Improvement in Cardiovascular Risk Prediction with Electronic Health Records Academic Article uri icon

Overview

MeSH Major

  • Cardiovascular Diseases
  • Data Mining
  • Decision Support Techniques
  • Electronic Health Records

abstract

  • 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.

publication date

  • March 9, 2016

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC4874910

Digital Object Identifier (DOI)

  • 10.1007/s12265-016-9687-z

PubMed ID

  • 26960568

Additional Document Info