Elsevier

The Lancet

Volume 391, Issue 10133, 12–18 May 2018, Pages 1897-1907
The Lancet

Articles
Cardiovascular disease risk prediction equations in 400 000 primary care patients in New Zealand: a derivation and validation study

https://doi.org/10.1016/S0140-6736(18)30664-0Get rights and content

Summary

Background

Most cardiovascular disease risk prediction equations in use today were derived from cohorts established last century and with participants at higher risk but less socioeconomically and ethnically diverse than patients they are now applied to. We recruited a nationally representative cohort in New Zealand to develop equations relevant to patients in contemporary primary care and compared the performance of these new equations to equations that are recommended in the USA.

Methods

The PREDICT study automatically recruits participants in routine primary care when general practitioners in New Zealand use PREDICT software to assess their patients' risk profiles for cardiovascular disease, which are prospectively linked to national ICD-coded hospitalisation and mortality databases. The study population included male and female patients in primary care who had no prior cardiovascular disease, renal disease, or congestive heart failure. New equations predicting total cardiovascular disease risk were developed using Cox regression models, which included clinical predictors plus an area-based deprivation index and self-identified ethnicity. Calibration and discrimination performance of the equations were assessed and compared with 2013 American College of Cardiology/American Heart Association Pooled Cohort Equations (PCEs). The additional predictors included in new PREDICT equations were also appended to the PCEs to determine whether they were independent predictors in the equations from the USA.

Findings

Outcome events were derived for 401 752 people aged 30–74 years at the time of their first PREDICT risk assessment between Aug 27, 2002, and Oct 12, 2015, representing about 90% of the eligible population. The mean follow-up was 4·2 years, and a third of participants were followed for 5 years or more. 15 386 (4%) people had cardiovascular disease events (1507 [10%] were fatal, and 8549 [56%] met the PCEs definition of hard atherosclerotic cardiovascular disease) during 1 685 521 person-years follow-up. The median 5-year risk of total cardiovascular disease events predicted by the new equations was 2·3% in women and 3·2% in men. Multivariable adjusted risk increased by about 10% per quintile of socioeconomic deprivation. Māori, Pacific, and Indian patients were at 13–48% higher risk of cardiovascular disease than Europeans, and Chinese or other Asians were at 25–33% lower risk of cardiovascular disease than Europeans. The PCEs overestimated of hard atherosclerotic cardiovascular disease by about 40% in men and by 60% in women, and the additional predictors in the new equations were also independent predictors in the PCEs. The new equations were significantly better than PCEs on all performance metrics.

Interpretation

We constructed a large prospective cohort study representing typical patients in primary care in New Zealand who were recommended for cardiovascular disease risk assessment. Most patients are now at low risk of cardiovascular disease, which explains why the PCEs based mainly on old cohorts substantially overestimate risk. Although the PCEs and many other equations will need to be recalibrated to mitigate overtreatment of the healthy majority, they also need new predictors that include measures of socioeconomic deprivation and multiple ethnicities to identify vulnerable high-risk subpopulations that might otherwise be undertreated.

Funding

Health Research Council of New Zealand, Heart Foundation of New Zealand, and Healthier Lives National Science Challenge.

Introduction

More than 40 years ago, Framingham Heart Study investigators developed multivariable cardiovascular disease risk prediction equations that identified high-risk patients much more accurately than traditional classifications based on blood pressure or blood cholesterol concentrations alone.1 As the benefits of interventions that reduce the risk of cardiovascular disease are proportional to pretreatment risk,2, 3 treating patients who are assessed as high-risk with multivariable prediction equations is also more effective than treating patients with high levels of single risk factors. Most existing guidelines on cardiovascular disease risk factor management therefore recommend using risk prediction equations to inform treatment decisions.4, 5, 6, 7, 8, 9 Although more than 360 cardiovascular disease risk equations have been published since the pioneering Framingham research,10 most are based on cohort studies established last century. Participants in these older studies, including those used to derive the 2013 American College of Cardiology/American Heart Association Pooled Cohort Equations (PCEs)7 that are recommended at present, are very different to the patient populations the equations are now applied to, and their applicability is uncertain.

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.

In the 1990s, New Zealand developed the world's first national cardiovascular disease risk factor management guidelines based on multivariable predicted risk11 and recommended using 1991 Framingham Heart Study prediction equations12 to inform treatment decisions. At the time, no local cohort studies were available to validate the Framingham equations. In 2002, we developed a computerised decision support system that helped general practitioners implement the national guidelines while simultaneously generating a cohort study to investigate whether a 20th century Framingham equation was applicable to an ethnically and socioeconomically diverse New Zealand population in the 21st century. Here we describe the derivation and validation of new equations based on the Framingham equations that also include measures of deprivation, ethnicity, and other predictors of increased risk. For comparison, we externally validated the PCEs7 that have replaced Framingham equations and are integral to current cholesterol and blood pressure management guidelines in the USA.8, 9

Section snippets

Study design and participants

PREDICT is an ongoing, prospectively designed, open cohort study in New Zealand that automatically recruits participants when primary health-care practitioners complete standardised cardiovascular disease risk assessments using PREDICT decision support software.13 When opened, the software attempts to auto-populate PREDICT risk factor templates from patient records. Clinicians must fill in any missing fields before a cardiovascular disease risk can be calculated and recruitment completed.

Results

The study population included 452 092 men and women aged 30–74 years at the time of their first PREDICT risk assessment (index assessment) between Aug 27, 2002, and Oct 12, 2015. More than half of participants were recruited after Dec 31, 2010 (figure 1). We excluded 50 260 people with prior cardiovascular disease, impaired renal function, or heart failure and 80 people with missing risk factor data. The remaining 401 752 people constituted the PREDICT-1° cohort used in these analyses. The

Discussion

PREDICT is a large prospective cohort study representing patients in primary care who are recommended for cardiovascular disease risk assessment in New Zealand, a country with relatively similar cardiovascular disease event rates to many high-income nations, including the USA.33 All 401 752 participants had cardiovascular disease risk assessments completed by general practitioners or their practice nurses. More than half of the participants were assessed after 2010, and no data on standard risk

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