Performance of the ASSIGN cardiovascular disease risk score on a UK cohort of patients from general practice
- 1School of Computing Sciences, University of East Anglia, Norwich, UK
- 2School of Medicine, Health Policy and Practice, University of East Anglia, Norwich, UK
- 3ICCH, Imperial College London, London, UK
- Correspondence to Beatriz de la Iglesia, School of Computing Sciences, University of East Anglia, UEA Campus, Norwich, NR4 7TJ, UK;
Contributors Margaret Robins conducted data pre-processing and implementation of some of the risk models to obtain the different risk scores for performance analysis.Beatriz de la Iglesia supervised the project, contributed to implementation of the models, conducted the calibration and discrimination analysis and wrote the paper. Jane Skinner checked our implementation of the models, supplied advice and discrimination analysis on Stata, and contributed to the editing of the paper. Both Neil Poulter and John Potter provided clinical expertise and contributed to the editing of the paper.
- Accepted 28 September 2010
- Published Online First 20 November 2010
Objective To evaluate the performance of ASSIGN against the Framingham equations for predicting 10 year risk of cardiovascular disease in a UK cohort of patients from general practice and to make the evaluation comparable to an independent evaluation of QRISK on the same cohort.
Design Prospective open cohort study.
Setting 288 practices from England and Wales contributing to The Health Improvement Network (THIN) database.
Participants Patients registered with 288 UK practices for some period between January 1995 and March 2006. The number of records available was 1 787 169.
Main outcome measures First diagnosis of myocardial infarction, coronary heart disease, stroke and transient ischaemic attacks recorded.
Methods We implemented the Anderson Framingham Coronary Heart Disease and Stroke models, ASSIGN, and a more recent Framingham Cox proportional-hazards model and analysed their calibration and discrimination.
Results Calibration showed that all models tested over-estimated risk particularly for men. ASSIGN showed better discrimination with higher AUROC (0.756/0.792 for men/women), D statistic (1.35/1.58 for men/women), and R2 (30.47%/37.39% for men/women). The performance of ASSIGN was comparable to that of QRISK on the same cohort. Models agreed on 93–97% of categorical (high/lower) risk assessments and when they disagreed, ASSIGN was often closer to the estimated Kaplan-Meier incidence. ASSIGN also provided a steeper gradient of deprivation and discriminated between those with and without recorded family history of CVD. The estimated incidence was twice/three times as high for women/men with a recorded family history of CVD.
Conclusions For systematic CVD risk assessment all models could usefully be applied, but ASSIGN improved on the gradient of deprivation and accounted for recorded family history whereas the Framingham equations did not. However, all models display relatively low specificity and sensitivity. An additional conclusion is that the recording of family history of CVD in primary care databases needs to improve given its importance in risk assessment.