From Guideline-Based Risk Stratification To Dynamic Blood Resource Prediction: A Prospective Comparison Of Chatgpt-5 And Specialist Anesthesiologists İn Preoperative Assessment
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 703
- 试验地点
- 1
- 主要终点
- Agreement Between Anesthesiologist-Assigned and ChatGPT-5-Generated ASA Physical Status Classification
研究概览
简要总结
Accurate preoperative risk stratification is essential for perioperative planning, resource allocation, and patient safety. The American Society of Anesthesiologists Physical Status (ASA-PS) classification remains the most widely used global system for assessing preoperative health status. However, ASA classification relies on clinician judgment and may demonstrate inter-observer variability.
Recent advances in artificial intelligence (AI), particularly large language models (LLMs), have shown potential for assisting clinical decision-making by synthesizing structured and unstructured medical information. In perioperative medicine, AI systems may support more standardized risk assessment and laboratory testing strategies.
The objective of this observational study is to evaluate the agreement between ASA classifications assigned by anesthesiologists and those generated by a large language model (ChatGPT-5) using anonymized preoperative clinical information. The study will also examine differences in laboratory test recommendations and explore the relationship between clinician- and AI-generated risk assessments and perioperative erythrocyte suspension utilization.
Adult patients scheduled for elective surgery who undergo routine preoperative anesthesia assessment will be included. For each patient, the ASA classification assigned by the anesthesiologist will be recorded and compared with the classification generated by the AI system using the same anonymized clinical information.
This study aims to assess whether AI-assisted preoperative evaluation may support more consistent risk stratification and potentially contribute to more standardized perioperative resource utilization.
详细描述
Background and Rationale Preoperative risk assessment is a fundamental component of perioperative medicine and plays a central role in anesthetic planning, patient safety, and perioperative resource allocation. The American Society of Anesthesiologists Physical Status (ASA-PS) classification system remains the most widely used global method for describing preoperative health status. Despite its widespread adoption, ASA classification depends on clinician interpretation and may vary between evaluators.
Advances in artificial intelligence (AI), particularly large language models (LLMs), have introduced new opportunities for supporting clinical decision-making. These systems can process both structured and unstructured clinical information and may assist in standardizing certain medical classification tasks. In perioperative medicine, AI-assisted evaluation may help interpret patient comorbidities and clinical information in a consistent manner.
Another important component of preoperative assessment is laboratory test utilization. Preoperative laboratory testing is commonly used to identify potential perioperative risks; however, the number and type of tests ordered may vary among clinicians and institutions. AI-based systems may provide standardized recommendations for laboratory investigations and potentially contribute to more efficient resource utilization.
In addition, perioperative erythrocyte suspension (packed red blood cell, PRBC) transfusion represents an objective indicator of surgical physiological stress and perioperative resource use. Evaluating the relationship between risk classification and actual blood product utilization may help determine whether AI-assisted risk assessment has potential clinical relevance.
Study Design and Procedures This study is designed as a single-center observational study conducted at the preoperative anesthesia outpatient clinic of Antalya City Hospital. Adult patients undergoing routine preoperative anesthesia assessment before elective surgery during the study period will be included in the analysis.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age ≥ 18 years
- •Scheduled for elective surgery
- •Completed standardized preoperative anesthesia evaluation form
- •ASA Physical Status classification assigned by a specialist anesthesiologist
- •Written informed consent
- •Clinical documentation suitable for anonymization
排除标准
- •Emergency surgery
- •ASA VI classification
- •Pregnancy
- •Pediatric patients (<18 years)
- •Incomplete or non-standardized clinical documentation
- •Inability to anonymize clinical records
- •More than 30 days between preoperative assessment and surgery
结局指标
主要结局
Agreement Between Anesthesiologist-Assigned and ChatGPT-5-Generated ASA Physical Status Classification
时间窗: At the time of preoperative anesthesia assessment (baseline).
Agreement between ASA Physical Status classifications assigned by board-certified anesthesiologists and those generated by ChatGPT-5 using anonymized preoperative clinical data. Agreement will be quantified using Cohen's kappa and weighted kappa statistics for ordinal ASA categories (I-V). The comparison will be performed using identical anonymized preoperative clinical summaries.
次要结局
未报告次要终点
研究者
Damla Kaytancı Özçelik
specialist in anaesthesiology and reanimation, Principal Investigator
Antalya City Hospital