Predicting the 10 year risk of cardiovascular disease in the United Kingdom: independent and external validation of an updated version of QRISK2

Objective To evaluate the performance of the QRISK2-2011 score for predicting the 10 year risk of cardiovascular disease in an independent UK cohort of patients from general practice and to compare it with earlier versions of the model and a National Institute for Health and Clinical Excellence version of the Framingham equation. Design Prospective cohort study to validate a cardiovascular risk score with routinely collected data between June 1994 and June 2008. Setting 364 practices from the United Kingdom contributing to The Health Improvement Network (THIN) database. Participants Two million patients aged 30 to 84 years (11.8 million person years) with 93 564 cardiovascular events. Main outcome measure First diagnosis of cardiovascular disease (myocardial infarction, angina, coronary heart disease, stroke, and transient ischaemic attack) recorded in general practice records. Results Results from this independent and external validation of QRISK2-2011 indicate good performance data when compared with the NICE version of the Framingham equation. QRISK2-2011 had better ability to identify those at high risk of developing cardiovascular disease than did the NICE Framingham equation. QRISK2-2011 is well calibrated, with reasonable agreement between observed and predicted outcomes, whereas the NICE Framingham equation seems to consistently over-predict risk in men by about 5% and shows poor calibration in women. Conclusions QRISK2-2011 seems to be a useful model, with good discriminative and calibration properties when compared with the NICE version of the Framingham equation. Furthermore, based on current high risk thresholds, concerns exist on the clinical usefulness of the NICE version of the Framingham equation for identifying women at high risk of developing cardiovascular disease. At current thresholds the NICE version of the Framingham equation has no clinical benefit in either men or women.

[1]  Patrick Royston,et al.  A new measure of prognostic separation in survival data , 2004, Statistics in medicine.

[2]  Elena B. Elkin,et al.  Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers , 2008, BMC Medical Informatics Decis. Mak..

[3]  J. Robson,et al.  Lipid modification: cardiovascular risk assessment and the modification of blood lipids for the primary and secondary prevention of cardiovascular disease , 2007, Heart.

[4]  N. Cook,et al.  Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. , 2007, JAMA.

[5]  Tx Station Stata Statistical Software: Release 7. , 2001 .

[6]  N. Paynter,et al.  C-Reactive Protein and Parental History Improve Global Cardiovascular Risk Prediction: The Reynolds Risk Score for Men , 2008, Circulation.

[7]  J. Hippisley-Cox,et al.  Performance of the QRISK cardiovascular risk prediction algorithm in an independent UK sample of patients from general practice: a validation study , 2007, Heart.

[8]  D G Altman,et al.  Missing covariate data within cancer prognostic studies: a review of current reporting and proposed guidelines , 2004, British Journal of Cancer.

[9]  E. Elkin,et al.  Decision Curve Analysis: A Novel Method for Evaluating Prediction Models , 2006, Medical decision making : an international journal of the Society for Medical Decision Making.

[10]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[11]  Patrick Royston,et al.  Explained Variation for Survival Models , 2006 .

[12]  Gary S Collins,et al.  An independent and external validation of QRISK2 cardiovascular disease risk score: a prospective open cohort study , 2010, BMJ : British Medical Journal.

[13]  K. Anderson,et al.  Cardiovascular disease risk profiles. , 1991, American heart journal.

[14]  Y. Vergouwe,et al.  Validation, updating and impact of clinical prediction rules: a review. , 2008, Journal of clinical epidemiology.

[15]  F. Harrell,et al.  Evaluating the yield of medical tests. , 1982, JAMA.

[16]  J. Hippisley-Cox,et al.  Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study , 2007, BMJ : British Medical Journal.

[17]  N. Obuchowski,et al.  Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures , 2010, Epidemiology.

[18]  P. Royston,et al.  Prognosis and prognostic research: application and impact of prognostic models in clinical practice , 2009, BMJ : British Medical Journal.

[19]  H. Young Putting prevention first - vascular checks: risk assessment and management , 2010 .

[20]  J. Hippisley-Cox,et al.  Advantages of QRISK2 (2010): the key issue is ethnicity and extent of reallocation , 2011, Heart.

[21]  A. Sheikh,et al.  Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2 , 2008, BMJ : British Medical Journal.

[22]  Gary S Collins,et al.  An independent external validation and evaluation of QRISK cardiovascular risk prediction: a prospective open cohort study , 2009, BMJ : British Medical Journal.

[23]  Yvonne Vergouwe,et al.  Development and validation of a prediction model with missing predictor data: a practical approach. , 2010, Journal of clinical epidemiology.