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Identifying Risk Indicators of Cardiovascular Disease in Fasa Cohort Study (FACS): An Application of Generalized

Fariba Asadi1, Reza Homayounfar2, Mojtaba Farjam3

  • 1Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Archives of Iranian Medicine
|May 1, 2024
PubMed

Insights

Cardiovascular disease (CVD) prediction was improved using a generalized linear mixed model (GLMM) tree. This statistical framework offers simpler interpretation and fewer assumptions for proactive patient detection planning.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Cardiovascular disease (CVD) is a leading global cause of mortality.
  • Accurate prediction of CVD is crucial for public health initiatives.
  • Early detection frameworks are needed to mitigate CVD impact.

Purpose of the Study:

  • To predict cardiovascular disease (CVD) using key indicators.
  • To develop and evaluate a tree-based statistical framework for CVD detection.
  • To compare the predictive performance of different statistical models for CVD.

Main Methods:

  • Utilized baseline data from the Fasa Cohort Study (FACS).
  • Fitted ordinary Tree, generalized linear mixed models (GLMM), and GLMM tree models.
  • Compared predictive power using metrics like Area Under the Curve (AUC) and specificity/sensitivity.

Main Results:

  • Analyzed data from 9499 participants aged 35-70 years.
  • Identified age, cholesterol, smoking, glucose, and family history as significant CVD predictors.
  • GLMM achieved the highest AUC (0.81), closely followed by the GLMM tree (0.80), outperforming the ordinary tree (0.58).

Conclusions:

  • The GLMM tree offers a simpler interpretation and fewer assumptions compared to standard tree models.
  • The GLMM tree demonstrates comparable performance to GLMM for CVD prediction.
  • Updated statistical models like the GLMM tree can enhance proactive patient detection and planning for CVD.
Abstract

Keywords:
Cardiovascular diseasesFasa Cohort StudyGLMM treeMixed-effect model

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