Machine Learning Models for Predicting Unforeseen Hospital Admissions or Discharges After Anesthesia

NCT06582407 | Completed

• 1 other identifier: HUmani_ODanesth
• Study Type: observational
• Target: 68,683
• Locations: 1 country
• Sites: 1

Brief Summary

Unexpected hospital admissions after ambulatory surgery not only bring discomfort to patients but also causes a decrease in the efficiency of the healthcare system. In addition, unanticipated patient's orientation carry the risk of unsuitable post operative orders. The hypothesis of this project is that artificial intelligence models will outperform traditional models in predicting which patients will require hospital admission after ambulatory surgery or unforeseen hospital discharge after surgery.

Conditions

Anesthesia Complication; Surgery-Complications; Pain, Postoperative

Keywords

anesthesiology; ambulatory surgery; artificial intelligence; machine learning; post operative complication

Outcome Measures

Primary Outcomes (1)

• Rate of patient reorientation

  Rate of unforeseen hospital admission after an ambulatory surgery and rate of discharge after an hospitalised surgery

  On the day of the operation

Study Arms (2)

Ambulatory Patients

Patient undergoing anesthesia in an ambulatory setting.

Other: Mathematical Prediction of unforseen patient reorientation

Hospitalised Patients

Patient undergoing anesthesia in a hospitalisation setting.

Other: Mathematical Prediction of unforseen patient reorientation

Interventions

Mathematical Prediction of unforseen patient reorientation OTHER

The goal of this project is to develop models to predict in the preoperative period which patients will require hospital admission after ambulatory surgery or unforeseen hospital discharge after surgery

Ambulatory Patients; Hospitalised Patients

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All hospitalized or outpatient patients who have undergone anesthesia for a diagnostic or therapeutic procedure, in a scheduled or emergency condition, in the institution's hospitals.

You may qualify if:

• Patient undergoing anesthesia for a therapeutic or diagnostic procedure

You may not qualify if:

• Incomplete informatic data
• Error in the encoding system

Sponsors & Collaborators

• HUmani: lead

Study Sites (1)

Université de Mons

Mons, 7000, Belgium

Location

MeSH Terms

Conditions

Pain, Postoperative

Condition Hierarchy (Ancestors)

Postoperative Complications; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Pain; Neurologic Manifestations; Signs and Symptoms

Study Officials

• Rémi Florquin, MD

  Université de Mons, Belgium

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: NETWORK
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Doctor

Study Record Dates

• First Submitted: August 30, 2024
• First Posted: September 3, 2024
• Study Start: January 1, 2020
• Primary Completion: June 30, 2024
• Study Completion: July 30, 2024
• Last Updated: October 18, 2024

Record last verified: 2024-10

Data Sharing

• IPD Sharing: Will not share

Locations

BE (1)