Analysis of Adverse Events in Anesthesia Using Artificial Intelligence

Allergic Reaction Clinical Trial

— ADVENTURE  

Official title:

Analysis of ADVerse evENTs in Anesthesia Using ARtificial IntelligencE

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05185479
• Other study ID #: 8505
• Status: Completed
• Start date: November 12, 2020
• Est. completion date: November 12, 2021

Study information

• Verified date: December 2023
• Source: University Hospital, Strasbourg, France
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

The interest of health databases in anesthesia is no longer to be demonstrated. The aim of this research was to develop a natural language processing approach to establish a classification of adverse events observed during the perioperative period and to facilitate their analysis:

The main objective of the study was to identify what a "naïve" unsupervised model would discover based on Adverse Event (AE) descriptions. Our second goal was to identify apparently unrelated events whose combination could favor the occurrence of an AE

Recruitment information / eligibility

• Status: Completed
• Enrollment: 9559
• Est. completion date: November 12, 2021
• Est. primary completion date: October 12, 2021
• Accepts healthy volunteers: No
• Gender: All
• Age group: 1 Year and older

Eligibility

Inclusion Criteria:

• Minors and adults having had an allergic reaction associated with care

• Having had an adverse event reported by an anesthetist between January 01, 2009 and June 30, 2020

Exclusion Criteria:

• Patient not meeting the inclusion criteria

Related Conditions & MeSH terms

• Allergic Reaction
• Hypersensitivity

Locations

Country	Name	City	State
France	Service d'Anesthésie et Réanimation chirurgicale - CHU de Strasbourg - France	Strasbourg

Sponsors (1)

Lead Sponsor	Collaborator
University Hospital, Strasbourg, France

Country where clinical trial is conducted

France, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Development of a natural language processing approach to establish a classification of adverse events observed during the perioperative period and to facilitate their analysis.	The aim of this research was to develop a natural language processing approach to establish a classification of adverse events observed during the perioperative period and to facilitate their analysis: The main objective of the study was to identify what a "naïve" unsupervised model would discover based on Adverse Event (AE) descriptions. Our second goal was to identify apparently unrelated events whose combination could favor the occurrence of an AE	Files analysed retrospectively from January 01, 2009 to June 30, 2020 will be examined]