Published online before print January 22, 2008, doi: 10.1161/HYPERTENSIONAHA.107.102145
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(Hypertension. 2008;51:642.)
© 2008 American Heart Association, Inc.
Original Articles |
Impacts of Measurement Protocols on Blood Pressure Tracking From Childhood Into Adulthood
A Metaregression Analysis
From the Center for Human Nutrition, Department of International Health, Bloomberg School of Public Health (X.C., Y.W.), and Welch Center for Prevention, Epidemiology, and Clinical Research (L.J.A.), Johns Hopkins University, Baltimore, Md; and the Department of Epidemiology (J.M.), Capital Institute of Pediatrics, Beijing, China.
Correspondence to Youfa Wang, Center for Human Nutrition, Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205. E-mail ywang{at}jhsph.edu
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Abstract |
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The best approach for blood pressure (BP) measurement in children remains controversial, specifically regarding the choice of Korotkoff phase 4 versus Korotkoff phase 5 for diastolic BP (DBP) and the use of automated devices. To examine the impacts of different BP measurement protocols on BP tracking from childhood into adulthood, we conducted a meta-analysis of 50 related studies published between 1970 and 2006 identified based on a systematic search of PubMed. These studies provided 617 data points (tracking correlation coefficient, our outcome variable) for systolic BP and 547 data points for DBP for our meta-analysis. The explanatory variables included the use of Korotkoff phase 4/Korotkoff phase 5, BP device, and number of BP measurements per visit. Analyses were adjusted for potential confounders, including sex, baseline age, follow-up length, publication year, and study country. Tracking correlation coefficients for DBP measured using Korotkoff phase 4 was higher than that of Korotkoff phase 5 by 0.035 but not significant. DBP tracking assessed by automated device was higher than that of Korotkoff phase 5 by 0.152 (P=0.024) and higher than the mercury manometer by 0.223 (P=0.005). BP tracking was slightly higher with multiple BP measurements per visit, but measurements of
3 times did not improve the tracking further compared with 2 measurements. Although policy-making bodies currently recommend the use of Korotkoff phase 5 to assess DBP in children, our metaregression analysis did not support the recommendation. In general, Korotkoff phase 4 seems to be different from Korotkoff phase 5, and automated device is a promising approach for BP assessment in childhood.
Key Words: blood pressure • measurement protocol • Korotkoff phase • tracking • child • hypertension
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Introduction |
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Childhood blood pressure (BP) levels are predictive of BP levels in adulthood.1 Elevated BP (prehypertension and hypertension) is a major public health problem, affecting
60% of the US adult population.2,3 Control of high BP and cardiovascular risk rely on accurate assessment of BP.4 In adults, the basis for evaluating different BP measurement protocols is to compare their ability to accurately predict cardiovascular disease outcomes. Because cardiovascular disease outcomes are rare in children, a reasonable alternative is to compare the ability of different techniques to predict adult BP. However, it remains controversial as to how diastolic BP (DBP) and systolic BP (SBP) should be measured in childhood, ie, the choice of Korotkoff phase 4 (K4; the muffling of the sound) or Korotkoff phase 5 (K5; the disappearance of sound) as the best representation of DBP when using the mercury manometer.5,6 K5 has been recommended and universally used to measure DBP in adults, but the related previous and recently published recommendations for children’s DBP assessment have varied considerably, from abandoning DBP in childhood altogether,7 reporting readings using both K4 and K5,8–10 using K4 up to a certain age and then shifting to K5,11–13 or using only K414–20 or K5.20–24 The use of DBP measured at K4 or K5 is likely to affect the classification of childhood hypertension.25 Compared with direct intraarterial BP measurement, the manual auscultatory technique using Korotkoff sounds tends to give SBP values slightly lower and DBP values slightly higher.4,26 The mercury manometer depends on the trained observer using the manual auscultatory technique.4 Currently, given environmental concerns related to mercury and the difficulties in recording BP manually, electronic/digital BP equipment was developed.4,27–31 The use of automated devices may rectify some of these problems.4 However, to our knowledge, little is known about the impact of different BP measurement protocols on BP tracking patterns. Such information can help determine which protocol is superior. It is well recognized that BP tracks from children into adults.1,32–34 Comparisons within studies using K4, K5, and automated devices are the best; however, few studies have used these at all. Thus far, only 2 published studies have compared DBP tracking based on K4 and K5.6,34 The present study aimed to examine whether the choice of using different BP measurement protocols would affect BP tracking from childhood to adulthood based on previous studies. Our specific interests were the use of K4 versus K5 and the use of automated devices.
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Methods |
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Literature Search Strategy
We conducted a systematic literature search of the PubMed database using related key words, including "child," "adolescent," "adult," "tracking," "persistence," "stability," "maintenance," "consistency," "blood pressure," "hypertension," "cohort," "follow-up," "longitudinal study," and "cardiovascular disease," for related studies published between January 1970 and July 2006. Titles and abstracts of studies uncovered by the electronic searches were examined on-screen first. Only studies that examined the tracking of BP from childhood to adulthood and were published in English, Chinese, and Japanese were included. This initial screening resulted in 301 studies, which were then further examined. The full articles that met our selection criteria (see below) were carefully reviewed. Additional studies identified in the course of reading or brought to our attention by colleagues consulted were included. Studies were also identified by searching on authors who had contributed
1 relevant article, by using the "related articles" function in PubMed, and hand searching some cross-references from retrieved articles.
Study Inclusion Criteria
Only cohort studies that examined BP tracking from childhood to adulthood and met the following criteria were included: (1) BP tracking correlation coefficient (Pearson or Spearman approach) was reported; (2) cohorts’ baseline age was <18 years; (3) sample size was >50; and (4) the study was published in English, Chinese, or Japanese. Note that multiple follow-up studies based on the same cohort with different lengths of follow-up were included as separate data points. We defined "data points" as the reported BP tracking correlation for each subgroup included in a certain study, ie, a study might provide different BP tracking results by sex, baseline age, and follow-up periods. Note that some studies only reported SBP or DBP alone.
Data Extraction
Using a standardized data extraction form, we extracted and tabulated related data. Information extracted included first author’s name, study publication year, country of data collection, sample characteristics (eg, baseline age, sex, ethnicity/population, and sample size), length of follow-up, BP measurement instruments, readings of DBP measurement (eg, K4), number of BP measurements per visit, and outcome assessment (eg, correlation coefficient). A data set based on this data extraction was created using Microsoft Excel (Microsoft Co). From 301 retrieved articles, 50 cohort studies met our inclusion criteria and provided 617 data points (ie, correlation coefficients) for SBP and 547 data points for DBP, which were used in our metaregression analysis.
Key Study Variables
BP Tracking
Our outcome variable was the reported BP tracking correlations, including Pearson or Spearman rank correlation coefficients. We calculated the average of tracking correlations if studies only reported the range of BP tracking correlation for our meta-analysis.
BP Measurement Protocol
As the primary explanatory variable, BP measurement protocol included the use of a specific Korotkoff phase (K4 or K5), measurement instrument (eg, mercury manometer), and number of measurements per visit. K5 was treated as the reference group in our analysis, because it was recommended in the Fourth Report of National High Blood Pressure Education Program and by the American Heart Association.3,35 BP measurement instruments were classified into 5 categories: (1) mercury manometer (treated as the reference); (2) random-zero manometer; (3) ultrasound device; (4) automated device; and (5) unknown, meaning no information was reported about BP measurement instruments. The number of BP measurements per visit was grouped as once (as reference), twice, 3 times, and unknown (no detailed information available).
Study Site/Country
Studies were grouped into 4 categories: European (as reference), American, Asian, and other populations. Study site/country was included in our analysis as an important potential confounder because we suspected that the quality of data collection and BP tracking patterns might vary across countries and regions.
Statistical Analysis
Using the Fisher’s z transformation, we converted BP tracking correlation coefficients (rs) to zs to obtain approximate normality and then calculated a mean transformed correlation weighted by the study sample size. We inverted the z transformation to obtain the overall tracking correlation and its 95% CI.34,36 To examine the influence of the choice of BP measurement protocols on BP tracking, fixed- and random-effects models were used. The homogeneity of the effect size among studies was tested using the Q test. Because our tests for heterogeneity were significant for SBP and DBP across studies (Q=847.7, P<0.001; Q=384.5, P<0.001, respectively), we present results based on a random-effects model adjusted for clustering effect of multiple correlation coefficients reported from the same cohort. The primary outcome variable was BP tracking correlation coefficient, whereas primary explanatory variables were selected aspects of BP measurement (K4 versus K5 for DBP, measurement device, and number of BP measurements per visit). Other variables (sex, baseline age, length of follow-up, study publication year, and study site/country) were included in our analyses as potential confounders. Note that these results from random-effects models seemed to be more conservative than those from multiple linear regression analyses (data not reported).
Locally weighted regression was used as a nonparametric smoothing technique to show the relationship between baseline age and DBP tracking correlation stratified by K4 and K5, with a bandwidth of 0.4.37 In addition, we assessed publication bias by plotting sample sizes against SBP/DBP tracking correlation coefficients and by 2 formal tests, the Begg’s adjusted rank correlation test and the Egger’s regression asymmetry test.38–40 All of the analyses were performed using SAS version 9.1 (SAS Institute, Inc) and Stata release 9.41 Statistical significance was set at P<0.05 and was marginally significant at P<0.1.
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Results |
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Table 1 and Appendix 1 (available online at http://hyper.ahajournals.org) summarized the main characteristics of 50 cohort studies. More than half (n=29; 58%) were conducted in the United States, 11 (22%) in Europe, 6 (12%) in Asia, and 4 (8%) in other countries. Regarding DBP measurement, 50% used K4 and 30% used K5; 6% recorded K4 and K5 but did not specify which one was used; 4% used an automated device; and 8% did not provide the details. In Europe, 36.4% used K4, and 54.5% used K5. In the United States, the figures were 62.1% versus 20.7%, respectively. In total, 60.3% of data points used K4 and 16.3% used K5.
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Of these studies, for DBP, 64.6% used mercury manometer, 6.3% used random-zero manometer, 6.3% used ultrasound device, and 4.2% used an automated device. In the United States, 65.5% used mercury manometer, 6.9% used random-zero manometer, 10.3% used ultrasound device, and 3.4% used an automated device. Regarding the study data points for DBP, 446 (81.5%) were from a mercury manometer, 17 (3.1%) were from a random-zero manometer, 15 (2.7%) were from an ultrasound device, and 19 (3.5%) were from an automated device. Regarding the number of DBP readings at each survey time point, 8.3% of studies recorded DBP once, 25.0% recorded twice, 47.9% recorded 3 times, and 18.8% did not provide detailed information. The patterns were similar for SBP.
The Figure shows the crude relationship between baseline age and DBP tracking and compares DBP tracking assessed using K4 versus K5. We observed an almost linear relationship between baseline age and DBP tracking for K4 but a nonlinear relationship for K5. It indicates that, with the increase in baseline age, K4 seems to predict stronger DBP tracking until around age 7 years, whereas the patterns for K5 change markedly with age. The difference in DBP tracking between K4 and K5 was small between 7 and 13 years of age.
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The choice of DBP measurement protocols affected DBP tracking after adjustment for potential confounders (Table 2). The tracking correlation coefficients for DBP measured using K4 were 0.035 higher than that of K5 but were not significant. The tracking assessed using an automated device was 0.152 higher than that of K5 (P=0.024). We conducted 2 sets of further analysis stratified by the cohorts’ baseline age: 1 was 13 years based on recommendations from reports of the Second Task Force on Blood Pressure Control in Children in 1987 and their update in 1996,11,24 and the other was 10 years based on the World Health Organization’s age category for adolescents. Among those aged <13 years, no significant difference was found between K4 and K5 in DBP tracking, whereas the automated device showed stronger DBP tracking than K5 (β=0.161; P=0.038) or K4 (β=0.134; P=0.085). For those with baseline age 13 years, DBP tracking based on K4 was 0.058 higher than that using K5 but was not significant. For baseline age <10 years, no difference was found between K4 and K5, whereas the automated device showed marginally higher DBP tracking than K5 (β=0.155; P=0.071) or K4 (β=0.159; P=0.062). Among those with baseline age 10 years, K4 gave higher DBP tracking than K5 (β=0.065; P=0.041), and so did the automated device (β=0.291; P=0.005), and it was stronger than K4 (β=0.220; P=0.030) as well.
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Table 3 shows that, compared with a mercury manometer, the automated device revealed higher DBP tracking (β=0.223; P=0.005), whereas a random-zero manometer or ultrasound device did not give different DBP tracking. Our further analyses stratified by age group show that, compared with a mercury manometer, the automated device revealed higher DBP in both younger children with baseline age <10 years (β=0.231; P=0.017) and older children (β=0.260; P=0.022). Multiple BP measurements revealed higher DBP tracking for younger children (β=0.219, P=0.015 for twice; β=0.150, P=0.030 for 3 times, respectively). However, in general, fewer advantages were observed if measurements were taken 3 times than if taken twice. We also conducted a similar analysis on SBP. However, no significant differences were found by using different types of monitors (Appendix 2). It seems that the choice of using different BP measurement protocols might not affect SBP tracking as much as for DBP. In addition, we tested but found no evidence of publication bias, and neither test was significant (P>0.05; see Appendix 3).
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Discussion |
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Based on findings from 50 cohort studies conducted in a variety of populations worldwide published since 1970, our meta-analysis shows that BP measurement protocol may affect BP tracking. Specifically, the tracking based on K4 was higher than that based on K5. Automated devices seem to give stronger tracking than mercury manometers. Multiple BP measurements revealed stronger DBP and SBP tracking but no apparent advantage if
3 readings were taken. These findings suggest that the current recommendation of using K5 in children should be re-examined and that current trend, in which automated devices replace mercury manometers, might be appropriate as long as the automated devices are validated. Our analysis shows that, in general, K4 provided a higher DBP tracking from childhood into adulthood than K5. To our knowledge, thus far, only 3 studies have used K4 and K5 simultaneously to compare the degree of DBP tracking (Table 4).6,34 The Bogalusa Heart Study measured BP (K4 and K5) with a mercury manometer at each screening on 2530 children aged 4 to 18 years and followed them for 15 years; K4 was found superior to K5 in children. However, another study showed no difference between the DBP tracking based on K4 and K5, which included a cohort of 333 urban US schoolchildren aged 8 to 15 years.34 A recent study of 412 Chinese children aged 6 to 18 years at baseline in Beijing shows a stronger DBP tracking from childhood to adulthood over a 15-year follow-up using K4 than using K5 (0.29 versus 0.22; Liang L, Mi J, Zhang MM, Wang YF, Wang TY, personal communication, 2007). This provides further evidence to support our findings.
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Overall, the currently available evidence, summarized in Table 5, suggests that K4 is superior to K5, which is against the current recommendations in the United States (see below). Several previous studies have argued that K4 is preferable to K5 for measuring DBP in children, or at least at very young ages, because of it has more advantages.1,5,6,42–44 However, some other studies found that reliable and repeatable DBPs were best achieved with K5 readings.21 Some other studies suggest that the within-person variability is similar between K4 and K5 readings.10,45 The Bogalusa Heart Study and other studies show that DBP measured at K4 and K5 can vary considerably in some age groups or for some individuals (eg, 
20 to 30 mm Hg), whereas in others groups the difference is very small.6,22,25
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The choice of using K4 or K5 to measure children’s DBP can have some important clinical implications. The difference between DBP readings at K4 and K5 can be substantial, and the difference tends to be greater in older than in younger children. Thus, misclassifications can occur when fixed DBP cut points (eg, the 95th percentiles) are used to classify childhood elevated BP status. Although frequently K4 and K5 phases occur simultaneously (or K5 does not occur),11 in children, particularly preadolescents, a difference of several millimeters of mercury is frequently present between DBPs measured at K4 and K5.22,46 Sinaiko et al46 evaluated K4 and K5 in 19 274 school children aged 10 to 15 years in 1986–1987. Approximately 50% of the children only had K4 (or K4 and K5 occurred simultaneously); 20% had a difference of 5 to 10 mm Hg between K4 and K5; 10% had a difference of 11 to 20 mm Hg; and 3% had a difference of >21 mm Hg. The K4 to K5 phase difference tended to be greater in older than younger children, was positively related to SBP and K4 DBP, and was negatively related to K5 DBP.46 Uhari et al21 measured DBP in Finnish children aged 6 to 18 years over a 6-year period and found that the mean difference between K4 and K5 DBP was 7 mm Hg (range: 6.5 to 9.2 mm Hg).
The 1977 First Task Force on Blood Pressure Control in Children recommended the use of K420; the 1987 Second Task Force recommended the use of K4 in infants and children 3 to 12 years old but a switch to K5 in adolescents aged 13 to 18 years.11,13 The Second Task Force report also released a set of BP-age distribution curves, which indicated that K5 was a reliable measure of DBP for children aged 13 years.24 The 1996 update of the Second Task Force Report suggested universal reporting of only K5 in children and adolescents.24 The most recent 2004 Fourth Report of National High Blood Pressure Education Program, as well as the American Heart Association, recommended the use of K5, and only if the very low K5 persists should K4 be recorded.35 However, some concerns and doubts have been raised regarding the use of K5 versus K4 in children even after the release of the 2004 report.47 Our study indicates that, before 14 years of age, K5 seems to be equal to K4 in the degree of DBP tracking. After 14 years of age, K4 seems to be different from K5 to predict stronger DBP tracking (see Figure). Further well-designed studies are needed to provide conclusive evidence regarding which one is favored in clinical practice and epidemiologic studies.
Our another important finding is that childhood DBP measured using automated devices shows a stronger tracking into adulthood than that with mercury manometers, although there are concerns regarding the accuracy of BP measurements using automated devices beyond their advantages.48–51 Recent US reports still recommend mercury manometers as the "gold standard."4,52 The human error in BP measurement by using the manual auscultatory technique includes inaccurate cuff selection and application, incorrect cuff positioning, inadequate rest period, rapid cuff deflation rate, poor observer concentration, digit bias, and lack of repeated measurement.4 Over the past decade, difficulty in obtaining a measurement by auscultation has prompted the use of commercial Doppler and oscillometric devices, and automated BP measurement became widely used with the banning of mercury manometers in some countries.53–55 Compared with mercury manometers, the main advantages of automated devices include their ease of use, the minimization of observer bias or digit preference, and possibly less risk for mercury contamination.4,48,56 Use of such automated devices is preferred for BP measurement in infants and young children, in whom auscultation is difficult.57 Our findings also indicate that, in children <10 years old, DBP measured using an automated device seems to be better for predicting DBP tracking. The use of Dinamap to measure BP in clinical settings has been criticized,50,51 whereas some studies confirm the strong temporal stability of BP measurements using Dinamap.58 The availability of alternative devices for the mercury manometer is improving, but the problem of independent validation is a serious issue.27,28,49 Our results provide some evidence to support the use of automated devices in clinical settings and in large epidemiologic studies. However, it is worth noting that high tracking correlations do not necessary preclude a systematic measurement error, and the small number of such available studies included in the present study could not exclude chance phenomena.
BP tracking correlations are likely to be attenuated by within-person variability. Combining multiple measurements might help reduce this measurement error. For the first time, our study quantifies the differences in observed BP tracking from childhood into adulthood by the number of BP readings per visit. We found that multiple BP measurements reveal slightly higher DBP tracking, but there are few additional advantages of collecting 3 readings. It has been reported previously that the within-person BP variability over time in children was greater than in adults, partially because of anxiety that accompanies the actual BP measurements in children.24,45 Children’s BP levels are dynamic and vary continually, which is especially true for the single BP readings obtained in health care facilities.47 The first (or single) readings are usually higher than the average of multiple readings.48 The average of multiple BP readings is closer to the basal BP levels and is more reproducible, and its use has been recommended.11,24,59,60 Others have argued that multiple visits are more important than multiple readings per visit, and 2 visits are recommended.34,48
Our metaregression analysis has its strengths. It is based on the findings of a large number of studies conducted in the general pediatric populations in different countries. A total of 617 data points for SBP and 547 data points for DBP provided in 50 cohort studies were used in our analysis. The systematic and quantitative assessments provide strong evidence to help quantify the influence of BP measurement protocols on BP tracking. On the other hand, our analysis has some limitations, which are common to these types of studies, such as potential selection bias. Another limitation is the lack of the original individual-level BP data in our study. Current BP cut points for hypertension diagnosis in young people are based on BP percentiles in reference populations stratified for age, sex, and height. Because we defined BP tracking correlation by using Pearson and Spearman correlations, we could not define and study hypertension or tracking in hypertension in our meta-analysis. Third, considerable heterogeneities in the BP instruments and measurements, including data quality, may exist in the studies included in our analysis, which may have affected our findings. Fourth, one may be concerned because we used >1 data point from some studies in which different follow-up periods or baseline ages were included, but such data in fact can help better test the influences of follow-up and baseline age on BP tracking. Our models have controlled for the possible dependence between multiple correlations from the same cohort. In addition, other factors, such as arm circumference, cuff size, and observers (eg, nurse versus technician) may affect the findings. Systematic errors in the measurement method might also be present. Furthermore, only 2 of our included studies used automated devices. In the Child and Adolescent Trial for Cardiovascular Health Study, Kelder et al61 reported 5-year follow-up BP tracking among 5106 US elementary students by using Dinamap Automated Device model 8100 XT.62 The other is a 9-year follow-up study of 1036 nine–year-old Australian children,63 in which BP was measured using a Dinamap 1846 SX/P BP recorder.57,64 Further study is needed to examine the impact of using automated devices on BP tracking.
Perspectives
In conclusion, our metaregression analysis shows that the choice of BP measurement protocols may affect BP tracking. Our findings do not support that K5 is superior to K4 when using an auscultatory technique to measure DBP. Thus, the current recommendation of using K5 may need to be reevaluated. An automated BP device is equally good or even better than other devices in predicting long-term DBP tracking. A collection of multiple BP measurements will help to measure BP more accurately and to improve BP tracking, but it is not necessary to obtain 3 readings considering the constraints in routine medical examinations and in large epidemiologic studies.
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Acknowledgments |
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Sources of Funding
The study was supported in part by the Johns Hopkins University Bloomberg School of Public Health and by a research grant from the National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases (R01 DK63383). J.M. was supported by grant H030930030130 from Beijing Municipal Science and Technology Commission and grant 30671804 from National Natural Science Foundation.
Disclosures
None.
Received October 2, 2007; first decision October 27, 2007; accepted December 19, 2007.
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