Long-term follow-up after GBP for morbid obesity showed better scores in most aspects of HRQoL compared to obese controls but did not achieve the levels of the general population. Patients with better medical outcome after gastric bypass operation had better HRQoL.
No significant differences were observed in objectively measured changes in PA or time spent sedentary from 3 months pre-surgery to 9 months postsurgery among women undergoing RYGB. However, women with higher pre-surgery PA decreased their PA postsurgery while women with lower pre-surgery PA increased their PA.
LRYGB is an efficient method for sustained long-term body weight loss. There is, however, a concomitant decrease in BMD and S-calcium, and an increase in fP-PTH.
Severe obesity has been associated with numerous comorbidities and reduced health-related quality of life (HRQoL). Although many studies have reported changes in HRQoL after bariatric surgery, few were long-term prospective studies. We examined the performance of the convolution neural network (CNN) for predicting 5-year HRQoL after bariatric surgery based on the available preoperative information from the Scandinavian Obesity Surgery Registry (SOReg). CNN was used to predict the 5-year HRQoL after bariatric surgery in a training dataset and evaluated in a test dataset. In general, performance of the CNN model (measured as mean squared error, MSE) increased with more convolution layer filters, computation units, and epochs, and decreased with a larger batch size. The CNN model showed an overwhelming advantage in predicting all the HRQoL measures. The MSEs of the CNN model for training data were 8% to 80% smaller than those of the linear regression model. When the models were evaluated using the test data, the CNN model performed better than the linear regression model. However, the issue of overfitting was apparent in the CNN model. We concluded that the performance of the CNN is better than the traditional multivariate linear regression model in predicting long-term HRQoL after bariatric surgery; however, the overfitting issue needs to be mitigated using more features or more patients to train the model.
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