Department of Physiology, University of Lausanne (UNIL), Lausanne, Switzerland.
Department of Visceral Surgery, University Hospital (CHUV), Lausanne, Switzerland.
Obes Surg. 2018 Nov;28(11):3393-3399. doi: 10.1007/s11695-018-3355-0.
Different factors, such as age, gender, preoperative weight but also the patient's motivation, are known to impact outcomes after Roux-en-Y gastric bypass (RYGBP). Weight loss prediction is helpful to define realistic expectations and maintain motivation during follow-up, but also to select good candidates for surgery and limit failures. Therefore, developing a realistic predictive tool appears interesting.
PATIENTS/METHODS: A Swiss cohort (n = 444), who underwent RYGBP, was used, with multiple linear regression models, to predict weight loss up to 60 months after surgery considering age, height, gender and weight at baseline. We then applied our model on two French cohorts and compared predicted weight to the one finally reached. Accuracy of our model was controlled using root mean square error (RMSE).
Mean weight loss was 43.6 ± 13.0 and 40.8 ± 15.4 kg at 12 and 60 months respectively. The model was reliable to predict weight loss (0.37 < R < 0.48) and RMSE between 5.0 and 12.2 kg. High preoperative weight and young age were positively correlated to weight loss, as well as male gender. Correlations between predicted weight and real weight were highly significant in both validation cohorts (R ≥ 0.7 and P < 0.01) and RMSE increased throughout follow-up between 6.2 and 15.4 kg.
Our statistical model to predict weight loss outcomes after RYGBP seems accurate. It could be a valuable tool to define realistic weight loss expectations and to improve patient selection and outcomes during follow-up. Further research is needed to demonstrate the interest of this model in improving patients' motivation and results and limit the failures.
已知多种因素，如年龄、性别、术前体重，还有患者的动机，会影响 Roux-en-Y 胃旁路术（RYGBP）后的结果。体重减轻的预测有助于确定现实的预期并在随访期间保持动力，还可以选择手术的合适候选人并减少失败。因此，开发一个现实的预测工具似乎很有意义。
患者/方法：使用来自瑞士的队列（n=444），他们接受了 RYGBP，并使用多元线性回归模型来预测术后 60 个月内的体重减轻，考虑了年龄、身高、性别和基线时的体重。然后，我们将我们的模型应用于两个法国队列，并将预测体重与最终达到的体重进行比较。我们使用均方根误差（RMSE）来控制模型的准确性。
12 个月和 60 个月时的平均体重减轻分别为 43.6±13.0 和 40.8±15.4kg。该模型可以可靠地预测体重减轻（0.37<R<0.48），RMSE 在 5.0 到 12.2kg 之间。高术前体重和年轻年龄与体重减轻呈正相关，而男性性别也是如此。在两个验证队列中，预测体重与真实体重之间的相关性均非常显著（R≥0.7，P<0.01），并且在随访过程中 RMSE 从 6.2 到 15.4kg 逐渐增加。
我们用于预测 RYGBP 后体重减轻结果的统计模型似乎是准确的。它可以成为一种有价值的工具，用于确定现实的体重减轻预期，并在随访期间改善患者的选择和结果。需要进一步的研究来证明该模型在提高患者动力和结果以及减少失败方面的兴趣。
