Department of Electrical Systems and Automation, University of Pisa, Pisa, Italy.
PLoS One. 2010 Oct 27;5(10):e13624. doi: 10.1371/journal.pone.0013624.
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.
肥胖被普遍认为是一种全球性的流行疾病，也是许多常见疾病发展的主要因素。腹腔镜可调节胃束带术（LAGB）是目前全球范围内最受欢迎的手术方法之一。然而，预期结果存在很大差异，手术失败率也很高，因此仍然存在争议，即哪种类型的患者更适合这种减肥手术。本研究旨在建立一个基于心理和生理数据的统计模型，以预测接受 LAGB 治疗的肥胖患者的体重减轻情况，并为选择可能受益于该手术的患者提供有价值的工具。
方法/主要发现：研究人群由 172 名肥胖女性组成，平均术前和术后体重指数（BMI）分别为 42.5 ± 5.1 和 32.4 ± 4.8 kg/m2。受试者接受了全面的心理病理学明尼苏达多相人格测验-2（MMPI-2）测试。本研究的主要目的是使用术前数据来预测个体治疗效果，即 2 年后的多余体重减轻（EWL）。使用 MMPI-2 评分、BMI 和年龄进行多元线性回归分析，以确定最佳预测 EWL 的变量。基于包括年龄和 3 个心理计量学量表在内的选定变量，人工神经网络（ANNs）被用于提高预测的准确性。在分类和预测任务中比较了线性和非线性模型：非线性模型在数据拟合方面表现更好（分别解释了 36%和 10%的方差），并为准确性和误分类率提供了更可靠的参数（分别为 70%和 30%，而 66%和 34%）。
结论/意义：ANN 模型可成功应用于预测接受 LAGB 治疗的肥胖女性的体重减轻情况。这种方法可以构成一种有价值的工具，用于选择最佳手术候选者，从而利用综合多学科方法的优势。
