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...
Main Authors: | , , , , , , , , , , , , , , , , |
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Format: | Article |
Language: | English |
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Wiley
2018-12-01
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Series: | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
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Online Access: | https://www.ahajournals.org/doi/10.1161/JAHA.118.009680 |
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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 |
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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 |
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