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Development of Machine Learning Models to Predict Postoperative GERD Symptom Resolution After Laparoscopic Nissen Fundoplication

NCT06862037 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 112

Last updated 2025-06-26

No results posted yet for this study

Summary

This study aims to develop machine learning models to predict postoperative gastroesophageal reflux symptom resolution after laparoscopic Nissen fundoplication using Elastic Net regression and synthetic minority oversampling technique (SMOTE).

Conditions

  • Gastroesophageal Reflux Disease (GERD)
  • Gastroesophageal Reflux (GER)

Interventions

PROCEDURE

Laparoscopic Nissen fundoplication

Laparoscopic Nissen fundoplication (LNF) is the most commonly performed anti-reflux surgery. LNF is performed in patients with GERD refractory to medication or those expected to require long-term medical treatment. During LNF, the fundus of the stomach is mobilized and wrapped 360 degrees around the lower esophagus to reinforce the lower esophageal sphincter (LES), preventing the reflux of gastric contents into the esophagus.

Sponsors & Collaborators

  • Korea University Anam Hospital

    lead OTHER

Eligibility

Min Age
20 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2017-02-01
Primary Completion
2024-02-28
Completion
2025-08-31

Countries

  • South Korea

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06862037 on ClinicalTrials.gov