Gastric CancerNCT05646290
Validation of a Model for Predicting Anastomotic Leakage
An observational study of Gastric Cancer, run by Jichao Qin at 1 site in China.
Trial summary
This study will validate a machine learning model for predicting anastomotic leakage of esophagogastrostomy and esophagojejunostomy. Read more about this study
- Who is it for?
- Ages 18 to 85 years
- Conditions the study is for
- Gastric Cancer
- How many people
- 512 participants
- Where
- 1 siteChina
- Study dates
- 6 Jan 2022to 6 May 2024
- 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 18 to 85 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Gastric cancer patients undergoing total or proximal gastrectomy in five medical centers (Tongji Hospital, Wuhan No. 1 Hospital, Xianning Central Hospital, Huanggang Central Hospital and Jingzhou Central Hospital) are included in this perspective study.
Who may be able to join
- Aged older than 18 years and younger than 85 years.
- Primary gastric adenocarcinoma confirmed by preoperative pathology.
- Expected curative resection via total or proximal gastrectomy.
- American Society of Anesthesiologists (ASA) class I, II, or III.
- Written informed consent.
Who cannot join
- Pregnant or breastfeeding women.
- Severe mental disorder or language communication disorder.
- Other surgical procedures of gastrectomy is performed.
- Interrupted of surgery for more than 30 minutes due to any cause.
- Malignant tumors with other organs
Trial locations and contacts
1 site in 1 country.
China1 site
Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology
Wuhan, Hubei, 430030
About this study
Official title: Validation of a Machine Learning Model for Predicting Anastomotic Leakage of Esophagogastrostomy and Esophagojejunostomy: A Multicenter Prospective Study
This study will validate a machine learning model for predicting anastomotic leakage of esophagogastrostomy and esophagojejunostomy.
Read the detailed description
Anastomotic leakage is a fatal complication after total and proximal gastrectomy in gastric cancer patients. Identifying patients with high-risk of AL is important for guiding the surgeons' decision making, such as a more rigorous anastomotic operation， placing a jejunal feeding tube and dual-lumen flushable drainage catheter. We have developed a high-performance machine learning model based on 1660 gastric cancer patients, which showed good discrimination of anastomotic leakage. Hence, this multi-center prospective study will validiate the usability of the model for predicting anastomotic leakage in gastric cancer patients who receive total and proximal gastrectomy.
- Conditions
- Gastric Cancer
- Keywords
- machine learning, anastomotic leakage, total and proximal gastrectomy
- Medical subject headings
- Stomach Neoplasms, Anastomotic Leak
What is being measured
The primary outcome is the main question the study is designed to answer.
Incidence of anastomotic leakage
Measured: Within 30 days after operation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Dec 2022
- Study start6 Jan 2022
- Primary completion15 Mar 2024
- Study completion6 May 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastric CancerStudies 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
- Prospective
- Enrolment
- 512 participants (actual)
Sponsor and investigators
- Lead sponsor
- Jichao Qin
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Jichao Qin, Tongji Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05646290
- Sponsor's protocol ID
- TJ-IRB20211255
- Status last verified
- Nov 2024
- First submitted
- 1 Dec 2022
- Last updated
- 21 Nov 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 21 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validation of a Machine Learning Model for Predicting Anastomotic Leakage of Esophagogastrostomy and Esophagojejunostomy: A Multicenter Prospective Study
- ClinicalTrials.gov ID
- NCT05646290
- Acronym / sponsor ID
- TJ-IRB20211255
- Conditions
- Gastric Cancer
- Keywords
- machine learning, anastomotic leakage, total and proximal gastrectomy
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 512 participants (actual)
- Lead sponsor
- Jichao QinUniversity, hospital or other
- Study start
- 6 Jan 2022 (actual)
- Primary completion
- 15 Mar 2024 (actual)
- Study completion
- 6 May 2024 (actual)
- First posted
- 12 Dec 2022 (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.
This study will validate a machine learning model for predicting anastomotic leakage of esophagogastrostomy and esophagojejunostomy.
Detailed description
Anastomotic leakage is a fatal complication after total and proximal gastrectomy in gastric cancer patients. Identifying patients with high-risk of AL is important for guiding the surgeons' decision making, such as a more rigorous anastomotic operation， placing a jejunal feeding tube and dual-lumen flushable drainage catheter. We have developed a high-performance machine learning model based on 1660 gastric cancer patients, which showed good discrimination of anastomotic leakage. Hence, this multi-center prospective study will validiate the usability of the model for predicting anastomotic leakage in gastric cancer patients who receive total and proximal gastrectomy.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 85 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Gastric cancer patients undergoing total or proximal gastrectomy in five medical centers (Tongji Hospital, Wuhan No. 1 Hospital, Xianning Central Hospital, Huanggang Central Hospital and Jingzhou Central Hospital) are included in this perspective study.
Key inclusion criteria (5 listed)
- Aged older than 18 years and younger than 85 years.
- Primary gastric adenocarcinoma confirmed by preoperative pathology.
- Expected curative resection via total or proximal gastrectomy.
- American Society of Anesthesiologists (ASA) class I, II, or III.
- Written informed consent.
Key exclusion criteria (5 listed)
- Pregnant or breastfeeding women.
- Severe mental disorder or language communication disorder.
- Other surgical procedures of gastrectomy is performed.
- Interrupted of surgery for more than 30 minutes due to any cause.
- Malignant tumors with other organs
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
Incidence of anastomotic leakage
Time frame: Within 30 days after operation
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 21 Nov 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 Gastric Cancer, run by Jichao Qin 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 5 inclusion and 5 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 21 Nov 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?
Incidence of anastomotic leakage (time frame: Within 30 days after operation). 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 21 Nov 2024.