SORG algorithm to predict 3- and 12-month survival in metastatic spinal disease: a cross-sectional population-based retrospective study.

Abstract

In this study, we wished to compare statistically the novel SORG algorithm in predicting survival in spine metastatic disease versus currently used methods. We recruited 40 patients with spinal metastatic disease who were operated at Geneva University Hospitals by the Neurosurgery or Orthopedic teams between the years of 2015 and 2020. We did an ROC analysis in order to determine the accuracy of the SORG ML algorithm and nomogram versus the Tokuhashi original and revised scores. The analysis of data of our independent cohort shows a clear advantage in terms of predictive ability of the SORG ML algorithm and nomogram in comparison with the Tokuhashi scores. The SORG ML had an AUC of 0.87 for 90days and 0.85 for 1year. The SORG nomogram showed a predictive ability at 90days and 1year with AUCs of 0.87 and 0.76 respectively. These results showed excellent discriminative ability as compared with the Tokuhashi original score which achieved AUCs of 0.70 and 0.69 and the Tokuhashi revised score which had AUCs of 0.65 and 0.71 for 3months and 1year respectively. The predictive ability of the SORG ML algorithm and nomogram was superior to currently used preoperative survival estimation scores for spinal metastatic disease.