Evaluation of the Agreement Between ChatGPT-5 and Anesthesiologists' Predictions and Actual Outcomes in Predicting Postoperative Intensive Care Unit Requirement Based on Preoperative Data
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 938
- 试验地点
- 1
- 主要终点
- Agreement Between Predicted and Actual Postoperative ICU Requirement
研究概览
简要总结
Accurate prediction of postoperative intensive care unit (ICU) requirement is essential for patient safety and efficient use of limited ICU resources. In routine clinical practice, decisions regarding postoperative ICU admission are primarily based on anesthesiologists' preoperative clinical judgment, which may vary among clinicians.
This prospective, observational study aims to evaluate the agreement between predictions made by ChatGPT-5(Chat Generative Pre-trained Transformer) and anesthesiologists regarding postoperative ICU requirement using routinely collected preoperative patient data, and to compare these predictions with actual postoperative ICU admission outcomes.
The study does not involve any intervention, treatment modification, or additional procedures beyond standard clinical care. All patient data are anonymized, and clinical management is not influenced by the model's predictions
详细描述
Postoperative intensive care unit (ICU) admission is a critical component of perioperative patient management, particularly in patients with increased surgical or anesthetic risk. Accurate preoperative identification of patients who will require postoperative ICU care may improve patient safety and optimize resource allocation.
This study is designed as a prospective, non-interventional observational cohort study conducted in adult patients undergoing elective surgical procedures. Routinely collected preoperative clinical data, including demographic characteristics, comorbidities, laboratory results, and anesthesiologists' assessments, are recorded for each participant.
For each patient, postoperative ICU requirement predictions generated by ChatGPT-5 using structured preoperative data are documented. These predictions are compared with anesthesiologists' preoperative ICU admission assessments and with actual postoperative ICU admission outcomes.
No additional diagnostic or therapeutic interventions are performed as part of the study. Patient care follows standard institutional practice at all times. All collected data are anonymized prior to analysis. Statistical analyses focus on agreement and predictive performance measures, including sensitivity, specificity, and concordance between prediction methods and actual outcomes.
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
- •Adult patients aged 18 years or older.
- •Patients undergoing elective surgical procedures requiring preoperative anesthesiology evaluation.
- •Availability of complete preoperative clinical data required for postoperative intensive care unit (ICU) need prediction.
- •Patients evaluated preoperatively by an anesthesiology specialist.
排除标准
- •Patients younger than 18 years of age.
- •Emergency surgical procedures.
- •Patients with incomplete or missing preoperative clinical data.
- •Patients who decline the use of their clinical data for research purposes.
结局指标
主要结局
Agreement Between Predicted and Actual Postoperative ICU Requirement
时间窗: Within the first 24 hours after surgery
Agreement between preoperative predictions of postoperative intensive care unit (ICU) requirement made by ChatGPT-5 and anesthesiologists, compared with actual postoperative ICU admission outcomes.
次要结局
未报告次要终点
研究者
Beste Mutlu Dağlıoğlu
Specialist in Anesthesiology and Reanimation
Antalya City Hospital