Childhood prediction models for hypertension later in life: a systematic review

Marleen Hamoen1, Marlou L A de Kroon1,2, Marieke Welten3

  • 1Department of Public Health, Erasmus University Medical Center, Rotterdam.

Journal of Hypertension
|October 27, 2018
PubMed

Insights

Childhood prediction models for hypertension exist but require further validation. These models aim to identify at-risk children for early cardiovascular disease prevention, but their clinical utility is currently limited due to methodological issues and lack of external validation.

Area of Science:

  • Pediatric Cardiology
  • Preventive Medicine
  • Biostatistics

Background:

  • Childhood hypertension is a risk factor for atherosclerosis and cardiovascular disease.
  • Early intervention and prevention strategies are crucial for lifelong cardiovascular health.
  • Prediction models can identify children at high risk for hypertension, enabling targeted primordial prevention.

Purpose of the Study:

  • To systematically review and summarize existing prediction models for hypertension in children.
  • To assess the development and validation status of these childhood prediction models.

Main Methods:

  • Systematic literature search of Embase and Medline databases.
  • Inclusion of studies on multivariable models for predicting future high blood pressure, prehypertension, or hypertension in children.
  • Data extraction using the CHARMS checklist for prediction modeling studies.

Main Results:

  • Six studies presented 18 models, predicting either adulthood hypertension or adolescent prehypertension/hypertension.
  • Body Mass Index (BMI) and current blood pressure were common predictors.
  • Significant heterogeneity in prediction timing and outcome measurement, with missing methodological details and insufficient data for application in 4 studies. Reported areas under the ROC curve ranged from 0.51 to 0.74.

Conclusions:

  • Childhood hypertension prediction models have been identified.
  • Clinical utility is limited by suboptimal methodology, inadequate performance data, and lack of external validation.
  • Further validation studies are essential to confirm the generalizability and reliability of these models.
Abstract

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