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Predicting Hypothermia in Gynecological Laparoscopic Surgery Using Machine Learning

ConditionsLaparoscopic SurgeryIntraoperative HypothermiaGynecological Surgery
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexFemale
Age18+
SponsorChengdu Jinjiang Maternity and Child Health Hospital

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

Female patients aged 18 years or older.

Patients scheduled for laparoscopic surgery.

Disqualifiers

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.

Trial 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.

Trial design

Design model

Case-control

Time perspective

Prospective

Treatments tested in this trial

Not listed

Trial groups

1,000 Participants
are grouped into 2 trial groups
Group A: Hypothermia Group
Group B: Normothermia Group

Trial outcomes

Primary outcomes

1

Area Under the Receiver Operating Characteristic Curve (AUC) of the machine learning model for predicting intraoperative hypothermia

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.

Time frame
During surgery

Secondary outcomes

1

Sensitivity

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.

Time frame
During surgery
2

Positive Predictive Value

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.

Time frame
During surgery
3

Negative Predictive Value

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.

Time frame
During surgery
4

F1-Score

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.

Time frame
During surgery

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Chengdu Jinjiang Maternity and Child Health Hospital

Lead sponsor

Sichuan Jinxin Xinan Women & Children's Hospital

Collaborator

People ' s Hospital of Dayi County

Collaborator

Medical Center Hospital of QiongLai City

Collaborator

Source ClinicalTrials.gov

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