Prediction of successful XXXX after laparoscopic adjustable gastric banding.

@entity1 @entity2313 @entity353 @entity28 @entity73095 @entity2690 @entity31814
BACKGROUND/AIM: Compared with conventional pharmacological therapies, bariatric surgery has been shown to cause greater and- sustained @entity353 . It was aimed to evaluate @entity353 in @entity28 @entity1 after laparoscopic adjustable gastric banding surgery using information typically available during the initial evaluation studied before bariatric surgery and genes. METHODOLOGY: 74 @entity1 undergoing laparoscopic adjustable gastric banding (LAGB) were enrolled. Artificial Neural Network technology was used to predict @entity353 . RESULTS: We studied 74 @entity1 consisting of 22 @entity1 and 52 @entity1 2 years after operation. Mean age was 31.7 +/- 9.1 years. 27 (36.5%) @entity1 had successful @entity353 (excess @entity353 >50%) while 47 (63.5%) did not. ANN provided predicted factors on gender, @entity2690 , @entity2313 and two genes: re4684846_r, @entity31814 _r which were associated with success. CONCLUSION: Artificial neural network is a better modeling technique and the predictive accuracy is higher on the basis of multiple variables related to laboratory tests. Our finding gave demonstrated result that @entity28 @entity1 of successful @entity353 after laparoscopic adjustable gastric banding surgery were @entity1 , having little lower @entity2690 and @entity2313 , and carrying GG genotype on @entity73095 and with at least one T allele on @entity31814 . In these cases, @entity353 will give better results.
