• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

利用五家机构的 4304 名患者,开发并外部验证了预测脊柱转移瘤 6 周死亡率的预测算法。

Development and external validation of predictive algorithms for six-week mortality in spinal metastasis using 4,304 patients from five institutions.

机构信息

Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA, USA.

Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA, USA; Tufts University School of Medicine, Boston, MA, USA.

出版信息

Spine J. 2022 Dec;22(12):2033-2041. doi: 10.1016/j.spinee.2022.07.089. Epub 2022 Jul 14.

DOI:10.1016/j.spinee.2022.07.089
PMID:35843533
Abstract

BACKGROUND CONTEXT

Historically, spine surgeons used expected postoperative survival of 3-months to help select candidates for operative intervention in spinal metastasis. However, this cutoff has been challenged by the development of minimally invasive techniques, novel biologics, and advanced radiotherapy. Recent studies have suggested that a life expectancy of 6 weeks may be enough to achieve significant improvements in postoperative health-related quality of life.

PURPOSE

The purpose of this study was to develop a model capable of predicting 6-week mortality in patients with spinal metastases treated with radiation or surgery.

STUDY DESIGN/SETTING: A retrospective review was conducted at five large tertiary centers in the United States and Taiwan.

PATIENT SAMPLE

The development cohort consisted of 3,001 patients undergoing radiotherapy and/or surgery for spinal metastases from one institution. The validation institutional cohort consisted of 1,303 patients from four independent, external institutions.

OUTCOME MEASURES

The primary outcome was 6-week mortality.

METHODS

Five models were considered to predict 6-week mortality, and the model with the best performance across discrimination, calibration, decision-curve analysis, and overall performance was integrated into an open access web-based application.

RESULTS

The most important variables for prediction of 6-week mortality were albumin, primary tumor histology, absolute lymphocyte, three or more spine metastasis, and ECOG score. The elastic-net penalized logistic model was chosen as the best performing model with AUC 0.84 on evaluation in the independent testing set. On external validation in the 1,303 patients from the four independent institutions, the model retained good discriminative ability with an area under the curve of 0.81. The model is available here: https://sorg-apps.shinyapps.io/spinemetssurvival/.

CONCLUSIONS

While this study does not advocate for the use of a 6-week life expectancy as criteria for considering operative management, the algorithm developed and externally validated in this study may be helpful for preoperative planning, multidisciplinary management, and shared decision-making in spinal metastasis patients with shorter life expectancy.

摘要

背景

历史上,脊柱外科医生使用术后 3 个月的预期生存率来帮助选择脊柱转移瘤手术干预的候选者。然而,微创技术、新型生物制剂和先进的放疗技术的发展对这一标准提出了挑战。最近的研究表明,6 周的预期寿命可能足以显著提高术后健康相关生活质量。

目的

本研究旨在建立一个能够预测接受放疗或手术治疗的脊柱转移瘤患者 6 周死亡率的模型。

研究设计/设置:回顾性研究在美国和台湾的五家大型三级中心进行。

患者样本

开发队列由一家机构的 3001 例接受放疗和/或手术治疗的脊柱转移瘤患者组成。验证机构队列由来自四个独立外部机构的 1303 例患者组成。

主要结局

6 周死亡率。

方法

考虑了 5 种模型来预测 6 周死亡率,具有最佳区分度、校准度、决策曲线分析和整体性能的模型被整合到一个开放访问的网络应用程序中。

结果

预测 6 周死亡率的最重要变量是白蛋白、原发肿瘤组织学、绝对淋巴细胞计数、3 个或更多脊柱转移灶和 ECOG 评分。弹性网络惩罚逻辑回归模型被选为表现最佳的模型,在独立测试集中的 AUC 为 0.84。在来自四个独立机构的 1303 例患者的外部验证中,该模型保持了良好的判别能力,曲线下面积为 0.81。该模型可在此处获得:https://sorg-apps.shinyapps.io/spinemetssurvival/。

结论

虽然本研究不主张将 6 周预期寿命作为考虑手术治疗的标准,但本研究中开发和外部验证的算法可能有助于脊柱转移瘤患者的术前规划、多学科管理和基于预期寿命的共享决策。

相似文献

1
Development and external validation of predictive algorithms for six-week mortality in spinal metastasis using 4,304 patients from five institutions.利用五家机构的 4304 名患者,开发并外部验证了预测脊柱转移瘤 6 周死亡率的预测算法。
Spine J. 2022 Dec;22(12):2033-2041. doi: 10.1016/j.spinee.2022.07.089. Epub 2022 Jul 14.
2
Does the SORG algorithm generalize to a contemporary cohort of patients with spinal metastases on external validation?SORG 算法在外验证队列中是否能推广应用于当代脊柱转移瘤患者?
Spine J. 2020 Oct;20(10):1646-1652. doi: 10.1016/j.spinee.2020.05.003. Epub 2020 May 16.
3
Does the Presence of Missing Data Affect the Performance of the SORG Machine-learning Algorithm for Patients With Spinal Metastasis? Development of an Internet Application Algorithm.缺失数据的存在是否会影响 SORG 机器学习算法在脊柱转移瘤患者中的性能?开发一种互联网应用算法。
Clin Orthop Relat Res. 2024 Jan 1;482(1):143-157. doi: 10.1097/CORR.0000000000002706. Epub 2023 Jun 12.
4
Are Current Survival Prediction Tools Useful When Treating Subsequent Skeletal-related Events From Bone Metastases?当前的生存预测工具在治疗骨转移后的骨骼相关事件时有用吗?
Clin Orthop Relat Res. 2024 Sep 1;482(9):1710-1721. doi: 10.1097/CORR.0000000000003030. Epub 2024 Mar 22.
5
External validation of the SORG machine learning algorithms for predicting 90-day and 1-year survival of patients with lung cancer-derived spine metastases: a recent bi-center cohort from China.用于预测肺癌脊柱转移患者90天和1年生存率的SORG机器学习算法的外部验证:来自中国的一项近期双中心队列研究
Spine J. 2023 May;23(5):731-738. doi: 10.1016/j.spinee.2023.01.008. Epub 2023 Jan 25.
6
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。
Neurosurgery. 2019 Jul 1;85(1):E83-E91. doi: 10.1093/neuros/nyy469.
7
International Validation of the SORG Machine-learning Algorithm for Predicting the Survival of Patients with Extremity Metastases Undergoing Surgical Treatment.国际验证 SORG 机器学习算法在预测接受手术治疗的肢体转移患者生存情况的应用。
Clin Orthop Relat Res. 2022 Feb 1;480(2):367-378. doi: 10.1097/CORR.0000000000001969.
8
A Machine Learning Algorithm for Predicting 6-Week Survival in Spinal Metastasis: An External Validation Study Using 2,768 Taiwanese Patients.一种用于预测脊柱转移 6 周生存率的机器学习算法:使用 2768 名台湾患者的外部验证研究。
J Am Acad Orthop Surg. 2023 Sep 1;31(17):e645-e656. doi: 10.5435/JAAOS-D-23-00091. Epub 2023 May 15.
9
International external validation of the SORG machine learning algorithms for predicting 90-day and one-year survival of patients with spine metastases using a Taiwanese cohort.使用台湾队列对 SORG 机器学习算法预测脊柱转移瘤患者 90 天和一年生存率进行国际外部验证。
Spine J. 2021 Oct;21(10):1670-1678. doi: 10.1016/j.spinee.2021.01.027. Epub 2021 Feb 2.
10
External validation of the SORG 90-day and 1-year machine learning algorithms for survival in spinal metastatic disease.SORG 90 天和 1 年机器学习算法在脊柱转移瘤患者生存中的外部验证。
Spine J. 2020 Jan;20(1):14-21. doi: 10.1016/j.spinee.2019.09.003. Epub 2019 Sep 7.

引用本文的文献

1
Artificial Intelligence Models for Predicting Outcomes in Spinal Metastasis: A Systematic Review and Meta-Analysis.预测脊柱转移瘤预后的人工智能模型:一项系统评价和荟萃分析
J Clin Med. 2025 Aug 20;14(16):5885. doi: 10.3390/jcm14165885.
2
Artificial Intelligence in bone Metastases: A systematic review in guideline adherence of 92 studies.人工智能在骨转移中的应用:对92项研究指南依从性的系统评价
J Bone Oncol. 2025 Apr 24;52:100682. doi: 10.1016/j.jbo.2025.100682. eCollection 2025 Jun.
3
The utilization of hypoalbuminemia as a prognostic metric in patients with spinal metastases: A scoping review.
低白蛋白血症在脊柱转移瘤患者中作为预后指标的应用:一项范围综述。
Brain Spine. 2025 Feb 25;5:104223. doi: 10.1016/j.bas.2025.104223. eCollection 2025.
4
Malnutrition in Spine Oncology: Where Are We and What Are We Measuring?脊柱肿瘤学中的营养不良:我们目前的状况及测量指标是什么?
Global Spine J. 2025 Jan;15(1_suppl):29S-46S. doi: 10.1177/21925682231213799.
5
Revolutionizing spinal interventions: a systematic review of artificial intelligence technology applications in contemporary surgery.颠覆脊柱介入治疗:人工智能技术在当代手术中应用的系统评价。
BMC Surg. 2024 Nov 5;24(1):345. doi: 10.1186/s12893-024-02646-2.
6
Clinical, oncological, and prognostic differences of patients with subsequent skeletal-related events in bone metastases.骨转移患者发生后续骨相关事件的临床、肿瘤学及预后差异。
Bone Joint Res. 2024 Sep 16;13(9):497-506. doi: 10.1302/2046-3758.139.BJR-2023-0372.R1.
7
Machine Learning in Spine Oncology: A Narrative Review.脊柱肿瘤学中的机器学习:一篇叙述性综述。
Global Spine J. 2025 Jan;15(1):210-227. doi: 10.1177/21925682241261342. Epub 2024 Jun 11.
8
Predictive Modeling for Spinal Metastatic Disease.脊柱转移性疾病的预测模型
Diagnostics (Basel). 2024 May 5;14(9):962. doi: 10.3390/diagnostics14090962.
9
Are Current Survival Prediction Tools Useful When Treating Subsequent Skeletal-related Events From Bone Metastases?当前的生存预测工具在治疗骨转移后的骨骼相关事件时有用吗?
Clin Orthop Relat Res. 2024 Sep 1;482(9):1710-1721. doi: 10.1097/CORR.0000000000003030. Epub 2024 Mar 22.
10
Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations.神经外科中的机器学习:迈向复杂输入、可操作预测和可推广转化
Cureus. 2024 Jan 9;16(1):e51963. doi: 10.7759/cureus.51963. eCollection 2024 Jan.