Analysis of Adverse Events in Anesthesia Using Artificial Intelligence

Completed

• Conditions: Allergic Reaction

• Registration Number: NCT05185479
• Lead Sponsor: University Hospital, Strasbourg, France

Brief 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

Detailed Description

Not available

Study Timeline

• Study Start: 2020-11-12
• Primary Completion: 2021-10-12
• Study Completion: 2021-11-12
• Study First Submitted: 2021-12-22
• Study First Submitted QC: 2021-12-22
• Study First Posted: 2022-01-11
• Status Verified: 2023-12
• Last Update Submitted: 2023-12-11
• Last Update Posted: 2023-12-12

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 9559

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Development of a natural language processing approach to establish a classification of adverse events observed during the perioperative period and to facilitate their analysis.	Files analysed retrospectively from January 01, 2009 to June 30, 2020 will be examined]	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

Trial Locations

Locations (1)

Service d'Anesthésie et Réanimation chirurgicale - CHU de Strasbourg - France; 🇫🇷 Strasbourg, France