Pre-anesthesia Imaging-based Respiratory Assessment and Analysis

This study is to establish a preoperative respiratory imaging assessment database and develop a difficult intubation risk prediction model and further risk analysis. We attempt to construct it into a pre-anesthesia intubation risk assessment software as the clinical decision support system.

Study Overview

• Status: Recruiting
• Conditions: Clinical Decision Support System
• Intervention / Treatment: Procedure: intubation for general anesthesia

Detailed Description

Anesthesia respiratory assessment is an important issue for anesthesiologists to evaluate the respiratory status and airway management of patients before surgery. The American Society of Anesthesiologists (ASA) updated its guidelines in 2022, emphasizing the importance of comprehensive respiratory assessment in the guidelines.

Various risk factors have been proposed in past literature for discussion, and corresponding to these risk factors, there is currently no single factor that can predict difficult intubation completely. Existing investigations into difficult intubation factors mostly focus on high-risk populations, including patients with morbid obesity, where significant differences have been identified but not developed into predictive models.

With the rapid development of AI-related technologies in recent years, numerous image-related AI frameworks have been proposed. In recent years, attempts have been made to combine various clinical risk factors using machine learning methods to create automated prediction models for difficult intubation. However, their effectiveness has not met expectations, reflecting the significant clinical problem of difficulty in prediction that remains unresolved.

This study is an observational study aimed at analyzing and establishing patient image data, refining various data engineering techniques, and optimizing existing prediction model frameworks to enhance their medical value. Additionally, the focus of this project will be on establishing more prediction models to improve existing clinical decision support systems.

• Study Type: Observational
• Enrollment (Estimated): 30000

Contacts and Locations

• Study Contact: Name: Tz Ping Gau, MD; Phone Number: +886912060962; Email: u9401066@gap.kmu.edu.tw
• Study Contact Backup: Name: Kuang-I Cheng, MD,Phd; Phone Number: +886975357568; Email: kuaich@gmail.com

Study Locations

• Taiwan
  • Sanmin Dist
    • Kaohsiung, Sanmin Dist, Taiwan, 80756
      • Recruiting
      • Kaohsiung Medical University Chung-Ho Memorial Hospital
      • Contact: Tz Ping Gau, MD; Phone Number: +886912060962; Email: u9401066@gap.kmu.edu.tw

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Patients undergoing regular general anesthesia with pre-anesthetic consultation and airway examination.

Description

Inclusion Criteria:

• Patients undergoing general anesthesia
• Patients who can undergo pre-anesthetic consultation and airway examination.

Exclusion Criteria:

• Patients unable to undergo pre-anesthetic consultation and airway examination.
• Patients requiring emergency surgery.
• Vulnerable populations.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
normal intubation; during general anesthesia, normal intubation without any difficult airway or difficult intubation were recorded in the note.	Procedure: intubation for general anesthesia; routine intubation for general anesthesia
difficult airway or difficult intubation; during general anesthesia, any type of difficult airway or difficult intubation were recorded in the note.	Procedure: intubation for general anesthesia; routine intubation for general anesthesia

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
A pre-anesthesia evaluation	The examination includes airway assessment and dental evaluation.	pre-anesthetic consultation about 20 min
Perform non-invasive imaging capture.	The capture involves non-invasive imaging of the patient's facial features through standard basic photography, excluding any additional radiographic imaging examinations.The patient's images will be stored in de-identified form.	pre-anesthetic consultation about 5 min
difficult intubation prediction	The prediction of difficult intubation from pre-anesthesia evaluation and non-invasive imaging capture	after pre-anesthetic consultation about 5 min

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
time to successfully extubate the nasotracheal tube after anesthesia	early extubation allowable	from the end of surgery to the post-anesthesia care, assessed up to one hour
safely discharged from post-anesthesia care unit (postoperative recovery room)	as calculating the time from patient is delivered to postoperative recovery room to be safely discharged from recovery room by using the aldrete scores (activities level, respiration, circulation, conscious level, oxygenation) full back to pre-operative level or ten scores.	2 hours
side effects and adverse events	records any abnormal surgical or anesthesia related findings during this admission	intraoperative and postoperative stages, assessed up to 48 hours

Collaborators and Investigators

• Sponsor: Kaohsiung Medical University Chung-Ho Memorial Hospital
• Investigators: Study Director: Kuang-I Cheng, MD,Phd, Kaohsiung Medical University Chung-Ho Memorial Hospital

Study record dates

Study Major Dates

• Study Start (Estimated): March 1, 2024
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026

Study Registration Dates

• First Submitted: February 14, 2024
• First Submitted That Met QC Criteria: February 14, 2024
• First Posted (Estimated): February 21, 2024

Study Record Updates

• Last Update Posted (Estimated): February 21, 2024
• Last Update Submitted That Met QC Criteria: February 14, 2024
• Last Verified: December 1, 2023

More Information

Terms related to this study

• Keywords: artificial intelligence; Enhanced Recovery After Surgery; knowledge graph
• Additional Relevant MeSH Terms: Physiological Effects of Drugs; Central Nervous System Depressants; Anesthetics
• Other Study ID Numbers: KMUHIRB-E(I)-20230184

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No