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.
