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Development and validation of hypertension prediction models: The Korean Genome and Epidemiology Study_Cardiovascular

Hyun Kyung Namgung1,2, Hye Won Woo2,3, Jinho Shin4

  • 1Department of Epidemiology and Health Statistics, Graduate School of Public Health, Hanyang University, Seoul, Korea.

Journal of Human Hypertension
|February 19, 2022
PubMed

Insights

The Framingham-based hypertension risk model (F-CAVAS-HTN) showed better internal validation than models with additional factors or from other populations. Further studies are needed to enhance its external validity for hypertension prediction.

Area of Science:

  • Cardiovascular Disease Epidemiology
  • Hypertension Risk Prediction Modeling
  • Biostatistics and Predictive Analytics

Background:

  • Accurate hypertension risk prediction is crucial for cardiovascular disease prevention.
  • Existing models like the Framingham risk score require validation and potential refinement in diverse populations.
  • The CArdioVascular disease Association Study (CAVAS) provides a unique dataset for developing and validating such models.

Purpose of the Study:

  • To develop and internally validate hypertension risk prediction models using CAVAS data.
  • To externally validate these models and compare them against established Framingham and population-specific models.
  • To assess the predictive performance, calibration, and reclassification accuracy of the developed models.

Main Methods:

  • 6,186 participants without hypertension were randomly assigned to derivation (60%) and internal validation (40%) sets.
  • Two models were derived: F-CAVAS-HTN (using Framingham factors) and CAVAS-HTN (including additional factors via Weibull regression).
  • External validation involved Ansan and Ansung (A&A) participants; internal validation of Framingham (F-HTN) and A&A (A&A-HTN) models used the CAVAS internal set. Performance metrics included C-statistic, Hosmer-Lemeshow tests, and net reclassification.

Main Results:

  • All four models demonstrated good discrimination (C-statistic ≥ 0.7).
  • F-CAVAS-HTN models exhibited good internal calibration (H-L χ² < 20, P ≥ 0.05).
  • CAVAS-HTN, F-HTN, and A&A-HTN models showed poor external calibration (H-L χ² ≥ 20 or P < 0.05).

Conclusions:

  • The F-CAVAS-HTN model, derived from Framingham factors within the CAVAS cohort, appears more reliable for internal prediction than models incorporating additional factors or derived from different populations.
  • Significant discrepancies in external calibration were observed for CAVAS-HTN, F-HTN, and A&A-HTN models, highlighting the challenges of generalizability.
  • While F-CAVAS-HTN shows promise, further research is necessary to improve its external validity for broader hypertension risk prediction.

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