Build a Decision Aid Tool to Help Emergency Intensive Care Specialists in the Context of Hypoxic Ischemic Encephalopathy

Completed

• Conditions: Encephalopathy

• Registration Number: NCT05114070
• Lead Sponsor: Assistance Publique - Hôpitaux de Paris

Brief Summary

The project aims at designing a machine learning solution able to recognize characteristics signals patterns of brain damages in full term babies born within a context of Hypoxic Ischemic Encephalopathy (HIE)

Detailed Description

Retrospective study based on a digital EEG signal library intending to design, train and test an efficient AI solution for hypothermia protocol start indications.

The output of the Project is to make available to pediatric resuscitation units an adequate tool to guide them in the decision of hypothermia protocol start in a general context of neurophysiologist competence scarcity. EEG signal that would allow the algorithm design will be based on several parameters of the conventional EEG and not only on signal amplitude

Study Timeline

• Study First Submitted: 2021-07-09
• Study First Submitted QC: 2021-10-28
• Study First Posted: 2021-11-09
• Study Start: 2022-09-08
• Primary Completion: 2023-09-08
• Study Completion: 2023-09-08
• Status Verified: 2024-02
• Last Update Submitted: 2024-02-02
• Last Update Posted: 2024-02-05

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 106

Inclusion Criteria

• Full term (> 36 weeks)
• HIE context
• EEG recording before 6 hours of life

Exclusion Criteria

-Opposition of parental authority holders of a patient born after 2015

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Decision of hypothermia protocol start	6 months	Use of EEG signal in order to develop an algorithm

Trial Locations

Locations (1)

Hôpital TROUSSEAU; 🇫🇷 Paris, France