Improvement in Cardiovascular Risk Prediction with Electronic Health Records

被引:0
|
作者
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
机构
[1] Mayo Clinic,Department of Health Sciences Research
[2] Mayo Clinic,Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery
[3] Mayo Clinic,Department of Medicine
[4] Mayo Clinic,Division of Cardiovascular Diseases in the Department of Internal Medicine
[5] Mayo Clinic,Department of Neurology
[6] Mayo Clinic,Department of Surgery
[7] Mayo Clinic,Department of Physiology and Biomedical Engineering
[8] Weill Cornell Medical College,Department of Healthcare Policy and Research
关键词
Cardiovascular; QRISK; Framingham risk score; ASCVD; Biobank;
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学科分类号
摘要
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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页码:214 / 222
页数:8
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