Research in context
Evidence before this study
In a 2016 systematic review of cardiovascular disease risk prediction models, 363 equations were identified, mainly from Europe and North America. The models had substantial variation in predictor and outcome definitions, and most models included only age, sex, smoking, diabetes, blood pressure, and blood lipids as predictors. More than 70 definitions of cardiovascular disease outcomes were reported, and the authors concluded that most prediction models are insufficiently reported to allow external validation by others, let alone be implemented. Moreover, models were largely derived in cohorts established last century, when cardiovascular disease event rates were more than double current rates and included participants who were less socioeconomically and ethnically diverse and less likely to be on preventive medications than the patients the models are applied to at present. Only the UK QRISK risk prediction equations are regularly updated in contemporary representative cohorts and include a comprehensive range of predictors, including deprivation measures, but they are complex and difficult to implement or validate outside UK general practice.
Added value of this study
We developed simple equations for predicting the 5-year risk of ICD-coded fatal cardiovascular disease and non-fatal cardiovascular disease hospitalisations that were designed to facilitate external validation and implementation. They were derived in a contemporary cohort of 401 752 New Zealanders aged 30–74 years without prior cardiovascular disease, congestive heart failure, or significant renal disease in the primary care setting where most risk assessments of cardiovascular disease are done. Aside from QRISK, we are unaware of any similar contemporary cohorts, yet such cohorts are necessary for developing accurate risk prediction equations. Median 5-year risk of cardiovascular disease was only 2·3% in women and 3·2% in men, highlighting the low risk in this typical high-income country population. This explains why the recommended 2013 American College of Cardiology/American Heart Association Pooled Cohort Equations (PCEs) were poorly calibrated in the PREDICT cohort, overestimating hard atherosclerotic cardiovascular disease events by up to 60%, although incidentally estimating total ischaemic cardiovascular disease hospitalisations and deaths reasonably well. Adding measures of socioeconomic status, ethnicity, and several other variables routinely available in clinical care to the PCEs would identify patient groups with predicted risk from about 25% lower to 65% higher than equations based on standard risk predictors. Moreover, the poor performance of the PCEs could not be explained by increasing use of preventive medications.
Implications of all the available evidence
Unless risk of cardiovascular disease is clearly defined and estimated using equations derived or recalibrated in contemporary populations that represent the patients they are applied to, substantial underestimation or overestimation of risk, and therefore substantial undertreatment or overtreatment, is likely. Furthermore, in the era of precision medicine, recalibrating old equations will be insufficient, and new predictors (including measures of socioeconomic deprivation and multiple ethnicities) that could be made routinely available in medical records should be included to avoid undertreatment of high-risk subpopulations. With increasing computerisation of medical practice, many countries or health-care organisations could replicate the PREDICT approach by linking primary care records to hospitalisations and deaths.