Abstract
BACKGROUND:
The association with cardiovascular disease (CVD) is stronger for mean systolic blood pressure (SBP) estimated using ambulatory blood pressure monitoring (ABPM) vs. office measurements. Determining whether this is due to ABPM providing more measurement reliability or greater ecological validity can inform its use.
METHODS:
We estimated the association of mean SBP based on 2 office measurements and 2, 5, 10, and 20 measurements on ABPM with incident CVD in the Jackson Heart Study (n = 773). Hazard ratios (HRs) for CVD were estimated per standard deviation higher mean SBP. CVD events were defined by incident fatal or non-fatal stroke, non-fatal myocardial infarction, or fatal coronary heart disease.
RESULTS:
There were 80 CVD events over a median of 15 years. The adjusted HRs for incident CVD were 1.03 (95% CI: 0.90–1.19) for mean office SBP and 1.30 (95% CI: 1.12–1.50), 1.34 (95% CI: 1.15–1.56), 1.36 (95% CI: 1.17–1.59), and 1.38 (95% CI: 1.17–1.63) for mean SBP using the first 2, 5, 10, and 20 ABPM readings. The difference in the HRs for incident CVD ranged from 0.26 (95% CI: 0.07–0.46) to 0.35 (95% CI: 0.15–0.54) when comparing mean office SBP vs. 2, 5, 10, or 20 sequential ABPM readings. The association with incident CVD was also stronger for mean SBP based on 2, 5, 10, and 20 randomly selected ABPM readings vs. 2 office readings.
CONCLUSIONS:
Mean SBP based on 2 ABPM readings vs. 2 office measurements had a stronger association with CVD events. The increase in the strength of the association with more ABPM readings was small.
Keywords: ABPM, African-Americans, ambulatory monitoring, blood pressure, cardiovascular disease, hypertension, Jackson Heart Study, mortality
The association between higher mean systolic blood pressure (SBP) with increased risk for cardiovascular disease (CVD) events is stronger when SBP is estimated using ambulatory BP monitoring (ABPM) vs. measurements obtained in the office setting.1–3 A possible reason for the stronger association is that ABPM obtains more SBP measurements, typically 48–72 over 24 hours, than the 1–3 SBP measurements usually obtained in the office. Another possible reason for the stronger association is that ABPM measures SBP while a person performs their usual activities of daily living outside of the office setting and, therefore, better reflects a person’s true BP under normal circumstances, a phenomenon known as ecological validity.4
If the stronger association with CVD events for mean SBP estimated by ABPM compared to office measurements can be explained by greater ecological validity, then obtaining only a few readings on ABPM may be sufficient for estimating mean SBP. This would reduce the burden of ABPM since it would not need to be performed for 24 hours. If the predictive value of SBP estimated on ABPM for CVD events is not stronger when based on the same number of measurements as office SBP, it would suggest that ABPM may provide a more reliable estimate because it is based on a larger number of readings. Under this scenario, a 24-hour ABPM recording may be needed for reliably estimating SBP.
Using data from the Jackson Heart Study (JHS), a community-based cohort of African-American adults, we compared the hazard for CVD events and all-cause mortality associated with mean BP estimated in the office vs. ABPM using 2 measurements for both approaches. Additionally, we estimated the hazard for CVD events and all-cause mortality based on mean BP estimated using progressively more measurements on ABPM.
METHODS
Study population
The JHS enrolled a community-based cohort of African-American adults from urban and rural areas of 3 counties (Hinds, Madison, and Rankin) comprising the Jackson, Mississippi, metropolitan area. Details of the study design and recruitment have been published previously.5–7 Participants were recruited from the Atherosclerosis Risk in Communities study site in Jackson, Mississippi, and a representative sample of residents from the three counties, volunteers, randomly contacted individuals, and eligible family members of participants. The JHS cohort of 5,306 African-American adults 21 years and older was enrolled between 2000 and 2004. The study protocol was approved by the Institutional Review Boards governing research in human subjects at the participating centers and all participants provided written informed consent.
There were 1,146 JHS participants who initiated ABPM following their baseline study visit. In an effort to maximize comparability with office BP measurements, which were taken during the daytime, the current analysis focused on daytime ABPM measurements. We restricted the analysis to participants with a complete ABPM recording during the daytime, defined based on the European Society of Hypertension criteria as ≥70% of the planned SBP and diastolic BP (DBP) readings from 10 am to 6 pm (n = 877).8 We excluded participants who did not consent to follow-up for CVD events (n = 20) or who had a history of CVD at baseline (n = 84), resulting in an analytic sample of 773 participants (Supplementary Figure S1).
Data collection
Data used for the current analysis were collected through an in-home interview, a study examination where blood and urine were collected after an overnight fast, and ABPM. Self-reported information on age, sex, education, cigarette smoking, and the use of antihypertensive and glucose-lowering medication were collected during the study interview. Diabetes was defined as a glycated hemoglobin level ≥6.5%, a fasting plasma glucose level ≥126 mg/dl, or the use of glucose-lowering medication.6 Estimated glomerular filtration rate (eGFR) was calculated via the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.9,10
During the JHS baseline exam, SBP and DBP were measured after a 5-minute rest with a Hawksley random-zero sphygmomanometer (Hawksley and Sons Ltd.) equipped with 1 of 4 cuff sizes selected following measurement of each participant’s arm circumference. At the second exam, the JHS switched from the random-zero sphygmomanometer to an oscillometric device. For calibration purposes, BP measurements from both devices were taken simultaneously with a y-tube connector in a subset of participants. The random-zero BP measurements were calibrated using robust regression to the Omron HEM-907XL oscillometric device for analysis.11 The average of 2 measurements was used to define mean office SBP and DBP. Uncontrolled BP was defined by office SBP ≥130 mm Hg or office DBP ≥80 mm Hg. Hypertension was defined by the presence of uncontrolled BP or self-reported use of antihypertensive medication. Quality control for office BP measurements included technician recertification, procedural checklists, and data review.6,12–14 Upon completion of the study visit, participants were asked if they would be willing to undergo ABPM over the next 24 hours. SBP and DBP measurements on ABPM were obtained with a portable, noninvasive oscillometric device (Spacelabs 90207; Spacelabs Healthcare, Snoqualmie, WA) with a cuff fitted to the participant’s non-dominant arm. Study staff instructed participants on the proper use of the ABPM device. The device was programmed to measure BP every 20 minutes for 24 hours, and participants were instructed to proceed with their normal daily activities but to keep their arms still and extended at their sides while each BP reading was obtained. Participants returned to the clinic after 24 hours for removal of the device. White-coat hypertension was defined by mean office SBP ≥130 or mean office DBP ≥80 mm Hg, with mean daytime SBP <130 and mean daytime DBP <80 mm Hg. Masked hypertension was defined as mean office SBP <130 and mean office DBP <80 mm Hg, with mean daytime SBP ≥130 or mean daytime DBP ≥80 mm Hg.
Outcome definitions
The primary outcome was incident CVD. CVD events were defined by the first occurrence of a fatal or non-fatal stroke, non-fatal myocardial infarction, or fatal coronary heart disease. A detailed description of the adjudication process has been published previously.15 In brief, study participants or their proxies were contacted to identify hospitalizations and possible CVD events at yearly intervals. If a hospitalization for CVD was suspected, medical records were obtained, and the event was adjudicated by trained physicians. For the current analysis, CVD events were adjudicated from the baseline exam (2000–2004) through 31 December 2016.
We examined all-cause mortality as a secondary outcome. Deaths were identified by report of next of kin, the National Death Index, or through online sources (e.g., the Social Security Death Index). The cause of death was determined using information obtained from proxies, medical history, death certificates, and autopsy reports. All-cause mortality was assessed from the baseline exam (2000–2004) through 31 December 2016.
Statistical analysis
Participant characteristics were summarized using means and standard deviations (SDs) for continuous variables and percentages for categorical variables. We conducted Cox regression with multivariable adjustment to estimate the hazard ratios (HRs) for CVD events and all-cause mortality, separately, with mean SBP based on 2 measurements obtained in the office and 2, 5, 10, and 20 measurements from ABPM. We defined mean SBP on ABPM using readings in sequential order beginning with the first ABPM measurement after the device initialization was completed in the clinic and also using a simple random sample of readings from the awake period, selected using a random number generator (Supplementary Figure S2). To standardize comparisons of HRs between SBP from the office and ABPM, all HRs were estimated modeling these variables per 1 SD higher level. Natural cubic splines were applied and we conducted a Wald F-test to determine if a non-linear relationship was present between SBP and CVD events and all-cause mortality, separately.16 Although the analyses compare 2 different methods of BP measurement in the same participants, all models were minimally adjusted for age and sex, as is standard practice.17,18 For each model, we assessed the proportionality of the hazards and no deviations were present (all P values ≥ 0.05; mean P-value = 0.60).
We compared the associations of mean SBP from the office vs. 2, 5, 10, and 20 measurements from ABPM with CVD events and all-cause mortality, separately, by calculating the difference in their HRs and estimating a confidence interval (CI) for the difference using 2.5th and 97.5th percentiles from bootstrap resampling.19 We computed Harrell’s -c-statistic from the regression models and compared differences between models with mean SBP in the office vs. ABPM to assess whether one approach produced a superior model in terms of discrimination.20 We calculated the prevalence of white-coat and masked hypertension and the overall percent agreement and Kappa statistics for white-coat and masked hypertension assessed using 2, 5, 10, and 20 measurements from ABPM vs. the full ABPM recording. In secondary analyses, we repeated the analyses described above (i) stratified by antihypertensive medication use and (ii) for DBP instead of SBP. We conducted sensitivity analyses (i) excluding participants with white-coat hypertension, and (ii) modeling office and awake SBP per 10 mm Hg increase and office and awake DBP per 7 mm Hg higher level vs. per SD. Data management and analysis were conducted using R version 4.1.3 (R Foundation for Statistical Computing, Vienna, Austria).
RESULTS
The mean age of the 773 participants included in the current analysis was 58 years and 68% were women (Table 1). Also, 15% of participants had less than a high school education, 10% were current or former smokers, 25% had diabetes, and 57% were taking antihypertensive medication. Overall, the mean office SBP was 127 mm Hg and the mean office DBP was 75 mm Hg (Table 2). Depending on the number of measurements from ABPM, the mean SBP based on sequential ABPM measurements ranged from 130 to 132 mm Hg, and, for random measurements, the mean SBP ranged from 129 to 130 mm Hg. The mean DBP ranged from 78 to 80 mm Hg for sequential ABPM measurements and was 78 mm Hg for random measurements.
Table 1.
Characteristics of Jackson Heart Study participants included in the analysis (n = 773)
| Characteristic | Mean (SD) or percent |
|---|---|
| Age, y | 58 (11) |
| Women | 68% |
| Less than high school education | 15% |
| Body mass index, kg/m2 | 31 (6) |
| Former or current smoker | 10% |
| eGFR <60 ml/min/1.73 m2 | 6% |
| Albumin-to-creatinine ratio >30 mg/g | 9% |
| Diabetes | 25% |
| Antihypertensive medication use | 57% |
Abbreviations: eGFR, estimated glomerular filtration rate; SD, standard deviation.
Table 2.
Mean systolic and diastolic blood pressure of Jackson Heart Study participants included in the analysis, overall, without hypertension, and having hypertension with and without controlled blood pressure (n = 773)
| Characteristic | Overall | No hypertension | Hypertension | |
|---|---|---|---|---|
| Controlled BP | Uncontrolled BP | |||
| Systolic BP, mm Hg | ||||
| Office BP measurements | 127 (16) | 115 (8) | 118 (8) | 139 (13) |
| ABPM measurements | ||||
| First measurements while ABPM was worn | ||||
| First 2 measurements | 132 (15) | 125 (12) | 129 (13) | 138 (15) |
| First 5 measurements | 131 (14) | 124 (11) | 128 (12) | 137 (14) |
| First 10 measurements | 130 (14) | 123 (10) | 127 (12) | 135 (14) |
| First 20 measurements | 130 (13) | 123 (10) | 126 (12) | 135 (14) |
| Random measurements while ABPM was worn | ||||
| Random 2 measurements | 129 (15) | 123 (12) | 126 (13) | 138 (15) |
| Random 5 measurements | 130 (14) | 123 (11) | 126 (12) | 135 (14) |
| Random 10 measurements | 129 (14) | 123 (10) | 126 (12) | 135 (14) |
| Random 20 measurements | 130 (13) | 123 (10) | 126 (11) | 135 (14) |
| Diastolic BP, mm Hg | ||||
| Office BP measurements | 75 (8) | 71 (6) | 70 (6) | 79 (8) |
| ABPM measurements | ||||
| First measurements while ABPM was worn | ||||
| First 2 measurements | 80 (10) | 78 (9) | 78 (10) | 83 (11) |
| First 5 measurements | 80 (10) | 77 (8) | 78 (9) | 82 (10) |
| First 10 measurements | 79 (10) | 76 (8) | 77 (9) | 81 (10) |
| First 20 measurements | 78 (9) | 76 (8) | 76 (9) | 81 (10) |
| Random measurements while ABPM was worn | ||||
| Random 2 measurements | 78 (11) | 77 (9) | 76 (10) | 81 (11) |
| Random 5 measurements | 78 (10) | 76 (9) | 76 (10) | 81 (11) |
| Random 10 measurements | 78 (9) | 76 (9) | 75 (9) | 81 (10) |
| Random 20 measurements | 78 (9) | 76 (8) | 76 (9) | 81 (10) |
The numbers in the table are mean (standard deviation).
Abbreviations: ABPM, ambulatory blood pressure monitoring; BP, blood pressure.
HRs for CVD and all-cause mortality with SBP
To provide data on the absolute risk of CVD and all-cause mortality events accounting for competing risks, we estimated the cumulative incidence of CVD mortality and non-CVD mortality using the Fine and Gray model.21,22 There were 80 CVD events and 139 deaths over a median follow-up of 15 years. There was no evidence of non-linearity in the association between mean SBP or DBP measured in the office with CVD or all-cause mortality (Figure 1, Supplementary Figures S3 and S4).
Figure 1.
Cumulative incidence of cardiovascular mortality and non-cardiovascular mortality according to blood pressure measured twice in the office setting, measured twice immediately after leaving the office setting and measured twice at two random times within 24 hours of leaving the office setting.
After adjustment for age and sex, the HRs for CVD events were 1.05 (95% CI: 0.85–1.31) per 1 SD higher mean office SBP (Table 3). The age-sex adjusted HRs for CVD events were 1.48 (95% CI: 1.19–1.84), 1.51 (98% CI: 1.22–1.87), 1.53 (95% CI: 1.24–1.90), and 1.55 (95% CI: 1.24–1.92) for mean SBP based on the first 2, 5, 10, and 20 ABPM readings, respectively. When mean SBP was based on randomly selected measurements from ABPM, the adjusted HRs for CVD events according to a 1 SD higher mean SBP were 1.41 (95% CI: 1.14–1.74), 1.45 (95% CI: 1.17–1.81), 1.47 (95% CI: 1.18–1.84), and 1.53 (95% CI: 1.23–1.90), respectively. The estimated difference in the HR for CVD associated with SBP based on 2 sequential ABPM measurements vs. 2 sequential office measurements was 0.42 (95% CI: 0.21–0.64). Comparing the HR for CVD based on mean SBP estimated using 2 random measurements from ABPM vs. office SBP, the difference was 0.35 (95% CI: 0.17–0.54). The magnitude of the difference in the HR for CVD events ranged from 0.40 (95% CI: 0.18–0.61) to 0.49 (95% CI: 0.29–0.69) when 5, 10, or 20 sequential or random SBP readings were used to estimate mean SBP from ABPM.
Table 3.
Age- and sex-adjusted hazard ratios and differences in hazard ratios for cardiovascular disease and all-cause mortality associated with 1 SD higher systolic blood pressure
| Systolic blood pressure | Cardiovascular disease | All-cause mortality | ||||
|---|---|---|---|---|---|---|
| Location | Number of readings | SD, mm Hg | Hazard ratio (95% CI) | Difference (95% CI) | Hazard ratio (95% CI) | Difference (95% CI) |
| In the office setting | 2 | 16 | 1.05 (0.85, 1.31) | 0 (Reference) | 1.22 (1.04, 1.44) | 0 (Reference) |
| Sequentially after the ABPM device was initiated | 2 | 15 | 1.48 (1.19, 1.84) | 0.42 (0.21, 0.64) | 1.21 (1.03, 1.43) | −0.01 (−0.17, 0.15) |
| 5 | 14 | 1.51 (1.22, 1.87) | 0.46 (0.26, 0.66) | 1.27 (1.08, 1.50) | 0.05 (−0.12, 0.21) | |
| 10 | 14 | 1.53 (1.24, 1.90) | 0.48 (0.28, 0.68) | 1.20 (1.02, 1.42) | −0.02 (−0.18, 0.14) | |
| 20 | 13 | 1.55 (1.24, 1.92) | 0.49 (0.29, 0.69) | 1.20 (1.02, 1.42) | −0.02 (−0.18, 0.13) | |
| Randomly selected from awake ABPM recording | 2 | 15 | 1.41 (1.14, 1.74) | 0.35 (0.17, 0.54) | 1.18 (1.00, 1.39) | −0.04 (−0.18, 0.09) |
| 5 | 14 | 1.45 (1.17, 1.81) | 0.40 (0.18, 0.61) | 1.14 (0.96, 1.34) | −0.09 (−0.24, 0.07) | |
| 10 | 14 | 1.47 (1.18, 1.84) | 0.42 (0.20, 0.64) | 1.24 (1.05, 1.47) | 0.02 (−0.13, 0.18) | |
| 20 | 13 | 1.53 (1.23, 1.90) | 0.47 (0.26, 0.69) | 1.24 (1.05, 1.47) | 0.02 (−0.15, 0.19) | |
Abbreviations: ABPM, ambulatory blood pressure monitoring; CI, confidence interval; SD, standard deviation.
The age-sex adjusted HR for all-cause mortality per 1 SD higher mean office SBP was 1.22 (95% CI: 1.04–1.44). The age-sex adjusted HRs for all-cause mortality were 1.21 (95% CI: 1.03–1.43), 1.27 (95% CI: 1.08–1.50), 1.20 (95% CI: 1.02–1.42), and 1.20 (95% CI: 1.02–1.42) for mean SBP based on the first 2, 5, 10, and 20 ABPM readings, respectively. When mean SBP was based on randomly selected measurements from ABPM, the adjusted HRs for all-cause mortality according to a 1 SD higher mean SBP were 1.18 (95% CI: 1.00–1.39), 1.14 (95% CI: 0.96–1.34), 1.24 (95% CI: 1.05–1.47), and 1.24 (95% CI: 1.05–1.47) for 2, 5, 10, and 20 readings, respectively. Results stratified by antihypertensive medication use are shown in Supplementary Tables S1 and S2.
HRs for CVD and all-cause mortality with DBP
After adjustment for age and sex, the HR for CVD events per 1 SD higher mean office DBP was 1.03 (95% CI: 0.82–1.30; Supplementary Table S3). The age-sex-adjusted HRs were 1.53 (95% CI: 1.21–1.92), 1.53 (95% CI: 1.22–1.93), 1.50 (95% CI: 1.18–1.89), and 1.47 (95% CI: 1.17–1.85) for mean DBP based on the first 2, 5, 10, and 20 ABPM readings, respectively. When mean DBP was based on randomly selected measurements from ABPM, the age-sex adjusted HRs for CVD events according to a 1 SD higher mean DBP were 1.31 (95% CI: 1.05–1.65), 1.39 (95% CI: 1.11–1.75), 1.38 (95% CI: 1.10–1.73), and 1.41 (95% CI: 1.12–1.78) for 2, 5, 10, and 20 readings, respectively.
Agreement for white-coat hypertension and masked hypertension
The prevalence of white-coat hypertension was 13% when assessed using the full ABPM recording, and ranged from 10% to 13% when using 2, 5, 10, and 20 sequentially or randomly selected measurements from ABPM (Supplementary Table S4). The overall agreement ranged from 91% to 99% and the kappa statistic ranged from 0.53 to 0.94. The prevalence of masked hypertension was 21% when assessed using the full ABPM recording, and ranged from 21% to 29% when using 2, 5, 10, and 20 sequentially or randomly selected measurements from ABPM. The overall agreement ranged from 88% to 99% and the kappa statistic ranged from 0.69 to 0.98 depending on the number of ABPM measurements used to define masked hypertension and whether the measurements were sequentially or randomly selected.
Sensitivity analyses
Age, sex-adjusted HRs for cardiovascular disease, and all-cause mortality according to a 1 SD higher SBP among 678 participants who did not have white-coat hypertension at baseline are presented in Supplementary Table S5. Sensitivity analyses using the full study sample and modeling office and awake SBP per 10 mm Hg increase and office and awake DBP per 7 mm Hg increase vs. per SD are presented in Supplementary Tables S6 and S7.
Concordance statistics
The c-statistic for CVD events was 0.73 (95% CI: 0.68–0.78) for a model that included age, sex, and office SBP, and ranged from 0.74 to 0.76 for models that included age, sex, and SBP based on the first or randomly-selected 2, 5, 10, and 20 measurements from ABPM (Table 4). The c-statistics for all-cause mortality were 0.75 (95% CI: 0.71–0.79), regardless of whether mean SBP was based on office readings, the first 2, 5, 10, and 20 ABPM readings, or whether the readings were randomly selected from the entire awake ABPM period. The c-statistics for models that included age, sex, and DBP are presented in Supplementary Table S8. The c-statistics for models that included age, sex, and SBP, restricted to participants who did not have white-coat hypertension at baseline are presented in Supplementary Table S9.
Table 4.
Concordance statistics and difference in concordance statistics for cardiovascular disease and all-cause mortality according to models with age, sex, and different measurements of systolic blood pressure
| Systolic blood pressure | Cardiovascular disease | All-cause mortality | ||||
|---|---|---|---|---|---|---|
| Location | Number of readings | SD, mm Hg | c-statistic (95% CI) | Difference (95% CI) | c-statistic (95% CI) | Difference (95% CI) |
| In the office setting | 2 | 16 | 0.73 (0.68, 0.78) | 0 (Reference) | 0.75 (0.71, 0.79) | 0 (Reference) |
| Sequentially after the ABPM device was initiated | 2 | 15 | 0.75 (0.70, 0.80) | 0.02 (−0.01, 0.04) | 0.75 (0.71, 0.79) | 0.00 (−0.01, 0.01) |
| 5 | 14 | 0.75 (0.70, 0.80) | 0.02 (0.00, 0.04) | 0.75 (0.71, 0.79) | 0.00 (−0.01, 0.02) | |
| 10 | 14 | 0.75 (0.71, 0.80) | 0.02 (0.00, 0.05) | 0.75 (0.71, 0.79) | 0.00 (−0.01, 0.01) | |
| 20 | 13 | 0.75 (0.71, 0.80) | 0.02 (0.00, 0.05) | 0.75 (0.71, 0.79) | 0.00 (−0.01, 0.01) | |
| Randomly selected from awake ABPM recording | 2 | 15 | 0.74 (0.69, 0.79) | 0.01 (−0.01, 0.03) | 0.75 (0.71, 0.79) | 0.00 (−0.02, 0.01) |
| 5 | 14 | 0.75 (0.71, 0.80) | 0.02 (0.00, 0.05) | 0.75 (0.71, 0.79) | 0.00 (−0.01, 0.01) | |
| 10 | 14 | 0.75 (0.70, 0.80) | 0.02 (0.00, 0.04) | 0.75 (0.71, 0.79) | 0.00 (−0.01, 0.01) | |
| 20 | 13 | 0.76 (0.71, 0.80) | 0.02 (0.00, 0.05) | 0.75 (0.71, 0.79) | 0.00 (−0.01, 0.01) | |
Confidence intervals for c-statistics and differences in c-statistics were estimated via 2.5th and 97.5th percentiles from bootstrap resampling. Abbreviations: ABPM, ambulatory blood pressure monitoring; CI, confidence interval.
DISCUSSION
In the current study, out-of-office SBP and DBP based on 2 measurements of ABPM had stronger associations with CVD events compared to mean office SBP and DBP based on 2 measurements. The increase in the associations between mean out-of-office SBP and DBP with CVD with more SBP and DBP readings from ABPM was small. The association of SBP and DBP with all-cause mortality was similar when measured by ABPM vs. in the office.
Ecological validity refers to how closely measurements obtained in a controlled setting reflect measurements obtained in a person’s real-world setting.23 In the context of BP, this refers to the concept that measurements taken out of the office reflect a person’s BP during everyday life more accurately than measurements taken in the office.24 This is distinct from the reliability of BP measurement where, all else being equal, the larger number of readings averaged for ABPM yields a better estimate of the mean than the fewer readings averaged in the office.25,26 The findings of the current study suggest that the stronger association of SBP and DBP on ABPM vs. in the office setting with CVD events may be attributable to the greater ecological validity of ABPM compared to office SBP and DBP, rather than the higher number of readings providing a more reliable estimate.
A prior study of 166 participants compared the association of SBP and DBP measurements obtained in the office setting and from ABPM with left ventricular mass index using the average of 3 office measurements and 3 random awake measurements from ABPM.27 Higher SBP and DBP on ABPM were associated with left ventricular mass index while there were no associations with SBP and DBP based on office measurements. The current study extends these findings to a larger study population, as well as the inclusion of CVD events and mortality outcomes rather than subclinical CVD. Along with the prior study, the results from JHS indicate that the superior predictive value of ABPM vs. office SBP and DBP for outcomes may be explained by ABPM having greater ecological validity.
Home BP monitoring (HBPM) is another approach to measuring out-of-office BP.28 Two prior studies have demonstrated that the association of higher mean BP with increased CVD morbidity and mortality is stronger when BP is estimated using HBPM vs. office BP measurements.29,30 An analysis of 2,051 participants in the Pressioni Arteriose Monitorate e Loro Associazioni (PAMELA) study compared the predictive value of office, home, and ambulatory BP for CVD mortality and all-cause mortality and determined that HBPM, which was based on only 2 readings, was more strongly associated with CVD and all-cause mortality than office BP, which was based on 3 or 6 readings.31 In consideration of the findings of the current study, whether the superior predictive value of HBPM vs. office BP for CVD outcomes in the PAMELA study can be explained by HBPM having greater ecological validity should be evaluated. Although ABPM and HBPM are both approaches for measuring SBP and DBP outside of the office setting, HBPM differs from ABPM in that HBPM participants are instructed to be seated and resting with the feet flat on the ground with the arm stationary and the cuff at heart level.28 In contrast, there is no recommended approach to standardize posture and activity during the ABPM period.28 Additionally, ABPM confers potential advantages over HBPM with respect to measuring BP during sleep. A prior study found high agreement with nocturnal hypertension status based on 3 or 4 BP measurements during sleep, an approach applied by some HBPM devices, compared with measurements from a full night of ABPM.32
White-coat hypertension is defined as having high office BP without high out-of-office BP, and masked hypertension is defined as having high out-of-office BP without high office BP.33 Compared with sustained normotension, defined as not having high office BP and high out-of-office BP, white-coat hypertension has been associated with no increased risk to a modestly increased risk for CVD.34 In contrast, masked hypertension is associated with a substantially increased risk for CVD.35 Several hypertension guidelines recommend performing out-of-office BP monitoring to identify white-coat hypertension among individuals with high office BP and masked hypertension among those without high office BP.36,37 ABPM is considered to be the reference standard for out-of-office BP monitoring.38 The Systolic Blood Pressure Intervention Trial (SPRINT) ABPM ancillary study confirmed that intensive vs. standard antihypertensive treatment achieved lower nighttime and daytime SBP from ABPM compared to office SBP.39 However, the use of ABPM in the United States is low due to challenges including lack of patient tolerance, difficulty obtaining all the planned ABPM readings on patients who wear the device, and low reimbursement rates for providers who conduct ABPM.40 The results of the current study suggest that mean SBP and DBP using only a few readings on ABPM have a stronger association with CVD compared with office BP. Also, the first 2 ABPM readings after a person leaves the office may be sufficient to accurately predict CVD risk. These findings have the potential to greatly reduce the participant burden of ABPM since only a few readings on ABPM rather than a full 24-hour period may be needed.
Although the current study suggests that the association between mean awake SBP and DBP with CVD may be reliably estimated by as few as 2 ABPM readings, ABPM can be used to identify other BP phenotypes, including nocturnal hypertension, non-dipping BP, and BP variability. Prior studies have demonstrated that nocturnal hypertension and BP variability are associated with an increased risk of CVD events and mortality.41–43 Assessing these phenotypes may be useful in clinical practice, in which case a full ABPM recording may be necessary.
The current study has several strengths, including a large community-based cohort of African-American adults and the availability of ABPM data and adjudicated CVD and mortality outcomes. Additionally, office SBP, DBP, and ABPM were measured by trained technicians following standardized protocols. The findings of the current study should be considered in the context of known and potential limitations. First, only 22% of JHS participants consented to ABPM. Over half the study participants were taking antihypertensive medication, and the mean office SBP and DBP and ambulatory SBP and DBP were fairly well-controlled. Further, the HRs for CVD associated with office BP were lower than expected based on prior studies, all of which may influence the generalizability of our findings. Office SBP and DBP were measured with an observer present, and it is unknown whether the results would be similar if the office BP measurements had been unattended. It is possible that out-of-office BP had a stronger association with CVD than office BP due to the alerting reaction associated with the attended measurement. We did not analyze the association of nighttime BP, which is an important aspect of the BP profile from ABPM, with CVD risk. However, this comparison has somewhat limited utility given that office BP is measured during the day and most individuals’ BP is lower at night. Although the current findings suggest that 2 SBP readings from ABPM have similar prognostic value for CVD compared to a higher number of ABPM readings, the reproducibility of mean SBP based on 2 readings from a single ABPM recording period is unknown. Furthermore, while guidelines recommend using ABPM or HBPM to guide the decision to initiate and intensify antihypertensive medication, no randomized CVD outcome trials have used out-of-office BP to guide treatment decisions and it is unknown whether doing so will lower CVD risk to the same extent as or more than using office BP. Finally, although the c-statistics for CVD and all-cause mortality were statistically significant regardless of whether office BP or BP from ABPM was used, or the number and timing of BP measurements on ABPM, the lower bounds of many of their 95% CIs were ≤ 0.7, suggesting there may be limited clinical utility of these models.
In conclusion, out-of-office SBP and DBP estimated on ABPM were more strongly associated with CVD events compared to office SBP and DBP regardless of the number of ABPM measurements or whether the readings were sequential or random. While two SBP and DBP readings on ABPM had a stronger association with CVD events compared with office measurements, there was no significant increase in the strength of the association with a higher number of readings on ABPM. These findings suggest that the first 2 ABPM readings after a person leaves the office may be sufficient to estimate the association with CVD risk and may perform better than office SBP and DBP measurements.
Supplementary Material
Supplementary materials are available at American Journal of Hypertension (http://ajh.oxfordjournals.org).
ACKNOWLEDGMENTS
The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I/HHSN26800001) and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute for Minority Health and Health Disparities (NIMHD). This work was supported in part by NHLBI contracts R01HL117323 and R01HL159374. The authors also wish to thank the staff and participants of the JHS.
Footnotes
DISCLOSURES
This manuscript was sent to Guest Editor, Hillel W. Cohen, MPH, DrPH for editorial handling and final disposition.
DISCLAIMER
The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services.
CONFLICT OF INTEREST
The authors have no conflicts of interest to disclose.
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