Department of Orthopedics, National Taiwan University College of Medicine and National Taiwan University Hospital, Taipei, Taiwan; School of Medicine, National Taiwan University College of Medicine, Taipei, Taiwan.
Department of Orthopedics, National Taiwan University College of Medicine and National Taiwan University Hospital, Taipei, Taiwan.
Spine J. 2021 Oct;21(10):1670-1678. doi: 10.1016/j.spinee.2021.01.027. Epub 2021 Feb 2.
Accurately predicting the survival of patients with spinal metastases is important for guiding surgical intervention. The SORG machine-learning (ML) algorithm for the 90-day and one-year mortality of patients with metastatic cancer to the spine has been multiply validated, with a high degree of accuracy in both internal and external validation studies. However, prior external validations were conducted using patient groups located on the east coast of the United States, representing a generally homogeneous population. The aim of this study was to externally validate the SORG algorithms with a Taiwanese population.
STUDY DESIGN/SETTING: Retrospective study at a single tertiary care center in Taiwan PATIENT SAMPLE: Four hundred and twenty-seven patients who underwent surgery for metastatic spine disease from November 1, 2010 to December 31, 2018 OUTCOME MEASURES: 90-day and one-year mortality METHODS: The baseline characteristics of our validation cohort were compared with those of the previously published developmental and external validation cohorts. Discrimination (c-statistic and receiver operating curve), calibration (calibration plot, intercept, and slope), overall performance (Brier score), and decision curve analysis were used to assess the performance of the SORG ML algorithms in this cohort.
Ninety-day and one-year mortality rates were 110 of 427 (26%) and 256 of 427 (60%), respectively. The external validation cohort and the developmental cohort differed in body mass index (BMI), preoperative performance status, American Spinal Injury Association impairment scale, primary tumor histology and in several laboratory measurements. The SORG ML algorithm for 90-day and 1-year mortality demonstrated a high level of discriminative ability (c-statistics of 0.73 [95% confidence interval [CI], 0.67-0.78] and 0.74 [95% CI, 0.69-0.79]), overall performance, and had a positive net benefit throughout the range of threshold probabilities in decision curve analysis. The algorithm for 1-year mortality had a calibration intercept of 0.08, representing a good calibration. However, the 90-day mortality algorithm underestimated mortality for the lowest predicted probabilities, with an overall intercept of 0.81.
The SORG algorithms for predicting 90-day and 1-year mortality in patients with spinal metastatic disease generally performed well on international external validation in a predominately Taiwanese population. However, 90-day mortality was underestimated in this group. Whether this inconsistency was due to different primary tumor characteristics, body mass index, selection bias or other factors remains unclear, and may be better understood with further validative works that utilize international and/or diverse populations.
准确预测脊柱转移瘤患者的生存情况对于指导手术干预至关重要。SORG 机器学习（ML）算法可用于预测转移性癌症脊柱转移患者 90 天和 1 年的死亡率，其在内部和外部验证研究中均具有高度准确性。然而，先前的外部验证是在美国东海岸的患者群体中进行的，代表了一个相对同质的人群。本研究旨在使用台湾人群对 SORG 算法进行外部验证。
研究设计/地点：台湾一家三级医疗中心的回顾性研究
2010 年 11 月 1 日至 2018 年 12 月 31 日接受脊柱转移瘤手术的 427 例患者
90 天和 1 年死亡率
将我们验证队列的基线特征与之前发表的发展和外部验证队列进行比较。使用区分度（c 统计量和接收者操作曲线）、校准（校准图、截距和斜率）、总体性能（Brier 评分）和决策曲线分析来评估 SORG ML 算法在该队列中的性能。
90 天和 1 年的死亡率分别为 427 例中的 110 例（26%）和 256 例（60%）。外部验证队列和发展队列在体重指数（BMI）、术前功能状态、美国脊髓损伤协会损伤量表、原发肿瘤组织学和几项实验室检查方面存在差异。SORG ML 算法对 90 天和 1 年死亡率的预测具有较高的区分能力（90 天的 C 统计量为 0.73[95%置信区间（CI），0.67-0.78]和 1 年的 C 统计量为 0.74[95%CI，0.69-0.79]）、总体性能，并且在决策曲线分析中整个阈值概率范围内具有正净收益。1 年死亡率算法的校准截距为 0.08，表明校准良好。然而，90 天死亡率算法低估了最低预测概率下的死亡率，总体截距为 0.81。
SORG 算法可用于预测脊柱转移瘤患者的 90 天和 1 年死亡率，在以台湾人为主的国际外部验证中总体表现良好。然而，在这组人群中，90 天的死亡率被低估了。这种不一致是否是由于原发肿瘤特征、体重指数、选择偏倚或其他因素不同所致尚不清楚，进一步利用国际和/或多样化人群进行验证性研究可能会更好地理解这一点。
