高级别胶质瘤（HGG）是神经外科实践中最罕见、同时也最具侵袭性的肿瘤类型之一。在当前的文献中，鲜有研究评估这些肿瘤切除后早期结果的动力学，并研究它们与优质护理的关联性。本文作者旨在利用美国外科医师学院（ACS）全国外科质量改进计划（NSQIP）数据库，确定HGG手术后30天再入院和再手术的临床预测因素，并试图创建预测每个结果的基于网络的应用程序。作者采用ACS NSQIP数据库，对在2016年1月1日至2020年12月31日期间接受枕上HGG切除术的患者进行了回顾性、多中心队列分析。提取了人口统计学和合并症数据。主要结果为30天内非计划再入院和再手术。对可用数据进行了80:20的分层拆分。进行监督式机器学习算法的训练，以预测30天后的结果。 我们的队列中共有9418名患者。手术后30天内非计划再入院的观察率为13.0%（n = 1221）。在预测因素方面，体重、慢性类固醇使用、术前血尿素氮水平和白细胞计数与再入院风险增加相关。手术后30天内非计划再手术的观察率为5.2%（n = 489）。在预测因素方面，增加的体重、较长的手术时间和住院和手术之间的天数与早期再手术的风险增加相关。随机森林算法显示了早期再入院的最高预测性能（曲线下面积[AUC] = 0.967），而XGBoost算法显示了早期再手术的最高预测性能（AUC = 0.985）。针对这两个结果，部署了基于网络的工具（https://glioma-readmission.herokuapp.com/ and https://glioma-reoperation.herokuapp.com/）。在本研究中，作者首次提供了对接受枕上HGG切除术的患者短期结果的全国范围分析。多个患者、医院和入院因素与再入院和再手术相关，机器学习证实了预测患者预后的有效性，从而在术前有更好的规划和随后改善个体化患者护理。

High-grade gliomas (HGGs) are among the rarest yet most aggressive tumor types in neurosurgical practice. In the current literature, few studies have assessed the drivers of early outcomes following resection of these tumors and investigated their association with quality of care. The authors aimed to identify the clinical predictors for 30-day readmission and reoperation following HGG surgery using the American College of Surgeons (ACS) National Surgical Quality Improvement Project (NSQIP) database and sought to create web-based applications predicting each outcome.Using the ACS NSQIP database, the authors conducted a retrospective, multicenter cohort analysis of patients who underwent resection of supratentorial HGGs between January 1, 2016, and December 31, 2020. Demographics and comorbidities were extracted. The primary outcomes were 30-day unplanned readmission and reoperation. A stratified 80:20 split of the available data was carried out. Supervised machine learning algorithms were trained to predict 30-day outcomes.A total of 9418 patients were included in our cohort. The observed rate of unplanned readmission within 30 days of surgery was 13.0% (n = 1221). In terms of predictors, weight, chronic steroid use, preoperative blood urea nitrogen level, and white blood cell count were associated with a higher risk of readmission. The observed rate of unplanned reoperation within 30 days of surgery was 5.2% (n = 489). In terms of predictors, increased weight, longer operative time, and more days between hospital admission and operation were associated with an increased risk of early reoperation. The random forest algorithm showed the highest predictive performance for early readmission (area under the curve [AUC] = 0.967), while the XGBoost algorithm showed the highest predictive performance for early reoperation (AUC = 0.985). Web-based tools for both outcomes were deployed (https://glioma-readmission.herokuapp.com/ and https://glioma-reoperation.herokuapp.com/).In this study, the authors provide the first nationwide analysis for short-term outcomes in patients undergoing resection of supratentorial HGGs. Multiple patient, hospital, and admission factors were associated with readmission and reoperation, confirmed by machine learning predicting patients' prognosis, leading to better planning preoperatively and subsequently improved personalized patient care.