Journal
JOURNAL OF THE AMERICAN HEART ASSOCIATION
Volume 7, Issue 23, Pages -Publisher
WILEY
DOI: 10.1161/JAHA.118.009680
Keywords
electronic health record; peripheral artery disease; prognosis
Categories
Funding
- National Heart, Lung, and Blood Institute of the National Institutes of Health [K01HL124045]
- National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health [R01EB19403]
- National Center for Advancing Translational Sciences of the National Institutes of Health [U01TR02062]
- National Human Genome Research Institute of the National Institutes of Health, Electronic Medical Records and Genomics (eMERGE) Network [HG006379]
- National Institute on Aging of the National Institutes of Health [R01AG034676, RO1AGO 052425]
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Background-Automated individualized risk prediction tools linked to electronic health records (EHRs) are not available for management of patients with peripheral arterial disease. The goal of this study was to create a prognostic tool for patients with peripheral arterial disease using data elements automatically extracted from an EHR to enable real-time and individualized risk prediction at the point of care. Methods and Results-A previously validated phenotyping algorithm was deployed to an EHR linked to the Rochester Epidemiology Project to identify peripheral arterial disease cases from Olmsted County, MN, for the years 1998 to 2011. The study cohort was composed of 1676 patients: 593 patients died over 5-year follow-up. The c-statistic for survival in the overall data set was 0.76 (95% confidence interval [CI], 0.74-0.78), and the c-statistic across 10 cross-validation data sets was 0.75 (95% CI, 0.73-0.77). Stratification of cases demonstrated increasing mortality risk by subgroup (low: hazard ratio, 0.35 [95% CI, 0.21-0.58]; intermediate-high: hazard ratio, 2.98 [95% CI, 2.37-3.74]; high: hazard ratio, 8.44 [95% CI, 6.66-10.70], all P<0.0001 versus the reference subgroup). An equation for risk calculation was derived from Cox model parameters and beta estimates. Big data infrastructure enabled deployment of the real-time risk calculator to the point of care via the EHR. Conclusions-This study demonstrates that electronic tools can be deployed to EHRs to create automated real-time risk calculators to predict survival of patients with peripheral arterial disease. Moreover, the prognostic model developed may be translated to patient care as an automated and individualized real-time risk calculator deployed at the point of care.
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