Open AccessLeveraging the Electronic Health Record to Create an Automated Real‐Time Prognostic Tool for Peripheral Arterial Disease
Author(s) -
ArrudaOlson Adelaide M.,
Afzal Naveed,
Priya Mallipeddi Vishnu,
Said Ahmad,
Moussa Pacha Homam,
Moon Sungrim,
Chaudhry Alisha P.,
Scott Christopher G.,
Bailey Kent R.,
Rooke Thom W.,
Wennberg Paul W.,
Kaggal Vinod C.,
Oderich Gustavo S.,
Kullo Iftikhar J.,
Nishimura Rick A.,
Chaudhry Rajeev,
Liu Hongfang
Publication year - 2018
Publication title -
journal of the american heart association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.494
H-Index - 85
ISSN - 2047-9980
DOI - 10.1161/jaha.118.009680
Subject(s) - medicine , hazard ratio , confidence interval , electronic health record , cohort , proportional hazards model , time point , emergency medicine , epidemiology , data mining , health care , computer science , philosophy , economics , economic growth , aesthetics
Background Automated individualized risk prediction tools linked to electronic health records ( EHR s) 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 β 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 EHR s 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.