术后胰瘘（CR-POPF）是胰十二指肠切除术（PD）后的严重术后并发症，也是手术结果的主要决定因素。然而，目前大多数风险计算器仅利用术中和术后变量，限制了其在术前设置中的实用性。因此，我们的目标是使用最先进的机器学习（ML）算法和仅具有术前已知变量，开发一个用户友好的风险计算器，用于预测PD后的CR-POPF。 从ACS-NSQIP定向胰腺切除术数据集（2014-2019年）中筛选出行非转移性胰腺癌择期行PD的成年患者。主要终点是CR-POPF的发生（B或C级）。次要终点包括转院到医疗机构、30天死亡率和整体和重要并发症的综合终点。我们训练了四个模型（逻辑回归、神经网络、随机森林和XGBoost），进行了验证，并开发了一个用户友好的风险计算器。 在行择期PD的8666例患者中，13%（n = 1160）发生了CR-POPF。XGBoost模型表现最佳（AUC = 0.72），与CR-POPF相关的五个术前变量为非腺癌组织学、无术前辅助化疗、胰管直径小于3毫米、较高的BMI和较高的术前血清肌酐。对于30天死亡率、转院到医疗机构和整体和重要并发症，模型性能的AUC范围为0.62-0.78。 本研究开发和验证了一个ML模型，仅利用术前已知变量来预测PD后的CR-POPF。这个风险计算器可以在术前设置中用于临床决策和患者咨询。 © 2023年。这是美国政府的工作，不受美国版权保护，可能适用外国版权保护。

Clinically-relevant postoperative pancreatic fistula (CR-POPF) following pancreaticoduodenectomy (PD) is a major postoperative complication and the primary determinant of surgical outcomes. However, the majority of current risk calculators utilize intraoperative and postoperative variables, limiting their utility in the preoperative setting. Therefore, we aimed to develop a user-friendly risk calculator to predict CR-POPF following PD using state-of-the-art machine learning (ML) algorithms and only preoperatively known variables.Adult patients undergoing elective PD for non-metastatic pancreatic cancer were identified from the ACS-NSQIP targeted pancreatectomy dataset (2014-2019). The primary endpoint was development of CR-POPF (grade B or C). Secondary endpoints included discharge to facility, 30-day mortality, and a composite of overall and significant complications. Four models (logistic regression, neural network, random forest, and XGBoost) were trained, validated and a user-friendly risk calculator was then developed.Of the 8666 patients who underwent elective PD, 13% (n = 1160) developed CR-POPF. XGBoost was the best performing model (AUC = 0.72), and the top five preoperative variables associated with CR-POPF were non-adenocarcinoma histology, lack of neoadjuvant chemotherapy, pancreatic duct size less than 3 mm, higher BMI, and higher preoperative serum creatinine. Model performance for 30-day mortality, discharge to a facility, and overall and significant complications ranged from AUC 0.62-0.78.In this study, we developed and validated an ML model using only preoperatively known variables to predict CR-POPF following PD. The risk calculator can be used in the preoperative setting to inform clinical decision-making and patient counseling.© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.