Predicting Hypothermia in Gynecological Laparoscopic Surgery Using Machine Learning

January 18, 2026 updated by: Fei Jia, Chengdu Jinjiang Maternity and Child Health Hospital

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

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.

Study Overview

Status

Not yet recruiting

Conditions

Detailed Description

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.

Data Collection: Clinical data will be collected using electronic medical records (EMR). Core body temperature will be monitored intraoperatively using a wireless temperature monitoring system.

Statistical Analysis & Model Development: Data analysis will be performed using SPSS (v25.0) and R (v4.3.1). The dataset will be randomly split into training (80%) and testing (20%) sets. The Least Absolute Shrinkage and Selection Operator (LASSO) regression will be applied to the training set for feature selection. Six machine learning algorithms-Support Vector Machine (SVM), Logistic Regression (LR), Multilayer Perceptron (MLP), Random Forest (RF), Extreme Gradient Boosting (XGB), and Decision Tree (DT)-will be developed. Model hyperparameters will be optimized via 10-fold cross-validation.

Model Evaluation: The performance of all models will be independently validated on the test set. The primary metric for model comparison and selection will be the Area Under the Receiver Operating Characteristic Curve (AUC). Secondary performance metrics include sensitivity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. The optimal cutoff point for the final selected model will be determined by maximizing Youden's index.

Ethical Considerations: This study will be conducted following approval by the Institutional Review Boards/Ethics Committees of all participating centers. Written informed consent will be obtained from all participants. The study protocol will be registered in a clinical trial registry to ensure transparency. All participant data will be handled with strict confidentiality and in accordance with relevant data protection regulations.

Expected Outcomes: This study is expected to result in a validated machine learning model capable of accurately predicting IOH risk prior to gynecological laparoscopic surgery. The identification of key predictive factors and the deployment of this model aim to facilitate personalized preventative care, reduce the incidence of IOH, and improve patient safety and postoperative recovery outcomes.

Study Type

Observational

Enrollment (Estimated)

1000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Sichuan
      • Chengdu, Sichuan, China, 610011
      • Chengdu, Sichuan, China, 610011
        • Sichuan Jinxin Xinan Women & Children's Hospital
        • Contact:
      • Chengdu, Sichuan, China, 611300
        • People ' s Hospital of Dayi County
        • Contact:
      • Chengdu, Sichuan, China, 611532
        • Medical Center Hospital of QiongLai City
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

The study population consists of adult female patients undergoing gynecological laparoscopic surgery at multiple collaborating hospitals (including Chengdu Jinjiang District Maternal and Child Health Hospital, etc.). All participants must meet the inclusion criteria and not meet any exclusion criteria. Based on whether their intraoperative core temperature (measured by a wireless temperature monitoring system) falls below 36.0°C, patients will be categorized into either the "Hypothermia Group" (case) or the "Normothermia Group" (control). This is a prospective case-control study.

Description

Inclusion Criteria:

  • Female patients aged 18 years or older.
  • Patients scheduled for laparoscopic surgery.

Exclusion Criteria:

  • 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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Hypothermia Group
This cohort consists of patients who develop intraoperative hypothermia (IOH) during gynecological laparoscopic surgery. IOH is defined as a core body temperature (measured by a wireless temperature monitoring system) falling below 36.0°C at any time during the surgery.
Normothermia Group
This cohort comprises patients whose core body temperature (measured by a wireless temperature monitoring system) remains at or above 36.0°C throughout the entire gynecological laparoscopic surgery, and who do not develop intraoperative hypothermia (IOH). These patients serve as the control group for this study.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Area Under the Receiver Operating Characteristic Curve (AUC) of the machine learning model for predicting intraoperative hypothermia
Time Frame: During surgery
The primary outcome is the discriminatory performance of the developed machine learning model for predicting the occurrence of intraoperative hypothermia (defined as a core temperature < 36.0°C), as measured by the Area Under the Receiver Operating Characteristic Curve (AUC) evaluated on the independent testing set.
During surgery

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Sensitivity
Time Frame: During surgery
This metric measures the model's ability to correctly identify patients who will truly develop intraoperative hypothermia (true positive rate). It is calculated as the number of true positives divided by the sum of true positives and false negatives. This metric will be calculated on the model testing set.
During surgery
Positive Predictive Value
Time Frame: During surgery
This metric measures the proportion of patients predicted by the model to develop hypothermia who actually do so. It is calculated as the number of true positives divided by the sum of true positives and false positives. This metric will be calculated on the model testing set.
During surgery
Negative Predictive Value
Time Frame: During surgery
This metric measures the proportion of patients predicted by the model not to develop hypothermia who remain normothermic. It is calculated as the number of true negatives divided by the sum of true negatives and false negatives. This metric will be calculated on the model testing set.
During surgery
F1-Score
Time Frame: During surgery
This metric is the harmonic mean of precision (positive predictive value) and recall (sensitivity). It provides a single score that balances both concerns, especially useful when the class distribution is imbalanced. This metric will be calculated on the model testing set.
During surgery

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Chengdu Jinjiang Maternity and Child Health Hospital

Collaborators

Sichuan Jinxin Xinan Women & Children's Hospital
People ' s Hospital of Dayi County
Medical Center Hospital of QiongLai City

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

March 1, 2026

Primary Completion (Estimated)

March 1, 2026

Study Completion (Estimated)

July 1, 2026

Study Registration Dates

First Submitted

November 28, 2025

First Submitted That Met QC Criteria

January 18, 2026

First Posted (Actual)

January 20, 2026

Study Record Updates

Last Update Posted (Actual)

January 20, 2026

Last Update Submitted That Met QC Criteria

January 18, 2026

Last Verified

January 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • 202501 (National Health Education Center Research Grant)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

Individual participant data that underlie the results reported in this article, after de-identification (text, tables, figures, and appendices) will be shared. Researchers who provide a methodologically sound proposal for use in achieving the goals of the approved proposal will be granted access to the data. Proposals should be directed to the corresponding author via email. To gain access, data requestors will need to sign a data access agreement.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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