Predicting the 10 Year Risk of Cardiovascular Disease in the United Kingdom: Independent and External Validation of an Updated Version of QRISK2

Overview

Journal BMJ

Specialty General Medicine

Date 2012 Jun 23

PMID 22723603

Citations 104

Authors

Gary S Collins

Douglas G Altman

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis.

Liu T, Krentz A, Lu L, Curcin V Eur Heart J Digit Health. 2025; 6(1):7-22.

PMID: 39846062 PMC: 11750195. DOI: 10.1093/ehjdh/ztae080.

Estimating the burden of underdiagnosis within England: A modelling study of linked primary care data.

Anosova O, Head A, Collins B, Alexiou A, Darras K, Sutton M PLoS One. 2025; 20(1):e0313877.

PMID: 39813200 PMC: 11734898. DOI: 10.1371/journal.pone.0313877.

Effectiveness and feasibility of a theory-informed intervention to improve Mediterranean diet adherence, physical activity and cognition in older adults at risk of dementia: the MedEx-UK randomised controlled trial.

Jennings A, Shannon O, Gillings R, Lee V, Elsworthy R, Bundy R BMC Med. 2024; 22(1):600.

PMID: 39716203 PMC: 11667912. DOI: 10.1186/s12916-024-03815-z.

Bayesian Decision Curve Analysis With Bayesdca.

Cruz G, Korthauer K Stat Med. 2024; 43(30):6042-6058.

PMID: 39617734 PMC: 11639651. DOI: 10.1002/sim.10277.

Age and sex specific thresholds for risk stratification of cardiovascular disease and clinical decision making: prospective open cohort study.

Xu Z, Usher-Smith J, Pennells L, Chung R, Arnold M, Kim L BMJ Med. 2024; 3(1):e000633.

PMID: 39175920 PMC: 11340247. DOI: 10.1136/bmjmed-2023-000633.

References

Harrell Jr F, Califf R, Pryor D, Lee K, Rosati R . Evaluating the yield of medical tests. JAMA. 1982; 247(18):2543-6. View

Ridker P, Paynter N, Rifai N, Gaziano J, Cook N . C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation. 2008; 118(22):2243-51, 4p following 2251. PMC: 2752381. DOI: 10.1161/CIRCULATIONAHA.108.814251. View

Hippisley-Cox J, Coupland C, Robson J, Brindle P . Advantages of QRISK2 (2010): the key issue is ethnicity and extent of reallocation. Heart. 2011; 97(6):515. DOI: 10.1136/hrt.2010.221085. View

Steyerberg E, Vickers A, Cook N, Gerds T, Gonen M, Obuchowski N . Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2009; 21(1):128-38. PMC: 3575184. DOI: 10.1097/EDE.0b013e3181c30fb2. View

Ridker P, Buring J, Rifai N, Cook N . Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA. 2007; 297(6):611-9. DOI: 10.1001/jama.297.6.611. View

Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Minhas R, Sheikh A . Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ. 2008; 336(7659):1475-82. PMC: 2440904. DOI: 10.1136/bmj.39609.449676.25. View

Collins G, Altman D . An independent external validation and evaluation of QRISK cardiovascular risk prediction: a prospective open cohort study. BMJ. 2009; 339:b2584. PMC: 2714681. DOI: 10.1136/bmj.b2584. View

Anderson K, Odell P, Wilson P, Kannel W . Cardiovascular disease risk profiles. Am Heart J. 1991; 121(1 Pt 2):293-8. DOI: 10.1016/0002-8703(91)90861-b. View

Royston P, Sauerbrei W . A new measure of prognostic separation in survival data. Stat Med. 2004; 23(5):723-48. DOI: 10.1002/sim.1621. View

10.

Vickers A, Cronin A, Elkin E, Gonen M . Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak. 2008; 8:53. PMC: 2611975. DOI: 10.1186/1472-6947-8-53. View

11.

Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, Brindle P . Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ. 2007; 335(7611):136. PMC: 1925200. DOI: 10.1136/bmj.39261.471806.55. View

12.

Moons K, Altman D, Vergouwe Y, Royston P . Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ. 2009; 338:b606. DOI: 10.1136/bmj.b606. View

13.

Vickers A, Elkin E . Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006; 26(6):565-74. PMC: 2577036. DOI: 10.1177/0272989X06295361. View

14.

Vergouwe Y, Royston P, Moons K, Altman D . Development and validation of a prediction model with missing predictor data: a practical approach. J Clin Epidemiol. 2009; 63(2):205-14. DOI: 10.1016/j.jclinepi.2009.03.017. View

15.

Toll D, Janssen K, Vergouwe Y, Moons K . Validation, updating and impact of clinical prediction rules: a review. J Clin Epidemiol. 2009; 61(11):1085-94. DOI: 10.1016/j.jclinepi.2008.04.008. View

16.

Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Brindle P . Performance of the QRISK cardiovascular risk prediction algorithm in an independent UK sample of patients from general practice: a validation study. Heart. 2007; 94(1):34-9. DOI: 10.1136/hrt.2007.134890. View

17.

Burton A, Altman D . Missing covariate data within cancer prognostic studies: a review of current reporting and proposed guidelines. Br J Cancer. 2004; 91(1):4-8. PMC: 2364743. DOI: 10.1038/sj.bjc.6601907. View

18.

Collins G, Altman D . An independent and external validation of QRISK2 cardiovascular disease risk score: a prospective open cohort study. BMJ. 2010; 340:c2442. PMC: 2869403. DOI: 10.1136/bmj.c2442. View