Literature DB >> 35925406

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

Gregory Zegarek1, Enrico Tessitore2, Etienne Chaboudez2, Aria Nouri2, Karl Schaller2, Renato Gondar2.   

Abstract

PURPOSE: In this study, we wished to compare statistically the novel SORG algorithm in predicting survival in spine metastatic disease versus currently used methods.
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.
RESULTS: 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 90 days and 0.85 for 1 year. The SORG nomogram showed a predictive ability at 90 days and 1 year 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 3 months and 1 year respectively.
CONCLUSION: The predictive ability of the SORG ML algorithm and nomogram was superior to currently used preoperative survival estimation scores for spinal metastatic disease.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.

Entities:  

Keywords:  Hybrid therapy; NOMS algorithm; Outcome measures; SORG; Separation surgery; Spinal metastases; Survival prediction

Mesh:

Year:  2022        PMID: 35925406     DOI: 10.1007/s00701-022-05322-7

Source DB:  PubMed          Journal:  Acta Neurochir (Wien)        ISSN: 0001-6268            Impact factor:   2.816


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