• Skip to main content
  • Skip to secondary menu
  • Skip to primary sidebar
  • Skip to secondary sidebar
  • Skip to footer

  • Opinion
  • Health IT
    • Behavioral Health
    • Care Coordination
    • EMR/EHR
    • Interoperability
    • Patient Engagement
    • Population Health Management
    • Revenue Cycle Management
    • Social Determinants of Health
  • Digital Health
    • AI
    • Blockchain
    • Precision Medicine
    • Telehealth
    • Wearables
  • Startups
  • M&A
  • Value-based Care
    • Accountable Care (ACOs)
    • Medicare Advantage
  • Life Sciences
  • Research

Machine Learning Algorithm Can Predict Which Cardiac Patients Are High-Risk Post Discharge

by Fred Pennic 09/04/2019 Leave a Comment

  • LinkedIn
  • Twitter
  • Facebook
  • Email
  • Print
Machine Learning Algorithm Can Predict Which Cardiac Patients Are High-Risk Post Discharge

– New Mayo Clinic peer-reviewed study shows Medial EarlySign’s machine learning algorithm can predict which cardiac patients are at high-risk following discharge.


– The analysis was based on electronic health records (EHR), demographics, and social data collected from a cohort of 11,709 unique Mayo Clinic patients who underwent 14,349 PCIs during 14,024 hospital admissions.


– The study suggests AI solution can be more effective than traditional models to identify patients at risk of death or readmission for congestive heart failure.


Today, Medial EarlySign, a provider of machine-learning-based solutions to aid in early detection and prevention of high-burden diseases, today announced the results of new research with Mayo Clinic assessing the effectiveness of machine learning for predicting cardiac patients’ future risk trajectories following hospital discharge.

Study Background & Protocols

The peer-reviewed retrospective data study, Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention, published in JACC: Cardiovascular Interventions, evaluated the ability of machine learning models to assess risk for patients who underwent percutaneous coronary intervention (PCI) inside the hospital and following their discharge. The analyzed algorithm was developed by Medial EarlySign data scientists to identify patients at highest risk of complications and hospital readmission after undergoing PCI, one of the most frequently performed procedures in U.S. hospitals.

Study Results

The analysis was based on electronic health records (EHR), demographics, and social data collected from a cohort of 11,709 unique Mayo Clinic patients who underwent 14,349 PCIs during 14,024 hospital admissions. The patients’ mean age was 66.9, most were male (71.5%), 45.9% were obese, and 59.8% had a history of heart attacks.

The Bigger Picture

The study highlights the potential of AI solutions in supporting cardiology care teams in identifying and treating these high-risk patients. “Contemporary risk models have traditionally had little success in identifying patients’ post-PCI risks for complications, in-patient mortality, and hospital readmission. This study shows that machine learning tools may enable cardiology care teams to identify patients who may be on high-risk trajectories,” said Rajiv Gulati, MD, Ph.D., Interventional Cardiologist at Mayo Clinic.

The study revealed that Medial EarlySign’s algorithm had an excellent discriminatory ability using only data points available at time of admission or at discharge. Compared with standard regression methods, it was more predictive and discriminative at identifying in-patient sub-groups at high risk for 180-day post-PCI mortality and 30-day rehospitalization for congestive heart failure. 

The algorithm also proved effective at identifying patient subgroups at high risk of post-procedure complications and readmission, supporting the potential role for integrating machine learning into clinical practice.

Impact of Machine Learning Models for Clinicians & Patients

“Machine learning models can help clinicians assess patient risk at different points on their clinical pathways, including hospital admission, discharge and future re-admission,” said Yaron Kinar, Ph.D., Medial EarlySign’s Chief Data Scientist. “Collaborating with Mayo Clinic clinicians for this retroactive study provided the added benefit of assessing how social and demographic information, together with routine lab and existing EHR data, can provide further insights to stratify patient risk.”

Medial EarlySign Background

Founded in 2013, Medial EarlySign’s suite of outcome-focused software solutions (AlgoMarkers™) find subtle, early signs of high-risk patient trajectories in existing lab results and ordinary EHR data already collected in the course of routine care. EarlySign’s AlgoMarkers are currently helping clients identify patients at high risk for conditions such as lower GI disorders, prediabetic progression to diabetes, downstream diabetic complications, first coronary artery disease (CAD) and equivalent events, and chronic kidney disease (CKD).  As healthcare systems transition from volume to value-based care, EarlySign partners with health care organizations to support outcome-focused care delivery, while potentially preventing or delaying the onset of high-burden diseases, downstream complications, and their associated costs.

  • LinkedIn
  • Twitter
  • Facebook
  • Email
  • Print

Tagged With: AI, algorithms, Artificial Intelligence, Cardiac Monitoring, Cardiologist, care teams, diabetes, Heart, Kidney Disease, Machine Learning, Mayo Clinic, MD, Medial EarlySign, Partners, Patient Matching Algorithm, risk, Value-Based Care

Tap Native

Get in-depth healthcare technology analysis and commentary delivered straight to your email weekly

Reader Interactions

Primary Sidebar

Subscribe to HIT Consultant

Latest insightful articles delivered straight to your inbox weekly.

Submit a Tip or Pitch

Featured Insights

2025 EMR Software Pricing Guide

2025 EMR Software Pricing Guide

Featured Interview

Paradigm Shift in Diabetes Care with Studio Clinics: Q&A with Reach7 Founder Chun Yong

Most-Read

Medtronic to Separate Diabetes Business into New Standalone Company

Medtronic to Separate Diabetes Business into New Standalone Company

White House, IBM Partner to Fight COVID-19 Using Supercomputers

HHS Sets Pricing Targets for Trump’s EO on Most-Favored-Nation Drug Pricing

23andMe to Mine Genetic Data for Drug Discovery

Regeneron to Acquire Key 23andMe Assets for $256M, Pledges Continuity of Consumer Genome Services

CureIS Healthcare Sues Epic: Alleges Anti-Competitive Practices & Trade Secret Theft

The Evolving Role of Physician Advisors: Bridging the Gap Between Clinicians and Administrators

The Evolving Physician Advisor: From UM to Value-Based Care & AI

UnitedHealth Group Names Stephen Hemsley CEO as Andrew Witty Steps Down

UnitedHealth CEO Andrew Witty Steps Down, Stephen Hemsley Returns as CEO

Omada Health Files for IPO

Omada Health Files for IPO

Blue Cross Blue Shield of Massachusetts Launches "CloseKnit" Virtual-First Primary Care Option

Blue Cross Blue Shield of Massachusetts Launches “CloseKnit” Virtual-First Primary Care Option

Osteoboost Launches First FDA-Cleared Prescription Wearable Nationwide to Combat Low Bone Density

Osteoboost Launches First FDA-Cleared Prescription Wearable Nationwide to Combat Low Bone Density

2019 MedTech Breakthrough Award Category Winners Announced

MedTech Breakthrough Announces 2025 MedTech Breakthrough Award Winners

Secondary Sidebar

Footer

Company

  • About Us
  • Advertise with Us
  • Reprints and Permissions
  • Submit An Op-Ed
  • Contact
  • Subscribe

Editorial Coverage

  • Opinion
  • Health IT
    • Care Coordination
    • EMR/EHR
    • Interoperability
    • Population Health Management
    • Revenue Cycle Management
  • Digital Health
    • Artificial Intelligence
    • Blockchain Tech
    • Precision Medicine
    • Telehealth
    • Wearables
  • Startups
  • Value-Based Care
    • Accountable Care
    • Medicare Advantage

Connect

Subscribe to HIT Consultant Media

Latest insightful articles delivered straight to your inbox weekly

Copyright © 2025. HIT Consultant Media. All Rights Reserved. Privacy Policy |