Laparoscopic SurgeryNCT07353528
Predicting Hypothermia in Gynecological Laparoscopic Surgery Using Machine Learning
An observational study of Laparoscopic Surgery and Intraoperative Hypothermia, run by Chengdu Jinjiang Maternity and Child Health Hospital at 4 sites in China.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 years and older, women only
- Conditions the study is for
- Laparoscopic Surgery, Intraoperative Hypothermia, Gynecological Surgery
- How many people
- 1,000 participants
- Where
- 4 sitesChina
- Study dates
- 1 Mar 2026to 1 Jul 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Women only
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- Female patients aged 18 years or older.
- Patients scheduled for laparoscopic surgery.
Who cannot join
- 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 locations and contacts
4 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China4 sites
Chengdu Jinjiang District Women & Children Health Hospital
Chengdu, Sichuan, 610011
fei jia, BS, +86 18581877655, jiafei1980@sina.com
409440463@qq.com
Sichuan Jinxin Xinan Women & Children's Hospital
Chengdu, Sichuan, 610011
chunping li, MS, +86 18284535909, lichunpingjx@163.com
People ' s Hospital of Dayi County
Chengdu, Sichuan, 611300
chao liu, BS, +86 18190725356, 397619144@qq.com
Medical Center Hospital of QiongLai City
Chengdu, Sichuan, 611532
yunfei hu, BS, +86 13880189795, 409440463@qq.com
About this study
Official title: 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.
Read more
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.
Read the 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.
- Keywords
- Laparoscopic surgery, Intraoperative hypothermia, Machine learning, Gynecology
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: During surgery
Secondary outcomes (4)
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.
Measured: During surgery
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.
Measured: During surgery
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.
Measured: During surgery
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.
Measured: During surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Jan 2026
- Study start1 Mar 2026 (estimated)
- Primary completion1 Mar 2026 (estimated)
- Study completion1 Jul 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (10)Papers the sponsor has linked to this study.
- Other trials for Laparoscopic SurgeryStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chengdu Jinjiang Maternity and Child Health Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Sichuan Jinxin Xinan Women & Children's Hospital, People ' s Hospital of Dayi County, Medical Center Hospital of QiongLai City
- Responsible party
- Principal investigator, Fei Jia, Chengdu Jinjiang Maternity and Child Health Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07353528
- Sponsor's protocol ID
- 202501
- Status last verified
- Jan 2026
- First submitted
- 28 Nov 2025
- Last updated
- 20 Jan 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: 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.
Publications
Menzenbach J, Kirfel A, Guttenthaler V, Feggeler J, Hilbert T, Ricchiuto A, Staerk C, Mayr A, Coburn M, Wittmann M; PROPDESC Collaboration Group. PRe-Operative Prediction of postoperative DElirium by appropriate SCreening (PROPDESC) development and validation of a pragmatic POD risk screening score based on routine preoperative data. J Clin Anesth. 2022 Jun;78:110684. doi: 10.1016/j.jclinane.2022.110684. Epub 2022 Feb 18.
PubMed 35190344Lu Z, Chen X. Early prediction of intraoperative hypothermia in patients undergoing gynecological laparoscopic surgery: A retrospective cohort study. Medicine (Baltimore). 2024 Oct 4;103(40):e39038. doi: 10.1097/MD.0000000000039038.
PubMed 39465739Hosseini MP, Hosseini A, Ahi K. A Review on Machine Learning for EEG Signal Processing in Bioengineering. IEEE Rev Biomed Eng. 2021;14:204-218. doi: 10.1109/RBME.2020.2969915. Epub 2021 Jan 22.
PubMed 32011262Sessler DI, Pei L, Li K, Cui S, Chan MTV, Huang Y, Wu J, He X, Bajracharya GR, Rivas E, Lam CKM; PROTECT Investigators. Aggressive intraoperative warming versus routine thermal management during non-cardiac surgery (PROTECT): a multicentre, parallel group, superiority trial. Lancet. 2022 May 7;399(10337):1799-1808. doi: 10.1016/S0140-6736(22)00560-8. Epub 2022 Apr 4.
PubMed 35390321Cao B, Li Y, Liu Y, Chen X, Liu Y, Li Y, Wu Q, Ji F, Shu H. A multi-center study to predict the risk of intraoperative hypothermia in gynecological surgery patients using preoperative variables. Gynecol Oncol. 2024 Jun;185:156-164. doi: 10.1016/j.ygyno.2024.02.009. Epub 2024 Feb 29.
PubMed 38428331The nurse-nurse relationship. NLN Publ. 1990 Jun;(20-2294):257-61. No abstract available.
PubMed 2235395Gomez-Hidalgo NR, Pletnev A, Razumova Z, Bizzarri N, Selcuk I, Theofanakis C, Zalewski K, Nikolova T, Lanner M, Kacperczyk-Bartnik J, El Hajj H, Perez-Benavente A, Nelson G, Gil-Moreno A, Fotopoulou C, Sanchez-Iglesias JL. European Enhanced Recovery After Surgery (ERAS) gynecologic oncology survey: Status of ERAS protocol implementation across Europe. Int J Gynaecol Obstet. 2023 Jan;160(1):306-312. doi: 10.1002/ijgo.14386. Epub 2022 Aug 20.
PubMed 35929452Li L, Huang J, Chen X, Ma W, Hu Y, Li Y. A Retrospective Analysis of the Postoperative Effect of Intraoperative Hypothermia on Deep Vein Thrombosis After Intracranial Tumor Resection. World Neurosurg. 2022 Nov;167:e778-e783. doi: 10.1016/j.wneu.2022.08.099. Epub 2022 Aug 26.
PubMed 36038119Sessler DI. Perioperative thermoregulation and heat balance. Lancet. 2016 Jun 25;387(10038):2655-2664. doi: 10.1016/S0140-6736(15)00981-2. Epub 2016 Jan 8.
PubMed 26775126Carella M, Beck F, Piette N, Lecoq JP, Bonhomme VL. Effect of preoperative warming on intraoperative hypothermia and postoperative functional recovery in total hip arthroplasty: a randomized clinical trial. Minerva Anestesiol. 2024 Jan-Feb;90(1-2):41-50. doi: 10.23736/S0375-9393.23.17555-9. Epub 2023 Oct 25.
PubMed 37878246
Study at a glance
From the registry record, last updated by the sponsor on 20 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT07353528
- Acronym / sponsor ID
- 202501
- Keywords
- Laparoscopic surgery, Intraoperative hypothermia, Machine learning, Gynecology
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Chengdu Jinjiang Maternity and Child Health HospitalUniversity, hospital or other
- Collaborators
- Sichuan Jinxin Xinan Women & Children's Hospital, People ' s Hospital of Dayi County, Medical Center Hospital of QiongLai City
- Study start
- 1 Mar 2026 (estimated)
- Primary completion
- 1 Mar 2026 (estimated)
- Study completion
- 1 Jul 2026 (estimated)
- First posted
- 20 Jan 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (2 listed)
- Female patients aged 18 years or older.
- Patients scheduled for laparoscopic surgery.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
4 secondary outcomes
Sensitivity
Time frame: During surgery
Positive Predictive Value
Time frame: During surgery
Negative Predictive Value
Time frame: During surgery
F1-Score
Time frame: During surgery
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Jan 2026.
- Background: PubMed 35190344Menzenbach J, Kirfel A, Guttenthaler V, Feggeler J, Hilbert T, Ricchiuto A, Staerk C, Mayr A, Coburn M, Wittmann M; PROPDESC Collaboration Group. PRe-Operative Prediction of postoperative DElirium by appropriate SCreening (PROPDESC) development and validation of a pragmatic POD risk screening score based on routine preoperative data. J Clin Anesth. 2022 Jun;78:110684. doi: 10.1016/j.jclinane.2022.110684. Epub 2022 Feb 18.
- Background: PubMed 39465739Lu Z, Chen X. Early prediction of intraoperative hypothermia in patients undergoing gynecological laparoscopic surgery: A retrospective cohort study. Medicine (Baltimore). 2024 Oct 4;103(40):e39038. doi: 10.1097/MD.0000000000039038.
- Background: PubMed 32011262Hosseini MP, Hosseini A, Ahi K. A Review on Machine Learning for EEG Signal Processing in Bioengineering. IEEE Rev Biomed Eng. 2021;14:204-218. doi: 10.1109/RBME.2020.2969915. Epub 2021 Jan 22.
- Background: PubMed 35390321Sessler DI, Pei L, Li K, Cui S, Chan MTV, Huang Y, Wu J, He X, Bajracharya GR, Rivas E, Lam CKM; PROTECT Investigators. Aggressive intraoperative warming versus routine thermal management during non-cardiac surgery (PROTECT): a multicentre, parallel group, superiority trial. Lancet. 2022 May 7;399(10337):1799-1808. doi: 10.1016/S0140-6736(22)00560-8. Epub 2022 Apr 4.
- Background: PubMed 38428331Cao B, Li Y, Liu Y, Chen X, Liu Y, Li Y, Wu Q, Ji F, Shu H. A multi-center study to predict the risk of intraoperative hypothermia in gynecological surgery patients using preoperative variables. Gynecol Oncol. 2024 Jun;185:156-164. doi: 10.1016/j.ygyno.2024.02.009. Epub 2024 Feb 29.
- Background: PubMed 2235395The nurse-nurse relationship. NLN Publ. 1990 Jun;(20-2294):257-61. No abstract available.
- Background: PubMed 35929452Gomez-Hidalgo NR, Pletnev A, Razumova Z, Bizzarri N, Selcuk I, Theofanakis C, Zalewski K, Nikolova T, Lanner M, Kacperczyk-Bartnik J, El Hajj H, Perez-Benavente A, Nelson G, Gil-Moreno A, Fotopoulou C, Sanchez-Iglesias JL. European Enhanced Recovery After Surgery (ERAS) gynecologic oncology survey: Status of ERAS protocol implementation across Europe. Int J Gynaecol Obstet. 2023 Jan;160(1):306-312. doi: 10.1002/ijgo.14386. Epub 2022 Aug 20.
- Background: PubMed 36038119Li L, Huang J, Chen X, Ma W, Hu Y, Li Y. A Retrospective Analysis of the Postoperative Effect of Intraoperative Hypothermia on Deep Vein Thrombosis After Intracranial Tumor Resection. World Neurosurg. 2022 Nov;167:e778-e783. doi: 10.1016/j.wneu.2022.08.099. Epub 2022 Aug 26.
- Background: PubMed 26775126Sessler DI. Perioperative thermoregulation and heat balance. Lancet. 2016 Jun 25;387(10038):2655-2664. doi: 10.1016/S0140-6736(15)00981-2. Epub 2016 Jan 8.
- Background: PubMed 37878246Carella M, Beck F, Piette N, Lecoq JP, Bonhomme VL. Effect of preoperative warming on intraoperative hypothermia and postoperative functional recovery in total hip arthroplasty: a randomized clinical trial. Minerva Anestesiol. 2024 Jan-Feb;90(1-2):41-50. doi: 10.23736/S0375-9393.23.17555-9. Epub 2023 Oct 25.
- Individual participant dataSharing planned: Yes. 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.
Related trials for Laparoscopic Surgery
RecruitingObservational study
Robotic Inflammatory Bowel Disease Development, Learning and Efficacy Study
Inflammatory Bowel Diseases, Crohn Disease
Not yet recruiting
The Contribution of Accelerated Recovery (mERAS) Approach to Postoperative Recovery in Pediatric Laparoscopic Surgery Cases
mERAS Intervention, The ASA (American Society of Anesthesiologists) Score
Recruiting
The Impact of a Dexmedetomidine Perfusion on Intraoperative Remifentanil Consumption
Dexmedetomidine, Total Intravenous Anesthesia
Recruiting
Comparison of Two Surgical Techniques for Indirect Inguinal Hernia Repair
Indirect Inguinal Hernia
Not yet recruiting
Amisulpride Injection for the Prevention of Postoperative Nausea and Vomiting in Patients Undergoing Laparoscopic Surgery
PONV
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
4 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Laparoscopic Surgery and Intraoperative Hypothermia, run by Chengdu Jinjiang Maternity and Child Health Hospital at 4 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 20 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 4 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Bo Liu, MS (+8618502846036, liubojjfy@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the Receiver Operating Characteristic Curve (AUC) of the machine learning model for predicting intraoperative hypothermia (time frame: During surgery). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07353528, which also lists 10 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Jan 2026; AzTrials last refreshed its copy on 28 Oct 2026.