Prediction Model for Early Biliary Stasis After Bariatric Surgery (PM-EBS-BS)

April 28, 2024 updated by: Wang Siqi, China-Japan Friendship Hospital

Research on the Prediction Model for Early Biliary Stasis After Bariatric Surgery Based on Machine Learning Algorithms

Developing and validating a predictive model to estimate the risk of early biliary stasis following bariatric surgery

Study Overview

Status

Not yet recruiting

Conditions

Detailed Description

This study aims to create and validate a model that predicts the likelihood of early biliary stasis in obese patients following bariatric surgery (sleeve gastrectomy and Roux-en-Y gastric bypass)

Study Type

Observational

Enrollment (Estimated)

200

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 Contact Backup

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

This study aims to include patients who will undergo bariatric surgery.

Description

Inclusion Criteria:

  1. BMI ≥32.5 kg/m2 with one or more obesity-related comorbidities.
  2. BMI >35 kg/m2 without coexisting medical problems
  3. complete preoperative and follow-up data.

Exclusion Criteria:

  1. other bariatric procedures.
  2. contraindications for bariatric surgery.

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
early biliary stasis
Time Frame: one month
The incidence of early biliary stasis after bariatric surgery
one month
prediction model
Time Frame: six months
Area under the curve (AUC) of the prediction model
six months

Collaborators and Investigators

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

Sponsor

China-Japan Friendship Hospital

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)

July 1, 2024

Primary Completion (Estimated)

September 1, 2024

Study Completion (Estimated)

April 1, 2025

Study Registration Dates

First Submitted

April 23, 2024

First Submitted That Met QC Criteria

April 23, 2024

First Posted (Actual)

April 26, 2024

Study Record Updates

Last Update Posted (Actual)

April 30, 2024

Last Update Submitted That Met QC Criteria

April 28, 2024

Last Verified

April 1, 2024

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • GTMGS0423

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

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.

Clinical Trials on Obesity

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in Mali

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

Subscribe