The Success of ChatGPT in Providing American Society of Anesthesiologist (ASA) Scores

NCT06321445 | Completed

• 1 other identifier: ChatGptASAscore
• Study Type: observational
• Target: 2,851
• Locations: 1 country
• Sites: 1

Brief Summary

Patients applied to the anesthesia clinics of Health Science University Istanbul Kanuni Sultan Suleyman Training and Research Hospital and Basaksehir Cam and Sakura City Hospital were included in the study. Evaluation forms which will be filled in every preoperative examinations will be saved in the hospitals systems. Patients datas without indentification informations will be asked to ChatGpt to give anesthesiological risc scores. This scores will be compared with the scores already given by anesthesiologists.

Conditions

Artificial Intelligence; Preoperative Evaluation

Outcome Measures

Primary Outcomes (1)

• The Success of ChatGPT in Providing American Society of Anesthesiologist (ASA) Scores

  Comparison of ASA scores provided from ChatGpt and provided from anesthesiologists. ASA 1: A normal healthy patient. ASA 2: A patient with mild systemic disease. ASA 3: A patient with severe systemic disease that is not incapacitating. ASA 4: A patient with severe systemic disease that is a constant threat to life. ASA 5: A moribund patient who is not expected to survive without the operation. ASA 6: A declared brain-dead patient whose organs are being removed for donor purposes. Additional classifications include: E: This designation can be added to any of the above classifications (ASA 1E to ASA 6E) to indicate that the surgery is an emergency, which increases the risk of the procedure.

  2 weeks

Secondary Outcomes (1)

• The Success of ChatGPT in clinical decisions

  2 weeks

Study Arms (2)

Experts

ASA scores provided by anesthesiologists

Other: providing ASA score

ChatGpt

ASA scores provided by ChatGpt

Other: providing ASA score

Interventions

providing ASA score OTHER

ASA scoring, or the American Society of Anesthesiologists (ASA) Physical Status Classification System, is a framework used by anesthesiologists to assess the preoperative physical fitness of a patient before surgery. It categorizes patients into six classes based on their overall health status, ranging from ASA I (a healthy patient) to ASA VI (a declared brain-dead patient whose organs are being removed for donor purposes). The ASA score helps in predicting perioperative risks and assists healthcare professionals in making informed decisions about anesthesia management and the need for special precautions during and after surgery.

ChatGpt; Experts

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

All patients who admitted to anesthesia clinics of Health Science University Istanbul Kanuni Sultan Suleyman Training and Research Hospital and Basaksehir Cam and Sakura City Hospital.

You may qualify if:

• Patient who admitted to anesthesia clinics for both hospital
• Patient with ASA scores between I-IV

You may not qualify if:

• ASA scores higher than IV
• Emergency cases patients have symptoms like upper and lower airway infections.
• patients haven't got enough informations to provide ASA scores.
• Patients with symptoms of an active upper or lower respiratory tract infection

Sponsors & Collaborators

• Kanuni Sultan Suleyman Training and Research Hospital: lead

Study Sites (1)

Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital

Istanbul, 34303, Turkey (Türkiye)

Location

Related Publications (3)

• Yeo YH, Samaan JS, Ng WH, Ting PS, Trivedi H, Vipani A, Ayoub W, Yang JD, Liran O, Spiegel B, Kuo A. Assessing the performance of ChatGPT in answering questions regarding cirrhosis and hepatocellular carcinoma. Clin Mol Hepatol. 2023 Jul;29(3):721-732. doi: 10.3350/cmh.2023.0089. Epub 2023 Mar 22.

  PMID: 36946005 RESULT

• Wongtangman K, Aasman B, Garg S, Witt AS, Harandi AA, Azimaraghi O, Mirhaji P, Soby S, Anand P, Himes CP, Smith RV, Santer P, Freda J, Eikermann M, Ramaswamy P. Development and validation of a machine learning ASA-score to identify candidates for comprehensive preoperative screening and risk stratification. J Clin Anesth. 2023 Aug;87:111103. doi: 10.1016/j.jclinane.2023.111103. Epub 2023 Mar 8.

  PMID: 36898279 RESULT

• Turan EI, Baydemir AE, Ozcan FG, Sahin AS. Evaluating the accuracy of ChatGPT-4 in predicting ASA scores: A prospective multicentric study ChatGPT-4 in ASA score prediction. J Clin Anesth. 2024 Sep;96:111475. doi: 10.1016/j.jclinane.2024.111475. Epub 2024 Apr 23.

  PMID: 38657530 DERIVED

Study Officials

• Engin ihsan Turan, Specialist

  Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: anesthesiology and reanimation specialist

Study Record Dates

• First Submitted: March 11, 2024
• First Posted: March 20, 2024
• Study Start: February 8, 2024
• Primary Completion: April 1, 2024
• Study Completion: May 1, 2024
• Last Updated: May 6, 2024

Record last verified: 2024-05

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)