作者
Aditya V Karhade, Ali K Ahmed, Zach Pennington, Alejandro Chara, Andrew Schilling, Quirina CBS Thio, Paul T Ogink, Daniel M Sciubba, Joseph H Schwab
发表日期
2020/1/1
期刊
The Spine Journal
卷号
20
期号
1
页码范围
14-21
出版商
Elsevier
简介
BACKGROUND CONTEXT
Preoperative survival estimation in spinal metastatic disease helps determine the appropriateness of invasive management. The SORG ML 90-day and 1-year machine learning algorithms for survival in spinal metastatic disease were previously developed in a single institutional sample but remain to be externally validated.
PURPOSE
The purpose of this study was to externally validate these algorithms in an independent population from another institution.
STUDY DESIGN/SETTING
Retrospective study at a large, tertiary care center.
PATIENT SAMPLE
Patients 18 years or older who underwent surgery between 2003 and 2016.
OUTCOME MEASURES
Ninety-day and 1-year mortality.
METHODS
Baseline characteristics of the validation cohort were compared to the developmental cohort for the SORG ML algorithms. Discrimination (c-statistic and receiver operating curve), calibration …
学术搜索中的文章
AV Karhade, AK Ahmed, Z Pennington, A Chara… - The Spine Journal, 2020