Leveraging the Electronic Health Record to Create an Automated Real‐Time Prognostic Tool for Peripheral Arterial Disease

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...

Full description

Bibliographic Details
Main Authors: Adelaide M. Arruda‐Olson, Naveed Afzal, Vishnu Priya Mallipeddi, Ahmad Said, Homam Moussa Pacha, Sungrim Moon, Alisha P. Chaudhry, Christopher G. Scott, Kent R. Bailey, Thom W. Rooke, Paul W. Wennberg, Vinod C. Kaggal, Gustavo S. Oderich, Iftikhar J. Kullo, Rick A. Nishimura, Rajeev Chaudhry, Hongfang Liu
Format: Article
Language:English
Published: Wiley 2018-12-01
Series:Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
Subjects:
Online Access:https://www.ahajournals.org/doi/10.1161/JAHA.118.009680
_version_ 1811333986621849600
author Adelaide M. Arruda‐Olson
Naveed Afzal
Vishnu Priya Mallipeddi
Ahmad Said
Homam Moussa Pacha
Sungrim Moon
Alisha P. Chaudhry
Christopher G. Scott
Kent R. Bailey
Thom W. Rooke
Paul W. Wennberg
Vinod C. Kaggal
Gustavo S. Oderich
Iftikhar J. Kullo
Rick A. Nishimura
Rajeev Chaudhry
Hongfang Liu
author_facet Adelaide M. Arruda‐Olson
Naveed Afzal
Vishnu Priya Mallipeddi
Ahmad Said
Homam Moussa Pacha
Sungrim Moon
Alisha P. Chaudhry
Christopher G. Scott
Kent R. Bailey
Thom W. Rooke
Paul W. Wennberg
Vinod C. Kaggal
Gustavo S. Oderich
Iftikhar J. Kullo
Rick A. Nishimura
Rajeev Chaudhry
Hongfang Liu
author_sort Adelaide M. Arruda‐Olson
collection DOAJ
description 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 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 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.
first_indexed 2024-04-13T17:01:03Z
format Article
id doaj.art-0ed293ce0eb541a9a81e4df051d192e1
institution Directory Open Access Journal
issn 2047-9980
language English
last_indexed 2024-04-13T17:01:03Z
publishDate 2018-12-01
publisher Wiley
record_format Article
series Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
spelling doaj.art-0ed293ce0eb541a9a81e4df051d192e12022-12-22T02:38:39ZengWileyJournal of the American Heart Association: Cardiovascular and Cerebrovascular Disease2047-99802018-12-0172310.1161/JAHA.118.009680Leveraging the Electronic Health Record to Create an Automated Real‐Time Prognostic Tool for Peripheral Arterial DiseaseAdelaide M. Arruda‐Olson0Naveed Afzal1Vishnu Priya Mallipeddi2Ahmad Said3Homam Moussa Pacha4Sungrim Moon5Alisha P. Chaudhry6Christopher G. Scott7Kent R. Bailey8Thom W. Rooke9Paul W. Wennberg10Vinod C. Kaggal11Gustavo S. Oderich12Iftikhar J. Kullo13Rick A. Nishimura14Rajeev Chaudhry15Hongfang Liu16Department of Cardiovascular Medicine Mayo Clinic Rochester MNDepartment of Health Sciences Research Mayo Clinic Rochester MNDepartment of Cardiovascular Medicine Mayo Clinic Rochester MNDepartment of Cardiovascular Medicine Mayo Clinic Rochester MNDepartment of Cardiovascular Medicine Mayo Clinic Rochester MNDepartment of Health Sciences Research Mayo Clinic Rochester MNDepartment of Cardiovascular Medicine Mayo Clinic Rochester MNDepartment of Health Sciences Research Mayo Clinic Rochester MNDepartment of Health Sciences Research Mayo Clinic Rochester MNDepartment of Cardiovascular Medicine Mayo Clinic Rochester MNDepartment of Cardiovascular Medicine Mayo Clinic Rochester MNDepartment of Health Sciences Research Mayo Clinic Rochester MNDivision of Vascular and Endovascular Surgery Mayo Clinic Rochester MNDepartment of Cardiovascular Medicine Mayo Clinic Rochester MNDepartment of Cardiovascular Medicine Mayo Clinic Rochester MNDivision of Primary Care Medicine and Center of Translational Informatics and Knowledge Management Mayo Clinic Rochester MNDepartment of Health Sciences Research Mayo Clinic Rochester MNBackground 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 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 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.https://www.ahajournals.org/doi/10.1161/JAHA.118.009680electronic health recordperipheral artery diseaseprognosis
spellingShingle Adelaide M. Arruda‐Olson
Naveed Afzal
Vishnu Priya Mallipeddi
Ahmad Said
Homam Moussa Pacha
Sungrim Moon
Alisha P. Chaudhry
Christopher G. Scott
Kent R. Bailey
Thom W. Rooke
Paul W. Wennberg
Vinod C. Kaggal
Gustavo S. Oderich
Iftikhar J. Kullo
Rick A. Nishimura
Rajeev Chaudhry
Hongfang Liu
Leveraging the Electronic Health Record to Create an Automated Real‐Time Prognostic Tool for Peripheral Arterial Disease
Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
electronic health record
peripheral artery disease
prognosis
title Leveraging the Electronic Health Record to Create an Automated Real‐Time Prognostic Tool for Peripheral Arterial Disease
title_full Leveraging the Electronic Health Record to Create an Automated Real‐Time Prognostic Tool for Peripheral Arterial Disease
title_fullStr Leveraging the Electronic Health Record to Create an Automated Real‐Time Prognostic Tool for Peripheral Arterial Disease
title_full_unstemmed Leveraging the Electronic Health Record to Create an Automated Real‐Time Prognostic Tool for Peripheral Arterial Disease
title_short Leveraging the Electronic Health Record to Create an Automated Real‐Time Prognostic Tool for Peripheral Arterial Disease
title_sort leveraging the electronic health record to create an automated real time prognostic tool for peripheral arterial disease
topic electronic health record
peripheral artery disease
prognosis
url https://www.ahajournals.org/doi/10.1161/JAHA.118.009680
work_keys_str_mv AT adelaidemarrudaolson leveragingtheelectronichealthrecordtocreateanautomatedrealtimeprognostictoolforperipheralarterialdisease
AT naveedafzal leveragingtheelectronichealthrecordtocreateanautomatedrealtimeprognostictoolforperipheralarterialdisease
AT vishnupriyamallipeddi leveragingtheelectronichealthrecordtocreateanautomatedrealtimeprognostictoolforperipheralarterialdisease
AT ahmadsaid leveragingtheelectronichealthrecordtocreateanautomatedrealtimeprognostictoolforperipheralarterialdisease
AT homammoussapacha leveragingtheelectronichealthrecordtocreateanautomatedrealtimeprognostictoolforperipheralarterialdisease
AT sungrimmoon leveragingtheelectronichealthrecordtocreateanautomatedrealtimeprognostictoolforperipheralarterialdisease
AT alishapchaudhry leveragingtheelectronichealthrecordtocreateanautomatedrealtimeprognostictoolforperipheralarterialdisease
AT christophergscott leveragingtheelectronichealthrecordtocreateanautomatedrealtimeprognostictoolforperipheralarterialdisease
AT kentrbailey leveragingtheelectronichealthrecordtocreateanautomatedrealtimeprognostictoolforperipheralarterialdisease
AT thomwrooke leveragingtheelectronichealthrecordtocreateanautomatedrealtimeprognostictoolforperipheralarterialdisease
AT paulwwennberg leveragingtheelectronichealthrecordtocreateanautomatedrealtimeprognostictoolforperipheralarterialdisease
AT vinodckaggal leveragingtheelectronichealthrecordtocreateanautomatedrealtimeprognostictoolforperipheralarterialdisease
AT gustavosoderich leveragingtheelectronichealthrecordtocreateanautomatedrealtimeprognostictoolforperipheralarterialdisease
AT iftikharjkullo leveragingtheelectronichealthrecordtocreateanautomatedrealtimeprognostictoolforperipheralarterialdisease
AT rickanishimura leveragingtheelectronichealthrecordtocreateanautomatedrealtimeprognostictoolforperipheralarterialdisease
AT rajeevchaudhry leveragingtheelectronichealthrecordtocreateanautomatedrealtimeprognostictoolforperipheralarterialdisease
AT hongfangliu leveragingtheelectronichealthrecordtocreateanautomatedrealtimeprognostictoolforperipheralarterialdisease