Improvement in Cardiovascular Risk Prediction with Electronic Health Records
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The aim of this study was to compare the QRISKII, an electronic health data-based risk score, to the Framingham Risk Score (FRS) and atherosclerotic cardiovascular disease (ASCVD) score. Risk estimates were calculated for a cohort of 8783 patients, and the patients were followed up from November 29, 2012, through June 1, 2015, for a cardiovascular disease (CVD) event. During follow-up, 246 men and 247 women had a CVD event. Cohen’s kappa statistic for the comparison of the QRISKII and FRS was 0.22 for men and 0.23 for women, with the QRISKII classifying more patients in the higher-risk groups. The QRISKII and ASCVD were more similar with kappa statistics of 0.49 for men and 0.51 for women. The QRISKII shows increased discrimination with area under the curve (AUC) statistics of 0.65 and 0.71, respectively, compared to the FRS (0.59 and 0.66) and ASCVD (0.63 and 0.69). These results demonstrate that incorporating additional data from the electronic health record (EHR) may improve CVD risk stratification.
KeywordsCardiovascular QRISK Framingham risk score ASCVD Biobank
Atherosclerotic cardiovascular disease
Body mass index
Coronary heart disease
Chronic kidney disease
Electronic health record
Health Information Technology for Economic and Clinical Health
Framingham Risk Score
Rochester Epidemiology Project
United States of America
This study was made possible by using the resources of the Rochester Epidemiology Project, which is supported by the National Institute on Aging of the National Institutes of Health under award number R01AG034676. The Mayo Clinic Biobank is supported by the Mayo Clinic Center for Individualized Medicine.
Compliance with Ethical Standards
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. All research procedures were approved by the Institutional Review Committee of the Mayo Clinic.
Conflict of Interest
The authors declare that they have no competing interests.
Human and Animal Rights and Informed Consent
No animal studies were carried out by the authors for this article. The participants provided written and informed consent for the general research.
- 1.Go, A. S., Mozaffarian, D., Roger, V. L., Benjamin, E. J., Berry, J. D., Blaha, M. J., Dai, S., Ford, E. S., Fox, C. S., Franco, S., Fullerton, H. J., Gillespie, C., Hailpern, S. M., Heit, J. A., Howard, V. J., et al. (2014). Heart disease and stroke statistics—2014 update: a report from the American Heart Association. Circulation, 129(3), e28–e292.CrossRefPubMedGoogle Scholar
- 5.Goff, D. C., Jr., Lloyd-Jones, D. M., Bennett, G., Coady, S., D’Agostino, R. B., Gibbons, R., Greenland, P., Lackland, D. T., Levy, D., O’Donnell, C. J., Robinson, J. G., Schwartz, J. S., Shero, S. T., Smith, S. C., Jr., Sorlie, P., et al. (2014). 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation, 129(25 Suppl 2), S49–S73.CrossRefPubMedGoogle Scholar
- 6.Hippisley-Cox, J., Coupland, C., Robson, J., & Brindle, P. (2010). Derivation, validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database. BMJ, 341(c6624).Google Scholar
- 11.Bielinski, S. J., Pathak, J., Carrell, D. S., Takahashi, P. Y., Olson, J. E., Larson, N. B., Liu, H., Sohn, S., Wells, Q. S., Denny, J. C., Rasmussen-Torvik, L. J., Pacheco, J. A., Jackson, K. L., Lesnick, T. G., Gullerud, R. E., et al. (2015). A robust e-Epidemiology Tool in Phenotyping Heart Failure with Differentiation for Preserved and Reduced Ejection Fraction: the Electronic Medical Records and Genomics (eMERGE) Network. Journal of Cardiovascular Translational Research, 8(8), 475–483.CrossRefPubMedGoogle Scholar
- 14.Kho, A. N., Pacheco, J. A., Peissig, P. L., Rasmussen, L., Newton, K. M., Weston, N., Crane, P. K., Pathak, J., Chute, C. G., Bielinski, S. J., Kullo, I. J., Li, R., Manolio, T. A., Chisholm, R. L., & Denny, J. C. (2011). Electronic medical records for genetic research: results of the eMERGE consortium. Science Translational Medicine, 3(79), 79re71.CrossRefGoogle Scholar
- 15.Newton, K. M., Peissig, P. L., Kho, A. N., Bielinski, S. J., Berg, R. L., Choudhary, V., Basford, M., Chute, C. G., Kullo, I. J., Li, R., Pacheco, J. A., Rasmussen, L. V., Spangler, L., & Denny, J. C. (2013). Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. Journal of the American Medical Informatics Association, 20(e1), e147–e154.CrossRefPubMedPubMedCentralGoogle Scholar
- 17.Olson, J. E., Ryu, E., Johnson, K. J., Koenig, B. A., Maschke, K. J., Morrisette, J. A., Liebow, M., Takahashi, P. Y., Fredericksen, Z. S., Sharma, R. G., Anderson, K. S., Hathcock, M. A., Carnahan, J. A., Pathak, J., Lindor, N. M., et al. (2013). The Mayo Clinic Biobank: a building block for individualized medicine. Mayo Clinic Proceedings, 88(9), 952–962.Google Scholar
- 18.Kho, A. N., Hayes, M. G., Rasmussen-Torvik, L., Pacheco, J. A., Thompson, W. K., Armstrong, L. L., Denny, J. C., Peissig, P. L., Miller, A. W., Wei, W. Q., Bielinski, S. J., Chute, C. G., Leibson, C. L., Jarvik, G. P., Crosslin, D. R., et al. (2012). Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study. Journal of the American Medical Informatics Association, 19(2), 212–218.CrossRefPubMedPubMedCentralGoogle Scholar
- 20.Carletta, J. (1996). Assessing agreement on classification tasks: the kappa statistic. Computational Linguistics, 22(2), 249–254.Google Scholar
- 21.National Institute for Health and Care Excellence. Cardiovascular disease: risk assessment and reduction, including lipid modification. NICE Guidelines [CG181]. Published 18 July 2014. http://www.nice.org.uk/guidance/cg181. Accessed 13 Nov 2015.
- 22.Collins, G. S., & Altman, D. G. (2010), An independent and external validation of QRISK2 cardiovascular disease risk score: a prospective open cohort study. BMJ, 340(c2442).Google Scholar
- 24.Muntner, P., Colantonio, L. D., Cushman, M., Goff, D. C., Jr., Howard, G., Howard, V. J., Kissela, B., Levitan, E. B., Lloyd-Jones, D. M., & Safford, M. M. (2014). Validation of the atherosclerotic cardiovascular disease pooled cohort risk equations. JAMA, 311(14), 1406–1415.CrossRefPubMedPubMedCentralGoogle Scholar
- 25.Mosca, L., Benjamin, E. J., Berra, K., Bezanson, J. L., Dolor, R. J., Lloyd-Jones, D. M., Newby, L. K., Pina, I. L., Roger, V. L., Shaw, L. J., Zhao, D., Beckie, T. M., Bushnell, C., D’Armiento, J., Kris-Etherton, P. M., et al. (2011). Effectiveness-based guidelines for the prevention of cardiovascular disease in women—2011 update: a guideline from the American Heart Association. Journal of the American College of Cardiology, 57(12), 1404–1423.CrossRefPubMedPubMedCentralGoogle Scholar