NCT04527094

Machine Learning Model to Predict Postoperative Respiratory Failure

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

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 22,250 (actual)
• Sponsor: Seoul National University Hospital · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Noncardiac Surgery

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2021-05-26
• Primary completion: 2022-05-25
• Completion: 2022-06-25
• First posted: 2020-08-26
• Last updated: 2022-09-01

Locations

1 site across 1 country: South Korea