Clinical trial NCT06270797: Pre-anesthesia Imaging-based Respiratory Assessment and Analysis
Official title: Pre-anesthesia Imaging-based Respiratory Assessment and Analysis
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Clinical Decision Support System
Keywords: knowledge graph
artificial intelligence
Enhanced Recovery After Surgery
Lead sponsor: Kaohsiung Medical University Chung-Ho Memorial Hospital
Sponsor class: OTHER
First posted: 2024-02-21
Enrollment: 30000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 85
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients undergoing regular general anesthesia with pre-anesthetic consultation and airway examination.

Brief summary: 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.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: A pre-anesthesia evaluation
timeFrame: pre-anesthetic consultation about 20 min
measure: Perform non-invasive imaging capture.
timeFrame: pre-anesthetic consultation about 5 min
measure: difficult intubation prediction
timeFrame: after pre-anesthetic consultation about 5 min
Secondary outcomes:
measure: time to successfully extubate the nasotracheal tube after anesthesia
timeFrame: from the end of surgery to the post-anesthesia care, assessed up to one hour
measure: safely discharged from post-anesthesia care unit (postoperative recovery room)
timeFrame: 2 hours
measure: side effects and adverse events
timeFrame: intraoperative and postoperative stages, assessed up to 48 hours
Other outcomes:
Locations:
city: Kaohsiung
country: Taiwan
facility: Kaohsiung Medical University Chung-Ho Memorial Hospital
state: Sanmin Dist
Study officials:
affiliation: Kaohsiung Medical University Chung-Ho Memorial Hospital
name: Kuang-I Cheng, MD,Phd
role: STUDY_DIRECTOR
References: