Jump to content

Choose locationSutter Maternity & Surgery Center of Santa Cruz
  • Sign in or Enroll
    • Open I want to choose my medical group or hospital
    • Clear my location
Change Location
Sutter Health
  • Video Visits
  • Find Doctors
  • Find Locations
  • Treatments & Services
  • Locations
  • Sign in or Enroll
    • Video Visits
    • Find Doctors
    • Find Locations
    • Treatments & Services
    • COVID-19 Resources
    • Pay a Bill
    • Symptom Checker
    • Get Care Today
    • Health & Wellness
    • Classes & Events
    • Research & Clinical Trials
    • For Patients
    • About Sutter Health
    • Giving
    • Volunteering
    • Careers
    • News
    • For Medical Professionals
    • Other Business Services
Close Search
  • Home
  • Sutter Maternity & Surgery Center
  • Research
  • Medical Informatics
Content

Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population.

Description

Ward A, Sarraju A, Chung S, Li J, Harrington R, Heidenreich P, Palaniappan L, Scheinker D, Rodriguez F., NPJ Digit Med. 3:125. doi: 10.1038/s41746-020-00331-1. eCollection 2020., 2020 Sep 23

Investigators

Jiang Li, Ph.D., MPH, Assistant Scientist

Abstract

The pooled cohort equations (PCE) predict atherosclerotic cardiovascular disease (ASCVD) risk in patients with characteristics within prespecified ranges and has uncertain performance among Asians or Hispanics.

It is unknown if machine learning (ML) models can improve ASCVD risk prediction across broader diverse, real-world populations. We developed ML models for ASCVD risk prediction for multi-ethnic patients using an electronic health record (EHR) database from Northern California.

Our cohort included patients aged 18 years or older with no prior CVD and not on statins at baseline (n = 262,923), stratified by PCE-eligible (n = 131,721) or PCE-ineligible patients based on missing or out-of-range variables. We trained ML models [logistic regression with L2 penalty and L1 lasso penalty, random forest, gradient boosting machine (GBM), extreme gradient boosting] and determined 5-year ASCVD risk prediction, including with and without incorporation of additional EHR variables, and in Asian and Hispanic subgroups.

A total of 4309 patients had ASCVD events, with 2077 in PCE-ineligible patients. GBM performance in the full cohort, including PCE-ineligible patients (area under receiver-operating characteristic curve (AUC) 0.835, 95% confidence interval (CI): 0.825-0.846), was significantly better than that of the PCE in the PCE-eligible cohort (AUC 0.775, 95% CI: 0.755-0.794). Among patients aged 40-79, GBM performed similarly before (AUC 0.784, 95% CI: 0.759-0.808) and after (AUC 0.790, 95% CI: 0.765-0.814) incorporating additional EHR data.

Overall, ML models achieved comparable or improved performance compared to the PCE while allowing risk discrimination in a larger group of patients including PCE-ineligible patients. EHR-trained ML models may help bridge important gaps in ASCVD risk prediction.

Pubmed Abstract

Pubmed AbstractOpens New Window

Associated Topics

  • Cardiovascular Diseases
  • Medical Informatics

Related Publications

Tradeoffs of using administrative claims and medical records to identify the use of personalized medicine for patients with breast cancer.

Liang SY, Phillips KA, Wang G, Keohane C, Armstrong J, Morris WM, Haas JS.
Med Care. 49(6):e1-8. doi: 10.1097/MLR.0b013e318207e87e.
2011 Jun 01

Under diagnosis of hypertension using electronic health records.

Banerjee D, Chung S, Wong EC, Wang EJ, Stafford RS, Palaniappan LP.
Am J Manag Care. 16(2):e35-42
2010 Feb 01

Physicians’ well-being linked to in-basket messages generated by algorithms In EHRs.

Tai-Seale M, Dillon EC, Yang Y, Nordgren R, Steinberg R, Nauenberg T, Lee TC, Meehan A, Li J, Chan AS, Frosch D.
Health Aff. 38(7): https://doi.org/10.1377/hlthaff.2018.05509.
2019 Jul 01

Physicians’ well-being linked to in-basket messages generated by algorithms In electronic health records.

Tai-Seale M, Dillon EC, Yang Y, Nordgren R, Steinberg R, Nauenberg T, Lee TC, Meehan A, Li J, Chan AS, Frosch D.
Health Aff. 38(7): https://doi.org/10.1377/hlthaff.2018.05509.
2019 Jul 01

Comparative usability study of a newly created patient-centered tool and Medicare.gov plan finder to help Medicare beneficiaries choose prescription drug plans.

Stults CD, Fattahi S, Meehan A, Bundorf MK, Chan AS, Pun T, Tai-Seale M.
J Patient Exp. 6(1):81-86. doi: 10.1177/2374373518778343. Epub 2018 Jun 6.
2019 Mar 01
The Sutter Health Network of Care
Expertise to fit your needs
Primary Care

Check-ups, screenings and sick visits for adults and children.

Specialty Care

Expertise and advanced technologies in all areas of medicine.

Emergency Care

For serious accidents, injuries and conditions that require immediate medical care.

Urgent Care

After-hours, weekend and holiday services.

Walk-In Care

Convenient walk-in care clinics for your non-urgent health needs.

About Sutter

  • About Our Network
  • Annual Report
  • Awards
  • Community Benefit
  • Contact Us
  • News
  • Giving
  • Find Care

  • Birth Centers
  • Care Centers
  • Emergency Rooms
  • Hospitals
  • Imaging
  • Labs
  • Surgery Centers
  • Urgent Care
  • Walk-In Care
  • View All >
  • Featured Services

  • Behavioral Health
  • Cancer Services
  • Family Medicine
  • Home Health and Hospice
  • Orthopedics
  • Pediatrics
  • Pregnancy
  • Primary Care
  • Women's Health
  • View All >
  • Patient Resources

  • Accepted Health Plans
  • Classes and Events
  • Estimate Costs
  • Health and Wellness
  • Medical Records
  • Medicare Advantage
  • My Health Online
  • Pay a Bill
  • Symptom Checker
  • Our Team

  • For Employees
  • Physician Careers
  • Recruiting Events
  • Residencies and Fellowships
  • Sutter Careers
  • Vendors
  • Volunteers
    • ADA Accessibility
    • Contact
    • Privacy
    • Do Not Sell My Personal Information

    • LinkedIn Opens new window
    • YouTube Opens new window
    • Facebook Opens new window
    • Twitter Opens new window
    • Glassdoor Opens new window
    • Instagram Opens new window

    Copyright © 2022 Sutter Health. All rights reserved. Sutter Health is a registered trademark of Sutter Health ®, Reg. U.S. Patent & Trademark office.

    Cookie Policy

    We use cookies to give you the best possible user experience. By continuing to use the site, you agree to the use of cookies. Privacy Policy Cookie Preferences

    Privacy Policy Cookie Preferences