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

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dc.contributor.author Pylypchuk, Romana
dc.contributor.author Wells, Sue
dc.contributor.author Kerr, Andrew
dc.contributor.author Poppe, Katrina
dc.contributor.author Riddell, Tania
dc.contributor.author Harwood, Matire
dc.contributor.author Exeter, Dan
dc.contributor.author Mehta, Suneela
dc.contributor.author Grey, Corina
dc.contributor.author Wu, Billy P
dc.contributor.author Metcalf, Patricia
dc.contributor.author Warren, Jim
dc.contributor.author Harrison, Jeff
dc.contributor.author Marshall, Roger
dc.contributor.author Jackson, Rod
dc.coverage.spatial England
dc.date.accessioned 2023-01-19T03:37:17Z
dc.date.available 2023-01-19T03:37:17Z
dc.date.issued 2018-05
dc.identifier.citation (2018). The Lancet, 391(10133), 1897-1907.
dc.identifier.issn 0140-6736
dc.identifier.uri https://hdl.handle.net/2292/62498
dc.description.abstract <h4>Background</h4>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.<h4>Methods</h4>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.<h4>Findings</h4>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.<h4>Interpretation</h4>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.<h4>Funding</h4>Health Research Council of New Zealand, Heart Foundation of New Zealand, and Healthier Lives National Science Challenge.
dc.format.medium Print-Electronic
dc.language eng
dc.publisher Elsevier
dc.relation.ispartofseries Lancet (London, England)
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher.
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm
dc.subject Humans
dc.subject Cardiovascular Diseases
dc.subject Proportional Hazards Models
dc.subject Risk Assessment
dc.subject Risk Factors
dc.subject Cohort Studies
dc.subject Algorithms
dc.subject Socioeconomic Factors
dc.subject Adult
dc.subject Aged
dc.subject Middle Aged
dc.subject Primary Health Care
dc.subject New Zealand
dc.subject Female
dc.subject Male
dc.subject Ethnicity
dc.subject Racial Groups
dc.subject Patient Safety
dc.subject Clinical Research
dc.subject Prevention
dc.subject Cardiovascular
dc.subject Heart Disease
dc.subject 3 Good Health and Well Being
dc.subject Science & Technology
dc.subject Life Sciences & Biomedicine
dc.subject Medicine, General & Internal
dc.subject General & Internal Medicine
dc.subject BLOOD-PRESSURE
dc.subject METAANALYSIS
dc.subject DEPRIVATION
dc.subject RESIDUALS
dc.subject MODEL
dc.subject 1117 Public Health and Health Services
dc.subject 1102 Cardiorespiratory Medicine and Haematology
dc.subject Clinical
dc.subject Public Health
dc.subject Behavioral and Social Science
dc.subject 11 Medical and Health Sciences
dc.title Cardiovascular disease risk prediction equations in 400 000 primary care patients in New Zealand: a derivation and validation study.
dc.type Journal Article
dc.identifier.doi 10.1016/s0140-6736(18)30664-0
pubs.issue 10133
pubs.begin-page 1897
pubs.volume 391
dc.date.updated 2022-12-02T21:32:45Z
dc.rights.holder Copyright: The authors en
dc.identifier.pmid 29735391 (pubmed)
pubs.author-url https://www.ncbi.nlm.nih.gov/pubmed/29735391
pubs.end-page 1907
pubs.publication-status Published
dc.rights.accessrights http://purl.org/eprint/accessRights/RetrictedAccess en
pubs.subtype Research Support, Non-U.S. Gov't
pubs.subtype Validation Study
pubs.subtype Journal Article
pubs.elements-id 739762
pubs.org-id Medical and Health Sciences
pubs.org-id Science
pubs.org-id School of Computer Science
pubs.org-id Statistics
pubs.org-id Pharmacy
pubs.org-id Population Health
pubs.org-id Epidemiology & Biostatistics
pubs.org-id Gen.Practice& Primary Hlthcare
pubs.org-id School of Medicine
pubs.org-id Medicine Department
pubs.org-id Surgery Department
dc.identifier.eissn 1474-547X
dc.identifier.pii S0140-6736(18)30664-0
pubs.record-created-at-source-date 2022-12-03
pubs.online-publication-date 2018-05-04


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