Development and Validation of a Machine Learning Model to Predict Hypothermia in Gynecological Laparoscopic Surgery Based on Preoperative Clinical Indicators: A Multicenter Prospective Cohort Study
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 1,000
研究概览
简要总结
Brief Title: Predicting Hypothermia in Gynecological Laparoscopic Surgery Using Machine Learning
Brief Summary: This study aims to develop and validate a machine learning model for predicting intraoperative hypothermia (IOH) in patients undergoing gynecological laparoscopic surgery based on preoperative clinical indicators. This prospective, multicenter case-control study will enroll female patients aged 18 years and older who are scheduled for laparoscopic surgery across multiple hospitals from 2026 to 2027. The primary objective is to identify high-risk patients who may experience IOH, defined as a core temperature below 36.0°C during surgery.
Participants will be classified into two groups: the IOH group, consisting of patients who experience hypothermia, and the normal temperature group, comprising patients who maintain a core temperature of 36.0°C or higher. Data collection will include demographics, comorbidities, surgical details, anesthesia information, and preoperative laboratory results.
The primary outcome measure will be the area under the curve (AUC) of the model, assessing its predictive performance at various thresholds. Secondary outcomes will include sensitivity, positive predictive value, negative predictive value, and F1 score. The study hypothesizes that the developed machine learning model will significantly improve the accuracy and timeliness of predicting IOH, thereby enhancing patient safety during surgery and postoperative recovery. This research is expected to inform clinical practices related to preventative warming strategies, ultimately improving patient outcomes in gynecological laparoscopic surgery.
详细描述
Background: Intraoperative hypothermia (IOH), defined as a core body temperature below 36.0°C during surgery, is a common complication with an incidence as high as 50% in gynecological laparoscopic procedures. IOH is associated with adverse outcomes including surgical site infections, increased blood loss, cardiovascular complications, prolonged recovery, and higher healthcare costs. Accurate preoperative identification of patients at high risk for IOH is crucial for implementing targeted preventative measures and optimizing resource allocation.
Objective: The primary objective of this study is to develop and validate a machine learning model that utilizes preoperative clinical indicators to predict the occurrence of IOH specifically in patients undergoing gynecological laparoscopic surgery.
Study Design: This is a multicenter, prospective case-control study. Data will be prospectively collected from participating hospitals between 2026 and 2027.
Technical Methods:
Sample Size: Based on an estimated IOH incidence of 40% and 24 predictor variables, a minimum sample size of 1500 participants is planned to ensure adequate power for model development and validation.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •Female patients aged 18 years or older.
- •Patients scheduled for laparoscopic surgery.
排除标准
- •Preoperative body temperature exceeding 37.5°C or below 36.0°C.
- •History of hypothyroidism or hyperthyroidism.
- •Patients with thermoregulatory dysfunction, such as severe infection or central nervous system disorders.
- •Patients who refuse to sign the informed consent form.
研究者
Fei Jia
Director of Anesthesiology Department
Chengdu Jinjiang Maternity and Child Health Hospital