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Abstract

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Conclusions
  7. Acknowledgment
  8. References

Clinical implications of blood pressure variability (BPV) on subclinical organ damage in children are unknown. The authors sought to explore the potential utility of two newly derived BPV indices: weighted standard deviation (wBPSD) and real average variability (ARV), as well as two standard ambulatory blood pressure indices: average 24-hour systolic blood pressure (SBP) and 24-hour SBP load, to identify children at high risk for left ventricular (LV) hypertrophy (LVH). The study group consisted of 67 consecutive children who were referred to our institution for evaluation of suspected hypertension. LV mass was estimated by M-mode echocardiography using Devereux's formula according to the Penn convention and indexed for height2.7. We found a statistically significant, positive correlation between 24-hour wBPSD and LV mass index (LVMI) (ρ=0.389; P=.002) and no correlation between 24-hour ARV and LVMI (P>.05). However, partial correlation analysis of 24-hour wBPSD adjusted for body mass index (BMI) and LVMI showed only a weak correlation (ρ=0.3; P=.022). By using multiple linear regression analysis in a model with LVMI as a dependent variable and 24-hour wBPSD, 24-hour ARV, and BMI as independent variables, only BMI showed statistically significant independent positive associations with LVMI (P=.028). Results of our study showed that currently used BPV indices (24-hour wBPSD and 24-hour ARV) are not clinically reliable parameters to identify children at risk for LVH. Apparent contribution of the 24-hour wBPSD parameter to LVMI is negligible and is secondary to its close correlation with BMI (ρ=0.335 P=.009).

During the past decade, clinical and animal studies have examined an association between high blood pressure (BP) variability (BPV) and cardiovascular morbidity and mortality.[1-3] Although some animal studies clearly show that high BPV contributes to the development of left ventricular hypertrophy (LVH), the results of human studies are conflicting.[3, 4] Some human studies[1][5-8] have found no independent association between BP variability and cardiovascular events, while other studies[8-11] have reported an association between diastolic, systolic nighttime and daytime BPV, and cardiovascular outcome.[2]

This was explained mainly by the high prevalence of common confounding risk factors in adults such as obesity, older age, diabetes, autonomic dysfunction, and atherosclerosis, as well as by low prognostic validity of different BPV indices currently used.[12]

However, up to date, there are no relevant clinical data regarding BPV implications on subclinical organ damage in children.[13, 14]

The aim of this study was to explore the potential utility of two commonly used BPV indices: weighted standard deviation as well as average real variability (AVR) to identify hypertensive children at risk for LVH.

Methods

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Conclusions
  7. Acknowledgment
  8. References

The study involved 67 consecutive children referred from the local and regional basic health units to our clinic from January 2011 to August 2012 for evaluation of suspected hypertension.

Evaluation of all children was performed in accordance with our standard protocol, therefore we did not ask for special approval of the local ethic committee.

All children underwent 24-hour ambulatory BP monitoring as well as standard echocardiographic examination within the first week of initial examination. Additional clinical evaluation comprised medical history physical examination (including BP measurement using a mercury sphygmomanometer) and standard laboratory tests.

Ambulatory BP recording was performed according to the most recent criteria (American Heart Association Scientific Statement) on a day of typical activity, starting between 8 am and 9 am with the appropriate device (Scan Light III I.E.M GmbH, Germany, which is according to certificate of equivalence DIN EN 9001:2000, 13485, CMDCAS, functionally and technically identical with Mobil-O-Graph New Generation).[15, 16] By convention, cuff selection was determined by choosing a cuff whose bladder width was 40% of the upper arm circumference.[16] A potential study limitation is the fact that Mobil-O-Graph New Generation was not tested in children and has passed only validation procedure for adults.[15] Ambulatory BP readings were obtained at 15-minute intervals from 6 am to midnight, and at 30-minute intervals from midnight to 6 am. We accepted only recordings of good technical quality (at least 70% of valid readings). All children were asked to avoid sports activities but otherwise to continue usual activities, including school.

The following BPV parameters were evaluated: weighted standard deviation of BP (wBPSD) as well as the average real variability (ARV) for the entire 24-hour period. Weighted standard deviation (SD) was defined as the mean of day and night SD values, corrected for the number of hours included in each of these subperiods. Average real variability (ARV) was calculated from these readings by specifically designed software in Microsoft Office Excel 2003 statistical software, based on the previously reported formula

  • display math

where N stands for the number of valid BP measurements and K is the order of measurements from each patient monitoring.[3, 12]

From each BP recording we also calculated standard indices: the average 24-hour SBP (24-hour aSBP) and 24-hour SBP load (the percentage of readings greater than the 95th percentile during a 24-hour recording).

LV mass index (LVMI) as an index of LVH was estimated by M-mode echocardiography using Devereux's formula, according to the Penn convention and indexed for height2.7 to minimize effects of age, sex, and overweight status.[17, 18] LVH was defined as the LVMI >39.36 g/m2.7 in boys and >36.88 g/m2.7 in girls according to recommendations of the European Society of Hypertension.[19] Severe LVH was defined as LVMI >51 g/m2.7 according to the same recommendations.

Echocardiography and all measurements were performed in triplicate by the same cardiologist who was unaware of the patient's BP and the average values were used in subsequent analyses.

Statistics

Descriptive statistics on baseline variables are presented as median and interquartile range (IQR) or percentage as appropriate.

Potential predictors were analyzed for their relationship with the outcome using two-way tables with Mann-Whitney U test for continuous or ordinal variables, while Pearson χ2 tests or Fisher exact test were used for other variables.

We also used Pearson correlation test as well as multiple linear logistic regression models, enter method, and binary logistic regression model. All statistical analysis was performed with the SPSS 13.0 Windows XP environment program (SPSS Inc, Chicago, IL) as well as with STATA (version 11.2; StataCorp, College Station, TX) software. The results are presented in tabular and graphical views.

Sample Size

In order to show that an LVMI difference of 8 g/m2 is statistically significant, with a presumed LVMI standard deviation of 10 g/m2, for type I error 0.05 and type II error 0.20, minimal required sample size was 25 patients in the less frequent outcome category.

Results

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Conclusions
  7. Acknowledgment
  8. References

The study included 67 children, 23 girls and 44 boys, with a median age of 14 years (range, 12–17 years). Clinical data of the study population are summarized in the Table.

Table 1. Clinical Data of Study Population
VariableNo LVH (n=41)LVH (n=26)Total (N=67)
  1. Abbreviations: ARVBP, real average variability of blood pressure; BMI, body mass index; aSBP, ambulatory systolic blood pressure; IQR, interquartile range; LVH, left ventricular hypertrophy; SBP, systolic blood pressure; wBPSD, weighted standard deviation of blood pressure.

  2. a

    P<.05.

Women, No. (%)17 (41.46)6 (23.08)23 (34.33)
Men, No. (%)24 (58.54)20 (76.92)44 (65.67)
Age, median (IQR), y14 (12–16)14.5 (12–17)14 (12–16)
BMI, median (IQR), kg/m2a21.6 (19.8–25.8)28 (23.4–29.8)23.9 (20.4–28.4)
wBPSD, median (IQR), mm Hg10.8 (9.3–12.4)11.6 (10–13.6)11.2 (9.5–13.2)
24-H ARVBP, median (IQR), mm Hg8.6 (7.1–10.2)8.5 (7.6–10.6)8.6 (7.3–10.5)
24-H aSBP, median (IQR), mm Hga117 (105–124)129 (117–133)120 (111–129)
24-H SBP load, No. (%)19.5 (8.6–34.5)55.6 (31.7–6)32 (13–55.3)
Normotensive, No. (%)18 (43.9)3 (11.5)21 (31.3)
Ambulatory prehypertension, No. (%)10 (24.4)3 (11.5)13 (19.40)
Ambulatory hypertension, No. (%)2 (4.9)1 (3.8)3 (4.5)
Severe ambulatory hypertension, No. (%)a5 (12.2)16 (61.5)21 (31.3)
White-coat hypertension, No. (%)6 (14.6)3 (11.5)9 (13.4)

Of the total number of children, 21 (31.3%) were found to be normotensive, 13 (19.4%) had ambulatory prehypertension, 3 (4.5%) ambulatory hypertension, 21 (31.3%) severe ambulatory hypertension, and 9 (13.4%) white-coat hypertension. The classification was made in accordance with the American Heart Association recommended schema for ambulatory BP level staging in children.[16]

Echocardiographic examination revealed LVH in 26 patients (38.8%), of whom 8 (11.9%) had severe LVH. Children with LVH had significantly greater body mass index (BMI) vs children without LVH (median, 28 vs 21.6; P<.001).

There was no statistically significant difference between 24-hour wBPSD values of children without LVH vs children with LVH (median 10.8 vs 11.6; P>.05). Besides, there was no statistically significant difference between 24-hour ARV values of children with and without LVH (median, 8.6 vs 8.5; P>.05).

On the other hand, 24-hour aSBP was statistically significantly higher in the group of children with LVH compared with the children without LVH (median, 129 vs 117; P<.001). We also found significantly higher 24-hour SBP load in a group of children with LVH compared with children without LVH (median, 55.6 vs 19.5; P<.001).

By examining the correlation between analyzed BPV parameters and LVMI, we found a statistically moderate significant positive correlation between 24-hour wBPSD and LVMI (ρ=0.389; P=.002) (Figure).

image

Figure 1. Scatterplot with regression line and the fitted value with a confidence interval (CI) for left ventricular mass index (LVMI) and main independent variables. BMI indicates body mass index; ARVBP, real average variability of blood pressure; SBP, systolic blood pressure; wBPSD weighted standard deviation of blood pressure.

Download figure to PowerPoint

However, after partial correlation analysis between 24-hour wBPSD and LVMI adjusted for BMI, the correlation significantly weakened (ρ=0.3; P=.02). The correlation between ARV and LVMI was absent (P>.05).

By using multiple linear regression analysis in a model with LVMI as a dependent variable and 24-hour wBPSD, 24-hour ARV and BMI as independent variables, only BMI showed statistically significant positive associations with LVMI (P=.028). Similarly, in the binary regression models in which LVH (labeled either “yes” or “no”) was a dependent variable and 24-hour wBPSD, 24-hour ARV, and BMI were independent variables, only BMI was identified as an important independent predictor of LVH (P=.016). On the other hand, we found a statistically significant positive correlation between 24-hour wBPSD and BMI (ρ=0.335; P=.009) and no correlation between ARV and BMI (P<.05) (Figure).

By using linear logistic regression analysis in a model with LVMI as a dependent variable and 24-hour wBPSD, 24-hour ARV, BMI, and either 24-hour aSBP or 24-hour SBP load parameters, which were due to significant colinearity separately included in models, both parameters (24-hour aSBP or 24-hour SBP load) in separate models showed statistically significant independent positive associations with LVMI (P=.002 and P=.001, respectively). Similarly, this was the case in binary regression models where we found 24-hour aSBP and 24-hour SBP load to be important predictors of LVH (P=.049, P=.011, respectively).

Discussion

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Conclusions
  7. Acknowledgment
  8. References

The first result of our study is the finding that currently used BPV indices, which are thought to be pure measures of BPV such as 24-hour wBPSD and 24-hour ARV, are not clinically reliable parameters to identify children at risk for LVH.

This is in agreement with results of previous studies showing either no independent association between BPV with end-organ damage and cardiovascular risk, or its disappearance after adjusting for other cardiovascular, metabolic, and demographic variables.[1, 20, 21]

The second result of our study is the finding that 24-hour wBPSD and LVH association is secondary to their close correlation with BMI. The correlation between measures of BMI and BPV has been recently documented, although the explanation for this phenomenon is still being debated.[14] Our results are comparable with results of Abramson and colleagues,[22] who showed positive associations between BMI with high BPV (indexed by diastolic 24-hour wBPSD and systolic and diastolic ARV) in healthy, normotensive men and women. Although statistically unconvincing, independent predictive value of the ARV for the occurrence of adverse cardiovascular events was reported by Mena and colleagues.[3] However, unlike their results, we did not confirm the association of BMI and ARV. Nevertheless, we have shown that either the 24-hour aSBP or 24-hour SBP load are the most significant independent contributors for the development of LVH in children, which is in agreement with results of previous studies.[3, 16]

To the best of our knowledge, the present study is the first one to examine the clinical implications of BP variability (BPV) on subclinical organ damage (LVMI) in children. Moreover, to our knowledge, we are the first to examine the relationship between “pure” measures of BPV, based on intermittent BP measurements and their prognostic relevance on target organ damage in children. Unlike similar studies in adults, which are limited by the complexity of confounding risk factors such as diabetes, smoking, and aging, this wasn't the case in our research.

In order to improve the study design and prevent common methodology drawbacks (inappropriateness of BPV metrics as an index of BPV), we used wBPSD and ARV, which are thought to be pure measures of BPV sporadic component.[3, 12]

In comparison with assessing the real predictive importance of BPV on developing LVH based on intermittent ABP monitoring measurements, we are in the opinion that BPV analysis based on noninvasive continuous beat-to-beat BP monitoring will provide a definitive answer on the clinical implications of BPV on subclinical organ damage in children.[4, 23]

We would like to note, however, that we could not find any relevant literature data on the agreement between both of these “complementary” methods in assessing BPV in humans. Thus, our comparatively “crude method” may have led to some misapprehension of BPV clinical value in determining LVH in children.

Conclusions

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Conclusions
  7. Acknowledgment
  8. References

The results of this paper strengthen the previously reported usefulness of BMI in predicting cardiovascular risks and add to the understanding of the early development of obesity-related cardiovascular abnormalities in children.[24] We have demonstrated that the relationship of commonly used BPV indices, 24-hour wBPSD, and 24-hour ARV with target organ damage is negligible and is secondary to their close correlation with BMI.

Acknowledgment

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Conclusions
  7. Acknowledgment
  8. References

This work has been supported by the Ministry of Science and Technology of the Republic of Serbia by grant No. 175092.

References

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Conclusions
  7. Acknowledgment
  8. References
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