Improvement in Cardiovascular Risk Prediction with Electronic Health Records

Mindy M. Pike, Paul A. Decker, Nicholas B. Larson, Jennifer L. St. Sauver, Paul Y. Takahashi, Véronique L. Roger, Walter A. Rocca, Virginia M. Miller, Janet E. Olson, Jyotishman Pathak, Suzette J. Bielinski

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)214-222
Number of pages9
JournalJournal of cardiovascular translational research
Issue number3
StatePublished - Jun 1 2016


  • Biobank
  • Cardiovascular
  • Framingham risk score

ASJC Scopus subject areas

  • Molecular Medicine
  • Genetics
  • Pharmaceutical Science
  • Cardiology and Cardiovascular Medicine
  • Genetics(clinical)


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