Development and validation of cardiovascular risk prediction equations in 76 000 people with known cardiovascular disease.

ABSTRACT

AIMS: Multiple health administrative databases can be individually linked in Aotearoa New Zealand, using encrypted identifiers. These databases were used to develop cardiovascular risk prediction equations for patients with known cardiovascular disease (CVD). METHODS AND RESULTS: Administrative health databases were linked to identify all people aged 18-84 years with known CVD, living in Auckland and Northland, Aotearoa New Zealand, on 1 January 2014. The cohort was followed until study outcome, death, or 5 years. The study outcome was death or hospitalization due to ischaemic heart disease, stroke, heart failure, or peripheral vascular disease. Sex-specific 5-year CVD risk prediction equations were developed using multivariable Fine and Gray models. A total of 43 862 men {median age 67 years [interquartile range (IQR) 59-75]} and 32 724 women [median age 70 years (IQR 60-77)] had 14 252 and 9551 cardiovascular events, respectively. Equations were well calibrated with good discrimination. Increasing age and deprivation, recent cardiovascular hospitalization, Mori ethnicity, smoking history, heart failure, diabetes, chronic renal disease, atrial fibrillation, use of blood pressure lowering and anti-thrombotic drugs, haemoglobin A1c, total cholesterol/HDL cholesterol, and creatinine were statistically significant independent predictors of the study outcome. Fourteen per cent of men and 23% of women had predicted 5-year cardiovascular risk <15%, while 28 and 24% had ≥40% risk. CONCLUSION: Robust cardiovascular risk prediction equations were developed from linked routine health databases, a currently underutilized resource worldwide. The marked heterogeneity demonstrated in predicted risk suggests that preventive therapy in people with known CVD would be better informed by risk stratification beyond a one-size-fits-all high-risk categorization.

Using regionwide New Zealand health databases, methods of predicting hospitalization risk in patients with existing heart disease were developed. Using only data from health databases, it was possible to predict the risk accurately.Among patients with existing heart disease, the predicted risk varied markedly which could help improve preventive strategies.