BACKGROUND: Obesity is unanimously regarded as a global epidemic and a major contributing factor to the development of many common illnesses. Laparoscopic Adjustable Gastric Banding (LAGB) is one of the most popular surgical approaches worldwide. Yet, substantial variability in the results and significant rate of failure can be expected, and it is still debated which categories of patients are better suited to this type of bariatric procedure. The aim of this study was to build a statistical model based on both psychological and physical data to predict weight loss in obese patients treated by LAGB, and to provide a valuable instrument for the selection of patients that may benefit from this procedure. METHODOLOGY/PRINCIPAL FINDINGS: The study population consisted of 172 obese women, with a mean ± SD presurgical and postsurgical Body Mass Index (BMI) of 42.5 ± 5.1 and 32.4 ± 4.8 kg/m(2), respectively. Subjects were administered the comprehensive test of psychopathology Minnesota Multiphasic Personality Inventory-2 (MMPI-2). Main goal of the study was to use presurgical data to predict individual therapeutical outcome in terms of Excess Weight Loss (EWL) after 2 years. Multiple linear regression analysis using the MMPI-2 scores, BMI and age was performed to determine the variables that best predicted the EWL. Based on the selected variables including age, and 3 psychometric scales, Artificial Neural Networks (ANNs) were employed to improve the goodness of prediction. Linear and non linear models were compared in their classification and prediction tasks: non linear model resulted to be better at data fitting (36% vs. 10% variance explained, respectively) and provided more reliable parameters for accuracy and mis-classification rates (70% and 30% vs. 66% and 34%, respectively). CONCLUSIONS/SIGNIFICANCE: ANN models can be successfully applied for prediction of weight loss in obese women treated by LAGB. This approach may constitute a valuable tool for selection of the best candidates for surgery, taking advantage of an integrated multidisciplinary approach.
BACKGROUND: Obesity is unanimously regarded as a global epidemic and a major contributing factor to the development of many common illnesses. Laparoscopic Adjustable Gastric Banding (LAGB) is one of the most popular surgical approaches worldwide. Yet, substantial variability in the results and significant rate of failure can be expected, and it is still debated which categories of patients are better suited to this type of bariatric procedure. The aim of this study was to build a statistical model based on both psychological and physical data to predict weight loss in obese patients treated by LAGB, and to provide a valuable instrument for the selection of patients that may benefit from this procedure. METHODOLOGY/PRINCIPAL FINDINGS: The study population consisted of 172 obese women, with a mean ± SD presurgical and postsurgical Body Mass Index (BMI) of 42.5 ± 5.1 and 32.4 ± 4.8 kg/m(2), respectively. Subjects were administered the comprehensive test of psychopathology Minnesota Multiphasic Personality Inventory-2 (MMPI-2). Main goal of the study was to use presurgical data to predict individual therapeutical outcome in terms of Excess Weight Loss (EWL) after 2 years. Multiple linear regression analysis using the MMPI-2 scores, BMI and age was performed to determine the variables that best predicted the EWL. Based on the selected variables including age, and 3 psychometric scales, Artificial Neural Networks (ANNs) were employed to improve the goodness of prediction. Linear and non linear models were compared in their classification and prediction tasks: non linear model resulted to be better at data fitting (36% vs. 10% variance explained, respectively) and provided more reliable parameters for accuracy and mis-classification rates (70% and 30% vs. 66% and 34%, respectively). CONCLUSIONS/SIGNIFICANCE: ANN models can be successfully applied for prediction of weight loss in obese women treated by LAGB. This approach may constitute a valuable tool for selection of the best candidates for surgery, taking advantage of an integrated multidisciplinary approach.
Authors: Mustafa Bektaş; Beata M M Reiber; Jaime Costa Pereira; George L Burchell; Donald L van der Peet Journal: Obes Surg Date: 2022-06-17 Impact factor: 3.479
Authors: Sean Manning; Andrea Pucci; Nicholas C Carter; Mohamed Elkalaawy; Giorgia Querci; Silvia Magno; Anna Tamberi; Nicholas Finer; Alberic G Fiennes; Majid Hashemi; Andrew D Jenkinson; Marco Anselmino; Ferruccio Santini; Marco Adamo; Rachel L Batterham Journal: Surg Endosc Date: 2014-09-20 Impact factor: 4.584
Authors: Samuel Serisier; Alexandre Feugier; Sébastien Delmotte; Vincent Biourge; Alexander James German Journal: PLoS One Date: 2014-04-23 Impact factor: 3.240