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Surgical Endoscopy
Published in: Surgical Endoscopy 1/2021

01-01-2021 | Bariatric Surgery

Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database

Authors: Jacob Nudel, Andrew M. Bishara, Susanna W. L. de Geus, Prasad Patil, Jayakanth Srinivasan, Donald T. Hess, Jonathan Woodson

Published in: Surgical Endoscopy | Issue 1/2021

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Abstract

Background

Postoperative gastrointestinal leak and venous thromboembolism (VTE) are devastating complications of bariatric surgery. The performance of currently available predictive models for these complications remains wanting, while machine learning has shown promise to improve on traditional modeling approaches. The purpose of this study was to compare the ability of two machine learning strategies, artificial neural networks (ANNs), and gradient boosting machines (XGBs) to conventional models using logistic regression (LR) in predicting leak and VTE after bariatric surgery.

Methods

ANN, XGB, and LR prediction models for leak and VTE among adults undergoing initial elective weight loss surgery were trained and validated using preoperative data from 2015 to 2017 from Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program database. Data were randomly split into training, validation, and testing populations. Model performance was measured by the area under the receiver operating characteristic curve (AUC) on the testing data for each model.

Results

The study cohort contained 436,807 patients. The incidences of leak and VTE were 0.70% and 0.46%. ANN (AUC 0.75, 95% CI 0.73–0.78) was the best-performing model for predicting leak, followed by XGB (AUC 0.70, 95% CI 0.68–0.72) and then LR (AUC 0.63, 95% CI 0.61–0.65, p < 0.001 for all comparisons). In detecting VTE, ANN, and XGB, LR achieved similar AUCs of 0.65 (95% CI 0.63–0.68), 0.67 (95% CI 0.64–0.70), and 0.64 (95% CI 0.61–0.66), respectively; the performance difference between XGB and LR was statistically significant (p = 0.001).

Conclusions

ANN and XGB outperformed traditional LR in predicting leak. These results suggest that ML has the potential to improve risk stratification for bariatric surgery, especially as techniques to extract more granular data from medical records improve. Further studies investigating the merits of machine learning to improve patient selection and risk management in bariatric surgery are warranted.
Appendix
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Metadata
Title
Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database
Authors
Jacob Nudel
Andrew M. Bishara
Susanna W. L. de Geus
Prasad Patil
Jayakanth Srinivasan
Donald T. Hess
Jonathan Woodson
Publication date
01-01-2021
Publisher
Springer US
Published in
Surgical Endoscopy / Issue 1/2021
Print ISSN: 0930-2794
Electronic ISSN: 1432-2218
DOI
https://doi.org/10.1007/s00464-020-07378-x

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