Gastroesophageal Reflux Disease (GERD)NCT06862037
Development of Machine Learning Models to Predict Postoperative GERD Symptom Resolution After Laparoscopic Nissen Fundoplication
An observational study of Gastroesophageal Reflux Disease (GERD) and Gastroesophageal Reflux (GER), run by Korea University Anam Hospital at 1 site in South Korea.
Trial 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). Read more about this study
- Who is it for?
- Ages 20 years and older
- Conditions the study is for
- Gastroesophageal Reflux Disease (GERD), Gastroesophageal Reflux (GER)
- What is being tested?
- Laparoscopic Nissen fundoplication
- Type of treatment
- Surgery or procedure
- How many people
- 112 participants
- Where
- 1 siteSouth Korea
- Study dates
- 1 Feb 2017to 31 Aug 2025
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 20 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients who underwent laparoscopic Nissen fundoplication
Who may be able to join
- patients with age greater than 19 years
- patients who underwent laparoscopic Nissen fundoplication from February 2017 to February 2023
- patients who answered the GERD-HRQL questionnaire or the Korean version of the GERD questionnaire to assess preoperative and postoperative gastroesophageal reflux symptoms
- patients who underwent esophagogastroduodenoscopy before surgery
- patients who underwent esophageal manometry, 24-hour esophageal pH monitoring, or both before surgery
Who cannot join
- pregnant
- patients who were lost to follow-up before 3 months after surgery
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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).
Read the 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.
- Being tested
- Laparoscopic Nissen fundoplication
- Keywords
- Gastroesophageal reflux disease, Laparoscopic Nissen fundoplication, Machine learning, Elastic Net regression, SMOTE, endoscopy, esophagogastroduodenoscopy, esophageal pH monitoring, manometry
- Medical subject headings
- Gastroesophageal Reflux
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Laparoscopic Nissen fundoplicationProcedure or surgery
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Procedure: Laparoscopic Nissen fundoplication | |
| 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. Receives: Procedure: Laparoscopic Nissen fundoplication |
What is being measured
The primary outcome is the main question the study is designed to answer.
Model performance of novel models
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.
Measured: Symptoms were assessed before surgery and at 1, 3, 6, 9, and 12 months after surgery
Secondary outcomes (1)
Results from calibration analysis of novel models
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.
Measured: Symptoms were assessed before surgery and at 1, 3, 6, 9, and 12 months after surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Mar 2025
- Study start1 Feb 2017
- Primary completion28 Feb 2024
- Study completion31 Aug 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Gastroesophageal Reflux Disease (GERD)Studies 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
- Retrospective
- Enrolment
- 112 participants (actual)
Sponsor and investigators
- Lead sponsor
- Korea University Anam Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Sungsoo Park, Korea University Anam Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06862037
- Sponsor's protocol ID
- LNF-ML-D
- Status last verified
- Feb 2025
- First submitted
- 2 Mar 2025
- Last updated
- 26 Jun 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: According to private information law, the IPD generated and/or analyzed for this study will not be shared.
Publications
Tay JK, Narasimhan B, Hastie T. Elastic Net Regularization Paths for All Generalized Linear Models. J Stat Softw. 2023;106:1. doi: 10.18637/jss.v106.i01. Epub 2023 Mar 23.
PubMed 37138589Park S, Park SH, Kim MS, Kwak J, Lee I, Kwon Y, Lee CM, Choi HS, Keum B, Yang KS, Park JM, Park S. Exploring objective factors to predict successful outcomes after laparoscopic Nissen fundoplication. Int J Surg. 2023 May 1;109(5):1239-1248. doi: 10.1097/JS9.0000000000000274.
PubMed 37026848Jung SW, Lee I, Lee I, Kim JW, Alromi A, Seo WJ, Park SH, Kwon Y, Jang YJ, Lee CM, Kim JH, Park JM, Park S. Bolus exposure as a novel predictor of postoperative symptom resolution after laparoscopic Nissen fundoplication: a two-institutional retrospective cohort study. Int J Surg. 2024 Dec 1;110(12):7919-7928. doi: 10.1097/JS9.0000000000002124.
PubMed 39453844Farrell TM, Richardson WS, Trus TL, Smith CD, Hunter JG. Response of atypical symptoms of gastro-oesophageal reflux to antireflux surgery. Br J Surg. 2001 Dec;88(12):1649-52. doi: 10.1046/j.0007-1323.2001.01949.x.
PubMed 11736981Yamasaki T, Fass R. Reflux Hypersensitivity: A New Functional Esophageal Disorder. J Neurogastroenterol Motil. 2017 Oct 30;23(4):495-503. doi: 10.5056/jnm17097.
PubMed 28992673Zhang D, Liu S, Li Z, Wang R. Global, regional and national burden of gastroesophageal reflux disease, 1990-2019: update from the GBD 2019 study. Ann Med. 2022 Dec;54(1):1372-1384. doi: 10.1080/07853890.2022.2074535.
PubMed 35579516
Study at a glance
From the registry record, last updated by the sponsor on 26 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of Elastic Net Regression-SMOTE Models to Predict Postoperative Gastroesophageal Reflux Symptom Resolution After Laparoscopic Nissen Fundoplication
- ClinicalTrials.gov ID
- NCT06862037
- Acronym / sponsor ID
- LNF-ML-D
- Keywords
- Gastroesophageal reflux disease, Laparoscopic Nissen fundoplication, Machine learning, Elastic Net regression, SMOTE, endoscopy, esophagogastroduodenoscopy, esophageal pH monitoring
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 112 participants (actual)
- Lead sponsor
- Korea University Anam HospitalUniversity, hospital or other
- Study start
- 1 Feb 2017 (actual)
- Primary completion
- 28 Feb 2024 (actual)
- Study completion
- 31 Aug 2025 (estimated)
- First posted
- 6 Mar 2025 (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 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).
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Symptom resolution group | – | Procedure: Laparoscopic Nissen fundoplicationPreoperative 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. |
| Symptom non-resolution group | – | Procedure: Laparoscopic Nissen fundoplicationPreoperative 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. |
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients who underwent laparoscopic Nissen fundoplication
Key inclusion criteria (5 listed)
- patients with age greater than 19 years
- patients who underwent laparoscopic Nissen fundoplication from February 2017 to February 2023
- patients who answered the GERD-HRQL questionnaire or the Korean version of the GERD questionnaire to assess preoperative and postoperative gastroesophageal reflux symptoms
- patients who underwent esophagogastroduodenoscopy before surgery
- patients who underwent esophageal manometry, 24-hour esophageal pH monitoring, or both before surgery
Key exclusion criteria (2 listed)
- pregnant
- patients who were lost to follow-up before 3 months after surgery
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Laparoscopic Nissen fundoplicationLaparoscopic 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. | Procedure or surgery | Symptom non-resolution group, Symptom resolution group |
Primary outcome
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.
1 secondary outcome
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Jun 2025.
- Results publication: PubMed 37138589Tay JK, Narasimhan B, Hastie T. Elastic Net Regularization Paths for All Generalized Linear Models. J Stat Softw. 2023;106:1. doi: 10.18637/jss.v106.i01. Epub 2023 Mar 23.
- Results publication: PubMed 37026848Park S, Park SH, Kim MS, Kwak J, Lee I, Kwon Y, Lee CM, Choi HS, Keum B, Yang KS, Park JM, Park S. Exploring objective factors to predict successful outcomes after laparoscopic Nissen fundoplication. Int J Surg. 2023 May 1;109(5):1239-1248. doi: 10.1097/JS9.0000000000000274.
- Results publication: PubMed 39453844Jung SW, Lee I, Lee I, Kim JW, Alromi A, Seo WJ, Park SH, Kwon Y, Jang YJ, Lee CM, Kim JH, Park JM, Park S. Bolus exposure as a novel predictor of postoperative symptom resolution after laparoscopic Nissen fundoplication: a two-institutional retrospective cohort study. Int J Surg. 2024 Dec 1;110(12):7919-7928. doi: 10.1097/JS9.0000000000002124.
- Results publication: PubMed 11736981Farrell TM, Richardson WS, Trus TL, Smith CD, Hunter JG. Response of atypical symptoms of gastro-oesophageal reflux to antireflux surgery. Br J Surg. 2001 Dec;88(12):1649-52. doi: 10.1046/j.0007-1323.2001.01949.x.
- Results publication: PubMed 28992673Yamasaki T, Fass R. Reflux Hypersensitivity: A New Functional Esophageal Disorder. J Neurogastroenterol Motil. 2017 Oct 30;23(4):495-503. doi: 10.5056/jnm17097.
- Results publication: PubMed 35579516Zhang D, Liu S, Li Z, Wang R. Global, regional and national burden of gastroesophageal reflux disease, 1990-2019: update from the GBD 2019 study. Ann Med. 2022 Dec;54(1):1372-1384. doi: 10.1080/07853890.2022.2074535.
- Individual participant dataSharing planned: No. According to private information law, the IPD generated and/or analyzed for this study will not be shared.
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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 Gastroesophageal Reflux Disease (GERD) and Gastroesophageal Reflux (GER), run by Korea University Anam Hospital at 1 site in South Korea.
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 2 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: under way, not looking for participants, as of the sponsor's last update on 26 Jun 2025. 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 South Korea. 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?
This study is still under way. Results are not available in the source record yet.
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?
Model performance of novel models (time frame: Symptoms were assessed before surgery and at 1, 3, 6, 9, and 12 months after surgery). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06862037, which also lists 6 linked publications.
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 26 Jun 2025.