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Blood pressure (BP) visit-to-visit variability (VVV) influences the risk of vascular events and mortality. Research has suggested that antihypertensive medication classes may differentially impact VVV. This study evaluated whether antihypertensive medication class differentially impacted BP VVV among hypertensive individuals in a clinical, “real-world” setting as well as the association between VVV and patient characteristics. Clinical observational data were extracted for adults (mean age, 63; 56% female, 86% Caucasian) with hypertension from the Mercy EpicCare EHR-Derived Database (MEDD) (n=183,374) who had at least 4 outpatient visits with BP readings. A multilevel mixed model for change over time estimated between- and within-subject effects on the absolute real VVV of systolic BP. Diuretics significantly lowered VVV (β=−0.32[−0.39 to−0.25]) and α-/β-blockers resulted in the highest VVV (β=0.89 [0.77–1.00]). Being older, female, and having a higher systolic BP and certain comorbid conditions significantly raised VVV (P<.001). The findings from the MEDD were consistent in general with other research on BP VVV. However, the magnitude of effect of antihypertensive medication class and patient characteristics was relatively low (<10% of the BP VVV variance for any one variable). More research is needed to evaluate the extent to which the class of antihypertensive medication class impacts BP VVV in the outpatient setting.
Long-term poorly controlled blood pressure (BP) in hypertensive patients is associated with increased cardiovascular, renal, and cerebrovascular morbidity and mortality.[1-3] Although the individual's systolic BP level is considered of primary importance in assessing risk of vascular events, it is possible that other BP parameters might also be influential. One such parameter that has been proposed is BP visit-to-visit variability (VVV).[4-7] Although BP VVV has at times been considered a nuisance variable,[8-11] there is burgeoning literature on the topic.[6, 7, 12] Rothwell and colleagues reported that in two large trials (United Kingdom Transient Ischaemic Attack [UK-TIA aspirin] trial and the Anglo-Scandinavian Cardiac Outcomes Trial-Blood Pressure Lowering Arm [ASCOT-BPLA]) systolic BP (SBP) VVV was found to be predictive of stroke and coronary events, including mortality (and was independent of mean SBP).[6, 13] Munter and colleagues found that higher SBP VVV is associated with an increased risk of negative cardiovascular outcomes and end-organ damage. BP VVV has been previously shown to be associated with adverse outcomes such as mortality and cardiovascular events,[12, 14] yet there remains a gap to demonstrate what factors may explain higher levels of VVV.
Antihypertensive medications may have class-specific effects on BP VVV.[13, 15] A recent meta-analysis considering VVV across multiple randomized trials reported that some antihypertensive classes (calcium channel blockers, diuretics) decrease VVV, while others (angiotensin-converting enzyme inhibitors, β-blockers) increase VVV. Within the ASCOT-BPLA trial it was reported that using a calcium channel blocker resulted in a 24% decrease in cardiovascular mortality and a 23% decrease in stroke compared with β-blocker therapy. In at least one study, VVV was associated with being older and female and a predictor of myocardial infarction. Given the potential impact of BP VVV on vascular events, it is important to evaluate whether certain patient characteristics are associated with BP VVV. Due to the evolving understanding of BP VVV on clinical outcomes, more work needs to be conducted to evaluate whether BP VVV is differentially influenced by the various classes of antihypertensive medications as well as patient characteristics.
In order to appreciate these relationships in a “real-world” setting, it would be beneficial to examine a clinical electronic health record (EHR)–derived database. The current study had two main objectives. The first objective was to investigate whether antihypertensive class differentially affected VVV among individuals in an EHR-derived database, while the second objective was to investigate the effect of patient characteristics on VVV.
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The population was predominantly female (56.1%) and Caucasian (86.3%), with an age range of 45 to 75 years (mean, 63.4 years). The most common comorbid condition was diabetes (26.9%). Table 1 shows the population differences between patients taking antihypertensive medications (monotherapy or combination therapies) at the first observation and patients not taking antihypertensive medications. Results show that the most notable and significant differences (P<.05) in BP were found when comparing comorbid conditions and age.
Table 1. Comparing Patient Characteristics by Antihypertensive Medication Prescription Status
| ||Total Population (n=183,374)||Prescribed Antihypertensive Medication (n=105,010)||Not Prescribed Antihypertensive Medication (n=78,365)|
|Baseline systolic BP||131.7||132.0||131.3||.526|
|Baseline diastolic BP||77.3||77.4||77.0|| <.0001 |
|Systolic absolute real VVV|
|Visit 1 – baseline||13.8||13.8||13.7||.542|
|Visit 2 – visit 1||13.7||13.8||13.6|| .0002 |
|Visit 3 – visit 2||13.6||13.8||13.4|| <.0001 |
|Visit 4 – visit 3||13.7||13.8||13.4|| <.0001 |
|Visit 5 – visit 4||13.7||13.8||13.4|| <.0001 |
|Visit 6 – visit 5||13.8||13.9||13.4|| <.0001 |
|Age, y||63.4||63.7||63.3|| <.0001 |
|Female sex, %||56.1||56.3||55.9||.149|
|BMI||31.0||31.3||30.7|| <.0001 |
|African American||4.0||4.7||3.0|| <.0001 |
|Dyslipidemia||53.9||56.0||51.1|| <.0001 |
|Diabetes||26.9||27.5||26.1|| <.0001 |
|CAD without MI||20.3||20.3||20.4||.886|
|Stroke or TIA||10.1||9.9||10.4|| .001 |
|CHF||6.5||6.7||6.4|| .015 |
|PVD||5.1||4.8||5.4|| <.0001 |
|Renal disease||2.8||2.7||3.0|| .002 |
Table 2 shows the breakdown of patient medications, identifying the number of prescribed medications, medication classes, and common combination therapies prescribed. In the study population, 37.0% of patients were prescribed 1 antihypertensive medication, 21.2% were prescribed 2 antihypertensive medications, and <10% were prescribed ≥3 antihypertensive medications. For patients taking >1 medication, the most common combinations were diuretic+angiotensin-converting enzyme inhibitors (3.5%) and diuretic+β-blocker (3.0%).
Table 2. Patient Medication Descriptives
| ||No. (%)|
|Prescribed Antihypertensive Medications, No.|
|ACE inhibitors||229,542 (24.87)|
|Calcium channel blockers||151,834 (16.45)|
|Diuretic+ACE inhibitor||32,366 (3.51)|
|ACE inhibitor+β-blocker||25,566 (2.77)|
|ACE inhibitor+calcium channel blocker||15,666 (1.70)|
|Diuretic+calcium channel blocker||15,216 (1.65)|
|Diuretic+ACE inhibitor+β-blocker||14,122 (1.53)|
A propensity model was used to reduce selection bias and improve population comparisons in this observational design. The propensity model evaluated patient characteristics to estimate the probability of taking an antihypertensive medication. The actual rate of patients taking any antihypertensive medication was 68% and the propensity model estimated the probability of taking an antihypertensive medication to fall between 50% and 80% for nearly all patients (99.69%). The appropriateness of drawing inferences by comparing the medicated patients relative to the unmedicated patients was well supported given similar probability of taking an antihypertensive medication.
The estimates of the independent association of various antihypertensive medication classes on VVV after the initial treatment period are captured in Table 3 and visually demonstrated in Figure 3. Positive β coefficients indicate higher VVV. The mean effect varied among the various antihypertensive medication classes. As can be seen in Table 3 and Figure 3, both diuretics and calcium channel blockers significantly lowered VVV relative to all other classes (β=−0.32 [−0.39, −0.25], P<.0001, β=−0.11 [−0.19, −0.03], P=.005, respectively) whereas α-/β-blockers (β=0.89 [0.77, 1], P<.0001) showed the highest increase in VVV. Angiotensin-converting enzyme inhibitors, β-blockers, and angiotensin receptor blockers showed a statistically significant VVV increase relative to all other classes.
Table 3. Systolic BP Absolute Visit-to-Visit Variability Model
| ||β Estimate||95% CI|| t ||P Value|
|ACE inhibitors||0.34||(0.27, 0.41)||10.04|| <.0001 |
|β-Blockers||0.24||(0.17, 0.31)||6.93|| <.0001 |
|ARBs||0.34||(0.26, 0.42)||8.46|| <.0001 |
|Calcium channel blockers||−0.11||(−0.19, −0.03)||−2.8|| .005 |
|α-/β-Blockers||0.89||(0.77, 1)||14.99|| <.0001 |
|Diuretics||−0.32||(−0.39, −0.25)||−9.33|| <.0001 |
|Systolic BP||0.12||(0.12, 0.12)||156.95|| <.0001 |
|Sex (female vs male)||0.75||(0.68, 0.81)||24.01|| <.0001 |
|Age (centered at 62)||0.04||(0.04, 0.04)||29.05|| <.0001 |
|Atrial fibrillation||0.11||(0, 0.22)||2.03|| .042 |
|CAD without MI||0.54||(0.46, 0.62)||13.62|| <.0001 |
|Dyslipidemia||−0.39||(−0.46, −0.31)||−10.37|| <.0001 |
|MI||0.29||(0.13, 0.45)||3.55|| .0004 |
|Renal disease||0.72||(0.57, 0.88)||9.12|| <.0001 |
|Stroke or TIA||0.64||(0.55, 0.73)||13.46|| <.0001 |
|PVD||0.69||(0.56, 0.82)||10.72|| <.0001 |
|CHF||0.64||(0.52, 0.76)||10.52|| <.0001 |
|Diabetes||−0.09||(−0.16, − 0.02)||−2.57|| .010 |
|Visit count – 1||−0.07||(−0.26, 0.12)||−0.73||.463|
|Visit count – 2||−0.08||(−0.24, 0.07)||−1.06||.291|
|Visit count – 3||−0.13||(−0.26, −0.01)||−2.06|| .040 |
|Visit count – 4||−0.13||(−0.23, −0.02)||−2.32|| .020 |
|Visit count – 5||−0.12||(−0.20, −0.03)||−2.66|| .008 |
|Visit count – 6||0||–||–||–|
|Propensity score||−0.67||(−1.63, 0.30)||−1.35||<.177|
|Intercept||−1.98||(−2.72, −1.25)||−5.27|| <.0001 |
Figure 3. Medication class effect on systolic visit-to-visit variability (VVV). Error bars represent 95% confidence intervals. BP indicates blood pressure; ACE, angiotensin-converting enzyme inhibitors; ARBs, angiotensin receptor blockers..
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Table 3 also shows the demographic and comorbid influences on VVV. Sex (β=0.75 [0.68–0.81], P<.0001), age (β=0.04 [0.04–0.04], P<.0001), and SBP (β=0.12 [0.12–0.12], P<.0001) showed statistical significance. All comorbid conditions significantly increased VVV, with the exception of dyslipidemia (β=−0.39 [−0.46 to −0.31], P<.0001) and diabetes (β=−0.09 [−.16 to 0.02], P=.010). Having peripheral vascular disease (β=0.69 [0.56–0.82], P<.0001), renal disease (β=0.72 [0.57–0.88], P<.0001), stroke/transient ischemic attack (β=0.64 [0.55–0.73], P<.0001), or chronic heart failure (β=0.64 [0.52–0.76], P<.0001) resulted in the highest levels of VVV. Atrial fibrillation, coronary artery disease without myocardial infarction, and myocardial infarction also resulted in significantly higher levels of VVV.
Table 3 also includes the β estimates of the control variables (visit count and propensity of taking an antihypertensive medication). Visit sequence was analyzed; visit count 3, 4, and 5 had a significant effect on BP VVV (P<.052) with similar effect sizes to the medication and patient characteristic variables. The propensity score effect was not statistically significant (β=−0.67 [−1.63 to 0.30], P=.177), indicating that taking specific medications, rather than the characteristics of taking hypertensive medication (eg, demographics, comorbid conditions), accounted for the differences in BP VVV. Furthermore, including the propensity score improved the ability to properly estimate the effect of medication class after accounting for the dependency between patient characteristics and medication use. In the initial model we considered additional variables, including time between visits, time taking drug class, percent of time taking drug class (time taking drug class/time between visits), month of visit (seasonal trend indicator), and number of medications. However, these factors did not significantly contribute to VVV.
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Although the differences were small, we demonstrated diuretics and calcium channel blockers to be associated with lower VVV and α-/β-blockers to be associated with higher VVV, consistent with previous research. Additionally, antihypertensive medication class accounted for a small proportion of the variance. Given the strong statistical significance, it is tempting to assign clinical significance. However, in situations where the number of observations is extremely large, attention to statistical significance is necessary, but not sufficient. For example, being prescribed an α-/β-blocker raised VVV nearly 1 mm Hg on average, which represents 3.7% (.89/13.8 mm Hg) of the mean VVV. Similarly, the largest difference between any 2 classes (diuretics and α-/β-blockers) was 1.21 mm Hg, which equated to 8.8% on the average VVV. Furthermore, we did not assess clinical outcomes as they pertain to medication class. There are an array of issues that the clinician must consider when prescribing antihypertensive medication, including drug interactions, side effects, and cost. This analysis confirms that in a large clinical database, higher levels of VVV are associated with increased mortality, with the lowest level of variability associated with 2.25% mortality and the highest level of variability associated with 3.77% mortality. However, more work is needed prior to establishing VVV as another criterion for selecting optimal medical treatment regimens.
Several patient characteristics influenced VVV. Advanced age, female sex, and higher SBP were all associated with greater VVV. In terms of comorbid conditions, all evaluated conditions significantly affected VVV with the exception of diabetes and atrial fibrillation. These findings are consistent with previous research, except that in our study, diabetes was not associated with higher VVV. It has been suggested that increased VVV is caused by increased arterial stiffness, but the lack of effect on VVV when diabetes is present appears inconsistent with this notion. Similar to the impact of medication class on VVV, the magnitude of effect in absolute terms for both patient demographics and comorbid conditions raises questions as to the clinical significance of the findings.
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Although the current study provides insight into VVV in a database of real-world observations, there are limitations inherent to data of this nature. Selection bias exists given that antihypertensive medication prescriptions reflect a decision based on patient characteristics, provider preference, clinical concerns, and financial considerations. The impact of selection bias was mitigated by the use of propensity scoring. The EHR system has limited coverage of pharmacy prescription fill data causing a lack of patient medication compliance. Another limitation is the lack of a standardized method to measure BP due to differing standard operating procedures within each practice. This was partially eliminated by the fact that individual patients would likely have been assessed using similar procedures. Inaccurate or incomplete diagnosis coding practices and problem list completion are potential limitations to the accurate identification of hypertension and/or comorbid conditions. Sensitivity analysis was conducted to determine what portion of patients would be coded as having a comorbid condition under the least restrictive relative to the most restrictive set of ICD-9 codes and found that the patient population identified did not significantly vary between approaches. Lifestyle factors were not included in the analysis due to nonuniform collection in the EHR; however, future studies could address this limitation. Using a large clinical EHR-derived database posed potential limitations; however, the results of this study can be considered hypothesis-generating for future investigations of VVV.
Previous retrospective research[6, 12, 13, 16] has found that as VVV increases, the occurrence for vascular events increase;[7, 26, 27] however, it is unclear the extent to which reducing VVV via switching medication classes reduces the risk for vascular events. Future studies could prospectively evaluate the magnitude of effect of switching medication class. Furthermore, the interplay between VVV and SBP, medication class, nonhypertensive medications, and comorbidities on the risk of vascular events and other chronic-disease related events requires further investigation. Additionally, no clear parameters have been established that articulate what level of variability is considered too high and what amount of reduction in VVV would be of clinical benefit. Once a clinical benefit is demonstrated, translational studies, which determine the maximally efficient methodology for incorporating VVV in the EHR for clinical decision support, are needed, because multiple measures are required to establish a reliable VVV determination.