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CVD Risk Prediction Equation Validation Study
Study Questions:
How do new cardiovascular disease (CVD) risk predictors from a nationally representative cohort in New Zealand relevant to patients in contemporary primary care perform, and how do the new equations compare to equations that are recommended in the United States?
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 CVD, which are prospectively linked to national ICD-coded hospitalization and mortality databases. The study population includes male and female patients in primary care who have no prior CVD, renal disease, or congestive heart failure. New equations predicting total CVD 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 (ACC/AHA) 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 United States.
Results:
Outcome events were derived for 401,752 people aged 30–74 years at the time of their first PREDICT risk assessment between 2002 and 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. Four percent (15,386 people) had CV events, 10% were fatal, and 56% met the PCEs definition of hard atherosclerotic CVD (ASCVD) during 1,685,521 person-years of follow-up. The median 5-year risk of total CVEs 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 CV events than Europeans, and Chinese or other Asians were at 25–33% lower risk of CVD than Europeans. The PCEs overestimated ASCVD 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.
Conclusions:
In New Zealand, most patients are now at low risk of CVD, which explains why the AHA/ACC 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.
Perspective:
Amongst the goals of a universal health care database is to reduce cost and improve quality by electronic transfer of individual patient information (with appropriate informed consent and confidentiality). This has not happened in the United States, where medical information exchange between health care systems is relatively poor. The other major opportunity is that a de-identified national database could be used to help determine best and most cost-effective diagnostic and treatment practices, which is clearly possible in the New Zealand system.
Keywords: Atherosclerosis, Cardiovascular Diseases, General Practitioners, Heart Failure, Primary Health Care, Primary Prevention, Quality Improvement, Risk Assessment, Socioeconomic Factors
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