Frailty Assessment Reveals Cognitive Differences in ASA Classification: Anesthesiologists vs Large Language Models

NCT07399938 | Completed

• 1 other identifier: AI_ASA_FRAILTY_2026
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief Summary

The American Society of Anesthesiologists (ASA) Physical Status Classification System is widely used to assess perioperative risk, but it does not explicitly include frailty as a standardized variable. In daily clinical practice, anesthesiologists may implicitly incorporate frailty-related information into ASA classification based on individual clinical judgment, which may lead to variability between evaluators. In recent years, large language models (LLMs), a type of artificial intelligence, have been increasingly used in medical decision-support research. Unlike human clinicians, these models process information in a structured and explicit manner, without relying on intuition or implicit reasoning. The primary objective of this study is to compare ASA Physical Status classifications assigned by anesthesiologists and by two different large language models using standardized preoperative clinical data from adult patients undergoing elective surgery. A secondary objective is to evaluate how the addition of a frailty index influences ASA classification decisions made by human experts and artificial intelligence models. This prospective observational study aims to improve understanding of differences in clinical reasoning between anesthesiologists and artificial intelligence systems and to explore the role of frailty in perioperative risk assessment.

Conditions

Perioperative Risk Assessment

Keywords

ASA Physical Status Classification; Artificial Intelligence; Large Language Models; Anesthesiology; Frailty; Preoperative Assessment; Clinical Decision Support; Interrater Agreement; Kappa Statistics

Outcome Measures

Primary Outcomes (1)

• Interrater Agreement in ASA Physical Status Classification Between Anesthesiologists and Large Language Models

  Agreement in ASA Physical Status Classification (ASA I-V) between four anesthesiologists and two large language models based on standardized preoperative clinical data, assessed using interrater agreement statistics.

  At the time of preoperative evaluation, prior to surgery

Secondary Outcomes (3)

• Effect of Frailty Information on ASA Physical Status Classification

  At the time of preoperative evaluation, prior to surgery

• Agreement Between Large Language Models in ASA Physical Status Classification

  At the time of preoperative evaluation, prior to surgery

• Agreement Among Anesthesiologists in ASA Physical Status Classification

  At the time of preoperative evaluation, prior to surgery

Study Arms (1)

Adult Elective Surgery Patients

Adult patients aged 18 years and older scheduled for elective surgical procedures. Standardized preoperative clinical data will be evaluated by anesthesiologists and large language models for ASA Physical Status Classification, with and without the addition of frailty information.

Other: Observational Assessment Only

Interventions

Observational Assessment Only OTHER

This is an observational study with no clinical intervention. No treatment, procedure, drug, or device is assigned as part of the study. ASA Physical Status Classification is assessed using existing preoperative clinical data.

Adult Elective Surgery Patients

Eligibility Criteria

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

Study Population

Adult patients undergoing elective surgical procedures whose standardized preoperative clinical data are evaluated for ASA Physical Status Classification by anesthesiologists and large language models.

You may qualify if:

• Adult patients aged 18 years and older
• Scheduled for elective surgical procedures

You may not qualify if:

• Emergency surgical procedures
• Pediatric patients
• Patients with insufficient or incomplete clinical data preventing ASA Physical --Status assessment

Sponsors & Collaborators

• Fatih Sultan Mehmet Training and Research Hospital: lead

Study Sites (1)

Istanbul Provincial Health Directorate Fatih Sultan Mehmet Training and Research Hospital

Istanbul, Turkey (Türkiye)

Location

MeSH Terms

Conditions

Frailty

Condition Hierarchy (Ancestors)

Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• cansu ofluoglu, MD

  ISTANBUL PROVINCIAL HEALTH DIRECTORATE FATIH SULTAN MEHMET TRAINING AND RESEARCH HOSPITAL

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Specialist in Anesthesiology and Reanimation

Study Record Dates

• First Submitted: January 29, 2026
• First Posted: February 10, 2026
• Study Start: February 25, 2026
• Primary Completion: April 1, 2026
• Study Completion: April 1, 2026
• Last Updated: April 23, 2026

Record last verified: 2026-04

Locations

TU (1)