Clinical trial NCT04527094: Machine Learning Model to Predict Postoperative Respiratory Failure
Official title: Development and Prospective Evaluation of a Machine Learning Model to Predict Postoperative Respiratory Failure
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Noncardiac Surgery
Keywords: Artificial Intelligence
Risk Factors
Postoperative respiratory failure
Validation
Lead sponsor: Seoul National University Hospital
Sponsor class: OTHER
First posted: 2020-08-26
Enrollment: 22250
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adult patients undergoing general anesthesia for noncardiac surgery

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.

Eligibility criteria: 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.

Primary outcomes:
measure: the incidence of postoperative respiratory failure after general anesthesia
timeFrame: within postoperative day 7
Secondary outcomes:
Other outcomes:
Locations:
city: Seoul
country: Korea, Republic of
facility: Hyun-Kyu Yoon
Study officials:
References: