How to simplify the diagnostic criteria of hypertension in adolescents

J Hum Hypertens. 2011 Mar;25(3):159-63. doi: 10.1038/jhh.2010.46. Epub 2010 Apr 29.

Abstract

Diagnosis of hypertension in adolescents is complicated because blood pressure values vary with age, gender and height. How can we simplify the diagnostic criteria for hypertension in adolescents? In 2006, anthropometric measurements were assessed in a cross-sectional population-based study of 3136 Han adolescents aged 13-17 years. Hypertension was defined according to the 2004 National High Blood Pressure Education Program Working Group definition. The following equations for blood pressure-to-height ratio (BPHR) were used: systolic BPHR (SBPHR)=SBP (mm Hg)/height (cm) and diastolic BPHR (DBPHR)=DBP (mm Hg)/height (cm). Receiver-operating characteristic curve analyses were performed to assess the accuracy of SBPHR and DBPHR as diagnostic tests for elevated systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively. After the cutoff points were determined, hypertension was defined by SBPHR/DBPHR, and the sensitivity and specificity were calculated. The accuracy of SBPHR and DBPHR (assessed by area under the curve) for identifying elevated SBP and DBP was >0.85 (0.989-1.000). The optimal thresholds of SBPHR/DBPHR for defining hypertension (stages 1 and 2) were 0.75/0.48 for boys and 0.78/0.51 for girls, and for defining hypertension (stage 2) were 0.81/0.57 for boys and 0.84/0.63 for girls. In identifying hypertension, the sensitivity and specificity were both >90% (91.0-99.1%). In identifying stage 2 hypertension, when the sensitivity was 100%, the specificity was 98.6% for boys and 99.1% for girls. BPHR is a simple, accurate and non-age-dependent index for screening hypertension in Han adolescents, especially for stage 2 hypertension.

MeSH terms

  • Adolescent
  • Asian People / statistics & numerical data
  • Cross-Sectional Studies
  • Female
  • Humans
  • Hypertension / diagnosis*
  • Hypertension / epidemiology*
  • Male
  • ROC Curve
  • Sensitivity and Specificity