TY - JOUR
T1 - Leveraging the electronic health record to create an automated real-time prognostic tool for peripheral arterial disease
AU - Arruda-Olson, Adelaide M.
AU - Afzal, Naveed
AU - Mallipeddi, Vishnu Priya
AU - Said, Ahmad
AU - Pacha, Homam Moussa
AU - Moon, Sungrim
AU - Chaudhry, Alisha P.
AU - Scott, Christopher G.
AU - Bailey, Kent R.
AU - Rooke, Thom W.
AU - Wennberg, Paul W.
AU - Kaggal, Vinod C.
AU - Oderich, Gustavo S.
AU - Kullo, Iftikhar J.
AU - Nishimura, Rick A.
AU - Chaudhry, Rajeev
AU - Liu, Hongfang
N1 - Publisher Copyright:
© 2018 The Authors.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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 b 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.
AB - 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 b 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.
KW - Electronic health record
KW - Peripheral artery disease
KW - Prognosis
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U2 - 10.1161/JAHA.118.009680
DO - 10.1161/JAHA.118.009680
M3 - Article
C2 - 30571601
AN - SCOPUS:85058471955
SN - 2047-9980
VL - 7
JO - Journal of the American Heart Association
JF - Journal of the American Heart Association
IS - 23
M1 - e009680
ER -