Abstract
Automated office blood pressure measurement (AOBPM) is recommended for diagnosing hypertension; however, optimal treatment targets using this method are not established. Discrepancies between automated and office measurements of blood pressure have been described, producing uncertainty regarding the use of AOBPM in clinical practice. The Systolic Blood Pressure Intervention Trial (SPRINT) results improved our understanding of target AOBPM systolic blood pressure (SBP) levels; however, diastolic blood pressure (DBP) targets remain unknown. Therefore, we sought to determine the optimal on‐treatment DBP range. The analysis was performed on the participants of the SPRINT trial who had hypertension and prior cardiovascular disease. We analyzed the data of 1470 participants (mean age 70.3 ± 9.3 years, 24.1% female) selected from the SPRINT trial database of National Heart, Lung and Blood Institute. The mean achieved SBP and DBP were 127.9 ± 10.7 and 68.3 ± 9.4 mm Hg, respectively. Most of the participants (57.4%) had a DBP lower than 70 mm Hg, while only 11.7% had DPB ≥80 mm Hg. Clinical composite endpoint was defined as myocardial infarction, acute coronary syndrome not resulting in myocardial infarction, stroke, acute decompensated heart failure or death from cardiovascular causes. There were 159 (10.8%) clinical endpoint events. The participants with on‐treatment AOBPM DBP range of 68.6‐78.6 mm Hg showed the lowest hazard risk of a clinical composite endpoint. These results correspond to the office DBP range of 70‐80 mm Hg recommended in ESC guidelines. This is the first attempt to determine the range of optimal DBP values using population‐based AOBPM in patients with prior cardiovascular disease.
Keywords: clinical management of high blood pressure, coronary disease, hypertension‐vascular disease, risk assessment, treatment and diagnosis/guidelines
1. INTRODUCTION
The diagnosis of hypertension and the effectiveness of hypertension therapy should be guided and monitored using blood pressure (BP) measurements. Both office and home BP measurements are common methods of BP monitoring; however, office BP (whether auscultatory or oscillometric) remains the primary tool for assessing patients with hypertension.1, 2 Despite the well‐established role of office cuff‐based measurements of BP in clinical practice, the discussion regarding the best method for evaluating BP remains active.3, 4, 5, 6 Automated office BP measurement (AOBPM) has essential superiority over other methods of office BP measurements because of its well‐standardized procedure, lack of observer error and assurance of an appropriate resting period before BP measurement.7, 8
Current recommendations by American Heart Association (AHA) and similar Canadian and Australian bodies suggest using AOBPM to diagnose arterial hypertension.9, 10, 11 However, to date there are no recommended on‐treatment BP goals for AOBPM that are supported by clinical investigations that evaluated clinical endpoints.12 The Systolic Blood Pressure Intervention Trial (SPRINT) was the first randomized study evaluating systolic blood pressure (SBP) treatment goals using AOBPM. There were no trials performed examining diastolic blood pressure (DBP) targets using AOBPM. Joint guidelines of the European Society of Cardiology and European Society of Hypertension suggest a target office DBP range of 70‐80 mm Hg based on lowest‐risk criteria.13 We therefore sought to determine an optimal on‐treatment DBP assessed using AOBPM. We assumed that the optimal on‐treatment DBP, measured using AOBPM, should be expressed as a 10‐mm Hg wide range with the lowest cardiovascular risk.
2. METHODS
2.1. Study population and data source
The SPRINT trial sought to verify the hypothesis that intensive lowering of SBP will improve outcomes in non‐diabetic and high cardiovascular risk population. Participants aged ≥50 years with SBP readings within the range of 130‐180 mm Hg who were at high cardiovascular risk were enrolled in the study. High cardiovascular risk was defined as at least one of the following: clinical or subclinical cardiovascular disease (CVD) other than stroke, chronic kidney disease, Framingham Risk Score of >15% for 10‐year cardiovascular risk or age >75 years. The definitions of clinical and subclinical CVD are presented in Table 1. In the original study, 9361 participants were further randomized to either an intensive (SBP <120 mm Hg) or standard (SBP <140 mm Hg) treatment arm. Their results demonstrated that intensive treatment was associated with lowered risk of a primary composite endpoint, which was defined as having one of the following: myocardial infarction, acute coronary syndrome not resulting in myocardial infarction, stroke, acute decompensated heart failure or death from cardiovascular causes.
Table 1.
The definitions of clinical and subclinical cardiovascular disease according to the original SPRINT study protocol18
| Cardiovascular disease |
|
| Subclinical cardiovascular disease |
|
Both under‐ or over‐treatment of DBP may produce unfavorable clinical effects, as previously demonstrated in a high cardiovascular risk population.14, 15 This relationship is more pronounced in participants with histories of CVD.16, 17 Therefore, a subpopulation of 1877 (20.1%) SPRINT participants with prior clinical CVD, as defined in Table 1, was selected from the original study cohort. We analyzed the data of participants collected from the sixth month to the end of the trial. We chose this period because we expected that most participants would exhibit relatively stable on‐treatment BP.18 Finally, we analyzed the data of 1470 (78.3% of SPRINT participants with CVD) participants. Figure 1 presents the flow chart of the subpopulation selected for our analysis.
Figure 1.
Flow chart presenting selection of the SPRINT participants for the current analysis
The analysis was performed using limited SPRINT data (SPRINT_ POP Research Materials) obtained from the National Heart, Lung and Blood Institute (NHLBI) Biologic Specimen and Data Repository Information Coordinating Centre. This manuscript does not necessarily reflect the opinions or views of the SPRINT Research Group (SPRINT_POP) or the NHLBI. Our analysis received approval of our local Ethics Committee. Informed consent was obtained from all SPRINT participants. The data (SPRINT_POP Research Materials) are available at NHLBI Biologic Specimen and Data Repository Information Coordinating Centre upon reasonable request, as per the Centre's data sharing philosophy.
2.2. Blood pressure and clinical and laboratory measurements
In the SPRINT trial, office BP was measured using an automated system (Model 907, Omron Healthcare), three times per visit with 1‐minute intervals, and after 5 minutes of rest. The mean of these three measurements was computed. Analysis performed after the SPRINT trial showed that fewer than 50% of BP measurements were unattended.19 The SPRINT protocol assumed achievements of SBP <120 mm Hg for the intensive treatment arm and SBP <140 mm Hg for the standard treatment arm. In the standard treatment arm, antihypertensive agents were down‐titrated if SBP was <130 mm Hg at a single visit or <135 mm Hg at two consecutive visits, irrespective of DBP. The DBP target was not defined in any of the treatment arms; however, after meeting the SBP goal, participants were treated to achieve DBP <90 mm Hg.18 We calculated on‐treatment SBP and DBP as the mean of measurements made during the analyzed study period (from month 6 until trial end). Since the European Society of Cardiology recommends a treatment DBP range instead of a treatment threshold, we decided to determine the optimal 10 mm Hg DBP interval.13 We used other clinical and laboratory data gathered during the SPRINT trial and shared by NHLBI within the SPRINT_POP Research Materials.
2.3. Clinical study endpoint
Our clinical composite endpoint (CE) was defined identically as the primary composite outcome in the original trial: myocardial infarction, acute coronary syndrome not resulting in myocardial infarction, stroke, acute decompensated heart failure or death from cardiovascular causes. Individual components of the CE, death from any cause and the composite of the CE or death were also reported.
2.4. Statistical analysis
This was a retrospective analysis. All variables are presented as means followed by standard deviations, or as numbers with percentages. T‐ or chi‐square tests were used for between‐group comparisons. The differences were considered statistically significant when the P‐value was <0.05. Hazard ratio plots with cubic spline regression were applied to present non‐linear relationship between on‐treatment DBP and outcome. The optimal target DBP was selected on the basis of hazard ratio plot. We assumed that on‐treatment DPB treatment range should be 10 mm Hg wide and increases in the hazard ratio toward lower‐ and higher‐than‐optimal DBP should be linear and symmetric.
The comparison of survival depending on the on‐treatment DBP level was presented using Kaplan‐Meier curves. P‐value for pairwise comparison of Kaplan‐Meier survival curves was adjusted for multiple comparisons. All computations were performed in R 3.4.0 (R Foundation for Statistical Computing) environment for statistical programming using the “standard,” “smoothHR,” and “survival” packages.
3. RESULTS
The data of 1470 (354 females [24.1%], 1116 males [75.9%]) participants with clinical CVD were analyzed. The mean on‐treatment SBP and DBP were 127.9 ± 10.7 and 68.3 ± 9.4 mm Hg, respectively. In this analysis, 970 (66%) participants were older than 65 years, and 485 (33.0%) were older than 75 years. The baseline characteristics of the investigated subpopulation, clinical composite endpoint and its components occurrences are presented in Table 2.
Table 2.
Baseline characteristics and outcome of the analyzed population
| Parameter |
SPRINT participants with prior CVD N = 1470 |
|---|---|
| Age (y) | 70.3 ± 9.3 |
| Female (%) | 354 (24.1) |
| Black race (%) | 284 (19.3) |
| BMI (kg/m2) | 29.2 ± 5.4 |
| Smoking habits | |
| Current | 208 (14.1) |
| Former | 760 (51.7) |
| Prior CKD (%) | 518 (35.2) |
| Glucose (mg/dL) | 99.7 ± 13.8 |
| Total cholesterol (mg/dL) | 169.3 ± 41.5 |
| HDL (mg/dL) | 49.8 ± 12.8 |
| NON‐HDL (mg/dL) | 119.6 ± 40 |
| Triglyceride (mg/dL) | 126.1 ± 85 |
| Serum creatinine (mg/dL) | 1.1 ± 0.3 |
| eGFR (mL/min/1.73 m2) | 68.3 ± 19.9 |
| Aspirin (%) | 1204 (82.1) |
| Statin (%) | 1116 (76.3) |
| On‐treatment DBP (mm Hg) | 68.3 ± 9.4 |
| On‐treatment SBP (mm Hg) | 127.9 ± 10.7 |
| Baseline DBP (mm Hg) | 74.2 ± 12.1 |
| Baseline SBP (mm Hg) | 138.1 ± 15.8 |
| MI (%) | 62 (4.2) |
| Acute coronary syndrome other than MI (%) | 35 (2.4) |
| Acute exacerbation of heart failure (%) | 40 (2.7) |
| Stroke (%) | 33 (2.2) |
| Primary endpoint (%) | 159 (10.8) |
| CVD death (%) | 33 (2.2) |
| Death (%) | 91 (6.2) |
| Primary endpoint or death (%) | 203 (13.8) |
Abbreviations: BMI, body mass index; CKD, chronic kidney disease; CVD, cardiovascular disease; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate according to MDRD equation; MI, myocardial infarction; SBP, systolic blood pressure.
In the analyzed population, 38 (2.6%) participants had DBP <50 mm Hg, 239 (16.3%) participants had DBP ≥50 and <60 mm Hg, 567 (38.6%) participants had DBP ≥60 and <70 mm Hg, 454 (30.9%) participants had DBP ≥70, and <80 mm Hg and 172 (11.7%) participants had DBP ≥80 mm Hg. The distribution of on‐treatment DBP values is presented in a histogram plot (Figure 2, Panel A).
Figure 2.
Histogram of achieved on‐treatment diastolic blood pressure (A) unadjusted (B) and adjusted (C) hazard ratios according to achieved on‐treatment diastolic blood pressure for clinical composite study endpoint. The analysis (C) was adjusted for age, sex, on‐treatment systolic blood pressure, prior chronic kidney disease, current smoking status, and body mass index. Shaded areas indicate 95% confidence intervals
During the analysis period, 159 (10.8%) CE events occurred including 62 (4.2%) myocardial infarctions, 33 (2.2%) strokes, and 33 (2.2%) CVD deaths. The mean follow‐up period was 1183 ± 268 days.
The impact of on‐treatment DBP on cardiovascular outcomes is presented in Figure 2, and it includes CE hazard ratios. The plots present hazard ratios for CE before (Figure 2, Panel B) and after adjustment (Figure 2, Panel C) for potential covariates: age, sex, on‐treatment SBP, prior chronic kidney disease, current smoking status, and body mass index. According to the hazard ratio plot, we found the hazard ratio minimum at on‐treatment DBP = 74 mm Hg. Based on the hazard risk plot, we determined a 10‐mm Hg wide treatment range for DBP of 68.6‐78.6 mm Hg (Figure 2, Panel B). Hazard ratios for CE tended to increase linearly toward lower and higher on‐treatment DBP values beyond the selected optimal range. Table 3 presents characteristics of participants with lower‐than‐optimal DBP (<68.6 mm Hg), optimal DBP (68.6‐78.6 mm Hg), and DBP higher‐than‐optimal (>78.6 mm Hg). Only 498 (33.9%) participants were within the optimal DBP treatment range.
Table 3.
The clinical characteristics of participants with optimal DBP, lower‐than‐optimal or higher‐than‐optimal
| Parameter | Optimal, DBP 68.6‐78.6 mm Hg, N = 498 | Lower‐than‐optimal, DBP <68.6 mm Hg, N = 758 | Higher‐than‐optimal, DBP >78.6 mm Hg, N = 214 | P |
|---|---|---|---|---|
| Age (y) | 68.1 ± 8.7 | 73.9 ± 8.2 | 62.7 ± 7.9 | <0.001 |
| Female (%) | 117 (23.5) | 181 (23.9) | 56 (26.2) | 0.733 |
| Black race (%) | 106 (21.3) | 105 (13.9) | 73 (34.1) | <0.001 |
| BMI (kg/m2) | 29.9 ± 5.7 | 28.3 ± 4.9 | 31 ± 5.4 | <0.001 |
| Smoking habits | ||||
| Current (%) | 84 (16.9) | 88 (11.6) | 36 (16.8) | 0.017 |
| Former (%) | 239 (48) | 424 (55.9) | 97 (45.3) | |
| Prior CKD (%) | 139 (27.9) | 323 (42.6) | 56 (26.2) | <0.001 |
| Glucose (mg/dL) | 98.8 ± 12.4 | 100.5 ± 13.4 | 99.4 ± 17.5 | 0.102 |
| Total cholesterol (mg/dL) | 173 ± 43.7 | 164.5 ± 38.4 | 177.8 ± 44.8 | <0.001 |
| HDL (mg/dL) | 49.7 ± 13.4 | 50.2 ± 12.6 | 48.4 ± 11.6 | 0.213 |
| NON‐HDL (mg/dL) | 123.3 ± 41.2 | 114.3 ± 37.3 | 129.4 ± 43.4 | <0.001 |
| Triglyceride (mg/dL) | 128 ± 90.1 | 120.2 ± 81 | 142.1 ± 84.8 | 0.003 |
| Serum creatinine (mg/dL) | 1.1 ± 0.3 | 1.2 ± 0.4 | 1.1 ± 0.4 | <0.001 |
| eGFR (mL/min/1.73 m2) | 71.9 ± 19.7 | 65 ± 19.5 | 71.6 ± 19.8 | <0.001 |
| Aspirin (%) | 406 (81.7) | 642 (84.9) | 156 (72.9) | <0.001 |
| Statin (%) | 375 (75.8) | 596 (79) | 145 (67.8) | 0.003 |
| On‐treatment DBP (mm Hg) | 73 ± 2.9 | 61 ± 5.6 | 83.3 ± 3.6 | <0.001 |
| On‐treatment SBP (mm Hg) | 129.3 ± 9.9 | 125 ± 10.6 | 134.9 ± 9.4 | <0.001 |
| Baseline DBP (mm Hg) | 77.8 ± 10 | 68.4 ± 10.3 | 86.1 ± 10.3 | <0.001 |
| Baseline SBP (mm Hg) | 137.2 ± 15.2 | 138.5 ± 16.4 | 138.8 ± 15.3 | 0.298 |
| MI (%) | 12 (2.4) | 39 (5.1) | 11 (5.1) | 0.047 |
| Acute coronary syndrome other than MI (%) | 10 (2) | 19 (2.5) | 6 (2.8) | 0.773 |
| Acute exacerbation of heart failure (%) | 7 (1.4) | 24 (3.2) | 9 (4.2) | 0.061 |
| Stroke (%) | 6 (1.2) | 22 (2.9) | 5 (2.3) | 0.138 |
| Primary endpoint (%) | 32 (6.4) | 95 (12.5) | 32 (15) | <0.001 |
| CVD death (%) | 5 (1) | 19 (2.5) | 9 (4.2) | 0.024 |
| Death (%) | 24 (4.8) | 50 (6.6) | 17 (7.9) | 0.228 |
| Primary endpoint or death (%) | 47 (9.4) | 120 (15.8) | 36 (16.8) | 0.002 |
Abbreviations: BMI, body mass index; CKD, chronic kidney disease; CVD, cardiovascular; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HDL, high density lipoprotein concentration; MI, myocardial infarction; NON‐HDL, non‐high density lipoprotein concentration; SBP, systolic blood pressure.
Most of the participants (758; 51.6%) had lower‐than‐optimal DBP, but 214 (14.6%) had higher‐than‐optimal DBP. The participants with lower DBP values were older and often had chronic kidney disease, lower baseline DBP, and higher pulse pressure. The comparison of CE‐free survival within the groups selected on the basis of on‐treatment DBP is presented in Figure 3. The best survival was observed in participants with DBP within the range of 68.6‐78.6 mm Hg (P < 0.001 for optimal and lower‐than‐optimal range comparison, P < 0.001 for optimal and higher‐than‐optimal range comparison, and P = 0.329 for lower and higher‐than‐optimal range comparison).
Figure 3.
Kaplan‐Meier curves presenting clinical endpoint‐free survival in the group selected on the basis of on‐treatment diastolic blood pressure: optimal, lower‐than‐optimal, and higher‐than‐optimal. P‐values were computed to compare survival among these three groups of participants
Similar results were obtained following analysis of the intensive treatment arm participants with prior CVDs, as presented within the Figures S1, S2 and S3 and Tables S1 and S2.
4. DISCUSSION
In patients with hypertension and history of CVD, the optimal DBP range was 68.6‐78.6 mm Hg, selected on the basis of lowest cardiovascular risk. This range is similar to that recommended by the European Society of Cardiology, which is 70‐80 mm Hg. European Society of Cardiology guidelines also recommend treatment ranges rather than treatment thresholds.13
We found that 51.6% of analyzed patients had DBP levels within the lower‐than‐optimal range. Therefore, “lower is better strategy” for SBP should also prompt physicians to analyze DBP, since in some patients, DBP values may fall below optimal levels. In our study cohort, 33% of participants were older than 75 years. These individuals are especially prone to complications related to low DBP.
The SPRINT trial was the first randomized study that used AOBPM to monitor the effects of hypertension therapy. Current AHA guidelines changed the definition of hypertension (in addition to changing treatment goals) considering also the results of the SPRINT trial.11 However, AOBPM values (the method used in the SPRINT trial) differed from office BP values.20 There have been attempts to recalculate the trial results and compare them to other methods of BP measurement, but the results were incoherent with no clear conclusions.21 Tang et al22 found that AOBPM produced higher DBP values (by 3.8 mm Hg) than research‐grade methods for calculating BP. Other authors revealed that the mean difference between manual DBP readings and automated values was 1.55 ± 0.93 mm Hg, in favor of manual measurements.23 Relatively small differences between DBP AOBPM and office or research‐grade measurements (−3.0 and −2.4 mm Hg, respectively), with highly variable differences (standard deviation: 8.8 and 6.3 mm Hg, respectively), were previously reported by Rinfret et al24 Filipovsky et al25 showed a moderate correlation between the automated and auscultatory or home BP measurements with large limits of agreement. Similar results were reported between DBP AOBPM and ABPM.26, 27 Furthermore, the SPRINT Ambulatory Blood Pressure Study showed a lack of agreement between AOBPM and daytime ambulatory SBP using Bland‐Altman plots.28
Myers et al29 used linear regression to determine that AOBPM DBP 80 mm Hg corresponded to a mean awake ambulatory BP of 81.5 mm Hg. On the contrary, Seo et al30 found large discrepancies (>20 mm Hg) between AOBPM and awake ambulatory SBP, depending on individual patient's cardiovascular risk; however, discrepancies in DBP were not reported.
Even when the AOBPM method is used consistently, attended and unattended measurements (presence of medical staff or not) may produce different results.31 A study performed with pregnant females showed a small difference (mean difference −0.4 [95% confidence interval: −1.6 to 0.8], P = 0.46) between mean DBP values obtained with unattended vs attended AOBPM, but with a high standard deviation (5.3 mm Hg).32
We were able to determine an optimal DBP range of 68.6‐78.6 mm Hg, based on the lowest cardiovascular risk. Surprisingly, the presented optimal on‐treatment DBP range is similar to the DBP range for office BP measurements recommended by European Society of Cardiology (70‐80 mm Hg).13
We only evaluated participants with prior CVD since these patients have an increased risk of clinical complications if DBP is lowered too much. Since coronary blood flow usually occurs during diastole, at low DBP myocardial perfusion pressure, vascular autoregulation and collateral blood flow can be disrupted in the patients with coronary artery stenosis.33 In a population of Atherosclerosis Risk in Communities study, McEvoy et al34 showed that low DBP was associated with increased troponin levels and adverse cardiovascular events. Other studies found that low DBP was related to higher atherosclerotic burden and an increased risk of cardiovascular events in patients with CVD.14, 15, 17, 35, 36, 37, 38 Despite progress in the treatment of coronary artery disease, including use of newer invasive methods, our results confirmed the existence of a J‐shaped curve in patients with hypertension and prior CVD, according to on‐treatment DBP values. Even in SPRINT participants without prior CVD, Khan et al39 found that very low DBP (<55 mm Hg) could be harmful according to their post hoc analysis.
Some limitations of this analysis should be discussed. Our study involved a post hoc analysis which can produce bias. Due to the premature end of the original trial and selection of individuals with prior CVD, a limited number of events were analyzed. The results could have been influenced by the model of the automated device used in the SPRINT trial because small between‐device differences were reported.40, 41 The conclusions from our research are limited because of the different protocols used with the automated devices. For example, Colella et al42 reported a closer relationship between AOBPM and the mean awake ambulatory BP when the pre‐measure resting period was shortened from 5 to 0 minutes. It should also be stressed that probably only ~50% of measurements were truly unattended in the SPRINT trial.19 However, in their recently published meta‐analysis, Kollias et al31 showed only very small differences between attended and unattended BP measurements. Our study focused on a non‐diabetic population with CVD but without prior stroke, so our conclusions cannot be simply transferred to other populations.
There are limited data on how DBP control may influence cardiovascular outcomes in patients with well‐controlled SBP.37 In the SPRINT trial, participants in the standard treatment arm (targeting <140 mm Hg) did not meet criteria for SBP control according to current recommendations.11, 13 Thus, additional analyses were performed including only patients in the intensive treatment arm. These analyses are presented in the Figures S1, S2 and S3 and Tables S1 and S2 and suggest that both low and high DBP affect cardiovascular risk despite essential reduction of SBP. These findings remain in‐line with current guidelines, which recommend consideration of DBP targets once SBP is well controlled.13
In summary, AOBPM is currently the most objective available method for office BP measurement, mainly due to its lack of observer‐protocol related bias. Clinical studies performed to validate and compare various methods of office BP measurements have given conflicting results. Therefore, each time BP values are presented in various clinical contexts, the measurement method should be identified. Current analysis showed that a DBP range of 68.6‐78.6 mm Hg is related to the lowest hazard risk in participants with prior CVD. Due to the evidence from the SPRINT trial and other studies, intensive efforts to lower SBP in older patients with CVD are safe and beneficial.43, 44, 45, 46 Low DBP should not be a reason to avoid optimal SBP targets; however, in patients with CVD and low DBP, CVD risk factors should be searched and actively treated.
CONFLICT OF INTEREST
The authors report no conflicts of interest to disclose.
AUTHOR CONTRIBUTIONS
Piotr Sobieraj involved in study design, literature search, statistical analysis, data interpretation, and manuscript preparation. Jacek Lewandowski involved in study design, data interpretation, and manuscript preparation. Maciej Siński involved in study design, data interpretation, and manuscript preparation. Zbigniew Gaciong involved in data interpretation and manuscript preparation.
Supporting information
ACKNOWLEDGMENT
None.
Sobieraj P, Lewandowski J, Siński M, Gaciong Z. Determination of optimal on‐treatment diastolic blood pressure range using automated measurements in subjects with cardiovascular disease—Analysis of a SPRINT trial subpopulation. J Clin Hypertens. 2019;21:911–918. 10.1111/jch.13581
REFERENCES
- 1. O'Brien E, Dolan E, Stergiou GS. Achieving reliable blood pressure measurements in clinical practice: it's time to meet the challenge. J Clin Hypertens (Greenwich). 2018;20:1084‐1088. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Asayama K, Ohkubo T, Metoki H, et al. Cardiovascular outcomes in the first trial of antihypertensive therapy guided by self‐measured home blood pressure. Hypertens Res. 2012;35:1102‐1110. [DOI] [PubMed] [Google Scholar]
- 3. Boonyasai RT, Dietz KB, McCannon EL, Cooper LA. Automated blood pressure measurement may not improve efficiency if manual technique was suboptimal. J Clin Hypertens (Greenwich). 2018;20:821‐822. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Yarows SA. How to measure blood pressure in primary care offices to assure accuracy while maintaining efficiency. J Clin Hypertens (Greenwich). 2017;19:1386‐1387. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Myers MG, Asmar R, Staessen JA. Office blood pressure measurement in the 21st century. J Clin Hypertens (Greenwich). 2018;20:1104‐1107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Godwin M. Measuring blood pressure and diagnosing hypertension in the physicians office in the age of automated devices: time for guidelines to reflect reality. Can J Cardiol. 2017;33:963‐964. [DOI] [PubMed] [Google Scholar]
- 7. Stergiou GS, Kyriakoulis KG, Kollias A. Office blood pressure measurement types: different methodology‐different clinical conclusions. J Clin Hypertens (Greenwich). 2018;20:1683‐1685. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Chen Y, Lei L, Wang JG. Methods of blood pressure assessment used in milestone hypertension trials. Pulse (Basel). 2018;6:112‐123. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Nerenberg KA, Zarnke KB, Leung AA, et al. Hypertension Canada's 2018 guidelines for diagnosis, risk assessment, prevention, and treatment of hypertension in adults and children. Can J Cardiol. 2018;34:506‐525. [DOI] [PubMed] [Google Scholar]
- 10. Gabb GM, Mangoni AA, Anderson CS, et al. Guideline for the diagnosis and management of hypertension in adults ‐ 2016. Med J Aust. 2016;205:85‐89. [DOI] [PubMed] [Google Scholar]
- 11. Whelton PK, Carey RM, Aronow WS, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Hypertension. 2018;71:1269‐1324. [DOI] [PubMed] [Google Scholar]
- 12. Myers MG, Kaczorowski J, Dolovich L, Tu K, Paterson JM. Cardiovascular risk in hypertension in relation to achieved blood pressure using automated office blood pressure measurement. Hypertension. 2016;68:866‐872. [DOI] [PubMed] [Google Scholar]
- 13. Williams B, Mancia G, Spiering W, et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur Heart J. 2018;39:3021‐3104. [DOI] [PubMed] [Google Scholar]
- 14. Vidal‐Petiot E, Ford I, Greenlaw N, et al. Cardiovascular event rates and mortality according to achieved systolic and diastolic blood pressure in patients with stable coronary artery disease: an international cohort study. Lancet. 2016;388:2142‐2152. [DOI] [PubMed] [Google Scholar]
- 15. Huang C‐C, Leu H‐B, Yin W‐H, et al. Optimal achieved blood pressure for patients with stable coronary artery disease. Sci Rep. 2017;7:911‐12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Messerli FH, Mancia G, Conti CR, et al. Dogma disputed: can aggressively lowering blood pressure in hypertensive patients with coronary artery disease be dangerous? Ann Intern Med. 2006;144:884‐893. [DOI] [PubMed] [Google Scholar]
- 17. Bangalore S, Messerli FH, Wun C‐C, et al. J‐curve revisited: an analysis of blood pressure and cardiovascular events in the Treating to New Targets (TNT) Trial. Eur Heart J. 2010;31:2897‐2908. [DOI] [PubMed] [Google Scholar]
- 18. Reboussin D. Systolic blood pressure intervention trial (SPRINT) protocol, Version 4.0. https://www.sprinttrial.org/public/protocol_current.pdf. Accessed November 1, 2012.
- 19. Johnson KC, Whelton PK, Cushman WC, et al. Blood pressure measurement in SPRINT (Systolic Blood Pressure Intervention Trial). Hypertension. 2018;71:848‐857. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Kjeldsen SE, Mancia G. Unattended automated office vs. ambulatory blood pressure in people with high cardiovascular risk: implications for understanding the SPRINT results. J Hypertens. 2019;37(1):6‐918. [DOI] [PubMed] [Google Scholar]
- 21. Drawz PE, Ix JH. BP measurement in clinical practice: time to SPRINT to guideline‐recommended protocols. J Am Soc Nephrol. 2018;29:383‐388. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Tang O, Juraschek SP, Appel LJ, et al. Comparison of automated clinical and research blood pressure measurements: implications for clinical practice and trial design. J Clin Hypertens (Greenwich). 2018;20:1676‐1682. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Mirdamadi A, Etebari M. Comparison of manual versus automated blood pressure measurement in intensive care unit, coronary care unit, and emergency room. ARYA Atheroscler. 2017;13:29‐34. [PMC free article] [PubMed] [Google Scholar]
- 24. Rinfret F, Cloutier L, L'Archevêque H, et al. The gap between manual and automated office blood pressure measurements results at a hypertension clinic. Can J Cardiol. 2017;33:653‐657. [DOI] [PubMed] [Google Scholar]
- 25. Filipovský J, Seidlerová J, Kratochvíl Z, Karnosová P, Hronová M, Mayer O. Automated compared to manual office blood pressure and to home blood pressure in hypertensive patients. Blood Press. 2016;25:228‐234. [DOI] [PubMed] [Google Scholar]
- 26. Ayach O, Sarlon Bartoli G, Silhol F, et al. PASTIS study: evaluation of an automated office blood pressure measurement. Ann Cardiol Angeiol (Paris). 2018;67:180‐185. [DOI] [PubMed] [Google Scholar]
- 27. Seidlerová J, Gelžinský J, Mateřánková M, Ceral J, König P, Filipovský J. In the aftermath of SPRINT: further comparison of unattended automated office blood pressure measurement and 24‐hour blood pressure monitoring. Blood Press. 2018;27:256‐261. [DOI] [PubMed] [Google Scholar]
- 28. Drawz PE, Pajewski NM, Bates JT, et al. Effect of intensive versus standard clinic‐based hypertension management on ambulatory blood pressure: results from the SPRINT (Systolic Blood Pressure Intervention Trial) ambulatory blood pressure study. Hypertension. 2017;69:42‐50. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Myers MG, Matangi M, Kaczorowski J. Comparison of awake ambulatory blood pressure and automated office blood pressure using linear regression analysis in untreated patients in routine clinical practice. J Clin Hypertens (Greenwich). 2018;20:1696‐1702. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Seo J, Lee CJ, Oh J, et al. Large discrepancy between unobserved automated office blood pressure and ambulatory blood pressure in a high cardiovascular risk cohort. J Hypertens. 2019;37:42‐49. [DOI] [PubMed] [Google Scholar]
- 31. Kollias A, Stambolliu E, Kyriakoulis KG, Gravvani A, Stergiou GS. Unattended versus attended automated office blood pressure: systematic review and meta‐analysis of studies using the same methodology for both methods. J Clin Hypertens (Greenwich). 2018;21:148‐155. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Holm L, Stucke‐Brander T, Wagner S, et al. Automated blood pressure self‐measurement station compared to office blood pressure measurement for first trimester screening of pre‐eclampsia. Health Informatics J. 2018;911‐10. 10.1177/1460458218799505 [DOI] [PubMed] [Google Scholar]
- 33. Duncker DJ, Bache RJ. Regulation of coronary blood flow during exercise. Physiol Rev. 2008;88:1009‐1086. [DOI] [PubMed] [Google Scholar]
- 34. McEvoy JW, Chen Y, Rawlings A, et al. Diastolic blood pressure, subclinical myocardial damage, and cardiac events: implications for blood pressure control. J Am Coll Cardiol. 2016;68:1713‐1722. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Senthong V, Kukongviriyapan U, Settasatian N, Settasatian C, Komanasin N. Low diastolic blood pressure is associated with a high atherosclerotic burden in patients with obstructive coronary artery disease. Cardiol J. 2018;25:345‐352. [DOI] [PubMed] [Google Scholar]
- 36. Lu W. Could intensive anti‐hypertensive therapy produce the "J‐curve effect" in patients with coronary artery disease and hypertension after revascularization? Eur Rev Med Pharmacol Sci. 2016;20:1350‐1355. [PubMed] [Google Scholar]
- 37. Böhm M, Schumacher H, Teo KK, et al. Achieved diastolic blood pressure and pulse pressure at target systolic blood pressure (120–140 mmHg) and cardiovascular outcomes in high‐risk patients: results from ONTARGET and TRANSCEND trials. Eur Heart J. 2018;39:3105‐3114. [DOI] [PubMed] [Google Scholar]
- 38. Lonn EM, Bosch J, López‐Jaramillo P, et al. Blood‐pressure lowering in intermediate‐risk persons without cardiovascular disease. N Engl J Med. 2016;374:2009‐2020. [DOI] [PubMed] [Google Scholar]
- 39. Khan NA, Rabkin SW, Zhao Y, et al. Effect of lowering diastolic pressure in patients with and without cardiovascular disease: analysis of the SPRINT (Systolic Blood Pressure Intervention Trial). Hypertension. 2018;71:840‐847. [DOI] [PubMed] [Google Scholar]
- 40. Rinfret F, Cloutier L, Wistaff R, et al. Comparison of different automated office blood pressure measurement devices: evidence of nonequivalence and clinical implications. Can J Cardiol. 2017;33:1639‐1644. [DOI] [PubMed] [Google Scholar]
- 41. Nzelu D, Yeung F, Couderq D, Shennan A, Kametas NA. An inaccurate automated device negatively impacts the diagnosis and treatment of gestational hypertension. Pregnancy Hypertens. 2017;10:28‐33. [DOI] [PubMed] [Google Scholar]
- 42. Colella T, Tahsinul A, Gatto H, Oh P, Myers MG. Antecedent rest may not be necessary for automated office blood pressure at lower treatment targets. J Clin Hypertens (Greenwich). 2018;20:1160‐1164. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Wright JT Jr, Williamson JD, Whelton PK, et al. A randomized trial of intensive versus standard blood‐pressure control. N Engl J Med. 2015;373:2103‐2116. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Williamson JD, Supiano MA, Applegate WB, et al. Intensive vs standard blood pressure control and cardiovascular disease outcomes in adults aged >75 years: a randomized clinical trial. JAMA. 2016;315:2673‐2682. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Bavishi C, Bangalore S, Messerli FH. Outcomes of intensive blood pressure lowering in older hypertensive patients. J Am Coll Cardiol. 2017;69:486‐493. [DOI] [PubMed] [Google Scholar]
- 46. Oparil S, Lewis CE. Should patients with cardiovascular risk factors receive intensive treatment of hypertension to <120/80 mm Hg target? A protagonist view from the SPRINT trial (Systolic Blood Pressure Intervention Trial). Circulation. 2016;134:1308‐1310. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials