Machine Learning Model to Predict Postoperative Respiratory Failure

Noncardiac Surgery Clinical Trial

Official title:

Development and Prospective Evaluation of a Machine Learning Model to Predict Postoperative Respiratory Failure

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04527094
• Other study ID #: AI_PRF
• Status: Completed
• Start date: May 26, 2021
• Est. completion date: June 25, 2022

Study information

• Verified date: August 2022
• Source: Seoul National University Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 22250
• Est. completion date: June 25, 2022
• Est. primary completion date: May 25, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Adults patients undergoing general anesthesia for noncardiac surgery

Exclusion Criteria:

• 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

• 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.

Related Conditions & MeSH terms

• Noncardiac Surgery
• Respiratory Insufficiency

Intervention

Diagnostic Test:
• Prediction of postoperative respiratory failure using a machine learning
The performance of a machine learning model to predict postoperative respiratory failure after general anesthesia within postoperative day 7 was tested prospectively.

Locations

Country	Name	City	State
Korea, Republic of	Hyun-Kyu Yoon	Seoul

Sponsors (1)

Lead Sponsor	Collaborator
Seoul National University Hospital

Country where clinical trial is conducted

Korea, Republic of, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	within postoperative day 7