Population-Level Cardiovascular Risk Prediction Models Including Biochemical Predictors in 800,000 Individuals

Abstract Background Most countries lack the clinical cohort data required to create cardiovascular disease (CVD) risk prediction models and rely on models developed elsewhere. However, Individual-person linkage of health administrative databases enables the creation of local models. Objectives This study aimed to explore whether the addition 0f routine biochemical tests could improve the performance of “administrative data-based” models. Methods Estimated glomerular filtration rate, haemoglobin A1c, total cholesterol/high-density lipoprotein cholesterol ratio, and triglyceride tests performed on one-third of New Zealand adults were identified and linked to national administrative health datasets (11,564,665 tests). Sex-specific Cox models estimating 5-year CVD in people aged 30-74 without prior CVD were developed, internally and temporally validated, and compared, including and excluding the four biochemical predictors. Results 805,817 individuals were included, of whom 64% had all 4 examined test results available. 1.8% of women and 3.4% of men experienced a CVD event during 4.7-years mean follow-up. All biochemical predictors except triglyceride level in men were statistically significant independent predictors of CVD risk. The addition of these predictors improved global model performance metrics over models without biochemical predictors and identified 33% of men and 23% of women in the cohort who accounted for 74% and 75% of all CVD events, respectively. Conclusion Biochemical predictors improved the performance of administrative data-based CVD risk prediction models, illustrating the untapped potential of widely available databases. Similar models could be developed in many countries and healthcare organisations if health administrative databases were linked. These models could be remotely applied across populations to inform CVD prevention policy and practices.