Abstract
Previously published literature has identified a few predictors of health-related quality of life (HRQoL) after bariatric surgery. However, performance of the predictive models was not evaluated rigorously using real world data. To find better methods for predicting prognosis in patients after bariatric surgery, we examined performance of the Bayesian networks (BN) method in predicting long-term postoperative HRQoL and compared it with the convolution neural network (CNN) and multivariable logistic regression (MLR). The patients registered in the Scandinavian Obesity Surgery Registry (SOReg) were used for the current study. In total, 6542 patients registered in the SOReg between 2008 and 2012 with complete demographic and preoperative comorbidity information, and preoperative and postoperative 5-year HROoL scores and comorbidities were included in the study. HRQoL was measured using the RAND-SF-36 and the obesity-related problems scale. Thirty-five variables were used for analyses, including 19 predictors and 16 outcome variables. The Gaussian BN (GBN), CNN, and a traditional linear regression model were used for predicting 5-year HRQoL scores, and multinomial discrete BN (DBN) and MLR were used for 5-year comorbidities. Eighty percent of the patients were randomly selected as a training dataset and 20% as a validation dataset. The GBN presented a better performance than the CNN and the linear regression model; it had smaller mean squared errors (MSEs) than those from the CNN and the linear regression model. The MSE of the summary physical scale was only 0.0196 for GBN compared to the 0.0333 seen in the CNN. The DBN showed excellent predictive ability for 5-year type 2 diabetes and dyslipidemia (area under curve (AUC) = 0.942 and 0.917, respectively), good ability for 5-year hypertension and sleep apnea syndrome (AUC = 0.891 and 0.834, respectively), and fair ability for 5-year depression (AUC = 0.750). Bayesian networks provide useful tools for predicting long-term HRQoL and comorbidities in patients after bariatric surgery. The hybrid network that may involve variables from different probability distribution families deserves investigation in the future.
Highlights
Over the past two decades, obesity has been continuously increasing worldwide, which has become a major health issue worldwide and raised public concern across the globe [1]
We have examined the performance of the convolution neural network (CNN) for predicting 5-year health-related quality of life (HRQoL) after bariatric surgery based on the available preoperative information from the Scandinavian Obesity Surgery Registry (SOReg) [15]
In the two recently published studies, using the same database, we found that patients with postoperative complications had significantly less improvements in all aspects of HRQoL compared to those without any form of postoperative complication [16], and the ability of multilayer perceptron and CNN for predicting the postoperative serious complications after bariatric surgery is limited [17]
Summary
Over the past two decades, obesity has been continuously increasing worldwide, which has become a major health issue worldwide and raised public concern across the globe [1]. Gastric bypass and other weight-loss surgeries, known collectively as bariatric surgery, are currently considered the most effective treatment options for morbid obesity to help severe obese patients to lose excess weight and reduce potentially life-threatening risk of weight-related health problems, such as heart disease and stroke, hypertension, T2D, nonalcoholic fatty liver disease, and sleep apnea [5,6]. Published literature has identified a few predictors of HRQoL after bariatric surgery, including baseline demographic data and depression severity score [11,12,13,14]. None of these studies evaluated the models’ performances or the predictors’ predictive abilities rigorously using real world data. In the two recently published studies, using the same database, we found that patients with postoperative complications had significantly less improvements in all aspects of HRQoL compared to those without any form of postoperative complication [16], and the ability of multilayer perceptron and CNN for predicting the postoperative serious complications after bariatric surgery is limited [17]
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