Development of Machine Learning Models for the Prediction of BMI and Complications After Bariatric Surgery (CABS-Study)

Dr. Med Anas Taha10,000 enrolled

Overview

This Study aims to develop machine learning models with the ability to predict patients' BMI and complications after Bariatric Surgery (CABS-Score). This Study also aims to develop machine learning models with the ability to predict diabetic (DM II)patients' remission rate after Bariatric Surgery. The service mentioned above will be publicly available as a web-based application

Study Type

OBSERVATIONAL

Enrollment

10,000

Conditions

Body Weight Post-Op Complication Anastomosis, Leaking Hiatal Hernia GERD Internal Hernia Mesenteric Hernia Intussusception Diabetes Mellitus, Type 2

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria: * Patients undergoing bariatric surgery * Patients \>18 years Exclusion Criteria: * Patients \<18 years * Patients who cannot be followed up on for more than 6 month after surgery * Patients who are unable to provide informed approval to participate according to each centre's rules will be excluded.

Outcomes

Primary Outcomes

Complication after surgery

Time frame: [Time Frame: From index surgery up to 3 months postoperatively]

Body mass Index (KG/m2) after surgery

Time frame: [Time Frame: From index surgery up to 1 year postoperatively]

Diabetes mellitus type II remission rate postoperatively

Time frame: [Time Frame: From index surgery up to 2 year postoperatively]

Central Contacts

Anas Taha, MD

CONTACT

0041612075402 anas.taha@unibas.ch

Bassey Enodien, MD

CONTACT

Bassey.enodien@gzo.ch

Data from ClinicalTrials.gov

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