Machine Learning Model to Predict Postoperative Respiratory Failure
Study Details
Study Description
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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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
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| AI_PRF Adults patients undergoing general anesthesia |
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.
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Outcome Measures
Primary Outcome Measures
- the incidence of postoperative respiratory failure after general anesthesia [within postoperative day 7]
Postoperative respiratory failure which was defined as any reintubation in the operating room, general ward, or intensive care unit after primary extubation at the end of the surgery
Eligibility Criteria
Criteria
Inclusion Criteria:
- Adults patients undergoing general anesthesia for noncardiac surgery
Exclusion Criteria:
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Age under 18 years
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Surgery duration < 1 hr
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Surgery performed only under local anesthesia or peripheral nerve block
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Surgery related to a prior postoperative complication
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Organ transplantation
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Cardiac surgery
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Surgery performed outside the operating room
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Length of hospital stay < 24 h
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Patients who had tracheostoma prior to surgery
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Patients scheduled for tracheostomy
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Patients who were not extubated at the end of the surgery in the operating room
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Hyun-Kyu Yoon | Seoul | Korea, Republic of |
Sponsors and Collaborators
- Seoul National University Hospital
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- AI_PRF