Machine learning prediction of major adverse cardiac events after elective bariatric surgery
Gustavo Romero-Velez1, Jerry Dang2, Juan S Barajas-Gamboa3
1Endocrine and Metabolism Institute, Cleveland Clinic, Cleveland, OH, USA.
Background:
Machine learning (ML) is an emerging technology with the potential to predict and improve clinical outcomes including adverse events, based on complex pattern recognition. Major adverse cardiac events (MACE) after bariatric surgery have an incidence of 0.1% but carry significant morbidity and mortality. Prior studies have investigated these events using traditional statistical methods, however, studies reporting ML for MACE prediction in bariatric surgery remain limited. As such, the objective of this study was to evaluate and compare MACE prediction models in bariatric surgery using traditional statistical methods and ML.
Methods:
Cross-sectional study of the MBSAQIP database, from 2015 to 2019. A binary-outcome MACE prediction model was generated using three different modeling methods: (1) main-effects-only logistic regression, (2) neural network with a single hidden layer, and (3) XGBoost model with a max depth of 3. The same set of predictor variables and random split of the total data (50/50) were used to train and validate each model. Overall performance was compared based on the area under the receiver operating curve (AUC).
Results:
A total of 755,506 patients were included, of which 0.1% experienced MACE. Of the total sample, 79.6% were female, 47.8% had hypertension, 26.2% had diabetes, 23.7% had hyperlipidemia, 8.4% used tobacco within 1 year, 1.9% had previous percutaneous cardiac intervention, 1.2% had a history of myocardial infarction, 1.1% had previous cardiac surgery, and 0.6% had renal insufficiency. The AUC for the three different MACE prediction models was: 0.790 for logistic regression, 0.798 for neural network and 0.787 for XGBoost. While the AUC implies similar discriminant function, the risk prediction histogram for the neural network shifted in a smoother fashion.
Conclusion:
The ML models developed achieved good discriminant function in predicting MACE. ML can help clinicians with patient selection and identify individuals who may be at elevated risk for MACE after bariatric surgery.
