Medical Design and Outsourcing

  • Home
  • Medical Device Business
    • Mergers & Acquisitions
    • Financial
    • Regulatory
  • Applications
    • Cardiovascular
    • Devices
    • Imaging
    • Implantables
    • Medical Equipment
    • Orthopedic
    • Surgical
  • Technologies
    • Contract Manufacturing
    • Components
    • Electronics
    • Extrusions
    • Materials
    • Motion Control
    • Prototyping
    • Pumps
    • Tubing
  • Med Tech Resources
    • DeviceTalks Tuesdays
    • Digital Editions
    • eBooks
    • Manufacturer Search
    • Medical Device Handbook
    • MedTech 100 Index
    • Podcasts
    • Print Subscription
    • The Big 100
    • Webinars / Digital Events
    • Whitepapers
    • Video
  • 2022 Leadership in MedTech
    • 2022 Leadership Voting!
    • 2021 Winners
    • 2020 Winners
  • Women in Medtech

Machine Learning Model Provides Rapid Prediction Of C. Difficile Infection Risk

April 6, 2018 By Massachusetts General Hospital

Every year nearly 30,000 Americans die from an aggressive, gut-infecting bacteria called Clostridium difficile (C. difficile), which is resistant to many common antibiotics and can flourish when antibiotic treatment kills off beneficial bacteria that normally keep it at bay. Investigators from Massachusetts General Hospital (MGH), the University of Michigan (U-M), and Massachusetts Institute of Technology (MIT) now have developed investigational “machine learning” models, specifically tailored to individual institutions, that can predict a patient’s risk of developing C. difficile much earlier than it would be diagnosed with current methods. Preliminary data from their study is published in Infection Control and Hospital Epidemiology.

“Despite substantial efforts to prevent C. difficile infection and to institute early treatment upon diagnosis, rates of infection continue to increase,” says Erica Shenoy, MD, PhD, of the MGH Division of Infectious Diseases, co-senior author of the study and assistant professor of Medicine at Harvard Medical School. “We need better tools to identify the highest risk patients so that we can target both prevention and treatment interventions to reduce further transmission and improve patient outcomes.”

The authors note that most previous models of C. difficile infection risk were designed as “one size fits all” approaches and included only a few risk factors, which limited their usefulness. Co-lead authors Jeeheh Oh, a U-M graduate student in Computer Science and Engineering, and Maggie Makar, MS, of MIT’s Computer Science and Artificial Intelligence Laboratory and their colleagues took a “big data” approach that analyzed the whole electronic health record (EHR) to predict a patient’s C. difficile risk throughout the course of hospitalization. Their method allows the development of institution-specific models that could accommodate different patient populations, different EHR systems and factors specific to each institution.

“When data are simply pooled into a one-size-fits-all model, institutional differences in patient populations, hospital layouts, testing and treatment protocols, or even in the way staff interact with the EHR can lead to differences in the underlying data distributions and ultimately to poor performance of such a model,” says Jenna Wiens, PhD, assistant professor of Computer Science and Engineering at U-M and co-senior author of the study. “To mitigate these issues, we take a hospital-specific approach, training a model tailored to each institution.”

(Image credit: CDC)

Using their machine-learning-based model, the investigators analyzed de-identified data – including individual patient demographics and medical history, details of their admission and daily hospitalization, and the likelihood of exposure to C. difficile — from the EHRs of almost 257,000 patients admitted to either MGH or to Michigan Medicine – U-M’s academic medical center — over periods of two years and six years, respectively. The model generated daily risk scores for each individual patient that, when a set threshold is exceeded, classify patients as at high risk.

Overall, the models were highly successful at predicting which patients would ultimately be diagnosed with C. difficile. In half of those who were infected, accurate predictions could have been made at least five days before diagnostic samples were collected, which would allow highest-risk patients to be the focus of targeted antimicrobial interventions. If validated in prospective studies, the risk prediction score could guide early screening for C. difficile. For patients diagnosed earlier in the course of disease, initiation of treatment could limit the severity of the illness, and patients with confirmed C. difficile could be isolated and contact precautions instituted to prevent transmission to other patients.

The research team has made the algorithm code freely available for others to review and adapt for their individual institutions. Shenoy notes that facilities that explore applying similar algorithms to their own institutions will need to assemble the appropriate local subject-matter experts and validate the performance of the models in their institutions.

Study co-author Vincent Young, MD, PhD, the William Henry Fitzbutler Professor in the Department of Internal Medicine at U-M, adds, “This represents a potentially significant advance in our ability to identify and ultimately act to prevent infection with C. difficile. The ability to identify patients at greatest risk could allow us to focus expensive and potentially limited prevention methods on those who would gain the greatest potential benefit. I think that this project is a great example of a ‘team science’ approach to addressing complex biomedical questions to improve healthcare, which I expect to see more of as we enter the era of precision health.”

Related Articles Read More >

Lazurite ArthroFree wireless surgical camera system Minnetronix Medical
How Minnetronix Medical helped Lazurite with its wireless surgical camera
Medtronic Hugo robot-assisted surgery system
The road to a robot: Medtronic’s development process for its Hugo RAS system
A portrait of Stryker executive Siddarth Satish
How Stryker includes users for product design in the digital age
A portrait of Stryker executive Tracy Robertson
Stryker leaders talk medtech trends at DeviceTalks Boston: ‘If you’re slow, you’re going to lose’

DeviceTalks Weekly.

May 20, 2022
DeviceTalks Boston Post-Game – Editors’ Top Moments, Insulet’s Eric Benjamin on future of Omnipod 5
See More >

MDO Digital Edition

Digital Edition

Subscribe to Medical Design & Outsourcing. Bookmark, share and interact with the leading medical design engineering magazine today.

MEDTECH 100 INDEX

Medtech 100 logo
Market Summary > Current Price
The MedTech 100 is a financial index calculated using the BIG100 companies covered in Medical Design and Outsourcing.
DeviceTalks

DeviceTalks is a conversation among medical technology leaders. It's events, podcasts, webinars and one-on-one exchanges of ideas & insights.

DeviceTalks

New MedTech Resource

Medical Tubing

Enewsletter Subscriptions

Enewsletter Subscriptions

MassDevice

Mass Device

The Medical Device Business Journal. MassDevice is the leading medical device news business journal telling the stories of the devices that save lives.

Visit Website
MDO ad
Medical Design and Outsourcing
  • MassDevice
  • DeviceTalks
  • MedTech 100 Index
  • Medical Tubing + Extrusion
  • Drug Delivery Business News
  • Drug Discovery & Development
  • Pharmaceutical Processing World
  • R&D World
  • About Us/Contact
  • Advertise With Us
  • Subscribe to Print Magazine
  • Subscribe to E-newsletter
  • Attend our Monthly Webinars
  • Listen to our Weekly Podcasts
  • Join our DeviceTalks Tuesdays Discussion

Copyright © 2022 WTWH Media, LLC. All Rights Reserved. Site Map | Privacy Policy | RSS

Search Medical Design & Outsourcing

  • Home
  • Medical Device Business
    • Mergers & Acquisitions
    • Financial
    • Regulatory
  • Applications
    • Cardiovascular
    • Devices
    • Imaging
    • Implantables
    • Medical Equipment
    • Orthopedic
    • Surgical
  • Technologies
    • Contract Manufacturing
    • Components
    • Electronics
    • Extrusions
    • Materials
    • Motion Control
    • Prototyping
    • Pumps
    • Tubing
  • Med Tech Resources
    • DeviceTalks Tuesdays
    • Digital Editions
    • eBooks
    • Manufacturer Search
    • Medical Device Handbook
    • MedTech 100 Index
    • Podcasts
    • Print Subscription
    • The Big 100
    • Webinars / Digital Events
    • Whitepapers
    • Video
  • 2022 Leadership in MedTech
    • 2022 Leadership Voting!
    • 2021 Winners
    • 2020 Winners
  • Women in Medtech