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Analysis of Adverse Events in Anesthesia Using Artificial Intelligence

NCT ID: NCT05185479

Last Updated: 2023-12-12

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

9559 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-11-12

Study Completion Date

2021-11-12

Brief Summary

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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

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Conditions

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Allergic Reaction

Keywords

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Allergic Reaction Adverse events Health database Natural Language Processing

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

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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
Minimum Eligible Age

1 Year

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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University Hospital, Strasbourg, France

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Paul-Michel MERTES, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Service d'Anesthésie et Réanimation chirurgicale - CHU de Strasbourg - France

Locations

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Service d'Anesthésie et Réanimation chirurgicale - CHU de Strasbourg - France

Strasbourg, , France

Site Status

Countries

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France

References

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Mertes PM, Morgand C, Barach P, Jurkolow G, Assmann KE, Dufetelle E, Susplugas V, Alauddin B, Yavordios PG, Tourres J, Dumeix JM, Capdevila X. Validation of a natural language processing algorithm using national reporting data to improve identification of anesthesia-related ADVerse evENTs: The "ADVENTURE" study. Anaesth Crit Care Pain Med. 2024 Aug;43(4):101390. doi: 10.1016/j.accpm.2024.101390. Epub 2024 May 6.

Reference Type DERIVED
PMID: 38718923 (View on PubMed)

Other Identifiers

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8505

Identifier Type: -

Identifier Source: org_study_id