(Hypertension. 1997;29:1109-1113.)
© 1997 American Heart Association, Inc.
Articles |
Confirmation or Exclusion of Stage I Hypertension by Ambulatory Blood Pressure Monitoring
From the Hypertension Section–Cardiovascular Institute, Mount Sinai Medical Center, New York, NY.
Correspondence to Robert A. Phillips, MD, PhD, Hypertension Section–Cardiovascular Institute, Box 1085 Mount Sinai Medical Center, One Gustave Levy Place, New York, NY. E-mail robert_phillips{at}smtplink.mssm.edu
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Abstract |
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Abstract Criteria for the diagnosis or exclusion of hypertension using ambulatory blood pressure monitoring have not been agreed upon. We designed this study to provide a statistically based guide for using results of ambulatory blood pressure monitoring to resolve this issue. To generate this information, we used a database of 228 subjects (135 men, 93 women; average age, 45 years) referred by their primary physicians over the past 7 years for evaluation of borderline or stage I hypertension (average blood pressure, 148/92 mm Hg; SD, ±17.5/12.2 mm Hg). In this population, the pooled SDs of systolic and diastolic ambulatory blood pressures were 13.8 and 11.6 mm Hg, respectively. Using the pooled SD, we calculated the probability that a patient's blood pressure falls within the hypertensive range (>140/90 mm Hg). The 95% confidence interval for each subject's blood pressure was also determined. For example, if 40 ambulatory blood pressure measurements are performed on a subject and the average systolic ambulatory blood pressure is 137 mm Hg, then there is a 10% probability that the patient's "true" average blood pressure is actually in the hypertensive range. By contrast, if the systolic pressure is 143 mm Hg, there is a 90% probability that the patient is hypertensive. This approach may be useful for clinical decision making and also for the design of clinical trials.
Key Words: blood pressure monitoring, ambulatory • hypertension detection and control • statistics
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Introduction |
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Accurate determination of usual or average blood pressure (BP) is necessary for the assessment of risk for stroke and coronary heart disease. The variability inherent in casual office BP measurements,1 2 3 4 the variability of BP itself throughout the day,5 and the possibility of bias in the observer6 may lead to inaccurate assessment of true BP in subjects with borderline or stage I hypertension (for example, diastolic BPs between 85 and 95 mm Hg). Reliance on a small number of BP measurements may lead to inaccurate assessment of a subject's true BP due to regression dilution.7 By providing many measurements of BP within 1 day, ambulatory BP (ABP) monitoring can reduce error due to regression dilution and thus improve the estimate of the true BP.8 9 If a value of 140/90 mm Hg is used as the basis for classifying patients as hypertensive,10 it is important to know how to interpret average pressures derived from ambulatory monitoring near this level (ie, the likelihood that the true average pressure is definitely above or below 140/90 mm Hg).
ABP monitoring, now studied for more than 30 years, improves one's ability to determine which hypertensive individuals will develop cardiovascular disease11 12 13 14 ; however, the statistical criteria for the diagnosis or exclusion of hypertension using ABP monitoring have not been defined. Without the application of proper statistical criteria, this technique, like any other, can lead to misclassification of subjects. Therefore, the goal of this study was to develop valid criteria for the diagnosis or exclusion of hypertension with reasonable statistical certainty using ABP monitoring.
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Methods |
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Subjects
A total of 228 subjects (135 men, 93 women), aged 16 to 89 years (average age, 45), were referred by their primary physicians for ABP monitoring over the past 7 years for evaluation of borderline or stage I hypertension. All subjects had at least two casual office BP measurement of greater than or equal to 135/85 mm Hg. Antihypertensive therapy had been withdrawn for at least 2 months, and no subject had isolated systolic hypertension of the elderly. Subjects with clinical or laboratory signs of secondary hypertension were excluded.
BP Monitoring
In 51 subjects, BP was recorded with a SpaceLabs 5200
automatic BP monitor. In 147 subjects, BP was recorded with a
SpaceLabs 90202 (or 90207) automatic BP monitor. Finally, for the
remaining 30 subjects, the Suntech Accutracker BP monitor (Suntech
Medical Instruments) was used. These BP monitors have been validated in
previous studies.15 Readings were taken every 15 to 20
minutes during the daytime, which was arbitrarily defined as being from
6 AM to midnight. Readings were deleted automatically by
the system software for poor quality or if the pulse pressure was less
than 15 mm Hg.
Statistical Procedures
Calculation of the Probability That the Average of `n' ABP
Measurements Represents `True' Hypertension
The central limit theorem states that for sample sizes greater
than or equal to 30 (ie, n
30), the sample distribution can be assumed
to be normally distributed even if the population distribution is not.
The average of the sample distribution is equal to the average of the
population distribution, with the SD of the sample distribution equal
to 
/
, where is the SD of the population
distribution.16 17 Fig 1
shows how this
applies to ABP measurements. The "true" BP (population
distribution) of a subject over the course of a day has an unknown
probability distribution with an average (µ) and SD () (see Fig 1, solid curve). The shape of the probability distribution of BPs over the
course of a day is influenced by variables such as physical
activity, sleep, and morning surge. In contrast, the sample
distribution, which is the probability distribution of the average of
"n" ABP measurements, will be normally distributed (for n30)
about an average (µ) and SD (/) (see Fig 1, dotted
curve).
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As the null hypothesis, we assume that the test subject is hypertensive, with an average BP equal to 140/90 mm Hg (ie, µ=140/90 mm Hg). Based on the fifth report of the Joint National Committee on the detection, evaluation, and treatment of high blood pressure (JNC-V), a BP of 140/90 mm Hg was chosen as the lower limit of hypertension. Then for any given average of "n" ABP measurements performed on a test subject, the probability that this average ABP came from a hypertensive subject (ie, µ=140/90 mm Hg) is given by the area under the sample distribution curve to the left of the measurement. If this probability is small, then we have little confidence that the null hypothesis is true and therefore we reject it. However, as this probability increases, we have greater confidence that the null hypothesis is true (ie, the subject is "truly" hypertensive).
Therefore, to calculate the probability that the average of "n"
ABP measurements came from a "truly" hypertensive subject, one
must first calculate the respective test statistics with the null
hypothesis being that the average ABP is 140/90 mm Hg:
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sys and dias are systolic and
diastolic SDs, respectively. Then, using standard tables for a one-tailed test for a normal distribution, one can calculate the probability that the average of the "n" ABP measurements came from a "truly" hypertensive subject. We used this technique to calculate the probability that the average ABPs of the 228 subjects enrolled in this study represent "true" hypertension.
Individual and Pooled SDs
As discussed above, the probability that the average of
"n" ABP measurements came from a "truly" hypertensive
subject is a function of the SD (i, where i is the ith
subject), the average ABP, and the number of ABP measurements taken
(n). To develop a generalized methodology applicable to all subjects
with borderline or stage I hypertension, we assessed whether use of the
pooled SD (p), as opposed to each individual's SD,
significantly changed the calculated probability. If p
(which is a constant) can be substituted for i (which
varies from subject to subject), then the probability that the average
of "n" ABP measurements came from a "truly" hypertensive
subject becomes only a function of the average ABP and the number of
ABP measurements taken (n). For each subject, therefore, we performed
the calculations using both the pooled SD and each subject's
individual SD and compared the results. The pooled SD was calculated as
follows:
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p=
and p2 is the pooled estimated variance of
all 228 subjects in the study; ni is the number of ABP
measurements taken on the ith subject in the study; and
i is the SD of the ABP measurements taken on the ith
subject in the study. |
Results |
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Ambulatory BP Characteristics
An average (±SD) of 47±11 readings was obtained on the 228 subjects in the study. The average systolic ABP was 137±8.8 mm Hg (range, 109 to 172 mm Hg); the average diastolic ABP was 85±7.2 mm Hg (range, 58 to 112 mm Hg).
Individual and Pooled SDs
The pooled SDs for all three ABP monitoring devices (SpaceLabs
5200, SpaceLabs 90202 or 90207, and Accutracker) were calculated (see
Table 1) and found to be similar. When the SDs for all
228 subjects studied with the three devices were combined to calculate
the total pooled SD (p), the systolic and
diastolic values were 13.8 and 11.6 mm Hg,
respectively.
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Probability of Hypertension in the 228 Subjects Using
Individual SDs
Using each subject's individual SD, we calculated the probability
that the average of "n" ABP measurements taken on each of the 228
subjects represents "true" hypertension (see Table 2). If one arbitrarily defines as normotension an
average ABP that has less than a 10% probability of coming from a
"truly" hypertensive subject, then 126 (55.3%) and 142 (62.3%)
of the subjects are normotensive based on systolic and
diastolic BPs, respectively. Similarly, if one defines as
hypertension an average ABP that has a greater than 90% probability of
coming from a "truly" hypertensive subject, then 69 (30.3%) and
43 (18.9%) of the subjects are hypertensive based on systolic
and diastolic BPs, respectively.
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Probability of Hypertension in the 228 Subjects Using Pooled
SDs (p)
Using the pooled SDs, we calculated the probability that the
average of "n" ABP measurements taken on each of the 228 subjects
represents "true" hypertension (see Table 2
). Once again,
if one defines as normotension an average ABP that has less than a 10%
probability of coming from a "truly" hypertensive subject, then
126 (55.3%) and 136 (59.6%) of the subjects are normotensive based on
systolic and diastolic BPs, respectively.
Similarly, 69 (30.3%) and 42 (18.4%) of the subjects are hypertensive
based on systolic and diastolic BPs,
respectively.
Correlation of Results Using Individual and Pooled SDs
Table 2 shows all 228 subjects categorized with respect to the
probability that their average ABP represents "true"
hypertension (140/90 mm Hg). The calculations were done for both
individual and pooled SDs. When the categorization of all subjects
using the individual SDs was compared with the categorization of all
subjects using the pooled SDs, the correlation coefficients were .9997
and .998 for systolic and diastolic BPs,
respectively. The average difference in the calculated probabilities
using the pooled SD rather than the individual SD was 0.2% for both
systolic and diastolic BPs.
The correlation coefficients were similar (ie, >.99) for men, women, subjects younger than 50 years, and subjects 50 years and older when the categorization of subjects using each individual's SD was compared with the categorization of subjects using the pooled SD.
Therefore, it appears that for this population of borderline or stage I
hypertensive subjects, the probability that the average of "n"
ABP measurements represents "true" hypertension is not
significantly changed when the pooled SD is used rather than each
individual's SD. Substituting the pooled SD (p, which
is a constant) for the individual's SD in the equations for the test
statistic (Z) allows us to develop a generalized methodology defining
the probability that the average of "n" ABP measurements
represents "true" hypertension as a function of the
average ABP and the number of ABP measurements taken.
Generation of Probability Curves
With the use of the pooled SDs of 13.8 and 11.6 mm Hg for
systolic and diastolic BPs, respectively; the test
statistics become:
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Therefore, if the number of ABP measurements (n) is held constant,
the test statistics become only a function of the average measured ABP;
similarly, the probability that the average of "n" ABP
measurements represents "true" hypertension becomes only
a function of the average measured ABP. Figs 2 and 3 are a set of curves that show the probability that the
average of "n" ABP measurements represents "true"
hypertension as a function of the average measured ABP for n=30, n=40,
and n=50. The 95% confidence intervals as a function of the average
measured ABP are shown in Table 3.
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We realized that 140/90 mm Hg, chosen as the lower limit of
hypertension for this study, is somewhat arbitrary. A
meta-analysis performed by Staessen et al18 on
3746 normotensive subjects showed that the lower limit of hypertension
(ie, 2 SDs above the mean) for daytime ABP measurements was 146/91
mm Hg. If this value is used for the lower limit of hypertension, then
the curve for systolic BP (Fig 2) would be shifted 6
mm Hg to the right and the curve for diastolic BP (Fig 3)
would be shifted 1 mm Hg to the right. The 95% confidence
intervals, shown in Table 3, would remain unchanged.
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Discussion |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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The goal of the present study was to develop valid criteria to aid the clinician in the diagnosis or exclusion of hypertension with reasonable statistical certainty using ABP monitoring. If a value of 140/90 mm Hg is used as the basis for classifying individuals as hypertensive, it is important to know how to interpret average ABPs near this level. The potential for the misclassification of borderline or stage I hypertensive individuals using ABP monitoring is primarily due to the inherent uncertainty associated with taking a finite sample of a large population. The average obtained by sampling a large population is only an approximation of the "true" average of that population. Similarly, the average obtained from the finite number of ABP measurements taken over the course of a day is only an approximation of the "true" average BP and therefore also has an inherent uncertainty associated with it. However, if the number of ABP measurements (n) is at least 30, we may assume that the sampling distribution (ie, the probability distribution of the average of "n" ABP measurements) approximates a normal curve. Therefore, the uncertainty inherent in taking a finite number of ABP measurements can be quantified in terms of the probability that the average of "n" ABP measurements came from a "truly" hypertensive subject (ie, average BP=140/90 mm Hg). This method is used by many quality-assurance monitoring systems in which the error terms are known to hold across populations.19
Not only can the uncertainty inherent in ABP monitoring be quantified,
it can be minimized to a large extent. Previous studies have shown that
the average ABP of a given subject varies when that subject is tested
and then retested several days later (ie, test-retest variability) and
that this variability decreases as the number of ABP measurements (n)
recorded over 24 hours increases.20 21 The decrease in
test-retest variability (ie, the increase in test-retest
reproducibility) is due to the fact that as n increases, the
probability distribution curve of the average of "n" ABP
measurements becomes narrower (ie, 2 
1/n).
This means that as "n" increases, one becomes more confident that
the measured average ABP is an accurate representation of the
subject's "true" average BP.
Using this approach, we generated curves that will aid the clinician in
the confirmation or exclusion of hypertension with reasonable
statistical certainty. These curves are shown in Figs 2 and 3. One
immediately notices that the probability curves vary little when the
number of ABP measurements (n) is increased from 30 to 50. When the
number of ABP measurements is increased from 30 to 50, there is, at
most, a 6% increase in confidence that an individual is not
hypertensive when the average measured ABP is less than 140/90
mm Hg. Similarly, there is, at most, a 6% increase in confidence that
an individual is hypertensive when the average measured ABP is greater
than 140/90 mm Hg. Also, the 95% confidence intervals narrow
only slightly when the number of ABP measurements is increased from 30
to 50 (see Table 3).
Given the fact that very little is gained in statistical certainty when
the number of ABP measurements is increased from 30 to 50, we believe
that 40 ABP measurements are adequate when analyzing ABPs with this
statistical method. Forty ABP measurements satisfy the criteria of the
central limit theorem (ie, n30), while at the same time not
inconveniencing the subject with an excessive number of ABP
measurements.
An example using this method is as follows: A clinician might ask, if 40 ABP measurements are performed on a patient over the course of a day, what must the average systolic ABP be to have reasonable statistical certainty that the patient's "true" average systolic BP is not in the hypertensive range? The answer depends on the amount of uncertainty the clinician is willing to accept.
If the clinician is willing to accept no more than a 10% probability
that a patient's "true" average systolic BP is in the
hypertensive range, then an average systolic ABP of less than
137 mm Hg could reasonably be defined as normotension (see Fig 2
for n=40). Average systolic ABPs above 137 mm Hg would
exceed the level of uncertainty the clinician is willing to
accept, and at this point, the location of the patient's average
systolic ABP on the probability curve shown in Fig 2 must be
assessed. For example, if the average systolic ABP were
143 mm Hg, there is a 90% probability that the "true"
average systolic BP is in the hypertensive range (see Fig 2 for
n=40), with a 95% confidence interval of 138.7 to 147.3 mm Hg
(see Table 3 for n=40). Clearly, the patient would be considered
hypertensive with reasonable statistical certainty and the appropriate
nonpharmacological or antihypertensive therapy instituted. On the other
hand, if the average systolic ABP were 141 mm Hg, there
is a 70% probability that the patient's average systolic BP
is in the hypertensive range, with a 95% confidence interval of 136.7
to 145.3 mm Hg. At this point, the clinician can assess, on the
basis of other risk factors (such as age, diabetes, high
cholesterol, family history of coronary artery
disease, history of stroke, or tobacco use), whether it is appropriate
to treat the patient for hypertension or to hold off treatment and
remonitor the patient at a later date.
Finally, one potential limitation of our model is that it assumes that
the variability of BP for a given individual
(i2) does not change significantly from day
to day. This allows us to make the assumption that the variance
(2) of the probability distribution of the
average of "n" ABP measurements is equal to
i2/n. However, although several studies have
shown that the test-retest reproducibility of average ABP improves with
increasing "n," the improvement is not as great as would be
predicted by the equation
2=i2/n. There appears
to be a point reached (probably around n=30-60) where increasing
"n" does not result in corresponding improvement in test-retest
ABP reproducibility.20 21 It has been theorized that the
limitation on test-retest reproducibility is related to the
nonstandardization of subject activity and position during ABP
monitoring, both of which are likely to vary from day to day (ie,
i2 in a given individual significantly
varies from day to day). Evidence that this theory is true is supported
by the fact that Gerin et al22 showed an increase in ABP
test-retest reproducibility when they meticulously controlled for
subject activity and position. Subject reaction to the medical
environment and changes in body weight have also been implicated as
factors that may influence ABP test-retest
reproducibility.23
Because of the observed unresponsiveness of test-retest reproducibility
to increasing "n" above a particular threshold, Reeves and
Myers24 proposed an expanded model for the variance
(2) of the average of "n" ABP measurements
as
2=i2/n+b2,
where b2 represents a test-retest
variance that may be significant between any two measurements. Clearly,
further studies need to be done for determination of the etiology of
the test-retest variance (b2) that has been
observed in ABP monitoring to better quantify and minimize its effect
on test-retest variability. Already, Gerin et al22 have
shown that controlling for subject activity and position can
significantly decrease b2. Once
b2 is accurately quantified, it can be
incorporated into our model, thus resulting in an even more precise
estimation of the probability of hypertension. Until then, we believe
that the model presented in this article will be a useful tool
for the clinician to confirm or exclude hypertension with reasonable
statistical certainty in those individuals diagnosed with borderline or
stage I hypertension by office BP measurement.
Received October 25, 1996; first decision October 29, 1996; accepted October 29, 1996.
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