1Mayo Clinic Neuro-Informatics Laboratory.
2Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota.
Neurosurg Focus. 2023 Jun;54(6):E12. doi: 10.3171/2023.3.FOCUS22652.
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.
高级别胶质瘤（HGG）是神经外科实践中最罕见但侵袭性最强的肿瘤类型之一。在目前的文献中，很少有研究评估这些肿瘤切除后早期结果的驱动因素，并调查其与护理质量的关系。作者旨在使用美国外科医师学会（ACS）国家手术质量改进计划（NSQIP）数据库确定 HGG 手术后 30 天内再入院和再次手术的临床预测因素，并创建预测每种结果的基于网络的应用程序。
作者使用 ACS NSQIP 数据库，对 2016 年 1 月 1 日至 2020 年 12 月 31 日期间接受幕上 HGG 切除术的患者进行回顾性、多中心队列分析。提取人口统计学和合并症数据。主要结果是 30 天内计划外再入院和再次手术。对可用数据进行了 80:20 的分层分割。训练有监督的机器学习算法以预测 30 天的结果。
我们的队列共纳入 9418 名患者。手术 30 天内计划外再入院的观察率为 13.0%（n=1221）。就预测因素而言，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 30 天内计划外再次手术的观察率为 5.2%（n=489）。就预测因素而言，体重增加、手术时间延长以及住院和手术之间的天数增加与早期再次手术的风险增加相关。随机森林算法对早期再入院的预测性能最高（曲线下面积[AUC] = 0.967），而 XGBoost 算法对早期再手术的预测性能最高（AUC = 0.985）。还部署了这两种结果的基于网络的工具（https://glioma-readmission.herokuapp.com/ 和 https://glioma-reoperation.herokuapp.com/）。
在这项研究中，作者提供了首个全国性分析，评估了接受幕上 HGG 切除术的患者的短期结果。多个患者、医院和入院因素与再入院和再次手术相关，这通过机器学习预测患者的预后得到了证实，从而术前更好地计划，并随后改善了个性化患者护理。
