腹腔镜Roux-en-Y胃旁路术后体重过度减轻的预测：来自人工神经网络的数据。
Prediction of excess weight loss after laparoscopic Roux-en-Y gastric bypass: data from an artificial neural network.
作者信息
Wise Eric S, Hocking Kyle M, Kavic Stephen M
机构信息
Department of Surgery, Vanderbilt University Medical Center, 1161 21st Ave S, MCN T2121, Nashville, TN, 37232-2730, USA.
Department of General Surgery, University of Maryland Medical Center, Baltimore, MD, USA.
出版信息
Surg Endosc. 2016 Feb;30(2):480-488. doi: 10.1007/s00464-015-4225-7. Epub 2015 May 28.
INTRODUCTION
Laparoscopic Roux-en-Y gastric bypass (LRYGB) has become the gold standard for surgical weight loss. The success of LRYGB may be measured by excess body mass index loss (%EBMIL) over 25 kg/m(2), which is partially determined by multiple patient factors. In this study, artificial neural network (ANN) modeling was used to derive a reasonable estimate of expected postoperative weight loss using only known preoperative patient variables. Additionally, ANN modeling allowed for the discriminant prediction of achievement of benchmark 50% EBMIL at 1 year postoperatively.
METHODS
Six hundred and forty-seven LRYGB included patients were retrospectively reviewed for preoperative factors independently associated with EBMIL at 180 and 365 days postoperatively (EBMIL180 and EBMIL365, respectively). Previously validated factors were selectively analyzed, including age; race; gender; preoperative BMI (BMI0); hemoglobin; and diagnoses of hypertension (HTN), diabetes mellitus (DM), and depression or anxiety disorder. Variables significant upon multivariate analysis (P < .05) were modeled by "traditional" multiple linear regression and an ANN, to predict %EBMIL180 and %EBMIL365.
RESULTS
The mean EBMIL180 and EBMIL365 were 56.4 ± 16.5 % and 73.5 ± 21.5%, corresponding to total body weight losses of 25.7 ± 5.9% and 33.6 ± 8.0%, respectively. Upon multivariate analysis, independent factors associated with EBMIL180 included black race (B = -6.3%, P < .001), BMI0 (B = -1.1%/unit BMI, P < .001), and DM (B = -3.2%, P < .004). For EBMIL365, independently associated factors were female gender (B = 6.4%, P < .001), black race (B = -6.7%, P < .001), BMI0 (B = -1.2%/unit BMI, P < .001), HTN (B = -3.7%, P = .03), and DM (B = -6.0%, P < .001). Pearson r(2) values for the multiple linear regression and ANN models were 0.38 (EBMIL180) and 0.35 (EBMIL365), and 0.42 (EBMIL180) and 0.38 (EBMIL365), respectively. ANN prediction of benchmark 50% EBMIL at 365 days generated an area under the curve of 0.78 ± 0.03 in the training set (n = 518) and 0.83 ± 0.04 (n = 129) in the validation set.
CONCLUSIONS
Available at https://redcap.vanderbilt.edu/surveys/?s=3HCR43AKXR, this or other ANN models may be used to provide an optimized estimate of postoperative EBMIL following LRYGB.
引言
腹腔镜Roux-en-Y胃旁路术（LRYGB）已成为手术减肥的金标准。LRYGB的成功可以通过超过25kg/m²的超重体重指数损失（%EBMIL）来衡量，这部分取决于多个患者因素。在本研究中，人工神经网络（ANN）建模仅使用已知的术前患者变量来合理估计术后预期体重减轻。此外，ANN建模还可以对术后1年达到基准50%EBMIL进行判别预测。
方法
对647例接受LRYGB手术的患者进行回顾性分析，以确定与术后180天和365天（分别为EBMIL180和EBMIL365）的EBMIL独立相关的术前因素。对先前验证过的因素进行选择性分析，包括年龄、种族、性别、术前体重指数（BMI0）、血红蛋白以及高血压（HTN）、糖尿病（DM）和抑郁或焦虑症的诊断。多变量分析中具有显著性的变量（P < 0.05）通过“传统”多元线性回归和ANN进行建模，以预测%EBMIL180和%EBMIL365。
结果
EBMIL180和EBMIL365的平均值分别为56.4±16.5%和73.5±21.5%，分别对应总体重减轻25.7±5.9%和33.6±8.0%。多变量分析显示，与EBMIL180独立相关的因素包括黑人种族（B = -6.3%，P < 0.001）、BMI0（B = -1.1%/单位BMI，P < 0.001）和DM（B = -3.2%，P < 0.004）。对于EBMIL365，独立相关因素为女性性别（B = 6.4%，P < 0.001）、黑人种族（B = -6.7%，P < 0.001）、BMI0（B = -1.2%/单位BMI，P < 0.001）、HTN（B = -3.7%，P = 0.03）和DM（B = -6.0%，P < 0.001）。多元线性回归和ANN模型的Pearson r²值分别为0.38（EBMIL180）和0.35（EBMIL365），以及0.42（EBMIL180）和0.38（EBMIL365）。ANN对术后365天达到基准50%EBMIL的预测在训练集（n = 518）中的曲线下面积为0.78±0.03，在验证集（n = 129）中的曲线下面积为0.83±0.04。
结论
可在https://redcap.vanderbilt.edu/surveys/?s=3HCR43AKXR获取，此ANN模型或其他ANN模型可用于提供LRYGB术后EBMIL的优化估计。
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