Anesthesia ComplicationNCT06582407
Machine Learning Models for Predicting Unforeseen Hospital Admissions or Discharges After Anesthesia
An observational study of Anesthesia Complication and Surgery-Complications, run by HUmani at 1 site in Belgium.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Anesthesia Complication, Surgery-Complications, Pain, Postoperative
- What is being tested?
- Mathematical Prediction of unforseen patient reorientation
- How many people
- 68,683 participants
- Where
- 1 siteBelgium
- Study dates
- 1 Jan 2020to 30 Jul 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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.
Who may be able to join
- Patient undergoing anesthesia for a therapeutic or diagnostic procedure
Who cannot join
- Incomplete informatic data
- Error in the encoding system
Trial locations and contacts
1 site in 1 country.
About this study
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.
- Being tested
- Mathematical Prediction of unforseen patient reorientation
- Keywords
- anesthesiology, ambulatory surgery, artificial intelligence, machine learning, post operative complication
- Medical subject headings
- Pain, Postoperative
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Mathematical Prediction of unforseen patient reorientationOther
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
| Study group | Role | What they receive |
|---|---|---|
| Ambulatory Patients | Patient undergoing anesthesia in an ambulatory setting. Receives: Other: Mathematical Prediction of unforseen patient reorientation | |
| Hospitalised Patients | Patient undergoing anesthesia in a hospitalisation setting. Receives: Other: Mathematical Prediction of unforseen patient reorientation |
What is being measured
The primary outcome is the main question the study is designed to answer.
Rate of patient reorientation
Rate of unforeseen hospital admission after an ambulatory surgery and rate of discharge after an hospitalised surgery
Measured: On the day of the operation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Sep 2024
- Study start1 Jan 2020
- Primary completion30 Jun 2024
- Study completion30 Jul 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Anesthesia ComplicationStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 68,683 participants (actual)
Sponsor and investigators
- Lead sponsor
- HUmani
- Sponsor type
- Research network
- Responsible party
- Principal investigator, Rémi Florquin, University of Mons
- Investigators
- Rémi Florquin, MD, Principal investigator, Université de Mons, Belgium
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06582407
- Sponsor's protocol ID
- HUmani_ODanesth
- Status last verified
- Oct 2024
- First submitted
- 30 Aug 2024
- Last updated
- 18 Oct 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The investigators are not authorized to publish sensitive data by decision of the ethics committee
Study at a glance
From the registry record, last updated by the sponsor on 18 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Learning Models for Predicting Unforeseen Hospital Admissions or Discharges After Anesthesia
- ClinicalTrials.gov ID
- NCT06582407
- Acronym / sponsor ID
- HUmani_ODanesth
- Keywords
- anesthesiology, ambulatory surgery, artificial intelligence, machine learning, post operative complication
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 68,683 participants (actual)
- Lead sponsor
- HUmaniResearch network
- Study start
- 1 Jan 2020 (actual)
- Primary completion
- 30 Jun 2024 (actual)
- Study completion
- 30 Jul 2024 (actual)
- First posted
- 3 Sep 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Ambulatory Patients | – | Other: Mathematical Prediction of unforseen patient reorientationPatient undergoing anesthesia in an ambulatory setting. |
| Hospitalised Patients | – | Other: Mathematical Prediction of unforseen patient reorientationPatient undergoing anesthesia in a hospitalisation setting. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- Patient undergoing anesthesia for a therapeutic or diagnostic procedure
Key exclusion criteria (2 listed)
- Incomplete informatic data
- Error in the encoding system
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Mathematical Prediction of unforseen patient reorientationThe 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 | Other | Ambulatory Patients, Hospitalised Patients |
Primary outcome
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
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Oct 2024.
- Individual participant dataSharing planned: No. The investigators are not authorized to publish sensitive data by decision of the ethics committee
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Rémi Florquin, MD principal investigator
Université de Mons, Belgium
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Anesthesia Complication and Surgery-Complications, run by HUmani at 1 site in Belgium.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 18 Oct 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Belgium. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Rate of patient reorientation (time frame: On the day of the operation). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Oct 2024.