Endoscopic sleeve gastroplasty reintervention score using supervised machine learning

Hassam Ali; Pratik Patel; Talia Farrukh Malik; Rahul Pamarthy; Babu P Mohan; Ravishankar Asokkumar; Gontrand Lopez-Nava; Douglas G Adler

doi:10.1016/j.gie.2023.05.059

Endoscopic sleeve gastroplasty reintervention score using supervised machine learning

Gastrointest Endosc. 2023 Nov;98(5):747-754.e5. doi: 10.1016/j.gie.2023.05.059. Epub 2023 May 31.

Authors

Hassam Ali¹, Pratik Patel², Talia Farrukh Malik³, Rahul Pamarthy¹, Babu P Mohan⁴, Ravishankar Asokkumar⁵, Gontrand Lopez-Nava⁶, Douglas G Adler⁷

Affiliations

¹ Department of Internal Medicine, East Carolina University/Brody School of Medicine, Greenville, North Carolina, USA.
² Department of Gastroenterology, Mather Hospital/Hofstra University Zucker School of Medicine, Port Jefferson, New York, USA.
³ Department of Internal Medicine, Chicago Medical School at Rosalind Franklin University of Medicine and Science, Chicago, Illinois, USA.
⁴ Gastroenterology & Hepatology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
⁵ Gastroenterology & Hepatology, Singapore General Hospital, Duke National University, Singapore.
⁶ Bariatric Endoscopy, Hospital Universitario Madrid Sanchinarro, Madrid, Spain.
⁷ Center for Advanced Therapeutic Endoscopy, Centura Health, Porter Adventist Hospital, Denver, Colorado, USA.

PMID: 37263362
DOI: 10.1016/j.gie.2023.05.059

Abstract

Background and aims: Reintervention after endoscopic sleeve gastroplasty (ESG) can be indicated because of postprocedural adverse events from various preinterventional or postprocedural comorbidities. We developed and internally validated an ESG reintervention score (ESG-RS) that determines the individualized risk of reintervention within the first 30 days after ESG.

Methods: We used data from a sample of 3583 patients who underwent ESG in the Metabolic and Bariatric Surgery Accreditation Quality Improvement Program database (2016-2021). The least absolute shrinkage and selection operator (LASSO)-penalized regression was used to select the most promising predictors of reintervention after ESG within 30 days. The predictive variables extracted by LASSO regression were entered into multivariate analysis to generate an ESG-RS by using the coefficients of the statistically significant variables. The model performance was assessed using receiver-operator curves by 10-fold cross-validation.

Results: Eleven variables were selected by LASSO regression and used in the final multivariate analysis. The ESG-RS was inferred using 5 factors (history of previous foregut surgery, preoperative anticoagulation use, female gender, American Society of Anesthesiologists class ≥II, and hypertension) weighted by their regression coefficients in the multivariable logistic regression model. The area under the curve of the ESG-RS was .74 (95% confidence interval, .70-.78). For the ESG-RS, the optimal cutpoint was 67.9 (high risk vs low risk), with a sensitivity of .76 and specificity of .71.

Conclusions: The ESG-RS aids clinicians in preoperative risk stratification of patients undergoing ESG while clarifying factors contributing to a higher risk of reintervention.