Active — Not RecruitingNot Applicable

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

South Korea112 participantsStarted 2017-02-01

Plain-language 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).

Who can participate

Age range20 Years

SexALL

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Inclusion criteria

✓. 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

Exclusion criteria

✕. pregnant
✕. patients who were lost to follow-up before 3 months after surgery

What they're measuring

Model performance of novel models

Timeframe: Symptoms were assessed before surgery and at 1, 3, 6, 9, and 12 months after surgery

Trial details

NCT IDNCT06862037

SponsorKorea University Anam Hospital

Sponsor typeOTHER

Study typeOBSERVATIONAL

Primary completion2024-02-28

View on ClinicalTrials.gov More Gastroesophageal Reflux Disease (GERD) trials