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

Journal
The spine journal : official journal of the North American Spine Society
Journal Volume
22
Journal Issue
12
Pages
2033
Date Issued
2022-12
Author(s)
Karhade, Aditya V
Fenn, Brian
Groot, Olivier Q
Shah, Akash A
Yen, Hung-Kuan
Bilsky, Mark H
MING-HSIAO HU  
Laufer, Ilya
Park, Don Y
Sciubba, Daniel M
Steyerberg, Ewout W
Tobert, Daniel G
Bono, Christopher M
Harris, Mitchel B
Schwab, Joseph H
DOI
10.1016/j.spinee.2022.07.089
URI
https://scholars.lib.ntu.edu.tw/handle/123456789/632834
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.
Subjects
Artificial intelligence
External validation
Machine learning
Mortality
Prognosis
Spinal metastases
Publisher
ELSEVIER SCIENCE INC
Type
journal article

臺大位居世界頂尖大學之列,為永久珍藏及向國際展現本校豐碩的研究成果及學術能量,圖書館整合機構典藏(NTUR)與學術庫(AH)不同功能平台,成為臺大學術典藏NTU scholars。期能整合研究能量、促進交流合作、保存學術產出、推廣研究成果。

To permanently archive and promote researcher profiles and scholarly works, Library integrates the services of “NTU Repository” with “Academic Hub” to form NTU Scholars.

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開放取用是從使用者角度提升資訊取用性的社會運動,應用在學術研究上是透過將研究著作公開供使用者自由取閱,以促進學術傳播及因應期刊訂購費用逐年攀升。同時可加速研究發展、提升研究影響力,NTU Scholars即為本校的開放取用典藏(OA Archive)平台。(點選深入了解OA)

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