Height-Based Equations Can Improve the Diagnosis of Elevated Blood Pressure in Children

Height-Based Equations Can Improve the Diagnosis of Elevated Blood Pressure in Children Abstract Background High blood pressure (BP) is usually underdiagnosed in children and adolescents, particularly due to its complex diagnosis process. This study describes novel height-based equations for the detection of BP disorders (BP > 90th percentile) and compares the accuracy of this approach with previously described screening methods to identify BP disorders. Methods Height-based equations were built using the 90th percentile values for systolic and diastolic BP and respective height values from the current guideline of high-BP management in children. This guideline was also used as the gold standard method for identification of BP disorders. The equations were tested in Brazilian (n = 2,936) and American (n = 6,541) populations of children with 8–13 years old. Results The obtained equations were 70 + 0.3 × height (in cm) for systolic BP and 35 + 0.25 × height (in cm) for diastolic BP. The new equations presented sensitivity and negative predictive value of near 100% and specificity > 91% and showed higher specificity and positive predictive value when compared with other screening tools. Importantly, height-based equations had greater agreement (kappa coefficient = 0.75–0.81) with the gold standard method than the other methods (kappa coefficient = 0.53–0.73). Further analysis showed that alternative height-based equations designed to identify hypertension (BP ≥ 95th percentile) also showed superior performance (kappa coefficient = 0.89–0.92) compared with other screening methods (kappa coefficient = 0.43–0.85). Conclusions These findings suggest that the use of height-based equations may be a simple and feasible approach to improve the detection of high BP in the pediatric population. blood pressure, diagnosis, height, hypertension, screening, pediatrics Hypertension is progressively affecting the pediatric age group, raising the need for additional efforts to prevent such risk factor in this population.1,2 A recent guideline3 established that adolescents ≥13 years of age with systolic blood pressure (SBP) above 120 mm Hg or diastolic blood pressure (DBP) above 80 mm Hg are considered to have blood pressure (BP) disorders, which include elevated BP (formerly termed “prehypertension”) and hypertension. However, in children <13 years of age, those thresholds vary according to individuals’ age, sex, and height.3 This nuance makes the diagnosis of BP disorders more challenging in the pediatric age group, requiring the constant analysis of charts and tables of percentiles for correct diagnosis. Moreover, this diagnostic approach is pointed out as a major reason for the underdiagnosis of BP disorders in this population.4 Some methods have been described to simplify the diagnosis of BP disorders in the pediatric population.5 They include the use of simplified tables,6,7 cutoffs determined by the ratio between BP and height,8,9 equations to determinate the maximum normal BP value per patient,10,11 and simplified BP cutoffs.12 However, each method has specific disadvantages. Simplified tables require the use of the table itself, while cutoffs determined by BP-to-height ratio may vary among different populations and genders. BP equations, in turn, have shown lower sensitivity and specificity when compared with other screening methods.13 One potential explanation for the lower accuracy of BP equations to detect BP disorders may be the choice of the adjusting variable included in the formulae. BP equations built to date are based on regression analyses using age as the main adjusting variable.10,11 Interestingly, alternative methods that use height as the main adjusting variable have consistently shown good accuracy,13 raising the hypothesis that including height into BP equations might improve the detection of BP disorders. Additionally, recent guideline3 recommendations changed the values of BP percentiles for children, thus invalidating the cutoffs obtained from the aforementioned methods8–11 and supporting the need of novel analysis using data from current guidelines aiming to facilitate the diagnosis of BP disorders. The objective of this study is to describe simple height-based equations built from current guideline data3 for the detection of BP disorders and compare the accuracy of this new methodology with previously described methods in 2 different populations. METHODS Study populations Study population 1 was retrospectively obtained from a medical database including 17,083 children and adolescents evaluated in a pediatric cardiology center from northeast Brazil.14 Only individuals with age between 8 and 13 years old who had complete information on height, weight, sex, SBP, and DBP were included, leaving 2,936 subjects for the current analysis. Study population 2 included subjects from the National Health and Nutrition Examination Survey (NHANES) 1999–2014 database. The details of this survey have been described elsewhere.15 Only subjects with age between 8 (only patients with 8 years or more had their BP measured) and 13 years old and with complete information on height, weight, sex, SBP, and DBP were included, leaving 6,541 subjects for the current analysis. Clinical variables BP, height, and weight measurements from study population 1 were performed as previously described.14 Briefly, BP was measured by the auscultatory method using aneroid sphygmomanometers (BIC, Itupeva, Brazil), and appropriate cuffs’ size for the age. Height was measured using a stadiometer (Caumaq 101PL, Cachoeira do Sul, Brazil), and weight was measured using an electronic scale (Black&Decker BK30, Shandong, China). The methods of BP measurement in study population 2 were described elsewhere.16 Briefly, BP was measured using mercury sphygmomanometers following recommendations from the American Heart Association, while height and weight were measured according to NHANES Anthropometry Procedures Manual.17 This study only considered the first BP measurement in the analysis. BP disorder was defined as a value of SBP or DBP > 90th percentile, thus including BP levels > 90th percentile and < 95th percentile (elevated BP) and BP levels ≥ 95th percentile (hypertension). Body mass index (BMI) was calculated as weight (kg) divided by height (m2). Overweight was defined as a BMI between the 85th and 95th percentiles, and obesity was defined as a BMI greater than or equal to the 95th percentile according to CDC.18 Construction of BP equations For the main analysis, we built height-based equations using the 90th percentiles values for SBP and DBP and respective height values from the current guideline of management of high BP in children and adolescents.3 Height-based equations were constructed from linear regression relating SBP and DBP with height and used combined data from both boys and girls. Later, a mountain plot was created by computing a percentile for each ranked difference between the results of the new formulae and the 90th percentile of SBP and DBP by height, gender, and age reported in the current guideline.3 For sensitivity analysis, we used the same approach to construct alternative BP equations using the 95th percentiles values for SBP and DBP from the current guideline.3 For secondary analysis, we used the same approach to construct BP equations using the 90th percentiles values for SBP and DBP and height values from the previous guideline (The Fourth Report),19 instead of the current guideline.3 The construction of BP equations using data from the previous guideline was performed to allow the comparison of height-based BP equations with additional BP disorder screening methods6,7,10,20 described before the release of current guideline. Statistical analysis Descriptive data are presented as mean ± SD. Chi-square method was used to compare categorical variables. Each screening method was compared with the gold standard method for identification of BP disorders, which was based on the definitions of the current guideline3 for the main analysis or on the definitions of the previous guideline (The Fourth Report)19 for secondary analysis. The sensitivity, specificity, area under curve (AUC), positive predictive value (PPV), and negative predictive value (NPV) for each method were calculated in both populations. The agreement between each method and the respective gold standard method was calculated using the kappa coefficient and the strength of agreement categorized as poor (kappa < 0.20), fair (kappa between 0.21 and 0.40), moderate (kappa between 0.41 and 0.60), good (kappa between 0.61 and 0.80), and very good (kappa between 0.81 and 1.00).21 In the main analysis, we compared the performance of BP equations and other screening methods to detect BP disorders defined according to the current guideline.3 The other screening methods included (i) the new screening table described in the current clinical practice guideline (CPG table),3 (ii) the simplified cutoffs described by Xi et al.12 (SBP ≥ 110 mm Hg and/or DBP ≥ 70 mm Hg for children between 6 and 11 years and SBP ≥ 120 mm Hg and/or DBP ≥ 80 mm Hg for adolescents aged 12–17 years), and (iii) the BP-to-height ratio cutoffs.20 Regarding the latter method, we calculated new cutoffs for BP disorders with receiver operating characteristic (ROC) curves, using the current guideline as the gold standard (Supplementary Table 1). As a sensitivity analysis, we built alternative height-based equations aiming to identify hypertension (SBP or DBP ≥ 95th percentile) and compared their performance with 2 other screening methods: (i) BP-to-height ratio, with new calculated cutoffs for hypertension for both populations utilizing ROC curves (Supplementary Table 2) and (ii) the simplified cutoffs12 for hypertension (SBP ≥ 120 mm Hg and/or DBP ≥ 80 mm Hg for children between 6 and 11 years and SBP ≥ 130 mm Hg and/or DBP ≥ 85 mm Hg for adolescents aged 12–17 years). In secondary analysis, we compared the performance of BP equations (built using data from the previous guideline—The Fourth Report),19 with the table proposed by Kaelber et al.,6 the simplified cutoffs,12 and the BP-to-height ratio cutoffs to detect BP disorders defined according to the previous guideline.19 In addition, we evaluated the performance of the table proposed by Mitchell et al.7 and the equations described by Badeli et al.,10 which were built solely using data from the previous guideline. Regarding the BP-to-height method, we used cutoffs that were previously reported for population 114 (Supplementary Table 3). Although cutoffs were previously described for population 222, there were differences in patients’ selection in that aforementioned study in comparison with the present report. Therefore, we calculated new cutoffs for BP disorders for population 2 using ROC curves (Supplementary Table 4). Other studied methods are detailed in Supplementary Tables 5–7. P values < 0.05 were considered statistically significant. All analyses were performed using MedCalc 17.4 (Ostend, Belgium). RESULTS Table 1 describes the characteristics of the studied populations. BP disorders, determined according to the current guideline definitions,3 were present in 13% of the American population and 14% of the Brazilian population. Overweight and obesity were present in 42.7 and 37.5% of American and Brazilian individuals, respectively. Table 1. General characteristics of the studied populations Variables Brazilian population American population Sex Male Female Male Female Number of patients 1,695 1,241 3,243 3,298 Age, years 10.0 ± 1.4 9.9 ± 1.4 10.1 ± 1.4 10.1 ± 1.4 SBP, mm Hg 102.6 ± 10.0 102.1 ± 9.7 103.5 ± 9.7 102.7 ± 10.0 DBP, mm Hg 63.1 ± 8.3 62.9 ± 7.9 54.5 ± 12.0 55.6 ± 11.1 Height, cm 139.6 ± 10.3 139.2 ± 11.9 144.2 ± 11.3 145.1 ± 11.5 Weight, kg 35.5 ± 11.8 36.8 ± 11.5 42.3 ± 14.5 43.9 ± 15.2 Variables Brazilian population American population Sex Male Female Male Female Number of patients 1,695 1,241 3,243 3,298 Age, years 10.0 ± 1.4 9.9 ± 1.4 10.1 ± 1.4 10.1 ± 1.4 SBP, mm Hg 102.6 ± 10.0 102.1 ± 9.7 103.5 ± 9.7 102.7 ± 10.0 DBP, mm Hg 63.1 ± 8.3 62.9 ± 7.9 54.5 ± 12.0 55.6 ± 11.1 Height, cm 139.6 ± 10.3 139.2 ± 11.9 144.2 ± 11.3 145.1 ± 11.5 Weight, kg 35.5 ± 11.8 36.8 ± 11.5 42.3 ± 14.5 43.9 ± 15.2 Continuous variables are presented as mean ± SD. Abbreviations: DBP, diastolic blood pressure; SBP, systolic blood pressure. View Large Table 1. General characteristics of the studied populations Variables Brazilian population American population Sex Male Female Male Female Number of patients 1,695 1,241 3,243 3,298 Age, years 10.0 ± 1.4 9.9 ± 1.4 10.1 ± 1.4 10.1 ± 1.4 SBP, mm Hg 102.6 ± 10.0 102.1 ± 9.7 103.5 ± 9.7 102.7 ± 10.0 DBP, mm Hg 63.1 ± 8.3 62.9 ± 7.9 54.5 ± 12.0 55.6 ± 11.1 Height, cm 139.6 ± 10.3 139.2 ± 11.9 144.2 ± 11.3 145.1 ± 11.5 Weight, kg 35.5 ± 11.8 36.8 ± 11.5 42.3 ± 14.5 43.9 ± 15.2 Variables Brazilian population American population Sex Male Female Male Female Number of patients 1,695 1,241 3,243 3,298 Age, years 10.0 ± 1.4 9.9 ± 1.4 10.1 ± 1.4 10.1 ± 1.4 SBP, mm Hg 102.6 ± 10.0 102.1 ± 9.7 103.5 ± 9.7 102.7 ± 10.0 DBP, mm Hg 63.1 ± 8.3 62.9 ± 7.9 54.5 ± 12.0 55.6 ± 11.1 Height, cm 139.6 ± 10.3 139.2 ± 11.9 144.2 ± 11.3 145.1 ± 11.5 Weight, kg 35.5 ± 11.8 36.8 ± 11.5 42.3 ± 14.5 43.9 ± 15.2 Continuous variables are presented as mean ± SD. Abbreviations: DBP, diastolic blood pressure; SBP, systolic blood pressure. View Large Height-based equations obtained from the current guideline to detect BP disorders (BP > 90th percentile) The results of linear regression models relating the 90th percentiles values for SBP or DBP and respective height values from the current guideline3 are shown in Figure 1. The constant and the coefficient obtained for height were rounded to values that are easier to remember and within the confidence intervals. Therefore, the following equations for the lowest values of elevated BP (90th percentile) were obtained: Figure 1. View largeDownload slide Linear regression analysis between the 90th percentile values for systolic (a) and diastolic (b) blood pressure and height values obtained from the current guideline.3 Abbreviations: DBP, diastolic blood pressure; SBP, systolic blood pressure. Figure 1. View largeDownload slide Linear regression analysis between the 90th percentile values for systolic (a) and diastolic (b) blood pressure and height values obtained from the current guideline.3 Abbreviations: DBP, diastolic blood pressure; SBP, systolic blood pressure. • SBP 90th = 70 + 0.3 × height (in cm) • DBP 90th = 35 + 0.25 × height (in cm) Figure 2 shows the Mountain plots for the difference between the 90th percentile for SBP or DBP values derived from the current guideline3 and obtained SBP or DBP values from the formulae in children between 8 and 13 years. The maximum difference between the formulae and the gold standard method BP values was approximately 3 mm Hg for SBP and 6 mm Hg for DBP. Figure 2. View largeDownload slide Mountain plots for the difference in systolic (a) and diastolic (b) blood pressure between the 90th percentile values from the current guideline3 and the values obtained with height-based equations in children between 8 and 13 years. Abbreviations: DBP, diastolic blood pressure; SBP, systolic blood pressure. Figure 2. View largeDownload slide Mountain plots for the difference in systolic (a) and diastolic (b) blood pressure between the 90th percentile values from the current guideline3 and the values obtained with height-based equations in children between 8 and 13 years. Abbreviations: DBP, diastolic blood pressure; SBP, systolic blood pressure. Table 2 shows the results of sensitivity, specificity, AUC, PPV, and NPV, as well as kappa coefficients for the studied methods to detect BP disorders according to the gold standard method (current guideline definition).3 All studied methods had great sensitivity and NPV in both populations. In particular, the sensitivity and NPV of height-based equations were 100%. In both populations, height-based equations had greater specificity, AUC, and PPV than the CPG table, simplified cutoffs, and BP-to-height cutoffs. In American individuals, height-based equations showed a very good agreement with the gold standard method to detect BP disorders (kappa coefficient = 0.80), while the kappa coefficients for the other studied methods were remarkably lower (from 0.53 to 0.61). In Brazilian individuals, height-based equations showed good agreement for height-based equations (kappa coefficient = 0.75), slightly better than the CPG table (kappa coefficient = 0.73). Table 2. Performance of height-based equations and other methods to detect blood pressure disorders (blood pressure > 90th percentile) according to the current guideline3 Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  CPG table 99.5 (98.2–99.9) 90.6 (89.4–91.8) 0.95 (0.94–0.96) 63.4 (60.5–66.2) 99.9 (99.6–99.9) 0.73 (0.70–0.76)  Simplified cutoffs 100 (99.1–100) 81.3 (79.7–82.8) 0.91 (0.89–0.92) 46.5 (44.5–48.5) 100 0.55 (0.51–0.58)  BP-to-height ratio 97.2 (95.2–98.4) 76.3 (74.4–77.8) 0.86 (0.85–0.88) 42.3 (40.5–44.0) 99.3 (98.8–99.6) 0.48 (0.45–0.51)  Height-based equations 100 (99.1–100) 91.6 (90.5–92.7) 0.96 (0.95–0.97) 66.1 (63.1–68.9) 100 0.75 (0.72–0.78) American population  CPG table 99.9 (99.3–100) 85.8 (84.9–86.7) 0.93 (0.92–0.94) 51.4 (49.8–53.0) 99.9 (99.8–100) 0.61 (0.59–0.63)  Simplified cutoffs 94.7 (93.0–96.1) 86.2 (85.3–87.1) 0.90 (0.89–0.91) 50.7 (49.0–52.4) 99.1 (98.8–99.3) 0.59 (0.57–0.61)  BP-to-height ratio 94.5 (92.9–95.9) 81.3 (80.2–82.3) 0.88 (0.87–0.89) 46.8 (45.4–48.2) 98.8 (98.5–99.1) 0.53 (0.51–0.56)  Height-based equations 100 (99.5–100) 94.1 (93.5–94.7) 0.97 (0.96–0.98) 71.9 (69.8–74.0) 100 0.80 (0.79–0.83) Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  CPG table 99.5 (98.2–99.9) 90.6 (89.4–91.8) 0.95 (0.94–0.96) 63.4 (60.5–66.2) 99.9 (99.6–99.9) 0.73 (0.70–0.76)  Simplified cutoffs 100 (99.1–100) 81.3 (79.7–82.8) 0.91 (0.89–0.92) 46.5 (44.5–48.5) 100 0.55 (0.51–0.58)  BP-to-height ratio 97.2 (95.2–98.4) 76.3 (74.4–77.8) 0.86 (0.85–0.88) 42.3 (40.5–44.0) 99.3 (98.8–99.6) 0.48 (0.45–0.51)  Height-based equations 100 (99.1–100) 91.6 (90.5–92.7) 0.96 (0.95–0.97) 66.1 (63.1–68.9) 100 0.75 (0.72–0.78) American population  CPG table 99.9 (99.3–100) 85.8 (84.9–86.7) 0.93 (0.92–0.94) 51.4 (49.8–53.0) 99.9 (99.8–100) 0.61 (0.59–0.63)  Simplified cutoffs 94.7 (93.0–96.1) 86.2 (85.3–87.1) 0.90 (0.89–0.91) 50.7 (49.0–52.4) 99.1 (98.8–99.3) 0.59 (0.57–0.61)  BP-to-height ratio 94.5 (92.9–95.9) 81.3 (80.2–82.3) 0.88 (0.87–0.89) 46.8 (45.4–48.2) 98.8 (98.5–99.1) 0.53 (0.51–0.56)  Height-based equations 100 (99.5–100) 94.1 (93.5–94.7) 0.97 (0.96–0.98) 71.9 (69.8–74.0) 100 0.80 (0.79–0.83) Abbreviations: AUC, area under curve; BP, blood pressure; CI, confidence interval; CPG, current clinical practice guideline; NPV, negative predictive value; PPV, positive predictive value. View Large Table 2. Performance of height-based equations and other methods to detect blood pressure disorders (blood pressure > 90th percentile) according to the current guideline3 Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  CPG table 99.5 (98.2–99.9) 90.6 (89.4–91.8) 0.95 (0.94–0.96) 63.4 (60.5–66.2) 99.9 (99.6–99.9) 0.73 (0.70–0.76)  Simplified cutoffs 100 (99.1–100) 81.3 (79.7–82.8) 0.91 (0.89–0.92) 46.5 (44.5–48.5) 100 0.55 (0.51–0.58)  BP-to-height ratio 97.2 (95.2–98.4) 76.3 (74.4–77.8) 0.86 (0.85–0.88) 42.3 (40.5–44.0) 99.3 (98.8–99.6) 0.48 (0.45–0.51)  Height-based equations 100 (99.1–100) 91.6 (90.5–92.7) 0.96 (0.95–0.97) 66.1 (63.1–68.9) 100 0.75 (0.72–0.78) American population  CPG table 99.9 (99.3–100) 85.8 (84.9–86.7) 0.93 (0.92–0.94) 51.4 (49.8–53.0) 99.9 (99.8–100) 0.61 (0.59–0.63)  Simplified cutoffs 94.7 (93.0–96.1) 86.2 (85.3–87.1) 0.90 (0.89–0.91) 50.7 (49.0–52.4) 99.1 (98.8–99.3) 0.59 (0.57–0.61)  BP-to-height ratio 94.5 (92.9–95.9) 81.3 (80.2–82.3) 0.88 (0.87–0.89) 46.8 (45.4–48.2) 98.8 (98.5–99.1) 0.53 (0.51–0.56)  Height-based equations 100 (99.5–100) 94.1 (93.5–94.7) 0.97 (0.96–0.98) 71.9 (69.8–74.0) 100 0.80 (0.79–0.83) Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  CPG table 99.5 (98.2–99.9) 90.6 (89.4–91.8) 0.95 (0.94–0.96) 63.4 (60.5–66.2) 99.9 (99.6–99.9) 0.73 (0.70–0.76)  Simplified cutoffs 100 (99.1–100) 81.3 (79.7–82.8) 0.91 (0.89–0.92) 46.5 (44.5–48.5) 100 0.55 (0.51–0.58)  BP-to-height ratio 97.2 (95.2–98.4) 76.3 (74.4–77.8) 0.86 (0.85–0.88) 42.3 (40.5–44.0) 99.3 (98.8–99.6) 0.48 (0.45–0.51)  Height-based equations 100 (99.1–100) 91.6 (90.5–92.7) 0.96 (0.95–0.97) 66.1 (63.1–68.9) 100 0.75 (0.72–0.78) American population  CPG table 99.9 (99.3–100) 85.8 (84.9–86.7) 0.93 (0.92–0.94) 51.4 (49.8–53.0) 99.9 (99.8–100) 0.61 (0.59–0.63)  Simplified cutoffs 94.7 (93.0–96.1) 86.2 (85.3–87.1) 0.90 (0.89–0.91) 50.7 (49.0–52.4) 99.1 (98.8–99.3) 0.59 (0.57–0.61)  BP-to-height ratio 94.5 (92.9–95.9) 81.3 (80.2–82.3) 0.88 (0.87–0.89) 46.8 (45.4–48.2) 98.8 (98.5–99.1) 0.53 (0.51–0.56)  Height-based equations 100 (99.5–100) 94.1 (93.5–94.7) 0.97 (0.96–0.98) 71.9 (69.8–74.0) 100 0.80 (0.79–0.83) Abbreviations: AUC, area under curve; BP, blood pressure; CI, confidence interval; CPG, current clinical practice guideline; NPV, negative predictive value; PPV, positive predictive value. View Large Height-based equations obtained from the current guideline to detect hypertension (BP ≥ 95th percentile) As a sensitivity analysis, we built the following height-based equations (detailed in Supplementary Figures 1 and 2) to identify hypertension using the 95th percentiles values for SBP or DBP and respective height values from the current guideline3: • SBP 90th = 75 + 0.3 × height (in cm) • DBP 90th = 40 + 0.25 × height (in cm) The results of sensitivity, specificity, AUC, PPV, NPV, and kappa coefficient for height-based equations, simplified cutoffs, and BP-to-height cutoffs to detect hypertension (BP ≥ 95th percentile) using the current guideline3 as the gold standard are shown in Table 3. The kappa coefficient of height-based equations with the gold standard method was 0.92 and 0.89 in the Brazilian and American populations, respectively, while the other methods had inferior agreement with the gold standard. Table 3. Performance of height-based equations to detect hypertension (blood pressure ≥ 95th percentile) according to the current guideline3 Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  Simplified cutoffs 81.6 (76.8–85.8) 99.3 (99–99.6) 0.90 (0.89–0.91) 93.5 (89.9–95.9%) 97.9 (97.4–98.4) 0.85 (0.82–0.89)  BP-to-height ratio 98.3 (96.1–99.4) 79.2 (77.6–80.8) 0.89 (0.88–0.90) 35.0 (33.3–36.8) 99.7 (99.4–99.9) 0.43 (0.39–0.46)  Height-based equations 95.0 (91.8–97.2) 98.9 (98.4–99.2) 0.97 (0.96–0.98) 90.7 (87.2–93.4) 99.4 (99.0–99.7) 0.92 (0.89–0.94) American population  Simplified cutoffs 52.9 (48.4–57.4) 99.4 (99.2–99.6) 0.76 (0.75–0.77) 88.0 (84.0–91.2) 96.3 (96.0–96.6) 0.64 (0.60–0.68)  BP-to-height ratio 97.5 (95.8–98.7) 84.3 (83.4–85.2) 0.91 (0.90–0.92) 33.5 (32.2–34.8) 99.8 (99.6–99.9) 0.43 (0.41–0.46)  Height-based equations 87.1 (83.8–90.0) 99.1 (98.8–99.3) 0.93 (0.92–0.94) 88.7 (85.8–91.1) 98.9 (98.7–99.2) 0.89 (0.87–0.91) Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  Simplified cutoffs 81.6 (76.8–85.8) 99.3 (99–99.6) 0.90 (0.89–0.91) 93.5 (89.9–95.9%) 97.9 (97.4–98.4) 0.85 (0.82–0.89)  BP-to-height ratio 98.3 (96.1–99.4) 79.2 (77.6–80.8) 0.89 (0.88–0.90) 35.0 (33.3–36.8) 99.7 (99.4–99.9) 0.43 (0.39–0.46)  Height-based equations 95.0 (91.8–97.2) 98.9 (98.4–99.2) 0.97 (0.96–0.98) 90.7 (87.2–93.4) 99.4 (99.0–99.7) 0.92 (0.89–0.94) American population  Simplified cutoffs 52.9 (48.4–57.4) 99.4 (99.2–99.6) 0.76 (0.75–0.77) 88.0 (84.0–91.2) 96.3 (96.0–96.6) 0.64 (0.60–0.68)  BP-to-height ratio 97.5 (95.8–98.7) 84.3 (83.4–85.2) 0.91 (0.90–0.92) 33.5 (32.2–34.8) 99.8 (99.6–99.9) 0.43 (0.41–0.46)  Height-based equations 87.1 (83.8–90.0) 99.1 (98.8–99.3) 0.93 (0.92–0.94) 88.7 (85.8–91.1) 98.9 (98.7–99.2) 0.89 (0.87–0.91) Abbreviations: AUC, area under curve; BP, blood pressure; CI, confidence interval; NPV, negative predictive value; PPV, positive predictive value. View Large Table 3. Performance of height-based equations to detect hypertension (blood pressure ≥ 95th percentile) according to the current guideline3 Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  Simplified cutoffs 81.6 (76.8–85.8) 99.3 (99–99.6) 0.90 (0.89–0.91) 93.5 (89.9–95.9%) 97.9 (97.4–98.4) 0.85 (0.82–0.89)  BP-to-height ratio 98.3 (96.1–99.4) 79.2 (77.6–80.8) 0.89 (0.88–0.90) 35.0 (33.3–36.8) 99.7 (99.4–99.9) 0.43 (0.39–0.46)  Height-based equations 95.0 (91.8–97.2) 98.9 (98.4–99.2) 0.97 (0.96–0.98) 90.7 (87.2–93.4) 99.4 (99.0–99.7) 0.92 (0.89–0.94) American population  Simplified cutoffs 52.9 (48.4–57.4) 99.4 (99.2–99.6) 0.76 (0.75–0.77) 88.0 (84.0–91.2) 96.3 (96.0–96.6) 0.64 (0.60–0.68)  BP-to-height ratio 97.5 (95.8–98.7) 84.3 (83.4–85.2) 0.91 (0.90–0.92) 33.5 (32.2–34.8) 99.8 (99.6–99.9) 0.43 (0.41–0.46)  Height-based equations 87.1 (83.8–90.0) 99.1 (98.8–99.3) 0.93 (0.92–0.94) 88.7 (85.8–91.1) 98.9 (98.7–99.2) 0.89 (0.87–0.91) Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  Simplified cutoffs 81.6 (76.8–85.8) 99.3 (99–99.6) 0.90 (0.89–0.91) 93.5 (89.9–95.9%) 97.9 (97.4–98.4) 0.85 (0.82–0.89)  BP-to-height ratio 98.3 (96.1–99.4) 79.2 (77.6–80.8) 0.89 (0.88–0.90) 35.0 (33.3–36.8) 99.7 (99.4–99.9) 0.43 (0.39–0.46)  Height-based equations 95.0 (91.8–97.2) 98.9 (98.4–99.2) 0.97 (0.96–0.98) 90.7 (87.2–93.4) 99.4 (99.0–99.7) 0.92 (0.89–0.94) American population  Simplified cutoffs 52.9 (48.4–57.4) 99.4 (99.2–99.6) 0.76 (0.75–0.77) 88.0 (84.0–91.2) 96.3 (96.0–96.6) 0.64 (0.60–0.68)  BP-to-height ratio 97.5 (95.8–98.7) 84.3 (83.4–85.2) 0.91 (0.90–0.92) 33.5 (32.2–34.8) 99.8 (99.6–99.9) 0.43 (0.41–0.46)  Height-based equations 87.1 (83.8–90.0) 99.1 (98.8–99.3) 0.93 (0.92–0.94) 88.7 (85.8–91.1) 98.9 (98.7–99.2) 0.89 (0.87–0.91) Abbreviations: AUC, area under curve; BP, blood pressure; CI, confidence interval; NPV, negative predictive value; PPV, positive predictive value. View Large Height-based equations obtained from the previous guideline to detect BP disorders (BP > 90th percentile) As a secondary analysis, we built height-based equations to detect BP disorders (BP > 90th percentile) using data from the previous guideline (The Fourth Report)19: • SBP 90th = 70 + 0.33 × height (in cm) • DBP 90th = 40 + 0.25 × height (in cm) Table 4 shows the results of sensitivity, specificity, AUC, PPV, NPV, and kappa coefficient for several methods to detect BP disorders using the previous guideline19 as the gold standard. All studied methods showed great sensitivity and NPV, but height-based equations showed greater specificity, PPV, and AUC in both populations. The agreement of height-based equations with the previous guideline (kappa coefficient) was very good, while the other methods had remarkably lesser agreement with the gold standard method in both populations. Table 4. Performance of height-based equations and other methods to detect blood pressure disorders (blood pressure > 90th percentile) according to the previous guideline19 Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  Mitchell table 97.6 (95.0–99.0) 83.7 (82.3–85.1) 0.91 (0.90–0.92) 39.0 (36.9–41.1) 99.7 (99.4–99.8) 0.49 (0.45–0.52)  Kaelber table 97.6 (95.0–99.0) 90.6 (89.5–91.7) 0.94 (0.93–0.95) 52.6 (49.7–55.6) 99.7 (99.4–99.9) 0.64 (0.60–0.68)  BP-to-height ratio 91.6 (87.8–94.6) 87.1 (85.7–88.3) 0.89 (0.88–0.90) 43.0 (40.5–45.6) 99.00 (98.5–99.3) 0.52 (0.48–0.56)  Badeli equation 99.0 (98.5–99.3) 90.5 (89.4–91.6) 0.94 (0.93–0.95) 52.4 (49.5–55.4) 99.7 (99.4–99.9) 0.64 (0.60–0.68)  Simplified cutoffs 98.9 (97.0–99.8) 77.2 (75.6–78.8) 0.88 (0.87–0.89) 31.7 (30.2–33.2) 99.9 (99.6–99.9) 0.39 (0.36–0.42)  Height-based equations 96.2 (93.2–98.1) 98.4 (97.9–98.9) 0.97 (0.97–0.98) 86.8 (82.9–89.9) 99.6 (99.3–99.8) 0.90 (0.88–0.93) American population  Mitchell table 100 (99.2–100) 79.7 (78.7–80.7) 0.90 (0.89–0.91) 26.5 (25.5–27.5) 100 0.35 (0.32–0.37)  Kaelber table 100 (99.2–100) 83.0 (82.0–83.9) 0.91 (0.91–0.92) 29.9 (28.7–31.1) 100 0.40 (0.37–0.42)  BP-to-height ratio 97.1 (95.1–98.4) 81.8 (80.8–82.7) 0.89 (0.89–0.90) 27.8 (26.7–29.0) 99.7 (99.6–99.8) 0.37 (0.34–0.39)  Badeli equation 100 (99.2–100) 83.2 (82.2–84.1) 0.92 (0.91–0.92) 30.1 (28.9–31.3) 100 0.40 (0.37–0.43)  Simplified cutoffs 99.5 (98.4–99.9) 80.9 (79.9–81.9) 0.90 (0.90–0.91) 27.5 (26.4–28.5) 99.9 (99.8–100) 0.36 (0.34–0.39)  Height-based equations 97.7 (95.9–98.9) 99.1 (98.8–99.3) 0.98 (0.98–0.99) 88.4 (85.5–90.8) 99.8 (99.7–99.9) 0.92 (0.90–0.94) Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  Mitchell table 97.6 (95.0–99.0) 83.7 (82.3–85.1) 0.91 (0.90–0.92) 39.0 (36.9–41.1) 99.7 (99.4–99.8) 0.49 (0.45–0.52)  Kaelber table 97.6 (95.0–99.0) 90.6 (89.5–91.7) 0.94 (0.93–0.95) 52.6 (49.7–55.6) 99.7 (99.4–99.9) 0.64 (0.60–0.68)  BP-to-height ratio 91.6 (87.8–94.6) 87.1 (85.7–88.3) 0.89 (0.88–0.90) 43.0 (40.5–45.6) 99.00 (98.5–99.3) 0.52 (0.48–0.56)  Badeli equation 99.0 (98.5–99.3) 90.5 (89.4–91.6) 0.94 (0.93–0.95) 52.4 (49.5–55.4) 99.7 (99.4–99.9) 0.64 (0.60–0.68)  Simplified cutoffs 98.9 (97.0–99.8) 77.2 (75.6–78.8) 0.88 (0.87–0.89) 31.7 (30.2–33.2) 99.9 (99.6–99.9) 0.39 (0.36–0.42)  Height-based equations 96.2 (93.2–98.1) 98.4 (97.9–98.9) 0.97 (0.97–0.98) 86.8 (82.9–89.9) 99.6 (99.3–99.8) 0.90 (0.88–0.93) American population  Mitchell table 100 (99.2–100) 79.7 (78.7–80.7) 0.90 (0.89–0.91) 26.5 (25.5–27.5) 100 0.35 (0.32–0.37)  Kaelber table 100 (99.2–100) 83.0 (82.0–83.9) 0.91 (0.91–0.92) 29.9 (28.7–31.1) 100 0.40 (0.37–0.42)  BP-to-height ratio 97.1 (95.1–98.4) 81.8 (80.8–82.7) 0.89 (0.89–0.90) 27.8 (26.7–29.0) 99.7 (99.6–99.8) 0.37 (0.34–0.39)  Badeli equation 100 (99.2–100) 83.2 (82.2–84.1) 0.92 (0.91–0.92) 30.1 (28.9–31.3) 100 0.40 (0.37–0.43)  Simplified cutoffs 99.5 (98.4–99.9) 80.9 (79.9–81.9) 0.90 (0.90–0.91) 27.5 (26.4–28.5) 99.9 (99.8–100) 0.36 (0.34–0.39)  Height-based equations 97.7 (95.9–98.9) 99.1 (98.8–99.3) 0.98 (0.98–0.99) 88.4 (85.5–90.8) 99.8 (99.7–99.9) 0.92 (0.90–0.94) Abbreviations: AUC, area under curve; BP, blood pressure; CI, confidence interval; NPV, negative predictive value; PPV, positive predictive value. View Large Table 4. Performance of height-based equations and other methods to detect blood pressure disorders (blood pressure > 90th percentile) according to the previous guideline19 Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  Mitchell table 97.6 (95.0–99.0) 83.7 (82.3–85.1) 0.91 (0.90–0.92) 39.0 (36.9–41.1) 99.7 (99.4–99.8) 0.49 (0.45–0.52)  Kaelber table 97.6 (95.0–99.0) 90.6 (89.5–91.7) 0.94 (0.93–0.95) 52.6 (49.7–55.6) 99.7 (99.4–99.9) 0.64 (0.60–0.68)  BP-to-height ratio 91.6 (87.8–94.6) 87.1 (85.7–88.3) 0.89 (0.88–0.90) 43.0 (40.5–45.6) 99.00 (98.5–99.3) 0.52 (0.48–0.56)  Badeli equation 99.0 (98.5–99.3) 90.5 (89.4–91.6) 0.94 (0.93–0.95) 52.4 (49.5–55.4) 99.7 (99.4–99.9) 0.64 (0.60–0.68)  Simplified cutoffs 98.9 (97.0–99.8) 77.2 (75.6–78.8) 0.88 (0.87–0.89) 31.7 (30.2–33.2) 99.9 (99.6–99.9) 0.39 (0.36–0.42)  Height-based equations 96.2 (93.2–98.1) 98.4 (97.9–98.9) 0.97 (0.97–0.98) 86.8 (82.9–89.9) 99.6 (99.3–99.8) 0.90 (0.88–0.93) American population  Mitchell table 100 (99.2–100) 79.7 (78.7–80.7) 0.90 (0.89–0.91) 26.5 (25.5–27.5) 100 0.35 (0.32–0.37)  Kaelber table 100 (99.2–100) 83.0 (82.0–83.9) 0.91 (0.91–0.92) 29.9 (28.7–31.1) 100 0.40 (0.37–0.42)  BP-to-height ratio 97.1 (95.1–98.4) 81.8 (80.8–82.7) 0.89 (0.89–0.90) 27.8 (26.7–29.0) 99.7 (99.6–99.8) 0.37 (0.34–0.39)  Badeli equation 100 (99.2–100) 83.2 (82.2–84.1) 0.92 (0.91–0.92) 30.1 (28.9–31.3) 100 0.40 (0.37–0.43)  Simplified cutoffs 99.5 (98.4–99.9) 80.9 (79.9–81.9) 0.90 (0.90–0.91) 27.5 (26.4–28.5) 99.9 (99.8–100) 0.36 (0.34–0.39)  Height-based equations 97.7 (95.9–98.9) 99.1 (98.8–99.3) 0.98 (0.98–0.99) 88.4 (85.5–90.8) 99.8 (99.7–99.9) 0.92 (0.90–0.94) Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  Mitchell table 97.6 (95.0–99.0) 83.7 (82.3–85.1) 0.91 (0.90–0.92) 39.0 (36.9–41.1) 99.7 (99.4–99.8) 0.49 (0.45–0.52)  Kaelber table 97.6 (95.0–99.0) 90.6 (89.5–91.7) 0.94 (0.93–0.95) 52.6 (49.7–55.6) 99.7 (99.4–99.9) 0.64 (0.60–0.68)  BP-to-height ratio 91.6 (87.8–94.6) 87.1 (85.7–88.3) 0.89 (0.88–0.90) 43.0 (40.5–45.6) 99.00 (98.5–99.3) 0.52 (0.48–0.56)  Badeli equation 99.0 (98.5–99.3) 90.5 (89.4–91.6) 0.94 (0.93–0.95) 52.4 (49.5–55.4) 99.7 (99.4–99.9) 0.64 (0.60–0.68)  Simplified cutoffs 98.9 (97.0–99.8) 77.2 (75.6–78.8) 0.88 (0.87–0.89) 31.7 (30.2–33.2) 99.9 (99.6–99.9) 0.39 (0.36–0.42)  Height-based equations 96.2 (93.2–98.1) 98.4 (97.9–98.9) 0.97 (0.97–0.98) 86.8 (82.9–89.9) 99.6 (99.3–99.8) 0.90 (0.88–0.93) American population  Mitchell table 100 (99.2–100) 79.7 (78.7–80.7) 0.90 (0.89–0.91) 26.5 (25.5–27.5) 100 0.35 (0.32–0.37)  Kaelber table 100 (99.2–100) 83.0 (82.0–83.9) 0.91 (0.91–0.92) 29.9 (28.7–31.1) 100 0.40 (0.37–0.42)  BP-to-height ratio 97.1 (95.1–98.4) 81.8 (80.8–82.7) 0.89 (0.89–0.90) 27.8 (26.7–29.0) 99.7 (99.6–99.8) 0.37 (0.34–0.39)  Badeli equation 100 (99.2–100) 83.2 (82.2–84.1) 0.92 (0.91–0.92) 30.1 (28.9–31.3) 100 0.40 (0.37–0.43)  Simplified cutoffs 99.5 (98.4–99.9) 80.9 (79.9–81.9) 0.90 (0.90–0.91) 27.5 (26.4–28.5) 99.9 (99.8–100) 0.36 (0.34–0.39)  Height-based equations 97.7 (95.9–98.9) 99.1 (98.8–99.3) 0.98 (0.98–0.99) 88.4 (85.5–90.8) 99.8 (99.7–99.9) 0.92 (0.90–0.94) Abbreviations: AUC, area under curve; BP, blood pressure; CI, confidence interval; NPV, negative predictive value; PPV, positive predictive value. View Large DISCUSSION In this study, we described simple height-based equations as screening methods to identify BP disorders (BP > 90th percentile) and compared the ability of this novel approach and previously reported screening methods to detect BP disorders in large samples of American and Brazilian children. Our analysis highlights that height-based equations consistently showed superior ability to detect high BP and hypertension, as reflected by higher specificity, PPV, and AUC, and greater agreement with the gold standard method when compared to several other screening methods. These findings suggest that height-based equations may be a simple and feasible approach to improve the detection of high BP in children. The detection of BP disorders in children has classically required the joint analysis of various tables and charts, which is considered a major reason for the underdiagnosis of these conditions in the pediatric population.4,23 Although the current guideline3 establishes that adolescents (13 years or more) with SBP above 120 mm Hg or DBP above 80 mm Hg are considered to have elevated BP, the use of tables and charts are still necessary to detect elevated BP levels in children below 13 years. In this report, we described simple height-based equations to determine the 90th percentile of SBP and DBP according to the most recent guideline.3 Then, we compared the performance of these new formulae with other methods (CPG table,3 simplified cutoffs,12 and BP-to-height cutoffs20) to detect BP disorders in American and Brazilian children ranging from 8 to 13 years. Our analysis showed that all studied methods had high sensitivity and NPV. However, height-based equations showed higher PPV as well as higher specificity and AUC. These findings are significant because a higher PPV leads to a better identification of children with high BP. More importantly, novel height-based equations had high agreement with the gold standard method (kappa coefficients ranging from 0.75 to 0.81), which was greater than those observed from other methods. Likewise, results of sensitivity analysis demonstrated that alternative height-based equations aiming to detect hypertension (BP ≥ 95th percentile), instead of BP disorders, showed remarkably better performance as compared to other screening methods designed to detect hypertension. However, the novel height-based equations showed a lower sensitivity to identify hypertension than BP-to-height ratio, particularly in the American population, despite the better agreement with the gold standard. Therefore, in a scenario where the purpose is not missing patients with hypertension, the 95th percentile height-based equations might not necessarily be the best method. Height-based equations may offer some practical advantages when compared with previously reported BP screening tools. First, they do not require the use of additional tables, as proposed by other methods.5 Second, they can be uniformly used across different populations, which is not applicable to the BP-to-height ratio method, due to the variety of the cutoffs obtained in different studies.8,9,14,20,24,25 Third, our height-based equations were built using data from current guidelines,3 while most of the available screening methods were built using data from the previous guideline.19 Given that the current guideline excluded overweight and obese patients from normative BP tables,3,26 several screening methods reported hitherto7,11,20 may have become inappropriate to detect BP disorders. Fourth, height-based equations are simple, easy to memorize and do not vary according to age and gender, thus differing from other methods.4,7 These facts, along with the high sensitivity and a greater PPV compared with the other methods, suggest that height-based equations may be an easy and reliable tool to not only for screening but also for identifying children with BP disorders. At a first glance, the new equations described herein may appear as variants of Badeli et al.’s equations,10 which were built from regression analysis of 90th BP percentile values according to age. However, our equations were based on height, instead of age. This is an important difference because BP values have been demonstrated to vary more with height than with age.27 Furthermore, the Badeli et al.’s equations were built according to the 50th percentile for height,10 with consequent lower performance at the extremes of height.10,11 To compare the ability of our height-based equations and age-based equations reported by Badeli et al. to detect BP disorders, we performed secondary analysis considering data from the previous guideline19 as the gold standard method as well as the template to build the formulae. The previous guideline was used in such analysis because the equations reported by Badeli et al. have not been updated to the current guideline.3 Our results demonstrated that the accuracy of height-based equations to detect BP disorders was greater than that of age-based equations, strengthening the notion that screening methods based on height instead of age may have greater ability to detect BP disorders in children. Notably, height-based equations showed remarkably higher agreement with the gold standard method (kappa coefficient = 0.90 and 0.92 in the Brazilian and American population, respectively), than age-based equations (kappa coefficient = 0.64 and 0.40 in the Brazilian and American population, respectively). Furthermore, the accuracy of height-based equations was greater than several other studied screening methods that used the previous guideline as the gold standard method, demonstrating the superior ability of height-based equations to detect BP disorders regardless of the guideline used to define BP alterations. The idea that screening methods based on height might have better ability to identify children with actual high BP has also been supported by other studies. For instance, the comparison of 11 screening methods for the diagnosis of hypertension in children13 showed that methods based on height, particularly the table proposed by Chiolero et al.,28 had not only great screening ability, as reflected by high sensitivity and NPV, but also better PPV and therefore superior performance to identify patients with high BP. This study has some limitations. First, data from population 1 were based on retrospective analysis of patients’ charts, while a prospective design would have been more appropriate to obtain the data. Second, we used only one BP measurement from each studied subject, which might have overestimated the prevalence of BP disorders in the studied populations, given that an increasing number of measurements tends to reduce the obtained BP of children.29 We believe, however, that this fact is not a significant limitation to our study, because our aim was to evaluate the performance of height-based equations for the identification of BP disorders, and not to establish a precise prevalence of BP disorders in the studied populations. Third, we only used information from Brazilian and American children. Thus, it is necessary to confirm the current findings in other populations. In conclusion, our analysis showed that height-based equations have high sensitivity and NPV as well as great agreement with the gold standard method to detect BP disorders in American and Brazilian children. In addition, height-based equations showed superior ability to detect BP disorders and greater agreement with the gold standard method. These findings suggest that the use of height-based equations may be a simple and feasible approach to improve the detection of BP disorders in children. SUPPLEMENTARY MATERIAL Supplementary data are available at American Journal of Hypertension online. DISCLOSURE The authors declare no conflict of interest. ACKNOWLEDGMENT This study was supported by grant from the Brazilian National Council for Scientific and Technological Development (304245/2013–5 to W.N.). REFERENCES 1. Lackland DT , Weber MA . Global burden of cardiovascular disease and stroke: hypertension at the core . Can J Cardiol 2015 ; 31 : 569 – 571 . Google Scholar CrossRef Search ADS PubMed 2. Flynn J . The changing face of pediatric hypertension in the era of the childhood obesity epidemic . Pediatr Nephrol 2013 ; 28 : 1059 – 1066 . Google Scholar CrossRef Search ADS PubMed 3. Flynn JT , Kaelber DC , Baker-Smith CM , Blowey D , Carroll AE , Daniels SR , de Ferranti SD , Dionne JM , Falkner B , Flinn SK , Gidding SS , Goodwin C , Leu MG , Powers ME , Rea C , Samuels J , Simasek M , Thaker VV , Urbina EM . Clinical practice guideline for screening and management of high blood pressure in children and adolescents . Pediatrics 2017 ; 140 : e20171904 . Google Scholar CrossRef Search ADS PubMed 4. Hansen ML , Gunn PW , Kaelber DC . Underdiagnosis of hypertension in children and adolescents . JAMA 2007 ; 298 : 874 – 879 . Google Scholar CrossRef Search ADS PubMed 5. Chiolero A , Paradis G . User-friendly tools to identify elevated blood pressure in children . Paediatr Child Health 2013 ; 18 : 63 – 64 . Google Scholar CrossRef Search ADS PubMed 6. Kaelber DC , Pickett F . Simple table to identify children and adolescents needing further evaluation of blood pressure . Pediatrics 2009 ; 123 : e972 – e974 . Google Scholar CrossRef Search ADS PubMed 7. Mitchell CK , Theriot JA , Sayat JG , Muchant DG , Franco SM . A simplified table improves the recognition of paediatric hypertension . J Paediatr Child Health 2011 ; 47 : 22 – 26 . Google Scholar CrossRef Search ADS PubMed 8. Rabbia F , Rabbone I , Totaro S , Testa E , Covella M , Berra E , Bertello MC , Gioia E , Cerutti F , Veglio F . Evaluation of blood pressure/height ratio as an index to simplify diagnostic criteria of hypertension in Caucasian adolescents . J Hum Hypertens 2011 ; 25 : 623 – 624 . Google Scholar CrossRef Search ADS PubMed 9. Ejike CE , Yin FZ . Blood pressure-to-height ratio simplifies the diagnosis of hypertension in Nigerian children . J Trop Pediatr 2013 ; 59 : 160 – 161 . Google Scholar CrossRef Search ADS PubMed 10. Badeli H , Sajedi SA , Shakiba M . Simple formulas for screening abnormal blood pressure in children and adolescents . Iran J Kidney Dis 2010 ; 4 : 250 – 252 . Google Scholar PubMed 11. Somu S , Sundaram B , Kamalanathan AN . Early detection of hypertension in general practice . Arch Dis Child 2003 ; 88 : 302 . Google Scholar CrossRef Search ADS PubMed 12. Xi B , Zhang T , Li S , Harville E , Bazzano L , He J , Chen W . Can pediatric hypertension criteria be simplified? A prediction analysis of subclinical cardiovascular outcomes from the Bogalusa Heart Study . Hypertension 2017 ; 69 : 691 – 696 . Google Scholar CrossRef Search ADS PubMed 13. Ma C , Kelishadi R , Hong YM , Bovet P , Khadilkar A , Nawarycz T , Krzywińska-Wiewiorowska M , Aounallah-Skhiri H , Zong X , Motlagh ME , Kim HS , Khadilkar V , Krzyżaniak A , Ben Romdhane H , Heshmat R , Chiplonkar S , Stawińska-Witoszyńska B , El Ati J , Qorbani M , Kajale N , Traissac P , Ostrowska-Nawarycz L , Ardalan G , Parthasarathy L , Zhao M , Xi B . Performance of eleven simplified methods for the identification of elevated blood pressure in children and adolescents . Hypertension 2016 ; 68 : 614 – 620 . Google Scholar CrossRef Search ADS PubMed 14. Mourato FA , Nadruz W Jr , Moser LR , de Lima Filho JL , Mattos SS . A modified blood pressure to height ratio improves accuracy for hypertension in childhood . Am J Hypertens 2015 ; 28 : 409 – 413 . Google Scholar CrossRef Search ADS PubMed 15. Rosner B , Cook NR , Daniels S , Falkner B . Childhood blood pressure trends and risk factors for high blood pressure: the NHANES experience 1988–2008 . Hypertension 2013 ; 62 : 247 – 254 . Google Scholar CrossRef Search ADS PubMed 16. National Center for Health Statistics . NHANES Examination Data . <https://wwwn.cdc.gov/nchs/nhanes/Search/DataPage.aspx?Component=Examination>. 17. Centers for Disease Control and Prevention . NHANES – Anthropometry Procedures Manual . <https://wwwn.cdc.gov/nchs/data/nhanes/2013–2014/manuals/2013_anthropometry.pdf>. 18. Kuczmarski RJ , Ogden CL , Guo SS , Grummer-Strawn LM , Flegal KM , Mei Z , Wei R , Curtin LR , Roche AF , Johnson CL . CDC growth charts for the United States: methods and development . Vital Health Stat 2002 ; 11 : 1 – 190 . 19. Village G . The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents . Pediatrics 2004 ; 114 : 555 – 576 . Google Scholar CrossRef Search ADS PubMed 20. Lu Q , Ma CM , Yin FZ , Liu BW , Lou DH , Liu XL . How to simplify the diagnostic criteria of hypertension in adolescents . J Hum Hypertens 2011 ; 25 : 159 – 163 . Google Scholar CrossRef Search ADS PubMed 21. Altman D. Practical Statistics for Medical Research . Chapman and Hall : London , 1991 . 22. Xi B , Zhang M , Zhang T , Li S , Steffen LM . Simplification of childhood hypertension definition using blood pressure to height ratio among US youths aged 8–17 years, NHANES 1999–2012 . Int J Cardiol 2015 ; 180 : 210 – 213 . Google Scholar CrossRef Search ADS PubMed 23. Cunningham RJ . Is pediatric hypertension underdiagnosed ? Nat Clin Pract Cardiovasc Med 2008 ; 5 : 128 – 129 . Google Scholar CrossRef Search ADS PubMed 24. Lu Q , Ma C , Yin F , Wang R , Lou D , Liu X . Blood pressure-to-height ratio as a screening measure for identifying children with hypertension . Eur J Pediatr 2013 ; 172 : 99 – 105 . Google Scholar CrossRef Search ADS PubMed 25. Kelishadi R , Heshmat R , Ardalan G , Qorbani M , Taslimi M , Poursafa P , Keramatian K , Taheri M , Motlagh ME . First report on simplified diagnostic criteria for pre-hypertension and hypertension in a national sample of adolescents from the Middle East and North Africa: the CASPIAN-III study . J Pediatr (Rio J) 2014 ; 90 : 85 – 91 . Google Scholar CrossRef Search ADS PubMed 26. Rosner B , Cook N , Portman R , Daniels S , Falkner B . Determination of blood pressure percentiles in normal-weight children: some methodological issues . Am J Epidemiol 2008 ; 167 : 653 – 666 . Google Scholar CrossRef Search ADS PubMed 27. Regnault N , Kleinman KP , Rifas-Shiman SL , Langenberg C , Lipshultz SE , Gillman MW . Components of height and blood pressure in childhood . Int J Epidemiol 2014 ; 43 : 149 – 159 . Google Scholar CrossRef Search ADS PubMed 28. Chiolero A , Paradis G , Simonetti GD , Bovet P . Absolute height-specific thresholds to identify elevated blood pressure in children . J Hypertens 2013 ; 31 : 1170 – 1174 . Google Scholar CrossRef Search ADS PubMed 29. Salgado CM , Carvalhaes JT . Arterial hypertension in childhood . J Pediatr (Rio J) 2003 ; 79 ( Suppl 1 ): S115 – S124 . Google Scholar CrossRef Search ADS PubMed © American Journal of Hypertension, Ltd 2018. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png American Journal of Hypertension Oxford University Press

Height-Based Equations Can Improve the Diagnosis of Elevated Blood Pressure in Children

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Oxford University Press
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© American Journal of Hypertension, Ltd 2018. All rights reserved. For Permissions, please email: journals.permissions@oup.com
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0895-7061
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1941-7225
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10.1093/ajh/hpy028
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Abstract

Abstract Background High blood pressure (BP) is usually underdiagnosed in children and adolescents, particularly due to its complex diagnosis process. This study describes novel height-based equations for the detection of BP disorders (BP > 90th percentile) and compares the accuracy of this approach with previously described screening methods to identify BP disorders. Methods Height-based equations were built using the 90th percentile values for systolic and diastolic BP and respective height values from the current guideline of high-BP management in children. This guideline was also used as the gold standard method for identification of BP disorders. The equations were tested in Brazilian (n = 2,936) and American (n = 6,541) populations of children with 8–13 years old. Results The obtained equations were 70 + 0.3 × height (in cm) for systolic BP and 35 + 0.25 × height (in cm) for diastolic BP. The new equations presented sensitivity and negative predictive value of near 100% and specificity > 91% and showed higher specificity and positive predictive value when compared with other screening tools. Importantly, height-based equations had greater agreement (kappa coefficient = 0.75–0.81) with the gold standard method than the other methods (kappa coefficient = 0.53–0.73). Further analysis showed that alternative height-based equations designed to identify hypertension (BP ≥ 95th percentile) also showed superior performance (kappa coefficient = 0.89–0.92) compared with other screening methods (kappa coefficient = 0.43–0.85). Conclusions These findings suggest that the use of height-based equations may be a simple and feasible approach to improve the detection of high BP in the pediatric population. blood pressure, diagnosis, height, hypertension, screening, pediatrics Hypertension is progressively affecting the pediatric age group, raising the need for additional efforts to prevent such risk factor in this population.1,2 A recent guideline3 established that adolescents ≥13 years of age with systolic blood pressure (SBP) above 120 mm Hg or diastolic blood pressure (DBP) above 80 mm Hg are considered to have blood pressure (BP) disorders, which include elevated BP (formerly termed “prehypertension”) and hypertension. However, in children <13 years of age, those thresholds vary according to individuals’ age, sex, and height.3 This nuance makes the diagnosis of BP disorders more challenging in the pediatric age group, requiring the constant analysis of charts and tables of percentiles for correct diagnosis. Moreover, this diagnostic approach is pointed out as a major reason for the underdiagnosis of BP disorders in this population.4 Some methods have been described to simplify the diagnosis of BP disorders in the pediatric population.5 They include the use of simplified tables,6,7 cutoffs determined by the ratio between BP and height,8,9 equations to determinate the maximum normal BP value per patient,10,11 and simplified BP cutoffs.12 However, each method has specific disadvantages. Simplified tables require the use of the table itself, while cutoffs determined by BP-to-height ratio may vary among different populations and genders. BP equations, in turn, have shown lower sensitivity and specificity when compared with other screening methods.13 One potential explanation for the lower accuracy of BP equations to detect BP disorders may be the choice of the adjusting variable included in the formulae. BP equations built to date are based on regression analyses using age as the main adjusting variable.10,11 Interestingly, alternative methods that use height as the main adjusting variable have consistently shown good accuracy,13 raising the hypothesis that including height into BP equations might improve the detection of BP disorders. Additionally, recent guideline3 recommendations changed the values of BP percentiles for children, thus invalidating the cutoffs obtained from the aforementioned methods8–11 and supporting the need of novel analysis using data from current guidelines aiming to facilitate the diagnosis of BP disorders. The objective of this study is to describe simple height-based equations built from current guideline data3 for the detection of BP disorders and compare the accuracy of this new methodology with previously described methods in 2 different populations. METHODS Study populations Study population 1 was retrospectively obtained from a medical database including 17,083 children and adolescents evaluated in a pediatric cardiology center from northeast Brazil.14 Only individuals with age between 8 and 13 years old who had complete information on height, weight, sex, SBP, and DBP were included, leaving 2,936 subjects for the current analysis. Study population 2 included subjects from the National Health and Nutrition Examination Survey (NHANES) 1999–2014 database. The details of this survey have been described elsewhere.15 Only subjects with age between 8 (only patients with 8 years or more had their BP measured) and 13 years old and with complete information on height, weight, sex, SBP, and DBP were included, leaving 6,541 subjects for the current analysis. Clinical variables BP, height, and weight measurements from study population 1 were performed as previously described.14 Briefly, BP was measured by the auscultatory method using aneroid sphygmomanometers (BIC, Itupeva, Brazil), and appropriate cuffs’ size for the age. Height was measured using a stadiometer (Caumaq 101PL, Cachoeira do Sul, Brazil), and weight was measured using an electronic scale (Black&Decker BK30, Shandong, China). The methods of BP measurement in study population 2 were described elsewhere.16 Briefly, BP was measured using mercury sphygmomanometers following recommendations from the American Heart Association, while height and weight were measured according to NHANES Anthropometry Procedures Manual.17 This study only considered the first BP measurement in the analysis. BP disorder was defined as a value of SBP or DBP > 90th percentile, thus including BP levels > 90th percentile and < 95th percentile (elevated BP) and BP levels ≥ 95th percentile (hypertension). Body mass index (BMI) was calculated as weight (kg) divided by height (m2). Overweight was defined as a BMI between the 85th and 95th percentiles, and obesity was defined as a BMI greater than or equal to the 95th percentile according to CDC.18 Construction of BP equations For the main analysis, we built height-based equations using the 90th percentiles values for SBP and DBP and respective height values from the current guideline of management of high BP in children and adolescents.3 Height-based equations were constructed from linear regression relating SBP and DBP with height and used combined data from both boys and girls. Later, a mountain plot was created by computing a percentile for each ranked difference between the results of the new formulae and the 90th percentile of SBP and DBP by height, gender, and age reported in the current guideline.3 For sensitivity analysis, we used the same approach to construct alternative BP equations using the 95th percentiles values for SBP and DBP from the current guideline.3 For secondary analysis, we used the same approach to construct BP equations using the 90th percentiles values for SBP and DBP and height values from the previous guideline (The Fourth Report),19 instead of the current guideline.3 The construction of BP equations using data from the previous guideline was performed to allow the comparison of height-based BP equations with additional BP disorder screening methods6,7,10,20 described before the release of current guideline. Statistical analysis Descriptive data are presented as mean ± SD. Chi-square method was used to compare categorical variables. Each screening method was compared with the gold standard method for identification of BP disorders, which was based on the definitions of the current guideline3 for the main analysis or on the definitions of the previous guideline (The Fourth Report)19 for secondary analysis. The sensitivity, specificity, area under curve (AUC), positive predictive value (PPV), and negative predictive value (NPV) for each method were calculated in both populations. The agreement between each method and the respective gold standard method was calculated using the kappa coefficient and the strength of agreement categorized as poor (kappa < 0.20), fair (kappa between 0.21 and 0.40), moderate (kappa between 0.41 and 0.60), good (kappa between 0.61 and 0.80), and very good (kappa between 0.81 and 1.00).21 In the main analysis, we compared the performance of BP equations and other screening methods to detect BP disorders defined according to the current guideline.3 The other screening methods included (i) the new screening table described in the current clinical practice guideline (CPG table),3 (ii) the simplified cutoffs described by Xi et al.12 (SBP ≥ 110 mm Hg and/or DBP ≥ 70 mm Hg for children between 6 and 11 years and SBP ≥ 120 mm Hg and/or DBP ≥ 80 mm Hg for adolescents aged 12–17 years), and (iii) the BP-to-height ratio cutoffs.20 Regarding the latter method, we calculated new cutoffs for BP disorders with receiver operating characteristic (ROC) curves, using the current guideline as the gold standard (Supplementary Table 1). As a sensitivity analysis, we built alternative height-based equations aiming to identify hypertension (SBP or DBP ≥ 95th percentile) and compared their performance with 2 other screening methods: (i) BP-to-height ratio, with new calculated cutoffs for hypertension for both populations utilizing ROC curves (Supplementary Table 2) and (ii) the simplified cutoffs12 for hypertension (SBP ≥ 120 mm Hg and/or DBP ≥ 80 mm Hg for children between 6 and 11 years and SBP ≥ 130 mm Hg and/or DBP ≥ 85 mm Hg for adolescents aged 12–17 years). In secondary analysis, we compared the performance of BP equations (built using data from the previous guideline—The Fourth Report),19 with the table proposed by Kaelber et al.,6 the simplified cutoffs,12 and the BP-to-height ratio cutoffs to detect BP disorders defined according to the previous guideline.19 In addition, we evaluated the performance of the table proposed by Mitchell et al.7 and the equations described by Badeli et al.,10 which were built solely using data from the previous guideline. Regarding the BP-to-height method, we used cutoffs that were previously reported for population 114 (Supplementary Table 3). Although cutoffs were previously described for population 222, there were differences in patients’ selection in that aforementioned study in comparison with the present report. Therefore, we calculated new cutoffs for BP disorders for population 2 using ROC curves (Supplementary Table 4). Other studied methods are detailed in Supplementary Tables 5–7. P values < 0.05 were considered statistically significant. All analyses were performed using MedCalc 17.4 (Ostend, Belgium). RESULTS Table 1 describes the characteristics of the studied populations. BP disorders, determined according to the current guideline definitions,3 were present in 13% of the American population and 14% of the Brazilian population. Overweight and obesity were present in 42.7 and 37.5% of American and Brazilian individuals, respectively. Table 1. General characteristics of the studied populations Variables Brazilian population American population Sex Male Female Male Female Number of patients 1,695 1,241 3,243 3,298 Age, years 10.0 ± 1.4 9.9 ± 1.4 10.1 ± 1.4 10.1 ± 1.4 SBP, mm Hg 102.6 ± 10.0 102.1 ± 9.7 103.5 ± 9.7 102.7 ± 10.0 DBP, mm Hg 63.1 ± 8.3 62.9 ± 7.9 54.5 ± 12.0 55.6 ± 11.1 Height, cm 139.6 ± 10.3 139.2 ± 11.9 144.2 ± 11.3 145.1 ± 11.5 Weight, kg 35.5 ± 11.8 36.8 ± 11.5 42.3 ± 14.5 43.9 ± 15.2 Variables Brazilian population American population Sex Male Female Male Female Number of patients 1,695 1,241 3,243 3,298 Age, years 10.0 ± 1.4 9.9 ± 1.4 10.1 ± 1.4 10.1 ± 1.4 SBP, mm Hg 102.6 ± 10.0 102.1 ± 9.7 103.5 ± 9.7 102.7 ± 10.0 DBP, mm Hg 63.1 ± 8.3 62.9 ± 7.9 54.5 ± 12.0 55.6 ± 11.1 Height, cm 139.6 ± 10.3 139.2 ± 11.9 144.2 ± 11.3 145.1 ± 11.5 Weight, kg 35.5 ± 11.8 36.8 ± 11.5 42.3 ± 14.5 43.9 ± 15.2 Continuous variables are presented as mean ± SD. Abbreviations: DBP, diastolic blood pressure; SBP, systolic blood pressure. View Large Table 1. General characteristics of the studied populations Variables Brazilian population American population Sex Male Female Male Female Number of patients 1,695 1,241 3,243 3,298 Age, years 10.0 ± 1.4 9.9 ± 1.4 10.1 ± 1.4 10.1 ± 1.4 SBP, mm Hg 102.6 ± 10.0 102.1 ± 9.7 103.5 ± 9.7 102.7 ± 10.0 DBP, mm Hg 63.1 ± 8.3 62.9 ± 7.9 54.5 ± 12.0 55.6 ± 11.1 Height, cm 139.6 ± 10.3 139.2 ± 11.9 144.2 ± 11.3 145.1 ± 11.5 Weight, kg 35.5 ± 11.8 36.8 ± 11.5 42.3 ± 14.5 43.9 ± 15.2 Variables Brazilian population American population Sex Male Female Male Female Number of patients 1,695 1,241 3,243 3,298 Age, years 10.0 ± 1.4 9.9 ± 1.4 10.1 ± 1.4 10.1 ± 1.4 SBP, mm Hg 102.6 ± 10.0 102.1 ± 9.7 103.5 ± 9.7 102.7 ± 10.0 DBP, mm Hg 63.1 ± 8.3 62.9 ± 7.9 54.5 ± 12.0 55.6 ± 11.1 Height, cm 139.6 ± 10.3 139.2 ± 11.9 144.2 ± 11.3 145.1 ± 11.5 Weight, kg 35.5 ± 11.8 36.8 ± 11.5 42.3 ± 14.5 43.9 ± 15.2 Continuous variables are presented as mean ± SD. Abbreviations: DBP, diastolic blood pressure; SBP, systolic blood pressure. View Large Height-based equations obtained from the current guideline to detect BP disorders (BP > 90th percentile) The results of linear regression models relating the 90th percentiles values for SBP or DBP and respective height values from the current guideline3 are shown in Figure 1. The constant and the coefficient obtained for height were rounded to values that are easier to remember and within the confidence intervals. Therefore, the following equations for the lowest values of elevated BP (90th percentile) were obtained: Figure 1. View largeDownload slide Linear regression analysis between the 90th percentile values for systolic (a) and diastolic (b) blood pressure and height values obtained from the current guideline.3 Abbreviations: DBP, diastolic blood pressure; SBP, systolic blood pressure. Figure 1. View largeDownload slide Linear regression analysis between the 90th percentile values for systolic (a) and diastolic (b) blood pressure and height values obtained from the current guideline.3 Abbreviations: DBP, diastolic blood pressure; SBP, systolic blood pressure. • SBP 90th = 70 + 0.3 × height (in cm) • DBP 90th = 35 + 0.25 × height (in cm) Figure 2 shows the Mountain plots for the difference between the 90th percentile for SBP or DBP values derived from the current guideline3 and obtained SBP or DBP values from the formulae in children between 8 and 13 years. The maximum difference between the formulae and the gold standard method BP values was approximately 3 mm Hg for SBP and 6 mm Hg for DBP. Figure 2. View largeDownload slide Mountain plots for the difference in systolic (a) and diastolic (b) blood pressure between the 90th percentile values from the current guideline3 and the values obtained with height-based equations in children between 8 and 13 years. Abbreviations: DBP, diastolic blood pressure; SBP, systolic blood pressure. Figure 2. View largeDownload slide Mountain plots for the difference in systolic (a) and diastolic (b) blood pressure between the 90th percentile values from the current guideline3 and the values obtained with height-based equations in children between 8 and 13 years. Abbreviations: DBP, diastolic blood pressure; SBP, systolic blood pressure. Table 2 shows the results of sensitivity, specificity, AUC, PPV, and NPV, as well as kappa coefficients for the studied methods to detect BP disorders according to the gold standard method (current guideline definition).3 All studied methods had great sensitivity and NPV in both populations. In particular, the sensitivity and NPV of height-based equations were 100%. In both populations, height-based equations had greater specificity, AUC, and PPV than the CPG table, simplified cutoffs, and BP-to-height cutoffs. In American individuals, height-based equations showed a very good agreement with the gold standard method to detect BP disorders (kappa coefficient = 0.80), while the kappa coefficients for the other studied methods were remarkably lower (from 0.53 to 0.61). In Brazilian individuals, height-based equations showed good agreement for height-based equations (kappa coefficient = 0.75), slightly better than the CPG table (kappa coefficient = 0.73). Table 2. Performance of height-based equations and other methods to detect blood pressure disorders (blood pressure > 90th percentile) according to the current guideline3 Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  CPG table 99.5 (98.2–99.9) 90.6 (89.4–91.8) 0.95 (0.94–0.96) 63.4 (60.5–66.2) 99.9 (99.6–99.9) 0.73 (0.70–0.76)  Simplified cutoffs 100 (99.1–100) 81.3 (79.7–82.8) 0.91 (0.89–0.92) 46.5 (44.5–48.5) 100 0.55 (0.51–0.58)  BP-to-height ratio 97.2 (95.2–98.4) 76.3 (74.4–77.8) 0.86 (0.85–0.88) 42.3 (40.5–44.0) 99.3 (98.8–99.6) 0.48 (0.45–0.51)  Height-based equations 100 (99.1–100) 91.6 (90.5–92.7) 0.96 (0.95–0.97) 66.1 (63.1–68.9) 100 0.75 (0.72–0.78) American population  CPG table 99.9 (99.3–100) 85.8 (84.9–86.7) 0.93 (0.92–0.94) 51.4 (49.8–53.0) 99.9 (99.8–100) 0.61 (0.59–0.63)  Simplified cutoffs 94.7 (93.0–96.1) 86.2 (85.3–87.1) 0.90 (0.89–0.91) 50.7 (49.0–52.4) 99.1 (98.8–99.3) 0.59 (0.57–0.61)  BP-to-height ratio 94.5 (92.9–95.9) 81.3 (80.2–82.3) 0.88 (0.87–0.89) 46.8 (45.4–48.2) 98.8 (98.5–99.1) 0.53 (0.51–0.56)  Height-based equations 100 (99.5–100) 94.1 (93.5–94.7) 0.97 (0.96–0.98) 71.9 (69.8–74.0) 100 0.80 (0.79–0.83) Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  CPG table 99.5 (98.2–99.9) 90.6 (89.4–91.8) 0.95 (0.94–0.96) 63.4 (60.5–66.2) 99.9 (99.6–99.9) 0.73 (0.70–0.76)  Simplified cutoffs 100 (99.1–100) 81.3 (79.7–82.8) 0.91 (0.89–0.92) 46.5 (44.5–48.5) 100 0.55 (0.51–0.58)  BP-to-height ratio 97.2 (95.2–98.4) 76.3 (74.4–77.8) 0.86 (0.85–0.88) 42.3 (40.5–44.0) 99.3 (98.8–99.6) 0.48 (0.45–0.51)  Height-based equations 100 (99.1–100) 91.6 (90.5–92.7) 0.96 (0.95–0.97) 66.1 (63.1–68.9) 100 0.75 (0.72–0.78) American population  CPG table 99.9 (99.3–100) 85.8 (84.9–86.7) 0.93 (0.92–0.94) 51.4 (49.8–53.0) 99.9 (99.8–100) 0.61 (0.59–0.63)  Simplified cutoffs 94.7 (93.0–96.1) 86.2 (85.3–87.1) 0.90 (0.89–0.91) 50.7 (49.0–52.4) 99.1 (98.8–99.3) 0.59 (0.57–0.61)  BP-to-height ratio 94.5 (92.9–95.9) 81.3 (80.2–82.3) 0.88 (0.87–0.89) 46.8 (45.4–48.2) 98.8 (98.5–99.1) 0.53 (0.51–0.56)  Height-based equations 100 (99.5–100) 94.1 (93.5–94.7) 0.97 (0.96–0.98) 71.9 (69.8–74.0) 100 0.80 (0.79–0.83) Abbreviations: AUC, area under curve; BP, blood pressure; CI, confidence interval; CPG, current clinical practice guideline; NPV, negative predictive value; PPV, positive predictive value. View Large Table 2. Performance of height-based equations and other methods to detect blood pressure disorders (blood pressure > 90th percentile) according to the current guideline3 Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  CPG table 99.5 (98.2–99.9) 90.6 (89.4–91.8) 0.95 (0.94–0.96) 63.4 (60.5–66.2) 99.9 (99.6–99.9) 0.73 (0.70–0.76)  Simplified cutoffs 100 (99.1–100) 81.3 (79.7–82.8) 0.91 (0.89–0.92) 46.5 (44.5–48.5) 100 0.55 (0.51–0.58)  BP-to-height ratio 97.2 (95.2–98.4) 76.3 (74.4–77.8) 0.86 (0.85–0.88) 42.3 (40.5–44.0) 99.3 (98.8–99.6) 0.48 (0.45–0.51)  Height-based equations 100 (99.1–100) 91.6 (90.5–92.7) 0.96 (0.95–0.97) 66.1 (63.1–68.9) 100 0.75 (0.72–0.78) American population  CPG table 99.9 (99.3–100) 85.8 (84.9–86.7) 0.93 (0.92–0.94) 51.4 (49.8–53.0) 99.9 (99.8–100) 0.61 (0.59–0.63)  Simplified cutoffs 94.7 (93.0–96.1) 86.2 (85.3–87.1) 0.90 (0.89–0.91) 50.7 (49.0–52.4) 99.1 (98.8–99.3) 0.59 (0.57–0.61)  BP-to-height ratio 94.5 (92.9–95.9) 81.3 (80.2–82.3) 0.88 (0.87–0.89) 46.8 (45.4–48.2) 98.8 (98.5–99.1) 0.53 (0.51–0.56)  Height-based equations 100 (99.5–100) 94.1 (93.5–94.7) 0.97 (0.96–0.98) 71.9 (69.8–74.0) 100 0.80 (0.79–0.83) Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  CPG table 99.5 (98.2–99.9) 90.6 (89.4–91.8) 0.95 (0.94–0.96) 63.4 (60.5–66.2) 99.9 (99.6–99.9) 0.73 (0.70–0.76)  Simplified cutoffs 100 (99.1–100) 81.3 (79.7–82.8) 0.91 (0.89–0.92) 46.5 (44.5–48.5) 100 0.55 (0.51–0.58)  BP-to-height ratio 97.2 (95.2–98.4) 76.3 (74.4–77.8) 0.86 (0.85–0.88) 42.3 (40.5–44.0) 99.3 (98.8–99.6) 0.48 (0.45–0.51)  Height-based equations 100 (99.1–100) 91.6 (90.5–92.7) 0.96 (0.95–0.97) 66.1 (63.1–68.9) 100 0.75 (0.72–0.78) American population  CPG table 99.9 (99.3–100) 85.8 (84.9–86.7) 0.93 (0.92–0.94) 51.4 (49.8–53.0) 99.9 (99.8–100) 0.61 (0.59–0.63)  Simplified cutoffs 94.7 (93.0–96.1) 86.2 (85.3–87.1) 0.90 (0.89–0.91) 50.7 (49.0–52.4) 99.1 (98.8–99.3) 0.59 (0.57–0.61)  BP-to-height ratio 94.5 (92.9–95.9) 81.3 (80.2–82.3) 0.88 (0.87–0.89) 46.8 (45.4–48.2) 98.8 (98.5–99.1) 0.53 (0.51–0.56)  Height-based equations 100 (99.5–100) 94.1 (93.5–94.7) 0.97 (0.96–0.98) 71.9 (69.8–74.0) 100 0.80 (0.79–0.83) Abbreviations: AUC, area under curve; BP, blood pressure; CI, confidence interval; CPG, current clinical practice guideline; NPV, negative predictive value; PPV, positive predictive value. View Large Height-based equations obtained from the current guideline to detect hypertension (BP ≥ 95th percentile) As a sensitivity analysis, we built the following height-based equations (detailed in Supplementary Figures 1 and 2) to identify hypertension using the 95th percentiles values for SBP or DBP and respective height values from the current guideline3: • SBP 90th = 75 + 0.3 × height (in cm) • DBP 90th = 40 + 0.25 × height (in cm) The results of sensitivity, specificity, AUC, PPV, NPV, and kappa coefficient for height-based equations, simplified cutoffs, and BP-to-height cutoffs to detect hypertension (BP ≥ 95th percentile) using the current guideline3 as the gold standard are shown in Table 3. The kappa coefficient of height-based equations with the gold standard method was 0.92 and 0.89 in the Brazilian and American populations, respectively, while the other methods had inferior agreement with the gold standard. Table 3. Performance of height-based equations to detect hypertension (blood pressure ≥ 95th percentile) according to the current guideline3 Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  Simplified cutoffs 81.6 (76.8–85.8) 99.3 (99–99.6) 0.90 (0.89–0.91) 93.5 (89.9–95.9%) 97.9 (97.4–98.4) 0.85 (0.82–0.89)  BP-to-height ratio 98.3 (96.1–99.4) 79.2 (77.6–80.8) 0.89 (0.88–0.90) 35.0 (33.3–36.8) 99.7 (99.4–99.9) 0.43 (0.39–0.46)  Height-based equations 95.0 (91.8–97.2) 98.9 (98.4–99.2) 0.97 (0.96–0.98) 90.7 (87.2–93.4) 99.4 (99.0–99.7) 0.92 (0.89–0.94) American population  Simplified cutoffs 52.9 (48.4–57.4) 99.4 (99.2–99.6) 0.76 (0.75–0.77) 88.0 (84.0–91.2) 96.3 (96.0–96.6) 0.64 (0.60–0.68)  BP-to-height ratio 97.5 (95.8–98.7) 84.3 (83.4–85.2) 0.91 (0.90–0.92) 33.5 (32.2–34.8) 99.8 (99.6–99.9) 0.43 (0.41–0.46)  Height-based equations 87.1 (83.8–90.0) 99.1 (98.8–99.3) 0.93 (0.92–0.94) 88.7 (85.8–91.1) 98.9 (98.7–99.2) 0.89 (0.87–0.91) Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  Simplified cutoffs 81.6 (76.8–85.8) 99.3 (99–99.6) 0.90 (0.89–0.91) 93.5 (89.9–95.9%) 97.9 (97.4–98.4) 0.85 (0.82–0.89)  BP-to-height ratio 98.3 (96.1–99.4) 79.2 (77.6–80.8) 0.89 (0.88–0.90) 35.0 (33.3–36.8) 99.7 (99.4–99.9) 0.43 (0.39–0.46)  Height-based equations 95.0 (91.8–97.2) 98.9 (98.4–99.2) 0.97 (0.96–0.98) 90.7 (87.2–93.4) 99.4 (99.0–99.7) 0.92 (0.89–0.94) American population  Simplified cutoffs 52.9 (48.4–57.4) 99.4 (99.2–99.6) 0.76 (0.75–0.77) 88.0 (84.0–91.2) 96.3 (96.0–96.6) 0.64 (0.60–0.68)  BP-to-height ratio 97.5 (95.8–98.7) 84.3 (83.4–85.2) 0.91 (0.90–0.92) 33.5 (32.2–34.8) 99.8 (99.6–99.9) 0.43 (0.41–0.46)  Height-based equations 87.1 (83.8–90.0) 99.1 (98.8–99.3) 0.93 (0.92–0.94) 88.7 (85.8–91.1) 98.9 (98.7–99.2) 0.89 (0.87–0.91) Abbreviations: AUC, area under curve; BP, blood pressure; CI, confidence interval; NPV, negative predictive value; PPV, positive predictive value. View Large Table 3. Performance of height-based equations to detect hypertension (blood pressure ≥ 95th percentile) according to the current guideline3 Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  Simplified cutoffs 81.6 (76.8–85.8) 99.3 (99–99.6) 0.90 (0.89–0.91) 93.5 (89.9–95.9%) 97.9 (97.4–98.4) 0.85 (0.82–0.89)  BP-to-height ratio 98.3 (96.1–99.4) 79.2 (77.6–80.8) 0.89 (0.88–0.90) 35.0 (33.3–36.8) 99.7 (99.4–99.9) 0.43 (0.39–0.46)  Height-based equations 95.0 (91.8–97.2) 98.9 (98.4–99.2) 0.97 (0.96–0.98) 90.7 (87.2–93.4) 99.4 (99.0–99.7) 0.92 (0.89–0.94) American population  Simplified cutoffs 52.9 (48.4–57.4) 99.4 (99.2–99.6) 0.76 (0.75–0.77) 88.0 (84.0–91.2) 96.3 (96.0–96.6) 0.64 (0.60–0.68)  BP-to-height ratio 97.5 (95.8–98.7) 84.3 (83.4–85.2) 0.91 (0.90–0.92) 33.5 (32.2–34.8) 99.8 (99.6–99.9) 0.43 (0.41–0.46)  Height-based equations 87.1 (83.8–90.0) 99.1 (98.8–99.3) 0.93 (0.92–0.94) 88.7 (85.8–91.1) 98.9 (98.7–99.2) 0.89 (0.87–0.91) Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  Simplified cutoffs 81.6 (76.8–85.8) 99.3 (99–99.6) 0.90 (0.89–0.91) 93.5 (89.9–95.9%) 97.9 (97.4–98.4) 0.85 (0.82–0.89)  BP-to-height ratio 98.3 (96.1–99.4) 79.2 (77.6–80.8) 0.89 (0.88–0.90) 35.0 (33.3–36.8) 99.7 (99.4–99.9) 0.43 (0.39–0.46)  Height-based equations 95.0 (91.8–97.2) 98.9 (98.4–99.2) 0.97 (0.96–0.98) 90.7 (87.2–93.4) 99.4 (99.0–99.7) 0.92 (0.89–0.94) American population  Simplified cutoffs 52.9 (48.4–57.4) 99.4 (99.2–99.6) 0.76 (0.75–0.77) 88.0 (84.0–91.2) 96.3 (96.0–96.6) 0.64 (0.60–0.68)  BP-to-height ratio 97.5 (95.8–98.7) 84.3 (83.4–85.2) 0.91 (0.90–0.92) 33.5 (32.2–34.8) 99.8 (99.6–99.9) 0.43 (0.41–0.46)  Height-based equations 87.1 (83.8–90.0) 99.1 (98.8–99.3) 0.93 (0.92–0.94) 88.7 (85.8–91.1) 98.9 (98.7–99.2) 0.89 (0.87–0.91) Abbreviations: AUC, area under curve; BP, blood pressure; CI, confidence interval; NPV, negative predictive value; PPV, positive predictive value. View Large Height-based equations obtained from the previous guideline to detect BP disorders (BP > 90th percentile) As a secondary analysis, we built height-based equations to detect BP disorders (BP > 90th percentile) using data from the previous guideline (The Fourth Report)19: • SBP 90th = 70 + 0.33 × height (in cm) • DBP 90th = 40 + 0.25 × height (in cm) Table 4 shows the results of sensitivity, specificity, AUC, PPV, NPV, and kappa coefficient for several methods to detect BP disorders using the previous guideline19 as the gold standard. All studied methods showed great sensitivity and NPV, but height-based equations showed greater specificity, PPV, and AUC in both populations. The agreement of height-based equations with the previous guideline (kappa coefficient) was very good, while the other methods had remarkably lesser agreement with the gold standard method in both populations. Table 4. Performance of height-based equations and other methods to detect blood pressure disorders (blood pressure > 90th percentile) according to the previous guideline19 Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  Mitchell table 97.6 (95.0–99.0) 83.7 (82.3–85.1) 0.91 (0.90–0.92) 39.0 (36.9–41.1) 99.7 (99.4–99.8) 0.49 (0.45–0.52)  Kaelber table 97.6 (95.0–99.0) 90.6 (89.5–91.7) 0.94 (0.93–0.95) 52.6 (49.7–55.6) 99.7 (99.4–99.9) 0.64 (0.60–0.68)  BP-to-height ratio 91.6 (87.8–94.6) 87.1 (85.7–88.3) 0.89 (0.88–0.90) 43.0 (40.5–45.6) 99.00 (98.5–99.3) 0.52 (0.48–0.56)  Badeli equation 99.0 (98.5–99.3) 90.5 (89.4–91.6) 0.94 (0.93–0.95) 52.4 (49.5–55.4) 99.7 (99.4–99.9) 0.64 (0.60–0.68)  Simplified cutoffs 98.9 (97.0–99.8) 77.2 (75.6–78.8) 0.88 (0.87–0.89) 31.7 (30.2–33.2) 99.9 (99.6–99.9) 0.39 (0.36–0.42)  Height-based equations 96.2 (93.2–98.1) 98.4 (97.9–98.9) 0.97 (0.97–0.98) 86.8 (82.9–89.9) 99.6 (99.3–99.8) 0.90 (0.88–0.93) American population  Mitchell table 100 (99.2–100) 79.7 (78.7–80.7) 0.90 (0.89–0.91) 26.5 (25.5–27.5) 100 0.35 (0.32–0.37)  Kaelber table 100 (99.2–100) 83.0 (82.0–83.9) 0.91 (0.91–0.92) 29.9 (28.7–31.1) 100 0.40 (0.37–0.42)  BP-to-height ratio 97.1 (95.1–98.4) 81.8 (80.8–82.7) 0.89 (0.89–0.90) 27.8 (26.7–29.0) 99.7 (99.6–99.8) 0.37 (0.34–0.39)  Badeli equation 100 (99.2–100) 83.2 (82.2–84.1) 0.92 (0.91–0.92) 30.1 (28.9–31.3) 100 0.40 (0.37–0.43)  Simplified cutoffs 99.5 (98.4–99.9) 80.9 (79.9–81.9) 0.90 (0.90–0.91) 27.5 (26.4–28.5) 99.9 (99.8–100) 0.36 (0.34–0.39)  Height-based equations 97.7 (95.9–98.9) 99.1 (98.8–99.3) 0.98 (0.98–0.99) 88.4 (85.5–90.8) 99.8 (99.7–99.9) 0.92 (0.90–0.94) Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  Mitchell table 97.6 (95.0–99.0) 83.7 (82.3–85.1) 0.91 (0.90–0.92) 39.0 (36.9–41.1) 99.7 (99.4–99.8) 0.49 (0.45–0.52)  Kaelber table 97.6 (95.0–99.0) 90.6 (89.5–91.7) 0.94 (0.93–0.95) 52.6 (49.7–55.6) 99.7 (99.4–99.9) 0.64 (0.60–0.68)  BP-to-height ratio 91.6 (87.8–94.6) 87.1 (85.7–88.3) 0.89 (0.88–0.90) 43.0 (40.5–45.6) 99.00 (98.5–99.3) 0.52 (0.48–0.56)  Badeli equation 99.0 (98.5–99.3) 90.5 (89.4–91.6) 0.94 (0.93–0.95) 52.4 (49.5–55.4) 99.7 (99.4–99.9) 0.64 (0.60–0.68)  Simplified cutoffs 98.9 (97.0–99.8) 77.2 (75.6–78.8) 0.88 (0.87–0.89) 31.7 (30.2–33.2) 99.9 (99.6–99.9) 0.39 (0.36–0.42)  Height-based equations 96.2 (93.2–98.1) 98.4 (97.9–98.9) 0.97 (0.97–0.98) 86.8 (82.9–89.9) 99.6 (99.3–99.8) 0.90 (0.88–0.93) American population  Mitchell table 100 (99.2–100) 79.7 (78.7–80.7) 0.90 (0.89–0.91) 26.5 (25.5–27.5) 100 0.35 (0.32–0.37)  Kaelber table 100 (99.2–100) 83.0 (82.0–83.9) 0.91 (0.91–0.92) 29.9 (28.7–31.1) 100 0.40 (0.37–0.42)  BP-to-height ratio 97.1 (95.1–98.4) 81.8 (80.8–82.7) 0.89 (0.89–0.90) 27.8 (26.7–29.0) 99.7 (99.6–99.8) 0.37 (0.34–0.39)  Badeli equation 100 (99.2–100) 83.2 (82.2–84.1) 0.92 (0.91–0.92) 30.1 (28.9–31.3) 100 0.40 (0.37–0.43)  Simplified cutoffs 99.5 (98.4–99.9) 80.9 (79.9–81.9) 0.90 (0.90–0.91) 27.5 (26.4–28.5) 99.9 (99.8–100) 0.36 (0.34–0.39)  Height-based equations 97.7 (95.9–98.9) 99.1 (98.8–99.3) 0.98 (0.98–0.99) 88.4 (85.5–90.8) 99.8 (99.7–99.9) 0.92 (0.90–0.94) Abbreviations: AUC, area under curve; BP, blood pressure; CI, confidence interval; NPV, negative predictive value; PPV, positive predictive value. View Large Table 4. Performance of height-based equations and other methods to detect blood pressure disorders (blood pressure > 90th percentile) according to the previous guideline19 Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  Mitchell table 97.6 (95.0–99.0) 83.7 (82.3–85.1) 0.91 (0.90–0.92) 39.0 (36.9–41.1) 99.7 (99.4–99.8) 0.49 (0.45–0.52)  Kaelber table 97.6 (95.0–99.0) 90.6 (89.5–91.7) 0.94 (0.93–0.95) 52.6 (49.7–55.6) 99.7 (99.4–99.9) 0.64 (0.60–0.68)  BP-to-height ratio 91.6 (87.8–94.6) 87.1 (85.7–88.3) 0.89 (0.88–0.90) 43.0 (40.5–45.6) 99.00 (98.5–99.3) 0.52 (0.48–0.56)  Badeli equation 99.0 (98.5–99.3) 90.5 (89.4–91.6) 0.94 (0.93–0.95) 52.4 (49.5–55.4) 99.7 (99.4–99.9) 0.64 (0.60–0.68)  Simplified cutoffs 98.9 (97.0–99.8) 77.2 (75.6–78.8) 0.88 (0.87–0.89) 31.7 (30.2–33.2) 99.9 (99.6–99.9) 0.39 (0.36–0.42)  Height-based equations 96.2 (93.2–98.1) 98.4 (97.9–98.9) 0.97 (0.97–0.98) 86.8 (82.9–89.9) 99.6 (99.3–99.8) 0.90 (0.88–0.93) American population  Mitchell table 100 (99.2–100) 79.7 (78.7–80.7) 0.90 (0.89–0.91) 26.5 (25.5–27.5) 100 0.35 (0.32–0.37)  Kaelber table 100 (99.2–100) 83.0 (82.0–83.9) 0.91 (0.91–0.92) 29.9 (28.7–31.1) 100 0.40 (0.37–0.42)  BP-to-height ratio 97.1 (95.1–98.4) 81.8 (80.8–82.7) 0.89 (0.89–0.90) 27.8 (26.7–29.0) 99.7 (99.6–99.8) 0.37 (0.34–0.39)  Badeli equation 100 (99.2–100) 83.2 (82.2–84.1) 0.92 (0.91–0.92) 30.1 (28.9–31.3) 100 0.40 (0.37–0.43)  Simplified cutoffs 99.5 (98.4–99.9) 80.9 (79.9–81.9) 0.90 (0.90–0.91) 27.5 (26.4–28.5) 99.9 (99.8–100) 0.36 (0.34–0.39)  Height-based equations 97.7 (95.9–98.9) 99.1 (98.8–99.3) 0.98 (0.98–0.99) 88.4 (85.5–90.8) 99.8 (99.7–99.9) 0.92 (0.90–0.94) Method Sensitivity, % (95% CI) Specificity, % (95% CI) AUC (95% CI) PPV, % (95% CI) NPV, % (95% CI) Kappa coefficient (95% CI) Brazilian population  Mitchell table 97.6 (95.0–99.0) 83.7 (82.3–85.1) 0.91 (0.90–0.92) 39.0 (36.9–41.1) 99.7 (99.4–99.8) 0.49 (0.45–0.52)  Kaelber table 97.6 (95.0–99.0) 90.6 (89.5–91.7) 0.94 (0.93–0.95) 52.6 (49.7–55.6) 99.7 (99.4–99.9) 0.64 (0.60–0.68)  BP-to-height ratio 91.6 (87.8–94.6) 87.1 (85.7–88.3) 0.89 (0.88–0.90) 43.0 (40.5–45.6) 99.00 (98.5–99.3) 0.52 (0.48–0.56)  Badeli equation 99.0 (98.5–99.3) 90.5 (89.4–91.6) 0.94 (0.93–0.95) 52.4 (49.5–55.4) 99.7 (99.4–99.9) 0.64 (0.60–0.68)  Simplified cutoffs 98.9 (97.0–99.8) 77.2 (75.6–78.8) 0.88 (0.87–0.89) 31.7 (30.2–33.2) 99.9 (99.6–99.9) 0.39 (0.36–0.42)  Height-based equations 96.2 (93.2–98.1) 98.4 (97.9–98.9) 0.97 (0.97–0.98) 86.8 (82.9–89.9) 99.6 (99.3–99.8) 0.90 (0.88–0.93) American population  Mitchell table 100 (99.2–100) 79.7 (78.7–80.7) 0.90 (0.89–0.91) 26.5 (25.5–27.5) 100 0.35 (0.32–0.37)  Kaelber table 100 (99.2–100) 83.0 (82.0–83.9) 0.91 (0.91–0.92) 29.9 (28.7–31.1) 100 0.40 (0.37–0.42)  BP-to-height ratio 97.1 (95.1–98.4) 81.8 (80.8–82.7) 0.89 (0.89–0.90) 27.8 (26.7–29.0) 99.7 (99.6–99.8) 0.37 (0.34–0.39)  Badeli equation 100 (99.2–100) 83.2 (82.2–84.1) 0.92 (0.91–0.92) 30.1 (28.9–31.3) 100 0.40 (0.37–0.43)  Simplified cutoffs 99.5 (98.4–99.9) 80.9 (79.9–81.9) 0.90 (0.90–0.91) 27.5 (26.4–28.5) 99.9 (99.8–100) 0.36 (0.34–0.39)  Height-based equations 97.7 (95.9–98.9) 99.1 (98.8–99.3) 0.98 (0.98–0.99) 88.4 (85.5–90.8) 99.8 (99.7–99.9) 0.92 (0.90–0.94) Abbreviations: AUC, area under curve; BP, blood pressure; CI, confidence interval; NPV, negative predictive value; PPV, positive predictive value. View Large DISCUSSION In this study, we described simple height-based equations as screening methods to identify BP disorders (BP > 90th percentile) and compared the ability of this novel approach and previously reported screening methods to detect BP disorders in large samples of American and Brazilian children. Our analysis highlights that height-based equations consistently showed superior ability to detect high BP and hypertension, as reflected by higher specificity, PPV, and AUC, and greater agreement with the gold standard method when compared to several other screening methods. These findings suggest that height-based equations may be a simple and feasible approach to improve the detection of high BP in children. The detection of BP disorders in children has classically required the joint analysis of various tables and charts, which is considered a major reason for the underdiagnosis of these conditions in the pediatric population.4,23 Although the current guideline3 establishes that adolescents (13 years or more) with SBP above 120 mm Hg or DBP above 80 mm Hg are considered to have elevated BP, the use of tables and charts are still necessary to detect elevated BP levels in children below 13 years. In this report, we described simple height-based equations to determine the 90th percentile of SBP and DBP according to the most recent guideline.3 Then, we compared the performance of these new formulae with other methods (CPG table,3 simplified cutoffs,12 and BP-to-height cutoffs20) to detect BP disorders in American and Brazilian children ranging from 8 to 13 years. Our analysis showed that all studied methods had high sensitivity and NPV. However, height-based equations showed higher PPV as well as higher specificity and AUC. These findings are significant because a higher PPV leads to a better identification of children with high BP. More importantly, novel height-based equations had high agreement with the gold standard method (kappa coefficients ranging from 0.75 to 0.81), which was greater than those observed from other methods. Likewise, results of sensitivity analysis demonstrated that alternative height-based equations aiming to detect hypertension (BP ≥ 95th percentile), instead of BP disorders, showed remarkably better performance as compared to other screening methods designed to detect hypertension. However, the novel height-based equations showed a lower sensitivity to identify hypertension than BP-to-height ratio, particularly in the American population, despite the better agreement with the gold standard. Therefore, in a scenario where the purpose is not missing patients with hypertension, the 95th percentile height-based equations might not necessarily be the best method. Height-based equations may offer some practical advantages when compared with previously reported BP screening tools. First, they do not require the use of additional tables, as proposed by other methods.5 Second, they can be uniformly used across different populations, which is not applicable to the BP-to-height ratio method, due to the variety of the cutoffs obtained in different studies.8,9,14,20,24,25 Third, our height-based equations were built using data from current guidelines,3 while most of the available screening methods were built using data from the previous guideline.19 Given that the current guideline excluded overweight and obese patients from normative BP tables,3,26 several screening methods reported hitherto7,11,20 may have become inappropriate to detect BP disorders. Fourth, height-based equations are simple, easy to memorize and do not vary according to age and gender, thus differing from other methods.4,7 These facts, along with the high sensitivity and a greater PPV compared with the other methods, suggest that height-based equations may be an easy and reliable tool to not only for screening but also for identifying children with BP disorders. At a first glance, the new equations described herein may appear as variants of Badeli et al.’s equations,10 which were built from regression analysis of 90th BP percentile values according to age. However, our equations were based on height, instead of age. This is an important difference because BP values have been demonstrated to vary more with height than with age.27 Furthermore, the Badeli et al.’s equations were built according to the 50th percentile for height,10 with consequent lower performance at the extremes of height.10,11 To compare the ability of our height-based equations and age-based equations reported by Badeli et al. to detect BP disorders, we performed secondary analysis considering data from the previous guideline19 as the gold standard method as well as the template to build the formulae. The previous guideline was used in such analysis because the equations reported by Badeli et al. have not been updated to the current guideline.3 Our results demonstrated that the accuracy of height-based equations to detect BP disorders was greater than that of age-based equations, strengthening the notion that screening methods based on height instead of age may have greater ability to detect BP disorders in children. Notably, height-based equations showed remarkably higher agreement with the gold standard method (kappa coefficient = 0.90 and 0.92 in the Brazilian and American population, respectively), than age-based equations (kappa coefficient = 0.64 and 0.40 in the Brazilian and American population, respectively). Furthermore, the accuracy of height-based equations was greater than several other studied screening methods that used the previous guideline as the gold standard method, demonstrating the superior ability of height-based equations to detect BP disorders regardless of the guideline used to define BP alterations. The idea that screening methods based on height might have better ability to identify children with actual high BP has also been supported by other studies. For instance, the comparison of 11 screening methods for the diagnosis of hypertension in children13 showed that methods based on height, particularly the table proposed by Chiolero et al.,28 had not only great screening ability, as reflected by high sensitivity and NPV, but also better PPV and therefore superior performance to identify patients with high BP. This study has some limitations. First, data from population 1 were based on retrospective analysis of patients’ charts, while a prospective design would have been more appropriate to obtain the data. Second, we used only one BP measurement from each studied subject, which might have overestimated the prevalence of BP disorders in the studied populations, given that an increasing number of measurements tends to reduce the obtained BP of children.29 We believe, however, that this fact is not a significant limitation to our study, because our aim was to evaluate the performance of height-based equations for the identification of BP disorders, and not to establish a precise prevalence of BP disorders in the studied populations. Third, we only used information from Brazilian and American children. Thus, it is necessary to confirm the current findings in other populations. In conclusion, our analysis showed that height-based equations have high sensitivity and NPV as well as great agreement with the gold standard method to detect BP disorders in American and Brazilian children. In addition, height-based equations showed superior ability to detect BP disorders and greater agreement with the gold standard method. These findings suggest that the use of height-based equations may be a simple and feasible approach to improve the detection of BP disorders in children. SUPPLEMENTARY MATERIAL Supplementary data are available at American Journal of Hypertension online. DISCLOSURE The authors declare no conflict of interest. ACKNOWLEDGMENT This study was supported by grant from the Brazilian National Council for Scientific and Technological Development (304245/2013–5 to W.N.). REFERENCES 1. Lackland DT , Weber MA . Global burden of cardiovascular disease and stroke: hypertension at the core . Can J Cardiol 2015 ; 31 : 569 – 571 . Google Scholar CrossRef Search ADS PubMed 2. Flynn J . The changing face of pediatric hypertension in the era of the childhood obesity epidemic . Pediatr Nephrol 2013 ; 28 : 1059 – 1066 . Google Scholar CrossRef Search ADS PubMed 3. 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Int J Epidemiol 2014 ; 43 : 149 – 159 . Google Scholar CrossRef Search ADS PubMed 28. Chiolero A , Paradis G , Simonetti GD , Bovet P . Absolute height-specific thresholds to identify elevated blood pressure in children . J Hypertens 2013 ; 31 : 1170 – 1174 . Google Scholar CrossRef Search ADS PubMed 29. Salgado CM , Carvalhaes JT . Arterial hypertension in childhood . J Pediatr (Rio J) 2003 ; 79 ( Suppl 1 ): S115 – S124 . Google Scholar CrossRef Search ADS PubMed © American Journal of Hypertension, Ltd 2018. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

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American Journal of HypertensionOxford University Press

Published: Sep 1, 2018

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