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Interpretation of ambulatory blood pressure profile

a prognostic approach for clinical practice

Angeli, Fabioa; Reboldi, Gianpaolob; Verdecchia, Paoloc

doi: 10.1097/HJH.0000000000000497
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aDivision of Cardiology and Cardiovascular Pathophysiology, Hospital ‘S.M. della Misericordia’

bDepartment of Medicine, University of Perugia, Perugia

cDepartment of Internal Medicine, Hospital of Assisi, Assisi, Italy

Correspondence to Fabio Angeli, MD, Division of Cardiology and Cardiovascular Pathophysiology, Hospital ‘S.M. della Misericordia’, Perugia, Italy. E-mail: fangeli@cardionet.it

It is well established that ambulatory blood pressure (BP) reflects the true pattern of BP changes during usual daily life more accurately than clinic BP. Office-based BP readings are limited in the amount of information they provide, as they represent a single snapshot in time [1,2]. Conversely, BP readings recorded at predefined intervals throughout the 24 h portray a better picture of BP fluctuations associated with daily activities, including sleep. Ambulatory BP monitoring (ABPM) enables clinicians to obtain a more precise estimation of a patient's BP, assesses BP levels in the outpatient setting, and investigates BP variability and circadian BP profile [3]. The evidence that ABPM gives information over and above conventional BP measurement has been growing steadily over the past 25 years [3,4]. There is a solid body of evidence from longitudinal studies supporting the prognostic value of ABPM, and the contention that BP values obtained by 24-h ABPM are a better predictor of cardiovascular risk than office-based BP measurements [4–6].

A recent meta-analysis of 7030 participants [7] showed that average day-time ambulatory BP was superior to the conventional measurement of BP in predicting cardiovascular events. Specifically, in multivariate-adjusted continuous analyses, both conventional and ambulatory BP predicted cardiovascular outcomes [7]. Nevertheless, in fully adjusted models, including both office and ambulatory BP, conventional BP lost its predictive value, whereas systolic daytime ambulatory BPs retained their prognostic significance [7].

Although average 24-h, daytime (awake) and night-time (asleep) BP have been the principal components of the ambulatory BP profile to be investigated as prognostic determinants, other summary measures exist for describing different aspects of ambulatory readings. They include nocturnal dipping, morning surge, pulse pressure (PP), ambulatory arterial stiffness index (AASI), average heart rate, BP load, and BP variability [3,8–16].

Several of these measures have been assessed as prognostic factors. However, controversy exists as to which components have independent prognostic significance over and beyond average ambulatory BP [3]. Of note, the hypothesis that increased short-term BP variability estimated by non-invasive ABPM is a determinant of prognosis in hypertensive patients is attractive and in the past few years, a number of articles analysed its clinical relevance [2].

Some studies have reported associations between BP variability and end-organ damage [17–20], cardiovascular events [21–24] or mortality [24,25], whereas other failed to do so or found BP variability to be inferior to mean systolic pressure [26–28].

In the attempt to verify whether BP variability predicts risk over and beyond the BP level, the prognostic significance of night-time BP variability has received considerable attention.

In a prospective analysis of the Progetto Ipertensione Umbria Monitoraggio Ambulatoriale (PIUMA) study, an increased variability of systolic blood pressure (SBP) during the night-time (defined by a standard deviation (SD) >10.8 mmHg) was an independent predictor of cardiac events in initially untreated hypertensive patients [29].

Recently, this finding was examined in a large multinational cooperative database [24]. Palatini et al.[24] investigated the association of SD of night-time BP with mortality and cardiovascular events in 7112 untreated hypertensive patients. Specifically, in a multivariable Cox model, high BP variability, defined by SD of night-time SBP at least 12.2 mmHg, was associated with a significant 41% greater risk of cardiovascular events, a 55% greater risk of cardiovascular death and a 59% increased risk of all-cause mortality. In contrast, daytime BP variability was not an independent predictor of outcomes in any model. Notably, the addition of night-time BP variability to fully adjusted models had a significant impact on reclassification and discrimination statistics for all outcomes (relative integrated discrimination improvement for SBP variability: 9% cardiovascular events, 14.5% all-cause death and 8.5% cardiovascular death).

Although ambulatory BP variability is traditionally assessed from the SD of BP measurements over the 24 h (or more appropriately, daytime and night-time periods separately), other indices of BP variability have been proposed [3,30]. Calculation of the ‘weighted’ SD of the 24-h mean value (computed as the average of the daytime and night-time BP SD, each weighted for the duration of the respective day or night period) has been proposed as a method of excluding day-to-night BP changes from the quantification of overall 24-h SD, without discarding either daytime or night-time values [31]. Mena et al.[32,33] proposed average real variability (ARV) as the average of the absolute differences between consecutive BP measurements. Including measures of time interval between BP measurements, the ARV attempts to correct for the limitations of the commonly used SD, which accounts only for the dispersion of values around the mean and not for the order of the BP readings. Rothwell et al.[34] proposed BP variability independent of the mean (VIM) as a new index. The blood pressure VIM is calculated as the SD divided by the mean to the power, multiplied by the population mean to the power x. The power x is obtained by fitting a curve through a plot of SD against mean using the model SD = a × meanx, where x was derived by non-linear regression analysis. Finally, some investigators also proposed maximum minus minimum blood pressure (MMD) as alternative index of BP variability [30,35].

These parameters, which focus on short-term BP changes, have been shown to be better predictors of organ damage and cardiovascular risk than the conventional 24-h SD of BP. Nevertheless, their prognostic significance remains contentious [30,36].

In this context, the analysis by Gavish and Bursztyn [37] published in this issue of the Journal, adds additional evidence on the prognostic significance of BP variability. The main aim of this analysis was to explore whether some advanced techniques related to the estimation of BP variability (variability ratios) was associated with a worse prognosis in essential hypertension.

The study is a retrospective analysis of prospectively collected data, including 1246 hypertensive patients (60% treated, 46% men, mean age 56 ± 16 years) [37]. Heart period (T) in milliseconds (ms) was calculated for each heart rate datum (in beats/min) by the formula 60 000/heart rate. Estimates of 24-h SBP, DBP and T variability were computed by within-patient SD and variability ratios were defined by the following formulas:

  1. dS/dD = SD (SBP)/SD (DBP)
  2. dS/dT = SD (SBP)/SD (T) mmHg/ms
  3. dD/dT = SD (DBP)/SD (T) mmHg/ms

During the follow-up, 76 deaths were recorded. Non-survivors displayed reduced heart rate variability and greater variability ratio indices in comparison with survivors (all P < 0.05).

Exploring the 5-year risk of all-cause mortality, a somewhat significant positive association with all-cause death was demonstrated for dS/dT and dD/dT. Specifically, the adjusted hazard ratio estimates per 1 SD increase were 1.23 [95% confidence interval (CI) 1.03–1.47, P = 0.025] and 1.36 (95% CI 1.07–1.72, P = 0.011) for dS/dT and dD/dT, respectively. The prognostic significance of dS/dD was only demonstrated for elderly patients with slower heart rate (hazard ratio 2.39, 95% CI 1.27–4.5, P = 0.007).

Despite the interesting results on the relation between this new index with mortality, the new analysis by Gavish and Bursztyn [37] has some limitations that should be considered for a proper interpretation of its results. First, except for the diabetes status, information on other cardiovascular risk factors and comorbidities were not available (including cholesterol levels, previous vascular events and target organ damage). In this context, it is worth mentioning that in the previous reports, after adjustment for these covariates, the adverse prognostic relevance of some indices of BP variability was no longer noticeable [26–28]. In other words, it is not clear whether the addition of variability ratios improved a model, which already included all confounding variables and 24-h average BP levels as determinant of prognosis.

Second, the study population included treated and untreated patients undergoing 24-h ABPM. Although the attempt to adjust the survival model for antihypertensive drug treatment appears to be appropriate, it remains unclear whether the prognostic significance of BP variability equally applies to both treated and untreated cohorts.

Finally, computation of BP variability ratios appears somewhat complicated for routine clinical practice, requires ad-hoc software and can be affected by artifacts during ABPM recording.

In conclusion, the interpretation of ABPM in individual patients to optimize their management must be kept simple, and more sophisticated measures should be interpreted cautiously [38].

Although there is no consensus on the summary measures that should be preferred for risk stratification, clinicians should concentrate on average 24-h, daytime and night-time BP levels and on components of ABPM which proved to substantially refine risk profiling over and beyond the BP level [38,39].

In such a context, the addition of ambulatory PP, dipping status and SD of night-time BP variability to models of long-term outcomes improves risk stratification of patients with hypertension after accounting for the impact of average ambulatory BP levels [40,41].

From a practical standpoint, we suggested to use a mnemonic ‘Ambulatory Does Prediction Valid’ (ADPV): average ambulatory BP; dipping pattern; pulse pressure; variability of night-time SBP [3].

Figure 1 depicts an updated algorithm that could be used to refine cardiovascular risk stratification and adapt treatment strategies. Briefly, office BP (or home BP if available) is the first-line procedure to identify patients who could be candidates for commencing drug treatment. In the patients with office BP at least 140/90 mmHg, 24-h ambulatory BP identifies low-risk individuals with normal or optimal values of daytime ambulatory BP (i.e. white-coat hypertension) [42]. These patients may be eligible for lifestyle measures without antihypertensive drugs if they are free of comorbidities and target organ damage. In contrast, a non-dipping BP pattern, an increased 24-h PP or an increased night-time SBP variability in patients with elevated daytime BP (i.e. ambulatory hypertension) identify high-risk individuals, regardless of office BP values (office hypertension or masked hypertension) [43]. In these patients, drug treatment should be started as soon as possible. In the context of a high clinical suspicion of masked hypertension (pre-hypertension or high-normal BP, smoking status, regular alcohol consumption, male sex, diabetes, obesity and exposition to high environmental stress), ABPM can identify patients with hypertension undetected on the grounds of usual methods [43].

FIGURE 1

FIGURE 1

Since the cardiovascular risk in masked hypertension is equivalent to that in sustained hypertension, it is reasonable that patients with masked hypertension should undergo lifestyle changes and antihypertensive drug therapy [43]. Indications from current guidelines remain mandatory in patients with intermediate risk levels based on ambulatory BP, as well as in patients with white-coat hypertension and associated risk factors [1].

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ACKNOWLEDGEMENTS

This study was funded in part by the Fondazione Umbra Cuore e Ipertensione – ONLUS, Perugia, Italy.

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Conflicts of interest

None of the authors of this study has financial or other reasons that could lead to a conflict of interest.

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