Does the SORG algorithm generalize to a contemporary cohort of patients with spinal metastases on external validation?

Spine J. 2020 Oct;20(10):1646-1652. doi: 10.1016/j.spinee.2020.05.003. Epub 2020 May 16.


Background context: The SORG machine-learning algorithms were previously developed for preoperative prediction of overall survival in spinal metastatic disease. On sub-group analysis of a previous external validation, these algorithms were found to have diminished performance on patients treated after 2010.

Purpose: The purpose of this study was to assess the performance of these algorithms on a large contemporary cohort of consecutive spinal metastatic disease patients.

Study design/setting: Retrospective study performed at a tertiary care referral center.

Patient sample: Patients of 18 years and older treated with surgery for metastatic spinal disease between 2014 and 2016.

Outcome measures: Ninety-day and one-year mortality.

Methods: Baseline patient and tumor characteristics of the validation cohort were compared to the development cohort using bivariate logistic regression. Performance of the SORG algorithms on external validation in the contemporary cohort was assessed with discrimination (c-statistic and receiver operating curve), calibration (calibration plot, intercept, and slope), overall performance (Brier score compared to the null-model Brier score), and decision curve analysis.

Results: Overall, 200 patients were included with 90-day and 1-year mortality rates of 55 (27.6%) and 124 (62.9%), respectively. The contemporary external validation cohort and the developmental cohort differed significantly on primary tumor histology, presence of visceral metastases, American Spinal Injury Association impairment scale, and preoperative laboratory values. The SORG algorithms for 90-day and 1-year mortality retained good discriminative ability (c-statistic of 0.81 [95% confidence interval [CI], 0.74-0.87] and 0.84 [95% CI, 0.77-0.89]), overall performance, and decision curve analysis. The algorithm for 90-day mortality showed almost perfect calibration reflected in an overall calibration intercept of -0.07 (95% CI: -0.50, 0.35). The 1-year mortality algorithm underestimated mortality mainly for the lowest predicted probabilities with an overall intercept of 0.57 (95% CI: 0.18, 0.96).

Conclusions: The SORG algorithms for survival in spinal metastatic disease generalized well to a contemporary cohort of consecutively treated patients from an external institutional. Further validation in international cohorts and large, prospective multi-institutional trials is required to confirm or refute the findings presented here. The open-access algorithms are available here:

Keywords: External validation; Machine learning; Mortality; Prediction; Prognostication; Spinal metastases.

MeSH terms

  • Algorithms
  • Humans
  • Machine Learning
  • Prospective Studies
  • Retrospective Studies
  • Spinal Neoplasms* / secondary
  • Spinal Neoplasms* / surgery