Prediction of successful weight reduction after laparoscopic adjustable gastric banding.
OBJECTIVE
Compared with conventional pharmacological therapies, bariatric surgery has been shown to cause greater and- sustained weight loss. It was aimed to evaluate weight loss in obese patients after laparoscopic adjustable gastric banding surgery using information typically available during the initial evaluation studied before bariatric surgery and genes.


METHODS
74 patients undergoing laparoscopic adjustable gastric banding (LAGB) were enrolled. Artificial Neural Network technology was used to predict weight loss.


RESULTS
We studied 74 patients consisting of 22 men and 52 women 2 years after operation. Mean age was 31.7 +/- 9.1 years. 27 (36.5%) patients had successful weight reduction (excess weight loss >50%) while 47 (63.5%) did not. ANN provided predicted factors on gender, insulin, albumin and two genes: re4684846_r, rs660339_r which were associated with success.


CONCLUSIONS
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 obese patients of successful weight reduction after laparoscopic adjustable gastric banding surgery were women, having little lower insulin and albumin, and carrying GG genotype on rs4684846 and with at least one T allele on rs660339. In these cases, weight loss will give better results.
Lee
Yi-Chih
YC
Department of International Business, Ching Yun University, Taiwan. lyc6115@ms61.hinet.net
Liew
Phui-Ly
PL
Lee
Wei-Jei
WJ
Lin
Yang-Chu
YC
Lee
Chia Ko
CK
Huangs
Ming-Te
MT
Wang
Weu
W
Lin
Steven C H
SC
D000328:Adult; D005260:Female; D015391:Gastroplasty / Q000379:methods*; D006801:Humans; D010535:Laparoscopy; D008297:Male; D016571:Neural Networks, Computer; D009767:Obesity, Morbid / Q000601:surgery*; D011237:Predictive Value of Tests; D016896:Treatment Outcome; D015431:Weight Loss