:465-74. PubMed
 
Asch SM, McGlynn EA, Hiatt L, Adams J, Hicks J, DeCristofaro A. et al.  Quality of care for hypertension in the United States. BMC Cardiovasc Disord. 2005; 5:1. PubMed
 
Vijan S, Hayward RA.  Treatment of hypertension in type 2 diabetes mellitus: blood pressure goals, choice of agents, and setting priorities in diabetes care. Ann Intern Med. 2003; 138:593-602. PubMed
 
Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr. et al.  The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003; 289:2560-72. PubMed
 
Berlowitz DR, Ash AS, Hickey EC, Glickman M, Friedman R, Kader B.  Hypertension management in patients with diabetes: the need for more aggressive therapy. Diabetes Care. 2003; 26:355-9. PubMed
 
Hicks PC, Westfall JM, Van Vorst RF, Bublitz Emsermann C, Dickinson LM, Pace W. et al.  Action or inaction? Decision making in patients with diabetes and elevated blood pressure in primary care. Diabetes Care. 2006; 29:2580-5. PubMed
 
Godley PJ, Maue SK, Farrelly EW, Frech F.  The need for improved medical management of patients with concomitant hypertension and type 2 diabetes mellitus. Am J Manag Care. 2005; 11:206-10. PubMed
 
Schaars CF, Denig P, Kasje WN, Stewart RE, Wolffenbuttel BH, Haaijer-Ruskamp FM.  Physician, organizational, and patient factors associated with suboptimal blood pressure management in type 2 diabetic patients in primary care. Diabetes Care. 2004; 27:123-8. PubMed
 
Phillips LS, Branch WT, Cook CB, Doyle JP, El-Kebbi IM, Gallina DL. et al.  Clinical inertia. Ann Intern Med. 2001; 135:825-34. PubMed
 
O'Connor PJ.  Overcome clinical inertia to control systolic blood pressure [Editorial]. Arch Intern Med. 2003; 163:2677-8. PubMed
 
Fine LJ, Cutler JA.  Hypertension and the treating physician: understanding and reducing therapeutic inertia [Editorial]. Hypertension. 2006; 47:319-20. PubMed
 
Okonofua EC, Simpson KN, Jesri A, Rehman SU, Durkalski VL, Egan BM.  Therapeutic inertia is an impediment to achieving the Healthy People 2010 blood pressure control goals. Hypertension. 2006; 47:345-51. PubMed
 
Turchin A, Grant RW, Einbinder JS, Pendergrass ML.  Clinical inertia in management of hypertension in diabetic patients [Abstract]. Diabetes. 2006; 55:A3.
 
Berlowitz DR, Ash AS, Hickey EC, Friedman RH, Glickman M, Kader B. et al.  Inadequate management of blood pressure in a hypertensive population. N Engl J Med. 1998; 339:1957-63. PubMed
 
Andrade SE, Gurwitz JH, Field TS, Kelleher M, Majumdar SR, Reed G. et al.  Hypertension management: the care gap between clinical guidelines and clinical practice. Am J Manag Care. 2004; 10:481-6. PubMed
 
Rodondi N, Peng T, Karter AJ, Bauer DC, Vittinghoff E, Tang S. et al.  Therapy modifications in response to poorly controlled hypertension, dyslipidemia, and diabetes mellitus. Ann Intern Med. 2006; 144:475-84. PubMed
 
Oliveria SA, Lapuerta P, McCarthy BD, L'Italien GJ, Berlowitz DR, Asch SM.  Physician-related barriers to the effective management of uncontrolled hypertension. Arch Intern Med. 2002; 162:413-20. PubMed
 
Bolen SD, Samuels TA, Yeh HC, Marinopoulos SS, McGuire M, Abuid M. et al.  Failure to intensify antihypertensive treatment by primary care providers: a cohort study in adults with diabetes mellitus and hypertension. J Gen Intern Med. 2008. PubMed
 
Cook CB, Ziemer DC, El-Kebbi IM, Gallina DL, Dunbar VG, Ernst KL. et al.  Diabetes in urban African-Americans. XVI. Overcoming clinical inertia improves glycemic control in patients with type 2 diabetes. Diabetes Care. 1999; 22:1494-500. PubMed
 
Parchman ML, Pugh JA, Romero RL, Bowers KW.  Competing demands or clinical inertia: the case of elevated glycosylated hemoglobin. Ann Fam Med. 2007; 5:196-201. PubMed
 
Lin ND, Martins SB, Chan AS, Coleman RW, Bosworth HB, Oddone EZ. et al.  Identifying barriers to hypertension guideline adherence using clinician feedback at the point of care. AMIA Annu Symp Proc. 2006; 494-8. PubMed
 
Ferrari P, Hess L, Pechere-Bertschi A, Muggli F, Burnier M.  Reasons for not intensifying antihypertensive treatment (RIAT): a primary care antihypertensive intervention study. J Hypertens. 2004; 22:1221-9. PubMed
 
Haynes B, Haines A.  Barriers and bridges to evidence based clinical practice. BMJ. 1998; 317:273-6. PubMed
 
Casalino L, Gillies RR, Shortell SM, Schmittdiel JA, Bodenheimer T, Robinson JC. et al.  External incentives, information technology, and organized processes to improve health care quality for patients with chronic diseases. JAMA. 2003; 289:434-41. PubMed
 
Jaén CR, Stange KC, Nutting PA.  Competing demands of primary care: a model for the delivery of clinical preventive services. J Fam Pract. 1994; 38:166-71. PubMed
 
Nutting PA, Rost K, Smith J, Werner JJ, Elliot C.  Competing demands from physical problems: effect on initiating and completing depression care over 6 months. Arch Fam Med. 2000; 9:1059-64. PubMed
 
Stange KC, Fedirko T, Zyzanski SJ, Jaén CR.  How do family physicians prioritize delivery of multiple preventive services? J Fam Pract. 1994; 38:231-7. PubMed
 
Janz NK, Becker MH.  The Health Belief Model: a decade later. Health Educ Q. 1984; 11:1-47. PubMed
 
Green LW, Eriksen MP, Schor EL.  Preventive practices by physicians: behavioral determinants and potential interventions. Am J Prev Med. 1988;4:101-7; discussion 108-10. [PMID: 3079134]
 
Cummings KM, Becker MH, Maile MC.  Bringing the models together: an empirical approach to combining variables used to explain health actions. J Behav Med. 1980; 3:123-45. PubMed
 
Rubenstein LV, Mittman BS, Yano EM, Mulrow CD.  From understanding health care provider behavior to improving health care: the QUERI framework for quality improvement. Quality Enhancement Research Initiative. Med Care. 2000; 38:I129-41. PubMed
 
Wagner EH, Austin BT, Von Korff M.  Organizing care for patients with chronic illness. Milbank Q. 1996; 74:511-44. PubMed
 
Wagner EH, Austin BT, Davis C, Hindmarsh M, Schaefer J, Bonomi A.  Improving chronic illness care: translating evidence into action. Health Aff (Millwood). 2001; 20:64-78. PubMed
 
Bodenheimer T, Wagner EH, Grumbach K.  Improving primary care for patients with chronic illness: the chronic care model, Part 2. JAMA. 2002; 288:1909-14. PubMed
 
Weingarten SR, Henning JM, Badamgarav E, Knight K, Hasselblad V, Gano A Jr. et al.  Interventions used in disease management programmes for patients with chronic illness-which ones work? Meta-analysis of published reports. BMJ. 2002; 325:925. PubMed
 
Cabana MD, Rand CS, Powe NR, Wu AW, Wilson MH, Abboud PA. et al.  Why don't physicians follow clinical practice guidelines? A framework for improvement. JAMA. 1999; 282:1458-65. PubMed
 
Piette JD, Kerr EA.  The impact of comorbid chronic conditions on diabetes care. Diabetes Care. 2006; 29:725-31. PubMed
 
Yu W, Ravelo A, Wagner TH, Phibbs CS, Bhandari A, Chen S. et al.  Prevalence and costs of chronic conditions in the VA health care system. Med Care Res Rev. 2003; 60:146S-167S. PubMed
 
Hofer TP, Klamerus ML, Zikmund-Fisher B, Kerr EA.  Providers vary substantially in their propensity to intensify blood pressure treatment. J Gen Intern Med. 2006; 21:Suppl 4112-113.
 
Horne R, Weinman J, Hankins M.  The beliefs about medicines questionnaire: The development and evaluation of a new method for assessing the cognitive representation of medication. Psychol Health. 1999; 14:1-24.
 
Snijders TA, Bosker RJ.  Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modelings. Thousand Oaks, CA: Sage; 1999.
 
Kerr EA, Heisler M, Krein SL, Kabeto M, Langa KM, Weir D. et al.  Beyond comorbidity counts: how do comorbidity type and severity influence diabetes patients' treatment priorities and self-management? J Gen Intern Med. 2007; 22:1635-40. PubMed
 
Sechrest L.  Validity of measures is no simple matter. Health Serv Res. 2005; 40:1584-604. PubMed
 
Messerli FH, White WB, Staessen JA.  If only cardiologists did properly measure blood pressure. Blood pressure recordings in daily practice and clinical trials. J Am Coll Cardiol. 2002; 40:2201-3. PubMed
 
McManus RJ, Mant J, Hull MR, Hobbs FD.  Does changing from mercury to electronic blood pressure measurement influence recorded blood pressure? An observational study. Br J Gen Pract. 2003; 53:953-6. PubMed
 
Vaur L, Dubroca II, Dutrey-Dupagne C, Genès N, Chatellier G, Bouvier-d'Yvoire M. et al.  Superiority of home blood pressure measurements over office measurements for testing antihypertensive drugs. Blood Press Monit. 1998; 3:107-114. PubMed
 
Sassano P, Chatellier G, Billaud E, Alhenc-Gelas F, Corvol P, Ménard J.  Treatment of mild to moderate hypertension with or without the converting enzyme inhibitor enalapril. Results of a six-month double-blind trial. Am J Med. 1987; 83:227-35. PubMed
 
La Batide-Alanore A, Chatellier G, Bobrie G, Fofol I, Plouin PF.  Comparison of nurse- and physician-determined clinic blood pressure levels in patients referred to a hypertension clinic: implications for subsequent management. J Hypertens. 2000; 18:391-8. PubMed
 
Bruce NG, Shaper AG, Walker M, Wannamethee G.  Observer bias in blood pressure studies. J Hypertens. 1988; 6:375-80. PubMed
 
Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr. et al.  Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension. 2003; 42:1206-52. PubMed
 
Pickering TG, Hall JE, Appel LJ, Falkner BE, Graves J, Hill MN. et al.  Recommendations for blood pressure measurement in humans and experimental animals: part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Circulation. 2005; 111:697-716. PubMed
 
Parati G, Stergiou G.  Self blood pressure measurement at home: how many times? [Editorial]. J Hypertens. 2004; 22:1075-9. PubMed
 
Roumie CL, Elasy TA, Wallston KA, Pratt S, Greevy RA, Liu X. et al.  Clinical inertia: a common barrier to changing provider prescribing behavior. Jt Comm J Qual Patient Saf. 2007; 33:277-85. PubMed
 
Grant RW, Cagliero E, Dubey AK, Gildesgame C, Chueh HC, Barry MJ. et al.  Clinical inertia in the management of type 2 diabetes metabolic risk factors. Diabet Med. 2004; 21:150-5. PubMed
 
Asai Y, Heller R, Kajii E.  Hypertension control and medication increase in primary care. J Hum Hypertens. 2002; 16:313-8. PubMed
 
Safford MM, Shewchuk R, Qu H, Williams JH, Estrada CA, Ovalle F. et al.  Reasons for not intensifying medications: differentiating “clinical inertia” from appropriate care. J Gen Intern Med. 2007; 22:1648-55. PubMed
 

Letters

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Comments

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It must just happen to be spotted.
Posted on May 23, 2008
Eiichiro Sando
Department of General Medicine and Infectious Diseases, Kameda Medical Center
Conflict of Interest: None Declared

The article "The Role of Clinical Uncertainty in Treatment Decisions for Diabetic Patients with Uncontrolled Blood Pressure" by Eve A. Kerr et al is certainly interesting and clinically significant. As a generalist, I have a number of patients who have prescribed antihypertensive drugs. As mentioned, I also tend to delay changing my strategy of the antihypertensive therapy. Certainly we know the clinical uncertainty of the true blood pressure, and the uncertainty always means to us, just "elevated." White coats, timing or the way of measurement would usually produce increased blood pressure. Rarely found the cause of happen to bring down the blood pressure clinically. We want to believe that the high blood pressure just happen to be spotted, it must be lower in an ordinary way.

Conflict of Interest:

None declared

Additional Factors Causing Inertia
Posted on May 28, 2008
Michael S Karp
USC - Keck School of Medicine
Conflict of Interest: None Declared

I agree with many of the conclusions offered by Kerr et al regarding medical inertia. It is concerning that despite the knowledge of the benefits of blood pressure, blood sugar, and cholesterol control in the diabetic patient, the percent of patients that achieve medical goals is low. Many factors as outlined in the article contribute to this phenomenom. However, a major factor that wasn't studied in this article is medical insurance. It's been well established that those will less insurance are less likely to get the standard of care. Paz and colleagues studied risk factors for noncompliance with ophthalmologic exam in diabetics. They found, among other factors, the lack of insurance gave an odds ratio of 2.5 (95% CI, 1.7-3.7) for noncompliance. It's conceivable that physicians are consciously or unconsciously influenced by patient's insurance status when deciding to optimize or intensify medical management. Are we indirectly deciding who gets the best care? Are these decisions being made for us before the patient even comes into the clinic? How much does the presence of formulary restrictions and prior authorizations play a role? I'm concerned that we may be hardwiring our own thought processes around this issue more than we realize.

References: Paz et al. Noncompliance with vision care guidelines in Latinos with type 2 diabetes mellitus: the Los Angeles Latino Eye Study. Ophthalmology. 113(8):1372-7, 2006 Aug.

Conflict of Interest:

None declared

Tobacco Use qualifies as a Concordant Condition
Posted on June 22, 2008
Victor O. Kolade
University of Buffalo
Conflict of Interest: None Declared

Perhaps Kerr and colleagues should have classified tobacco use as a concordant condition (1). It is a chronic disease characterized by relapses and remissions (2) that is associated with some of the concordant conditions included in the study. Besides, smoking may be more prevalent among Veterans (3), possibly driven by previous Department of Defense practices offering tobacco to soldiers at a discount (4).

References

1. Kerr EA, Zikmund-Fisher BJ, Klamerus ML, Subramanian U, Hogan MM, Hofer TP. The role of clinical uncertainty in treatment decisions for diabetic patients with uncontrolled blood pressure. Ann Intern Med. 2008;148(10):717-27.

2. Steinberg MB, Schmelzer AC, Richardson DL, Foulds J. The case for treating tobacco dependence as a chronic disease. Ann Intern Med. 2008;148(7):554-6.

3. Ross JS, Keyhani S, Keenan PS, et al. Use of Recommended Ambulatory Care Services: Is the Veterans Affairs Quality Gap Narrowing? Archives of Internal Medicine. 2008;168(9):950-958.

4. Smith EA, Blackman VS, Malone RE. Death at a discount: how the tobacco industry thwarted tobacco control policies in US military commissaries. Tobacco Control. 2007;16(1):38-46.

Conflict of Interest:

None declared

Uncertainty or Good Judgement
Posted on July 17, 2008
Lawrence R Krakoff
Mount Sinai School of Medicine
Conflict of Interest: None Declared

Letter to Editors Annals of Internal Medicine Sent July 7, 2008 Ré Uncertainty in management of hypertension in diabetes. Kerr et al and editorial by Phillips.

The article by Kerr et al (1) is a valuable description of choices made by physicians in the VA health care clinics for treatment of hypertension in diabetic patients. Such studies are useful in trying to understand physician behavior in relation to recommendations from guidelines, such as the JNC-7 (2). However, the authors' interpretation of their results and the opinions given in the accompanying editorial by Phillips and Twombly (3) should not go unchallenged as they fail to recognize advances in the role of home blood pressures for management of hypertension.

In about half of the visits, providers relied on screening blood pressures for their treatment decisions regarding hypertension. Is this a reflection of uncertainty or awareness that screening pressures may be inaccurate for treatment decisions in individual cases? The providers who used either home pressures or additional clinic pressures may have been uncertain about the accuracy or the screening pressures, but the basis of their choice to use additional pressures is medically sound and, in fact, recommended. In this case uncertainty reflects better judgment. In particular, the case for relying on home blood pressures for treatment choices is well supported by a robust evidence base(4;5). Relying on limited blood pressure measurements from clinics alone, enhances the likelihood of regression dilution with the potential consequences of over- treatment or undertreatment.

Effective management of hypertension, especially in diabetics, is clearly a mainstay of preventive cardiovascular medicine. The VA health care system has been a major resource for both the clinical trials and the demonstration that control can be achieved in clinic populations. That being the case, attention must now be paid to optimization of all the complex issues of individual patient care for more accurate assessment of usual blood pressure and more nuanced recognition of decisions that will maximize patient satisfaction and prevention of future disease.

References

(1) Kerr EA, Zikmund-Fisher BJ, Klamerus ML, Subramanian U, Hogan MM, Hofer TP. The role of clinical uncertainty in treatment decisions for diabetic patients with uncontrolled blood pressure. Ann Intern Med. 2008;148:717-27.

(2) Chobanian AV, Bakris GL, Black HR, Green L, Izzo JLJr, Jones DW et al. The seventh report of the Joint National Committee on Prevention, Detection, Evaluation and Treatment of High Blood Pressure: The JNC 7 Report. JAMA. 2003;289:2560-2572.

(3) Phillips LS, Twombly JG. It's time to overcome clinical inertia. Ann Intern Med. 2008;148:783-85.

(4) Verberk WJ, Kroon AA, Kessels AG, de Leeuw PW. Home blood pressure measurement: a systematic review. J Am Coll Cardiol. 2005;46:743- 51.

(5) Pickering TG, Miller NH, Ogedegbe G, Krakoff LR, Artinian NT, Goff D. Call to Action on Use and Reimbursement for Home Blood Pressure Monitoring. A Joint Scientific Statement From the American Heart Association, American Society of Hypertension, and Preventive Cardiovascular Nurses Association. Hypertension. 2008.

Lawrence R Krakoff MD Professor of Medicine Mount Sinai School of Medicine New York NY 10029 E-mail Lawrence.krakoff@mssm.edu

Conflict of Interest:

None declared

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