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

June 23, 2025 updated by: Sungsoo Park, Korea University Anam Hospital

Development of Elastic Net Regression-SMOTE Models to Predict Postoperative Gastroesophageal Reflux Symptom Resolution After Laparoscopic Nissen Fundoplication

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).

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

Detailed Description

In patients with gastroesophageal reflux disease (GERD) refractory to medication or those expected to require long-term medical treatment, anti-reflux surgery (ARS), including Nissen fundoplication, has been performed. GERD is usually diagnosed as esophageal mucosal damage or pathological esophageal acid exposure. However, about 35% of patients with gastroesophageal reflux symptoms do not exhibit abnormal findings on esophagogastroduodenoscopy (EGD) and esophageal pH monitoring. Meanwhile, about 10% of patients with typical GERD symptoms and 30-50% of those with atypical GERD symptoms do not experience symptom improvement even after undergoing ARS. Therefore, the importance of predicting symptom improvement after ARS and appropriately selecting surgical candidates has been increasingly emphasized.

Though previous studies have suggested several predictors-including the length of the lower esophageal sphincter (LES), resting pressure of the LES, and bolus exposure time-to predict GERD symptom resolution after ARS, no model comprehensively integrated the results of EGD, esophageal pH monitoring, and manometry.

Elastic Net regression is a machine learning method that utilizes regularized regression analysis, combining L1 (Lasso) and L2 (Ridge) penalties. This approach makes the model relatively robust against overfitting and is suitable for datasets with a small sample size, a large number of variables, and severe multicollinearity. Synthetic minority oversampling technique (SMOTE) is a method that enhances the interpretability of the minority class in a model by oversampling minority class data using the k-nearest neighbors (k-NN) algorithm. Therefore, this study aims to develop machine learning models to predict postoperative gastroesophageal reflux symptom resolution after laparoscopic Nissen fundoplication using Elastic Net regression and SMOTE.

A total of 112 patients who underwent LNF between February 2017 to February 2023 will be included in this study. Preoperative and postoperative gastroesophageal symptoms, including heartburn and regurgitation, were evaluated using the GERD Health-Related Quality of Life (GERD-HRQL) questionnaire and the Korean version of the GERD questionnaire. Postoperative symptoms were assessed at 1, 3, 6, 9, and 12 months after surgery. Patients with more than a 70% improvement in symptoms at the last follow-up will be classified as the symptom resolution group. A total of 21 models will be developed to predict the resolution of heartburn, regurgitation, or atypical symptoms using the results of manometry, 24-hour esophageal pH monitoring, or both, with seven models for each symptom. All models will also incorporate the results of EGD. Elastic Net regression and the SMOTE method will be applied to oversample the minority class and develop the model. Model performance will be validated using 5-fold cross-validation. In addition to assessing model discrimination, calibration analysis will be performed to evaluate how well the predicted probabilities align with observed outcomes. The predictive performance of conventional predictors and possible predictors, including the length of LES, resting pressure of the LES, and bolus exposure time, will be compared with the model performance of the novel model.

Study Type

Observational

Enrollment (Actual)

112

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

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

Patients who underwent laparoscopic Nissen fundoplication

Description

Inclusion Criteria:

  1. patients with age greater than 19 years
  2. patients who underwent laparoscopic Nissen fundoplication from February 2017 to February 2023
  3. patients who answered the GERD-HRQL questionnaire or the Korean version of the GERD questionnaire to assess preoperative and postoperative gastroesophageal reflux symptoms
  4. patients who underwent esophagogastroduodenoscopy before surgery
  5. patients who underwent esophageal manometry, 24-hour esophageal pH monitoring, or both before surgery

Exclusion Criteria:

  1. pregnant
  2. patients who were lost to follow-up before 3 months after 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

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Symptom resolution group
Preoperative and postoperative gastroesophageal symptoms, including heartburn and regurgitation, were evaluated using the GERD-HRQL and the Korean version of the GERD questionnaire. Postoperative symptoms were assessed at 1, 3, 6, 9, and 12 months after laparoscopic Nissen fundoplication. Patients with more than a 70% improvement in symptoms at the last follow-up will be classified as the symptom resolution group.
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.
Symptom non-resolution group
Preoperative and postoperative gastroesophageal symptoms, including heartburn and regurgitation, were evaluated using the GERD-HRQL and the Korean version of the GERD questionnaire. Postoperative symptoms were assessed at 1, 3, 6, 9, and 12 months after laparoscopic Nissen fundoplication. Patients with less than a 70% improvement in symptoms at the last follow-up will be classified as the symptom non-resolution group.
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Model performance of novel models
Time Frame: Symptoms were assessed before surgery and at 1, 3, 6, 9, and 12 months after surgery
A total of 21 models will be developed to predict the resolution of heartburn, regurgitation, or atypical symptoms using the results of manometry, 24-hour esophageal pH monitoring, or both, with seven models for each symptom. All models will also incorporate the results of EGD. Elastic Net regression and the SMOTE method will be applied to oversample the minority class and develop the model. Model performance including AUC, sensitivity (or recall), specificity, accuracy, precision, and F1 score will be validated using 5-fold cross-validation.
Symptoms were assessed before surgery and at 1, 3, 6, 9, and 12 months after surgery

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Results from calibration analysis of novel models
Time Frame: Symptoms were assessed before surgery and at 1, 3, 6, 9, and 12 months after surgery
A total of 21 models will be developed to predict the resolution of heartburn, regurgitation, or atypical symptoms using the results of manometry, 24-hour esophageal pH monitoring, or both, with seven models for each symptom. All models will also incorporate the results of EGD. Elastic Net regression and the SMOTE method will be applied to oversample the minority class and develop the model. Calibration analysis will be performed to evaluate how well the predicted probabilities align with observed outcomes.
Symptoms were assessed before surgery and at 1, 3, 6, 9, and 12 months after surgery

Other Outcome Measures

Outcome Measure
Measure Description
Time Frame
Predictive performance of conventional predictors and possible predictors
Time Frame: Symptoms were assessed before surgery and at 1, 3, 6, 9, and 12 months after surgery
A total of 21 models will be developed to predict the resolution of heartburn, regurgitation, or atypical symptoms using the results of manometry, 24-hour esophageal pH monitoring, or both, with seven models for each symptom. All models will also incorporate the results of EGD. Elastic Net regression and the SMOTE method will be applied to oversample the minority class and develop the model. Model performance will be validated using 5-fold cross-validation. The predictive performance of conventional predictors and possible predictors, including the length of LES, resting pressure of the LES, and bolus exposure time, will be compared with the model performance of the novel model.
Symptoms were assessed before surgery and at 1, 3, 6, 9, and 12 months after surgery

Collaborators and Investigators

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

Sponsor

Korea University Anam Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

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 (Actual)

February 1, 2017

Primary Completion (Actual)

February 28, 2024

Study Completion (Estimated)

August 31, 2025

Study Registration Dates

First Submitted

March 2, 2025

First Submitted That Met QC Criteria

March 2, 2025

First Posted (Actual)

March 6, 2025

Study Record Updates

Last Update Posted (Actual)

June 26, 2025

Last Update Submitted That Met QC Criteria

June 23, 2025

Last Verified

February 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • LNF-ML-D

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

According to private information law, the IPD generated and/or analyzed for this study will not be shared.

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.

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