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脊柱转移性疾病的预后模型:方法学的演变、局限性及未来机遇

Prognostic models for spinal metastatic disease: evolution of methodologies, limitations, and future opportunities.

作者信息

Karhade Aditya V, Shin John H, Schwab Joseph H

机构信息

Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

出版信息

Ann Transl Med. 2019 May;7(10):219. doi: 10.21037/atm.2019.04.87.

DOI:10.21037/atm.2019.04.87
PMID:31297384
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6595198/
Abstract

Advances in cancer biology and therapy have increased survival of metastatic disease patients and, in turn, the rates of metastatic epidural spinal cord compression (MESCC). Surgery can improve patient quality of life, but accurate estimation of postoperative survival is critical for appropriate patient selection, multidisciplinary management, and shared decision making. Survival estimation on the basis of clinician judgement alone has been shown to be inaccurate and unreliable. Numerous prognostic scoring systems have been developed to address this need but the inputs to these models, the modeling methodologies, and the model outputs have evolved significantly over time. Here we discuss the available scoring systems, existing limitations, and future opportunities.

摘要

癌症生物学和治疗方面的进展提高了转移性疾病患者的生存率,进而也提高了转移性硬膜外脊髓压迫(MESCC)的发生率。手术可以改善患者的生活质量,但准确估计术后生存率对于恰当的患者选择、多学科管理以及共同决策至关重要。仅基于临床医生判断的生存估计已被证明是不准确和不可靠的。为满足这一需求,已经开发了许多预后评分系统,但这些模型的输入、建模方法以及模型输出随着时间的推移都有了显著发展。在此,我们讨论现有的评分系统、存在的局限性以及未来的机遇。

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本文引用的文献

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Neurosurgery. 2019 Oct 1;85(4):E671-E681. doi: 10.1093/neuros/nyz070.
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Development of Machine Learning Algorithms for Prediction of 30-Day Mortality After Surgery for Spinal Metastasis.机器学习算法在预测脊柱转移术后 30 天死亡率中的应用研究。
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Assessment and Management of Patients With Metastatic Spinal Cord Compression: A Multidisciplinary Review.评估和管理伴发转移性脊髓压迫的患者:多学科综述。
J Clin Oncol. 2019 Jan 1;37(1):61-71. doi: 10.1200/JCO.2018.78.1211. Epub 2018 Nov 5.
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