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. 2014 Oct 1;9(10):e106455.
doi: 10.1371/journal.pone.0106455. eCollection 2014.

Prediction of cardiovascular risk using Framingham, ASSIGN and QRISK2: how well do they predict individual rather than population risk?

Tjeerd-Pieter van Staa  1 , Martin Gulliford  2 , Edmond S-W Ng  3 , Ben Goldacre  3 , Liam Smeeth  3
Affiliations

Prediction of cardiovascular risk using Framingham, ASSIGN and QRISK2: how well do they predict individual rather than population risk?

Tjeerd-Pieter van Staa et al. PLoS One. .

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.

Methods and findings: 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.

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Conflict of interest statement

Competing Interests: The authors have the following interests: Utrecht Institute for Pharmaceutical Sciences has received unrestricted funding for pharmacoepidemiological research from GlaxoSmithKline and Novo Nordisk. There are no patents, products in development or marketed products to declare. Two authors (TvS and EN) were previously employed by the Clinical Practice Research Datalink (CPRD). CPRD provides data and trial services on a commercial basis for both academic and pharmaceutical industry researchers. CPRD did not have any role in writing the report, or any input into the content of the report. This does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. Absolute differences in individual 10-year CVD risk prediction between the Framingham, ASSIGN and QRISK2 risk scores and the individual risks estimated in CPRD using competing risk regression stratified by vigintiles of predicted risk.
X-axis: Vigintiles of predicted risk. Y-axis: Percentage of persons.
See this image and copyright information in PMC

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