在行使了细胞毒性手术（CRS）和腹腔加热化疗（HIPEC）的弥散性阑尾癌（dAC）患者中，对于那些可能早期复发的患者进行特征化和预测，可以为个性化随访提供一个框架。本研究旨在：（1）对于在接受CRS ± HIPEC之后的2年内有可能复发的dAC患者进行特征化；（2）利用自动机器学习（AutoML）来预测有风险的患者；（3）识别对预测有影响的因素。我们使用了一个由12个医疗机构组成的cohort，其中收治了2000年至2017年间接受CRS ± HIPEC的dAC患者。使用H2O.ai的AutoML训练预测模型。将早期复发（ER）患者与无复发或2年后复发的患者（对照组；C）进行比较，但是75％的数据用于训练，25％用于验证，模型进行了5倍交叉验证。总共纳入了949名患者，其中早期复发（ER）患者为337名（35.5％）。早期复发（ER）患者的炎症标志物更高，病情负担更重，反应不佳，并接受更多的术中液体/血液制品。最佳性能的AutoML模型是Stacked Ensemble（曲线下面积=0.78，曲线下面积精度召回=0.66，阳性预测值=85％，阴性预测值=63％）。预测受血液标志物、手术过程和与病情负担更重相关的因素的影响。在这个接受CRS ± HIPEC的dAC患者的多机构研究中，AutoML在预测ER患者方面表现良好。提示肿瘤生物学较差的变量对预测最有影响。我们的工作为在手术后早期较短间隔随访中受益于识别ER患者提供了一个框架。版权所有 © 2023 Elsevier Inc.。保留所有权利。

In patients with disseminated appendiceal cancer (dAC) who underwent cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC), characterizing and predicting those who will develop early recurrence could provide a framework for personalizing follow-up. This study aims to: (1) characterize patients with dAC that are at risk for recurrence within 2 y following of CRS ± HIPEC (early recurrence; ER), (2) utilize automated machine learning (AutoML) to predict at-risk patients, and (3) identifying factors that are influential for prediction.A 12-institution cohort of patients with dAC treated with CRS ± HIPEC between 2000 and 2017 was used to train predictive models using H2O.ai's AutoML. Patients with early recurrence (ER) were compared to those who did not have recurrence or presented with recurrence after 2 y (control; C). However, 75% of the data was used for training and 25% for validation, and models were 5-fold cross-validated.A total of 949 patients were included, with 337 ER patients (35.5%). Patients with ER had higher markers of inflammation, worse disease burden with poor response, and received greater intraoperative fluids/blood products. The highest performing AutoML model was a Stacked Ensemble (area under the curve = 0.78, area under the curve precision recall = 0.66, positive predictive value = 85%, and negative predictive value = 63%). Prediction was influenced by blood markers, operative course, and factors associated with worse disease burden.In this multi-institutional cohort of dAC patients that underwent CRS ± HIPEC, AutoML performed well in predicting patients with ER. Variables suggestive of poor tumor biology were the most influential for prediction. Our work provides a framework for identifying patients with ER that might benefit from shorter interval surveillance early after surgery.Copyright © 2023 Elsevier Inc. All rights reserved.