• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
Saturday, August 2, 2025
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News Health

Researchers use health data tools to rapidly detect sepsis in newborns

Bioengineer by Bioengineer
February 28, 2019
in Health
Reading Time: 4 mins read
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

CHOP study uses automated models to identify sepsis before clinical recognition

IMAGE

Credit: Children’s Hospital of Philadelphia

Automated programs can identify which sick infants in a neonatal intensive care unit (NICU) have sepsis hours before clinicians recognize the life-threatening condition. A team of data researchers and physician-scientists tested machine-learning models in a NICU population, drawing only on routinely collected data available in electronic health records (EHRs).

“Because early detection and rapid intervention is essential in cases of sepsis, machine-learning tools like this offer the potential to improve clinical outcomes in these infants,” said first author Aaron J. Masino, PhD, who led the study team’s machine-learning efforts. Masino is an assistant professor in the Department of Anesthesiology and Critical Care Medicine and a member of the Department of Biomedical and Health Informatics at Children’s Hospital of Philadelphia (CHOP). “Follow-up clinical studies will allow researchers to evaluate how well such systems perform in a hospital setting.”

The research team published its findings in the retrospective case-control study Feb. 22 in PLOS ONE.

A major worldwide cause of infant mortality and morbidity, sepsis begins with a bacterial invasion of the bloodstream. An aggressive immune response can unfortunately cause a progression to septic shock, a severe systemic condition causing multiple organs to fail, sometimes fatally. While relatively rare in healthy, full-term infants, sepsis rates are 200 times higher in premature or chronically hospitalized infants. Survivors of infant sepsis may suffer long-term problems such as chronic lung disease, neurodevelopmental disabilities, and prolonged hospital stays.

Rapid diagnosis of sepsis is often difficult in hospitalized infants, due to ambiguous clinical signs and inaccuracies in screening tests. Delays in recognizing sepsis cause delays in intervention, including antibiotic treatment and supportive care. However, unnecessary use of antibiotics carries its own risks and increases antibiotic resistance, so a clear-cut early diagnosis is important.

The current study aimed to develop a machine-learning model able to recognize sepsis in NICU infants at least four hours before clinical suspicion. “To our knowledge, this was the first study to investigate machine learning to identify sepsis before clinical recognition using only routinely collected EHR data,” said Masino.

Machine learning uses computational and statistical techniques to train computational models to detect patterns from data, then perform a desired task. In this case, the study team evaluated how well eight machine-learning models were able to analyze patient data to predict which infants had sepsis. Because the data came from a retrospective sample of NICU infants, the researchers were able to compare each model’s predictions to subsequent findings–whether or not an individual patient was found to develop sepsis.

The study team drew on EHR data from 618 infants in the CHOP NICU, from 2014 to 2017. Many of the infants in the patient registry were premature; the cohort had a median gestational age of 34 weeks. Co-occurring conditions included chronic lung disease, congenital heart disease, necrotizing enterocolitis (a severe intestinal infection) and surgical conditions.

Among the co-authors were pediatrician and biomedical informatics expert Robert W. Grundmeier, MD, and neonatologist and sepsis expert Mary Catherine Harris, MD. Both drew on their clinical experience and knowledge of medical literature to help develop groups of sepsis-related features available in EHR data. Masino, Grundmeier and Harris, in addition to their CHOP positions, also are faculty members of the Perelman School of Medicine at the University of Pennsylvania.

Grundmeier and Harris, the study’s lead clinical investigators, developed a list of 36 features associated or suspected to be associated with infant sepsis. Those features, grouped under vital signs, laboratory values, co-morbidities and clinical factors, such as whether an infant was on a ventilator, were extracted from EHR entries, and provided input data for the machine-learning models. “The biomedical informatics specialists like myself collaborated with our clinician colleagues to select relevant features from the EHR data,” said Masino.

Six of the eight models performed well in accurately predicting sepsis up to four hours before clinical recognition of the condition.

The team’s findings, said Masino, are a preliminary step toward developing a real-time clinical tool for hospital practice. The researchers plan to follow up this study with further research to refine their models and investigate the software tools in a carefully designed prospective clinical study. “If research validates some of these models, we may develop a tool to support clinical decisions and improve outcomes in critically ill infants,” he added.

###

Aaron J. Masino, et al, “Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data,” PLOS ONE, published Feb. 22, 2019. http://doi.org/10.1371/journal.pone.0212665

Children’s Hospital of Philadelphia: Children’s Hospital of Philadelphia was founded in 1855 as the nation’s first pediatric hospital. Through its long-standing commitment to providing exceptional patient care, training new generations of pediatric healthcare professionals, and pioneering major research initiatives, Children’s Hospital has fostered many discoveries that have benefited children worldwide. Its pediatric research program is among the largest in the country. In addition, its unique family-centered care and public service programs have brought the 564-bed hospital recognition as a leading advocate for children and adolescents. For more information, visit http://www.chop.edu

Media Contact
John Ascenzi
[email protected]

Related Journal Article

http://dx.doi.org/10.1371/journal.pone.0212665

Tags: Computer ScienceCritical Care/Emergency MedicineInfectious/Emerging DiseasesInternal MedicineMedicine/HealthPediatricsTechnology/Engineering/Computer Science
Share12Tweet8Share2ShareShareShare2

Related Posts

blank

Amyloid Fibrils Connect CHCHD10, CHCHD2 to Neurodegeneration

August 2, 2025
Mapping the Human Hippocampus: Single-Nucleus to Spatial Transcriptomics

Mapping the Human Hippocampus: Single-Nucleus to Spatial Transcriptomics

August 2, 2025

Boosting ADMET Predictions for Key CYP450s

August 2, 2025

Fermentable Carbs and Metformin Boost Prediabetes Control

August 2, 2025
Please login to join discussion

POPULAR NEWS

  • Blind to the Burn

    Overlooked Dangers: Debunking Common Myths About Skin Cancer Risk in the U.S.

    60 shares
    Share 24 Tweet 15
  • Neuropsychiatric Risks Linked to COVID-19 Revealed

    45 shares
    Share 18 Tweet 11
  • Dr. Miriam Merad Honored with French Knighthood for Groundbreaking Contributions to Science and Medicine

    46 shares
    Share 18 Tweet 12
  • Study Reveals Beta-HPV Directly Causes Skin Cancer in Immunocompromised Individuals

    38 shares
    Share 15 Tweet 10

About

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Recent News

Intrapleural Anti-VEGF Boosts Nab-Paclitaxel Efficacy

Amyloid Fibrils Connect CHCHD10, CHCHD2 to Neurodegeneration

Mapping the Human Hippocampus: Single-Nucleus to Spatial Transcriptomics

  • Contact Us

Bioengineer.org © Copyright 2023 All Rights Reserved.

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In
No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

Bioengineer.org © Copyright 2023 All Rights Reserved.