Leveraging the electronic health record to create an automated real-time prognostic tool for peripheral arterial disease

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 Oderich, Iftikhar Jan Kullo, Rick A. Nishimura, Rajeev Chaudhry, Hongfang D Liu

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish (US)
Article numbere009680
JournalJournal of the American Heart Association
Volume7
Issue number23
DOIs
StatePublished - Dec 1 2018

Fingerprint

Electronic Health Records
Peripheral Arterial Disease
Point-of-Care Systems
Confidence Intervals
Survival
Proportional Hazards Models
Patient Care
Epidemiology
Cohort Studies
Mortality

Keywords

  • Electronic health record
  • Peripheral artery disease
  • Prognosis

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

Cite this

Leveraging the electronic health record to create an automated real-time prognostic tool for peripheral arterial disease. / Arruda-Olson, Adelaide M; Afzal, Naveed; Mallipeddi, Vishnu Priya; Said, Ahmad; Pacha, Homam Moussa; Moon, Sungrim; Chaudhry, Alisha P.; Scott, Christopher G.; Bailey, Kent R; Rooke, Thom W; Wennberg, Paul W.; Kaggal, Vinod C.; Oderich, Gustavo; Kullo, Iftikhar Jan; Nishimura, Rick A.; Chaudhry, Rajeev; Liu, Hongfang D.

In: Journal of the American Heart Association, Vol. 7, No. 23, e009680, 01.12.2018.

Research output: Contribution to journalArticle

Arruda-Olson, Adelaide M ; Afzal, Naveed ; Mallipeddi, Vishnu Priya ; Said, Ahmad ; Pacha, Homam Moussa ; Moon, Sungrim ; Chaudhry, Alisha P. ; Scott, Christopher G. ; Bailey, Kent R ; Rooke, Thom W ; Wennberg, Paul W. ; Kaggal, Vinod C. ; Oderich, Gustavo ; Kullo, Iftikhar Jan ; Nishimura, Rick A. ; Chaudhry, Rajeev ; Liu, Hongfang D. / Leveraging the electronic health record to create an automated real-time prognostic tool for peripheral arterial disease. In: Journal of the American Heart Association. 2018 ; Vol. 7, No. 23.
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abstract = "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.",
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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

AU - Kullo, Iftikhar Jan

AU - Nishimura, Rick A.

AU - Chaudhry, Rajeev

AU - Liu, Hongfang D

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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.

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