Key result
An artificial neural network model predicted 30-day composite morbidity after conversion to Roux-en-Y gastric bypass with an AUC of 0.604, compared to 0.587 for multivariable regression.
Why the study?
This study aimed to use a comprehensive database to define predictors of 30-day reoperation, readmission, reintervention, or mortality after conversion of sleeve gastrectomy to Roux-en-Y gastric bypass, employing an artificial neural network to optimize prediction.
Does an artificial neural network improve the prediction of 30-day morbidity and mortality compared to multivariable regression in patients undergoing conversion of sleeve gastrectomy to Roux-en-Y gastric bypass?
Cohort (n=8,895)
Yes
Does an artificial neural network improve the prediction of 30-day morbidity and mortality compared to multivariable regression in patients undergoing conversion of sleeve gastrectomy to Roux-en-Y gastric bypass?
An artificial neural network model demonstrated modest predictive ability for 30-day morbidity after conversion of sleeve gastrectomy to Roux-en-Y gastric bypass, slightly outperforming standard multivariable regression.
Modest neural network performance precludes clinical adoption for morbidity prediction after SG-to-RYGB conversion; leaves open need for larger validation studies.
Background Conversion of sleeve gastrectomy to Roux-en-Y gastric bypass is indicated primarily for unsatisfactory weight loss or gastroesophageal reflux disease (GERD). This study aimed to use a comprehensive database to define predictors of 30-day reoperation, readmission, reintervention, or mortality. An artificial neural network (ANN) was employed to optimize prediction of the composite endpoint (occurrence of 1+ morbid event). Methods Areview of 8895 patients who underwent conversion for weight-related or GERD-related indications was performed using the 2021 MBSAQIP national dataset. Demographics, comorbidities, laboratory values, and other factors were assessed for bivariate and subsequent multivariable associations with the composite endpoint ( P ≤ .05). Factors considered in the multivariable model were imputed into a three-node ANN with 20% randomly withheld for internal validation, to optimize predictive accuracy. Models were compared using receiver operating characteristic (ROC) curve analysis. Results 39% underwent conversion for weight considerations and 61% for GERD. Rates of 30-day reoperation, readmission, reintervention, mortality, and the composite endpoint were 3.0%, 7.1%, 2.1%, .1%, and 9.1%, respectively. Of the nine factors associated with the composite endpoint on bivariate analysis, only non-white race ( P < .001; odds ratio 1.4), lower body-mass index ( P < .001; odds ratio .22), and therapeutic anticoagulation ( P = .001; odds ratio 2.0) remained significant upon multivariable analysis. Areas under ROC curves for the multivariable regression, ANN training, and validation sets were .587, .601, and .604, respectively. Discussion Identification of risk factors for morbidity after conversion offers critical information to improve patient selection and manage postoperative expectations. ANN models, with appropriate clinical integration, may optimize prediction of morbidity.
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Scott et al. (2024) conducted a cohort in Conversion of sleeve gastrectomy to Roux-en-Y gastric bypass (n=8,895). Artificial neural network (ANN) model vs. Multivariable regression model was evaluated on Area under the ROC curve (AUC) for predicting the 30-day composite endpoint (occurrence of 1+ morbid event). An artificial neural network model predicted 30-day composite morbidity after conversion to Roux-en-Y gastric bypass with an AUC of 0.604, compared to 0.587 for multivariable regression.
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