Machine Learning Model to Predict Postoperative Respiratory Failure

Completed

• Conditions: Noncardiac Surgery
• Interventions: Diagnostic Test: Prediction of postoperative respiratory failure using a machine learning

• Registration Number: NCT04527094
• Lead Sponsor: Seoul National University Hospital

Brief 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.

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 Timeline

• Study First Submitted: 2020-08-21
• Study First Submitted QC: 2020-08-21
• Study First Posted: 2020-08-26
• Study Start: 2021-05-26
• Primary Completion: 2022-05-25
• Study Completion: 2022-06-25
• Status Verified: 2022-08
• Last Update Submitted: 2022-08-29
• Last Update Posted: 2022-09-01

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 22250

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
AI_PRF	Prediction of postoperative respiratory failure using a machine learning	Adults patients undergoing general anesthesia

Primary Outcome Measures

Name	Time	Method
the incidence of postoperative respiratory failure after general anesthesia	within postoperative day 7	Postoperative respiratory failure which was defined as mechanical ventilation \>48 h or any reintubation after surgery

Trial Locations

Locations (1)

Hyun-Kyu Yoon; 🇰🇷 Seoul, Korea, Republic of