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

doi:10.1007/s12265-016-9687-z

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 journal › Article › peer-review

20 Scopus citations

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.

Original language	English (US)
Pages (from-to)	214-222
Number of pages	9
Journal	Journal of cardiovascular translational research
Volume	9
Issue number	3
DOIs	https://doi.org/10.1007/s12265-016-9687-z
State	Published - Jun 1 2016

Keywords

ASCVD
Biobank
Cardiovascular
Framingham risk score
QRISK

ASJC Scopus subject areas

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

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10.1007/s12265-016-9687-z

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title = "Improvement in Cardiovascular Risk Prediction with 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{\textquoteright}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.",

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AU - Larson, Nicholas B.

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AU - Takahashi, Paul Y.

AU - Roger, Véronique L.

AU - Rocca, Walter A.

AU - Miller, Virginia M.

AU - Olson, Janet E.

AU - Pathak, Jyotishman

AU - Bielinski, Suzette J.

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AB - 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.

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Improvement in Cardiovascular Risk Prediction with Electronic Health Records

Abstract

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ASJC Scopus subject areas

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