Risk Stratification for Cardiovascular Disease: A Comparative Analysis of Cluster Analysis and Traditional Prediction Models.

Aim: Primary prevention of cardiovascular disease (CVD) relies on effective risk stratification to guide interventions. Current models, primarily developed using regression analysis, can lead to inaccurate estimates when applied to external populations. This study evaluates the utility of cluster analysis as an alternative method for developing CVD risk stratification models, comparing its performance with established CVD risk prediction models.

Methods: Using data from 3,416 individuals (mean age of 66 years and no prior CVD) followed for an average of 5.2 years for incidence of CVD, we developed a risk stratification model using cluster analysis based on established CVD risk factors. We compared our model to the Systematic Coronary Risk Evaluation (SCORE2), the Pooled Cohort Equations (PCE) and the Predicting Risk of Cardiovascular Disease Events (PREVENT) models. We used Poisson and Cox regression to compare CVD risk between risk categories in each model. Predictive accuracy of the models was evaluated using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and C-statistic.

Results: During the study, 161 CVD events were detected. The high-risk cluster had a sensitivity of 59.0%, a PPV of 7.5% a specificity of 64.2% and NPV of 96.9% to predict CVD. Compared to the high-risk groups of the SCORE2, PCE and PREVENT, the high-risk cluster had a high sensitivity and NPV, but a low specificity and PPV. No statistically significant differences were found in C-statistic between models.

Conclusions: Cluster analysis performed comparably to existing models and identified a larger high-risk group that included more individuals who developed CVD, though with more false positives. Further studies in larger, diverse cohorts are needed to validate the clinical utility of cluster analysis in CVD risk stratification.