Development and Validation of an Artificial Intelligence-Based Model to Predict Gastroesophageal Reflux Disease After Sleeve Gastrectomy

Sameh Hany Emile, Waleed Ghareeb, Hossam Elfeki, Mohamed El Sorogy, Amgad Fouad, Mohamed Elrefai, Sameh Hany Emile, Waleed Ghareeb, Hossam Elfeki, Mohamed El Sorogy, Amgad Fouad, Mohamed Elrefai

Abstract

Purpose: Prediction of the onset of de novo gastroesophageal reflux disease (GERD) after sleeve gastrectomy (SG) would be helpful in decision-making and selection of the optimal bariatric procedure for every patient. The present study aimed to develop an artificial intelligence (AI)-based model to predict the onset of GERD after SG to help clinicians and surgeons in decision-making.

Materials and methods: A prospectively maintained database of patients with severe obesity who underwent SG was used for the development of the AI model using all the available data points. The dataset was arbitrarily split into two parts: 70% for training and 30% for testing. Then ranking of the variables was performed in two steps. Different learning algorithms were used, and the best model that showed maximum performance was selected for the further steps of machine learning. A multitask AI platform was used to determine the cutoff points for the top numerical predictors of GERD.

Results: In total, 441 patients (76.2% female) of a mean age of 43.7 ± 10 years were included. The ensemble model outperformed the other models. The model achieved an AUC of 0.93 (95%CI 0.88-0.99), sensitivity of 79.2% (95% CI 57.9-92.9%), and specificity of 86.1% (95%CI 70.5-95.3%). The top five ranked predictors were age, weight, preoperative GERD, size of orogastric tube, and distance of first stapler firing from the pylorus.

Conclusion: An AI-based model for the prediction of GERD after SG was developed. The model had excellent accuracy, yet a moderate sensitivity and specificity. Further prospective multicenter trials are needed to externally validate the model developed.

Keywords: Artificial intelligence; Gastroesophageal reflux disease; Model; Predict; Sleeve gastrectomy.

Conflict of interest statement

The authors declare no competing interests.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
A Ranking predictors by importance; B comparing predictor importance estimates
Fig. 2
Fig. 2
Diagnostic accuracy parameters of the AI model developed
Fig. 3
Fig. 3
Accuracy of different models used to determine which orogastric tube size and distance from the pylorus are associated with the greatest risk of gastroesophageal reflux disease after sleeve gastrectomy
Fig. 4
Fig. 4
Visual summary of the process of predictor selection and development of the model

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