Abstract：
BACKGROUND: The pilot study addresses the challenge of predicting postoperative outcomes, particularly body mass index (BMI) trajectories, following bariatric surgery. The complexity of this task makes preoperative personalized obesity treatment challenging.
OBJECTIVE: To develop and validate sophisticated machine learning (ML) algorithms capable of accurately forecasting BMI reductions up to 5 years following bariatric surgery aiming to enhance planning and postoperative care. The secondary goal involves the creation of an accessible web-based calculator for healthcare professionals. This is the first article that compares these methods in BMI prediction.
METHODS: The study was carried out from January 2012 to December 2021 at GZOAdipositas Surgery Center, Switzerland. Preoperatively, data for 1004 patients were available. Six months postoperatively, data for 1098 patients were available. For the time points 12 months, 18 months, 2 years, 3 years, 4 years, and 5 years the following number of follow-ups were available: 971, 898, 829, 693, 589, and 453.
METHODS: We conducted a comprehensive retrospective review of adult patients who underwent bariatric surgery (Roux-en-Y gastric bypass or sleeve gastrectomy), focusing on individuals with preoperative and postoperative data. Patients with certain preoperative conditions and those lacking complete data sets were excluded. Additional exclusion criteria were patients with incomplete data or follow-up, pregnancy during the follow-up period, or preoperative BMI ≤30 kg/m2.
RESULTS: This study analyzed 1104 patients, with 883 used for model training and 221 for final evaluation, the study achieved reliable predictive capabilities, as measured by root mean square error (RMSE). The RMSE values for three tasks were 2.17 (predicting next BMI value), 1.71 (predicting BMI at any future time point), and 3.49 (predicting the 5-year postoperative BMI curve). These results were showcased through a web application, enhancing clinical accessibility and decision-making.
CONCLUSIONS: This study highlights the potential of ML to significantly improve bariatric surgical outcomes and overall healthcare efficiency through precise BMI predictions and personalized intervention strategies.
OBJECTIVE: To develop and validate sophisticated machine learning (ML) algorithms capable of accurately forecasting BMI reductions up to 5 years following bariatric surgery aiming to enhance planning and postoperative care. The secondary goal involves the creation of an accessible web-based calculator for healthcare professionals. This is the first article that compares these methods in BMI prediction.
METHODS: The study was carried out from January 2012 to December 2021 at GZOAdipositas Surgery Center, Switzerland. Preoperatively, data for 1004 patients were available. Six months postoperatively, data for 1098 patients were available. For the time points 12 months, 18 months, 2 years, 3 years, 4 years, and 5 years the following number of follow-ups were available: 971, 898, 829, 693, 589, and 453.
METHODS: We conducted a comprehensive retrospective review of adult patients who underwent bariatric surgery (Roux-en-Y gastric bypass or sleeve gastrectomy), focusing on individuals with preoperative and postoperative data. Patients with certain preoperative conditions and those lacking complete data sets were excluded. Additional exclusion criteria were patients with incomplete data or follow-up, pregnancy during the follow-up period, or preoperative BMI ≤30 kg/m2.
RESULTS: This study analyzed 1104 patients, with 883 used for model training and 221 for final evaluation, the study achieved reliable predictive capabilities, as measured by root mean square error (RMSE). The RMSE values for three tasks were 2.17 (predicting next BMI value), 1.71 (predicting BMI at any future time point), and 3.49 (predicting the 5-year postoperative BMI curve). These results were showcased through a web application, enhancing clinical accessibility and decision-making.
CONCLUSIONS: This study highlights the potential of ML to significantly improve bariatric surgical outcomes and overall healthcare efficiency through precise BMI predictions and personalized intervention strategies.
摘要：
背景：初步研究解决了预测术后结果的挑战，特别是体重指数(BMI)轨迹，减肥手术后。这项任务的复杂性使得术前个性化肥胖治疗具有挑战性。
目的：开发和验证复杂的机器学习（ML）算法，能够准确预测减肥手术后5年的BMI降低，旨在加强计划和术后护理。第二个目标涉及为医疗保健专业人员创建一个可访问的基于Web的计算器。这是第一篇比较这些方法在BMI预测中的文章。
方法：该研究于2012年1月至2021年12月在GZOAdipositas手术中心进行，瑞士。术前,获得了1004例患者的数据.术后六个月,可获得1098例患者的数据.对于12个月的时间点，18个月,2年,3年,4年,5年的随访次数如下:971,898,829,693,589和453.
方法：我们对接受减肥手术（Roux-en-Y胃旁路术或袖状胃切除术）的成年患者进行了全面的回顾性研究，专注于术前和术后数据的个体。排除具有某些术前条件和缺乏完整数据集的患者。其他排除标准为数据不完整或随访的患者，在随访期间怀孕，或术前BMI≤30kg/m2。
结果：本研究分析了1104例患者，883用于模型训练，221用于最终评估，这项研究获得了可靠的预测能力，以均方根误差(RMSE)衡量。三个任务的RMSE值为2.17（预测下一个BMI值），1.71（预测未来任何时间点的BMI），和3.49（预测术后5年BMI曲线）。这些结果是通过一个网络应用程序展示的，提高临床可及性和决策。
结论：本研究强调了ML通过精确的BMI预测和个性化干预策略显著改善减肥手术结果和整体医疗效率的潜力。
目的：开发和验证复杂的机器学习（ML）算法，能够准确预测减肥手术后5年的BMI降低，旨在加强计划和术后护理。第二个目标涉及为医疗保健专业人员创建一个可访问的基于Web的计算器。这是第一篇比较这些方法在BMI预测中的文章。
方法：该研究于2012年1月至2021年12月在GZOAdipositas手术中心进行，瑞士。术前,获得了1004例患者的数据.术后六个月,可获得1098例患者的数据.对于12个月的时间点，18个月,2年,3年,4年,5年的随访次数如下:971,898,829,693,589和453.
方法：我们对接受减肥手术（Roux-en-Y胃旁路术或袖状胃切除术）的成年患者进行了全面的回顾性研究，专注于术前和术后数据的个体。排除具有某些术前条件和缺乏完整数据集的患者。其他排除标准为数据不完整或随访的患者，在随访期间怀孕，或术前BMI≤30kg/m2。
结果：本研究分析了1104例患者，883用于模型训练，221用于最终评估，这项研究获得了可靠的预测能力，以均方根误差(RMSE)衡量。三个任务的RMSE值为2.17（预测下一个BMI值），1.71（预测未来任何时间点的BMI），和3.49（预测术后5年BMI曲线）。这些结果是通过一个网络应用程序展示的，提高临床可及性和决策。
结论：本研究强调了ML通过精确的BMI预测和个性化干预策略显著改善减肥手术结果和整体医疗效率的潜力。
