Key result
Registry-based model predicts 5-year CVD risk with ~80% AUC, outperforming standard age-sex benchmarks.
Why the study?
The study was designed to derive and validate a nationwide risk prediction model to estimate individual and population-level risk of cardiovascular disease.
Does a risk prediction model based on administrative registry data accurately predict the 5-year risk of first CVD event in individuals free of CVD?
Cohort (n=2,980,000)
Yes
Does a risk prediction model based on administrative registry data accurately predict the 5-year risk of first CVD event in individuals free of CVD?
A nationwide registry-based risk prediction model accurately predicts the 5-year risk of first CVD event in the Danish population, which may inform primary prevention efforts.
May support registry-based CVD risk stratification in primary prevention; leaves open prospective validation and outcome impact.
Aims: The aim of this study was to derive and validate a risk prediction model with nationwide coverage to predict the individual and population-level risk of cardiovascular disease (CVD). Methods and results: All 2.98 million Danish residents aged 30-85 years free of CVD were included on 1 January 2014 and followed through 31 December 2018 using nationwide administrative healthcare registries. Model predictors and outcome were pre-specified. Predictors were age, sex, education, use of antithrombotic, blood pressure-lowering, glucose-lowering, or lipid-lowering drugs, and a smoking proxy of smoking-cessation drug use or chronic obstructive pulmonary disease. Outcome was 5-year risk of first CVD event, a combination of ischaemic heart disease, heart failure, peripheral artery disease, stroke, or cardiovascular death. Predictions were computed using cause-specific Cox regression models. The final model fitted in the full data was internally-externally validated in each Danish Region. The model was well-calibrated in all regions. Area under the receiver operating characteristic curve (AUC) and Brier scores ranged from 76.3% to 79.6% and 3.3 to 4.4. The model was superior to an age-sex benchmark model with differences in AUC and Brier scores ranging from 1.2% to 1.5% and -0.02 to -0.03. Average predicted risks in each Danish municipality ranged from 2.8% to 5.9%. Predicted risks for a 66-year old ranged from 2.6% to 25.3%. Personalized predicted risks across ages 30-85 were presented in an online calculator (https://hjerteforeningen.shinyapps.io/cvd-risk-manuscript/). Conclusion: A CVD risk prediction model based solely on nationwide administrative registry data provided accurate prediction of personal and population-level 5-year first CVD event risk in the Danish population. This may inform clinical and public health primary prevention efforts.
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Christensen et al. (2021) conducted a cohort in Cardiovascular disease (n=2,980,000). Registry-based CVD risk prediction model vs. Age-sex benchmark model was evaluated on 5-year risk of first CVD event (ischaemic heart disease, heart failure, peripheral artery disease, stroke, or cardiovascular death). A nationwide registry-based CVD risk prediction model accurately predicted 5-year first CVD event risk, achieving an AUC of 76.3% to 79.6% and outperforming an age-sex benchmark model.
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