Noncardiac SurgeryNCT04527094
Machine Learning Model to Predict Postoperative Respiratory Failure
An observational study of Noncardiac Surgery, run by Seoul National University Hospital at 1 site in South Korea.
Trial summary
The main objective of this study is to develop a machine learning model that predicts postoperative respiratory failure within 7 postoperative day using a real-world, local preoperative and intraoperative electronic health records, not administrative codes. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Noncardiac Surgery
- What is being tested?
- Prediction of postoperative respiratory failure using a machine learning
- How many people
- 22,250 participants
- Where
- 1 siteSouth Korea
- Study dates
- 26 May 2021to 25 Jun 2022
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adult patients undergoing general anesthesia for noncardiac surgery
Who may be able to join
- Adults patients undergoing general anesthesia for noncardiac surgery
Who cannot join
- Age under 18 years
- Surgery duration < 1 hr
- Cardiac surgery
- Surgery performed only regional or local anesthesia, peripheral nerve block, or monitored anesthesia care
- Organ transplantation
- Patient with preoperative tracheal intubation
Show all 11Show fewer
- Patients who had tracheostoma prior to surgery
- Patients scheduled for tracheostomy
- Surgery performed outside the operating room
- Length of hospital stay < 24 h
- If the patients had multiple surgeries during the same hospital stays, we included the first surgical cases in the dataset.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Development and Prospective Evaluation of a Machine Learning Model to Predict Postoperative Respiratory Failure
The main objective of this study is to develop a machine learning model that predicts postoperative respiratory failure within 7 postoperative day using a real-world, local preoperative and intraoperative electronic health records, not administrative codes.
Read the detailed description
Postoperative pulmonary complications are known to increase the length of hospital stay and healthcare cost. One of the most serious form of these complications is postoperative respiratory failure, which is also associated with morbidity and mortality. A lot of risk stratification models have been developed for identifying patients at increased risk of postoperative respiratory failure. However, these models were built by using a traditional logistic regression analysis. A logistic regression analysis had disadvantages of assuming the relationship between dependent and independent variables as linear. Recent advances in artificial intelligence make it possible to manage and analyze big data. Prediction model using a machine learning technique and large-scale data can improve the accuracy of prediction performance than those of previous models using traditional statistics. Furthermore, a machine learning technique may be a useful adjuvant tool in making clinical decisions or real-time prediction if it is integrated into the healthcare system. However, to our knowledge, there was no study investigating the predictive factors of postoperative respiratory failure using a machine-learning approach. Therefore, the main objective of this study is to develop a machine learning model that predicts postoperative respiratory failure within 7 postoperative day using a real-world, local preoperative and intraoperative electronic health records, not administrative codes and evaluate its performance prospectively.
- Conditions
- Noncardiac Surgery
- Being tested
- Prediction of postoperative respiratory failure using a machine learning
- Keywords
- Artificial Intelligence, Risk Factors, Postoperative respiratory failure, Validation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Prediction of postoperative respiratory failure using a machine learningDiagnostic test
The performance of a machine learning model to predict postoperative respiratory failure after general anesthesia within postoperative day 7 was tested prospectively.
| Study group | Role | What they receive |
|---|---|---|
| AI_PRF | Adults patients undergoing general anesthesia Receives: Diagnostic Test: Prediction of postoperative respiratory failure using a machine learning |
What is being measured
The primary outcome is the main question the study is designed to answer.
the incidence of postoperative respiratory failure after general anesthesia
Postoperative respiratory failure which was defined as mechanical ventilation >48 h or any reintubation after surgery
Measured: within postoperative day 7
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Aug 2020
- Study start26 May 2021
- Primary completion25 May 2022
- Study completion25 Jun 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Noncardiac SurgeryStudies of the same condition, open and under way.
- More trials run by Seoul National University HospitalThe sponsor's other registered studies.
- 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
- Prospective
- Enrolment
- 22,250 participants (actual)
Sponsor and investigators
- Lead sponsor
- Seoul National University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Hyun-Kyu Yoon, Seoul National University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04527094
- Sponsor's protocol ID
- AI_PRF
- Status last verified
- Aug 2022
- First submitted
- 21 Aug 2020
- Last updated
- 1 Sep 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 1 Sep 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Prospective Evaluation of a Machine Learning Model to Predict Postoperative Respiratory Failure
- ClinicalTrials.gov ID
- NCT04527094
- Acronym / sponsor ID
- AI_PRF
- Conditions
- Noncardiac Surgery
- Keywords
- Artificial Intelligence, Risk Factors, Postoperative respiratory failure, Validation
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 22,250 participants (actual)
- Lead sponsor
- Seoul National University HospitalUniversity, hospital or other
- Study start
- 26 May 2021 (actual)
- Primary completion
- 25 May 2022 (actual)
- Study completion
- 25 Jun 2022 (actual)
- First posted
- 26 Aug 2020 (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.
The main objective of this study is to develop a machine learning model that predicts postoperative respiratory failure within 7 postoperative day using a real-world, local preoperative and intraoperative electronic health records, not administrative codes.
Detailed description
Postoperative pulmonary complications are known to increase the length of hospital stay and healthcare cost. One of the most serious form of these complications is postoperative respiratory failure, which is also associated with morbidity and mortality. A lot of risk stratification models have been developed for identifying patients at increased risk of postoperative respiratory failure. However, these models were built by using a traditional logistic regression analysis. A logistic regression analysis had disadvantages of assuming the relationship between dependent and independent variables as linear. Recent advances in artificial intelligence make it possible to manage and analyze big data. Prediction model using a machine learning technique and large-scale data can improve the accuracy of prediction performance than those of previous models using traditional statistics. Furthermore, a machine learning technique may be a useful adjuvant tool in making clinical decisions or real-time prediction if it is integrated into the healthcare system. However, to our knowledge, there was no study investigating the predictive factors of postoperative respiratory failure using a machine-learning approach. Therefore, the main objective of this study is to develop a machine learning model that predicts postoperative respiratory failure within 7 postoperative day using a real-world, local preoperative and intraoperative electronic health records, not administrative codes and evaluate its performance prospectively.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| AI_PRF | – | Diagnostic Test: Prediction of postoperative respiratory failure using a machine learningAdults patients undergoing general anesthesia |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients undergoing general anesthesia for noncardiac surgery
Key inclusion criteria (1 listed)
- Adults patients undergoing general anesthesia for noncardiac surgery
Key exclusion criteria (11 listed)
- Age under 18 years
- Surgery duration < 1 hr
- Cardiac surgery
- Surgery performed only regional or local anesthesia, peripheral nerve block, or monitored anesthesia care
- Organ transplantation
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 |
|---|---|---|
| Prediction of postoperative respiratory failure using a machine learningThe performance of a machine learning model to predict postoperative respiratory failure after general anesthesia within postoperative day 7 was tested prospectively. | Diagnostic test | AI_PRF |
Primary outcome
the incidence of postoperative respiratory failure after general anesthesia
Time frame: within postoperative day 7
Postoperative respiratory failure which was defined as mechanical ventilation >48 h or any reintubation after 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 1 Sep 2022.
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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.
The record lists no contact. Use the official record to reach the sponsor.
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 Noncardiac Surgery, run by Seoul National University Hospital at 1 site in South Korea.
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 11 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 1 Sep 2022. 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 South Korea. 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?
the incidence of postoperative respiratory failure after general anesthesia (time frame: within postoperative day 7). 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 1 Sep 2022.