Machine Learning Model to Predict Postoperative Respiratory Failure

NCT04527094 | Completed

• 1 other identifier: AI_PRF
• Study Type: observational
• Target: 22,250
• Locations: 1 country
• Sites: 1

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.

Conditions

Noncardiac Surgery

Keywords

Artificial Intelligence; Risk Factors; Postoperative respiratory failure; Validation

Outcome Measures

Primary Outcomes (1)

• 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

Study Arms (1)

AI_PRF

Adults patients undergoing general anesthesia

Diagnostic Test: Prediction of postoperative respiratory failure using a machine learning

Interventions

Prediction of postoperative respiratory failure using a machine learning DIAGNOSTIC_TEST

The performance of a machine learning model to predict postoperative respiratory failure after general anesthesia within postoperative day 7 was tested prospectively.

AI_PRF

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult patients undergoing general anesthesia for noncardiac surgery

You may qualify if:

• Adults patients undergoing general anesthesia for noncardiac surgery

You may not qualify if:

• 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.

Sponsors & Collaborators

• Seoul National University Hospital: lead

Study Sites (1)

Hyun-Kyu Yoon

Seoul, South Korea

Location

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: clinical assistant professor

Study Record Dates

• First Submitted: August 21, 2020
• First Posted: August 26, 2020
• Study Start: May 26, 2021
• Primary Completion: May 25, 2022
• Study Completion: June 25, 2022
• Last Updated: September 1, 2022

Record last verified: 2022-08

Locations

KR (1)