title: Prediction of cardiovascular risk using Framingham, ASSIGN and QRISK2: how well do they predict individual rather than population risk?
abstract: BACKGROUND
  The objective of this study was to evaluate the performance of risk scores (Framingham, Assign and QRISK2) in predicting high cardiovascular disease (CVD) risk in individuals rather than populations.
  
  
  RESULTS
  This study included 1.8 million persons without CVD and prior statin prescribing using the Clinical Practice Research Datalink. This contains electronic medical records of the general population registered with a UK general practice. Individual CVD risks were estimated using competing risk regression models. Individual differences in the 10-year CVD risks as predicted by risk scores and competing risk models were estimated; the population was divided into 20 subgroups based on predicted risk. CVD outcomes occurred in 69,870 persons. In the subgroup with lowest risks, risk predictions by QRISK2 were similar to individual risks predicted using our competing risk model (99.9% of people had differences of less than 2%); in the subgroup with highest risks, risk predictions varied greatly (only 13.3% of people had differences of less than 2%). Larger deviations between QRISK2 and our individual predicted risks occurred with calendar year, different ethnicities, diabetes mellitus and number of records for medical events in the electronic health records in the year before the index date. A QRISK2 estimate of low 10-year CVD risk (<15%) was confirmed by Framingham, ASSIGN and our individual predicted risks in 89.8% while an estimate of high 10-year CVD risk (≥ 20%) was confirmed in only 48.6% of people. The majority of cases occurred in people who had predicted 10-year CVD risk of less than 20%.
  
  
  CONCLUSIONS
  Application of existing CVD risk scores may result in considerable misclassification of high risk status. Current practice to use a constant threshold level for intervention for all patients, together with the use of different scoring methods, may inadvertently create an arbitrary classification of high CVD risk.
authors.affiliation: Health eResearch Centre, Farr Institute for Health Informatics Research, University of Manchester, Manchester, United Kingdom; Department of non-communicable diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom; Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, the Netherlands.
authors.forename: Tjeerd-Pieter
authors.initials: TP
authors.lastname: van Staa
authors.affiliation: Primary Care and Public Health Sciences, King's College, London, United Kingdom.
authors.forename: Martin
authors.initials: M
authors.lastname: Gulliford
authors.affiliation: Department of non-communicable diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.
authors.forename: Edmond S-W
authors.initials: ES
authors.lastname: Ng
authors.affiliation: Department of non-communicable diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.
authors.forename: Ben
authors.initials: B
authors.lastname: Goldacre
authors.affiliation: Department of non-communicable diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.
authors.forename: Liam
authors.initials: L
authors.lastname: Smeeth
mesh_terms: D000328:Adult; D000368:Aged; D002318:Cardiovascular Diseases / Q000453:epidemiology*; D015897:Comorbidity; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011159:Population Surveillance; D012306:Risk; D018570:Risk Assessment; D012307:Risk Factors