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

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.

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Key information

Conditions

Anesthesia Complication Neurologic Manifestations Pain Pain, Postoperative Pathologic Processes Pathological Conditions, Signs and Symptoms Postoperative Complications Signs and Symptoms Surgery-Complications

Sex eligibility

All sexes

Study type

Observational

Primary location

Université de Mons

Mons, 7000, Belgium

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Patient undergoing anesthesia for a therapeutic or diagnostic procedure

Exclusion criteria

Incomplete informatic data
Error in the encoding system

Treatment and study plan

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

Primary outcomes

Rate of patient reorientation

Time frame: On the day of the operation

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

Sponsors and collaborators

Lead sponsor

HUmani

Network

Registry information

Important dates

Study start: 2020
Primary completion: 2024
Study completion: 2024
First posted: Sep 3, 2024
Registry last updated: Oct 18, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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