预测髓内脊髓肿瘤手术不良后果的机器学习驱动的国家分析。

IF 2.6 3区 医学 Q2 CLINICAL NEUROLOGY
Marc Ghanem, Abdul Karim Ghaith, Sung Huang Laurent Tsai, Yu-Cheng Yeh, Oluwaseun O Akinduro, Loizos Michaelides, Victor Gabriel El-Hajj, Hassan Saad, Ali Tfaily, Antonio Bon Nieves, Alfredo Quiñones-Hinojosa, Kingsley Abode-Iyamah, Mohamad Bydon
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引用次数: 0

摘要

脊髓肿瘤占所有中枢神经系统恶性肿瘤的15%,髓内脊髓肿瘤(IMSCTs)很少见。主要是室管膜瘤和星形细胞瘤,imsct通常出现较晚,导致显著的发病率和死亡率。由于肿瘤的复杂性和侵袭性,手术切除是关键,但具有挑战性。治疗涉及多学科方法,考虑肿瘤类型和患者情况,从小全切除到全切除,可能辅以辅助治疗。本研究使用国家癌症数据库数据的机器学习来预测术后结果,旨在为临床医生开发一个风险计算器,以评估术后死亡率和延长住院时间的风险。目的:本研究利用国家癌症数据库(NCDB)确定关键变量,调查硬膜内髓内脊髓肿瘤(IMSCTs)手术切除患者的医疗保健结果。我们的目标是开发基于监督机器学习的风险计算器,以预测高危患者的死亡率和延长住院时间(eLOS),并根据组织学对imsct进行分层,以增强对不良结果的理解和指导干预策略。方法:采用NCDB对2004-2017年手术治疗的imsct患者进行研究。我们提取了人口统计和合并症数据,采用描述性统计和监督机器学习算法来预测死亡率和eLOS。结果:该研究纳入了7,243例手术治疗的IMSCT病例,包括612例星形细胞瘤(8.5%),6,041例室管膜瘤(83.4%)和590例血管母细胞瘤(8.1%)。死亡率为10.2%,eLOS为27.1%。在过去的12年里(2004-2016年),这些脊柱肿瘤类型的管理发生了重大变化。预测模型的死亡率auc为0.721,eLOS auc为0.586。死亡率的主要预测特征包括年龄、诊断年份、行为、组织学、放疗、保险状况、患者-医院距离、肿瘤分级和大小、住院时间、次全切除(STR)到总全切除(GTR)和性别。对于eLOS,额外的预测因素是诊断-手术间隔、Charlson/Deyo评分和手术入路。为这两项成果部署了基于网络的工具:https://imsct-elos-predict.herokuapp.com/;结论:我们的全国分析强调了IMSCT管理的发展,并证明了机器学习在预测死亡率和eLOS方面的有效性,为改善患者护理提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning-driven national analysis for predicting adverse outcomes in intramedullary spinal cord tumor surgery.

Spinal tumors represent 15% of all central nervous system malignancies, with intramedullary spinal cord tumors (IMSCTs) being rare. Predominantly ependymomas and astrocytomas, IMSCTs often present late, leading to significant morbidity and mortality. Surgical excision is key but challenging due to the tumors' complex, invasive nature. Treatment involves a multidisciplinary approach, considering tumor type and patient condition, ranging from subtotal to gross total resection, possibly with adjuvant therapy. This study uses machine learning on National Cancer Database data to predict postoperative outcomes, aiming to develop a risk calculator for clinicians to assess mortality and extended hospital stay risks post-surgery.

Objective: This study investigates healthcare outcomes in patients undergoing surgical resection for intradural intramedullary spinal cord tumors (IMSCTs), employing the National Cancer Data Base (NCDB) to identify key variables. We aimed to develop supervised machine learning-based risk calculators to predict high-risk patients for mortality and extended length of stay (eLOS), stratifying IMSCTs by histology to enhance understanding and guide intervention strategies for adverse outcomes.

Methods: Patients with surgically-treated IMSCTs (2004-2017) was conducted using the NCDB. We extracted demographic and comorbidity data, employing descriptive statistics and supervised machine learning algorithms to predict mortality and eLOS.

Results: The study encompassed 7,243 surgically treated IMSCT cases, including 612 astrocytomas (8.5%), 6,041 ependymomas (83.4%), and 590 hemangioblastomas (8.1%). Mortality and eLOS rates were observed at 10.2% and 27.1%, respectively. Over 12 years (2004-2016), significant management shifts were noted for these spinal tumor types. The predictive models achieved AUCs of 0.721 for mortality and 0.586 for eLOS. Key predictive features for mortality included age, diagnosis year, behavior, histology, radiation, insurance status, patient-hospital distance, tumor grade and size, length of stay, subtotal resection (STR) to gross total resection (GTR), and sex. For eLOS, additional predictors were diagnosis-surgery interval, Charlson/Deyo score, and surgical approach. Web-based tools for both outcomes have been deployed: https://imsct-elos-predict.herokuapp.com/ ; https://imsct-risk-calcualor.herokuapp.com/ .

Conclusion: Our nationwide analysis underscores the evolution in IMSCT management and demonstrates the efficacy of machine learning in predicting mortality and eLOS, providing valuable insights for improved patient care.

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来源期刊
European Spine Journal
European Spine Journal 医学-临床神经学
CiteScore
4.80
自引率
10.70%
发文量
373
审稿时长
2-4 weeks
期刊介绍: "European Spine Journal" is a publication founded in response to the increasing trend toward specialization in spinal surgery and spinal pathology in general. The Journal is devoted to all spine related disciplines, including functional and surgical anatomy of the spine, biomechanics and pathophysiology, diagnostic procedures, and neurology, surgery and outcomes. The aim of "European Spine Journal" is to support the further development of highly innovative spine treatments including but not restricted to surgery and to provide an integrated and balanced view of diagnostic, research and treatment procedures as well as outcomes that will enhance effective collaboration among specialists worldwide. The “European Spine Journal” also participates in education by means of videos, interactive meetings and the endorsement of educative efforts. Official publication of EUROSPINE, The Spine Society of Europe
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