Clinical trial NCT06862037: Development of Machine Learning Models to Predict Postoperative GERD Symptom Resolution After Laparoscopic Nissen Fundoplication
Official title: Development of Elastic Net Regression-SMOTE Models to Predict Postoperative Gastroesophageal Reflux Symptom Resolution After Laparoscopic Nissen Fundoplication
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Gastroesophageal Reflux Disease (GERD)
Gastroesophageal Reflux (GER)
Lapaparoscopic Nissen Fundoplication
Keywords: Gastroesophageal reflux disease
Laparoscopic Nissen fundoplication
Machine learning
Elastic Net regression
SMOTE
endoscopy
esophagogastroduodenoscopy
esophageal pH monitoring
manometry
Lead sponsor: Korea University Anam Hospital
Sponsor class: OTHER
First posted: 2025-03-06
Enrollment: 112
Sex: ALL
Minimum age (years): 20
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients who underwent laparoscopic Nissen fundoplication

Brief 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).

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.

Eligibility criteria: 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

Primary outcomes:
measure: Model performance of novel models
timeFrame: Symptoms were assessed before surgery and at 1, 3, 6, 9, and 12 months after surgery
Secondary outcomes:
measure: Results from calibration analysis of novel models
timeFrame: Symptoms were assessed before surgery and at 1, 3, 6, 9, and 12 months after surgery
Other outcomes:
measure: Predictive performance of conventional predictors and possible predictors
timeFrame: Symptoms were assessed before surgery and at 1, 3, 6, 9, and 12 months after surgery
Locations:
city: Seoul
country: Korea, Republic of
facility: Korea University Anam Hospital
Study officials:
References:
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citation: Park 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.
citation: Jung 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.
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