Venous ThromboembolismNCT06556953
Evaluating the Risk of Postoperative Venous Thromboembolism in Cervical Cancer Patients
An observational study of Venous Thromboembolism, run by Haike Lei at 1 site in China.
Trial summary
The aim of this study is to develop a machine learning model to accurately predict the risk of venous thromboembolism in patients with cervical cancer after surgery. Read more about this study
- Who is it for?
- Ages 20 to 89 years, women only
- Conditions the study is for
- Venous Thromboembolism
- How many people
- 1,174 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2019to 31 Dec 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
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 20 to 89 years
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: Our data was collected from electronic case of the cervical cancer patients who underwent surgical treatment at the Cancer Hospital affiliated with Chongqing University between January 2019 and December 2022
Who may be able to join
- Confirmation of cervical cancer through pathological examination.
- Receipt of surgical treatment for cervical cancer at Chongqing University Cancer Hospital in China.
- Provision of comprehensive case information.
Who cannot join
- Patients under the age of 18.
- History of VTE caused by other reasons before surgery.
- Secondary cervical cancer or accompanying primary malignant tumor.
Trial locations and contacts
1 site in 1 country.
China1 site
Chongqing University Cancer Hospital
Chongqing, Chongqing Municipality, 400030
About this study
Official title: Development and Validation of Machine Learning Models to Evaluate the Postoperative Venous Thromboembolism Risk of Cervical Cancer Patients in China
The aim of this study is to develop a machine learning model to accurately predict the risk of venous thromboembolism in patients with cervical cancer after surgery.
Read the detailed description
Venous thromboembolism (VTE) is a common and life-threatening complication in patients with cervical cancer following surgery. The objective of this study is to develop a machine learning model with the potential to predict the risk of VTE in these patients postoperatively. We plan to employ partial dependence (PD) curves, breakdown (BD) curves, Ceteris-paribus (CP), and SHapley additive exPlanations (SHAP) values for a comprehensive analysis. The goal is to explore how different machine learning algorithms can be utilized as tools for personalized postoperative VTE risk assessment in cervical cancer patients.
- Conditions
- Venous Thromboembolism
- Keywords
- machine learning, postoperative VTE, cervical cancer
- Medical subject headings
- Venous Thromboembolism, Uterine Cervical Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
Whether the patient has developed VTE is determined based on the diagnostic criteria in the "Guidelines for the Prevention and Treatment of Tumor-Associated Venous Thromboembolism (2019 Edition)."
The diagnosis of VTE primarily includes the diagnosis of DVT and PE. According to the guidelines, DVT is diagnosed using venous compression ultrasound or venography, while PE is diagnosed using CT pulmonary angiography (CTPA) or nuclear lung ventilation/perfusion imaging.
Measured: December 31, 2023
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Aug 2024
- Study start1 Jan 2019
- Primary completion31 Dec 2023
- Study completion31 Dec 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Venous ThromboembolismStudies 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,174 participants (actual)
Sponsor and investigators
- Lead sponsor
- Haike Lei
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Haike Lei, Chongqing University Cancer Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06556953
- Sponsor's protocol ID
- CZLS2023337-A
- Status last verified
- Aug 2024
- First submitted
- 6 Aug 2024
- Last updated
- 16 Aug 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 16 Aug 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of Machine Learning Models to Evaluate the Postoperative Venous Thromboembolism Risk of Cervical Cancer Patients in China
- ClinicalTrials.gov ID
- NCT06556953
- Acronym / sponsor ID
- CZLS2023337-A
- Conditions
- Venous Thromboembolism
- Keywords
- machine learning, postoperative VTE, cervical cancer
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,174 participants (actual)
- Lead sponsor
- Haike LeiUniversity, hospital or other
- Study start
- 1 Jan 2019 (actual)
- Primary completion
- 31 Dec 2023 (actual)
- Study completion
- 31 Dec 2023 (actual)
- First posted
- 16 Aug 2024 (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.
The aim of this study is to develop a machine learning model to accurately predict the risk of venous thromboembolism in patients with cervical cancer after surgery.
Detailed description
Venous thromboembolism (VTE) is a common and life-threatening complication in patients with cervical cancer following surgery. The objective of this study is to develop a machine learning model with the potential to predict the risk of VTE in these patients postoperatively. We plan to employ partial dependence (PD) curves, breakdown (BD) curves, Ceteris-paribus (CP), and SHapley additive exPlanations (SHAP) values for a comprehensive analysis. The goal is to explore how different machine learning algorithms can be utilized as tools for personalized postoperative VTE risk assessment in cervical cancer patients.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 20 to 89 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Our data was collected from electronic case of the cervical cancer patients who underwent surgical treatment at the Cancer Hospital affiliated with Chongqing University between January 2019 and December 2022
Key inclusion criteria (3 listed)
- Confirmation of cervical cancer through pathological examination.
- Receipt of surgical treatment for cervical cancer at Chongqing University Cancer Hospital in China.
- Provision of comprehensive case information.
Key exclusion criteria (3 listed)
- Patients under the age of 18.
- History of VTE caused by other reasons before surgery.
- Secondary cervical cancer or accompanying primary malignant tumor.
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
Whether the patient has developed VTE is determined based on the diagnostic criteria in the "Guidelines for the Prevention and Treatment of Tumor-Associated Venous Thromboembolism (2019 Edition)."
Time frame: December 31, 2023
The diagnosis of VTE primarily includes the diagnosis of DVT and PE. According to the guidelines, DVT is diagnosed using venous compression ultrasound or venography, while PE is diagnosed using CT pulmonary angiography (CTPA) or nuclear lung ventilation/perfusion imaging.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Aug 2024.
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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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Venous Thromboembolism, run by Haike Lei at 1 site 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 3 inclusion and 3 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: finished, as of the sponsor's last update on 16 Aug 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Whether the patient has developed VTE is determined based on the diagnostic criteria in the "Guidelines for the Prevention and Treatment of Tumor-Associated Venous Thromboembolism (2019 Edition)." (time frame: December 31, 2023). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 16 Aug 2024.