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

Zegarek, Gregory; Tessitore, Enrico; Nouri, Aria; Schaller, Karl; Gondar, Renato

doi:10.1016/j.bas.2021.100122

Cited by 2 publications

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“…Prior publishing in conference proceedings: This work has been published by the same authors in the conference proceedings of Brain and Spine, Volume 1, Supplement 1, 2021 [32].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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In this study, 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 90-days and 0.85 for 1-year. The SORG Nomogram showed a predictive ability at 90-days and 1-year with AUC's of 0.87 and 0.76 respectively.These results showed excellent discriminative ability as compared with the Tokuhashi Original Score which achieved AUC's of 0.70 and 0.69 and the Tokuhashi Revised Score which had AUC's of 0.65 and 0.71 for 3-month and 1-year respectively. The predictive ability of the SORG ML algorithm and nomogram were superior to currently used preoperative survival estimation scores for spinal metastatic disease.

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“…Prior publishing in conference proceedings: This work has been published by the same authors in the conference proceedings of Brain and Spine, Volume 1, Supplement 1, 2021 [32].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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SORG Algorithm to Predict 3- and 12-month Survival in Metastatic Spinal Disease: A Cross-Sectional Population-Based Retrospective Study.

Zegarek

Tessitore

Chaboudez

et al. 2021

Preprint

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In this study, 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 90-days and 0.85 for 1-year. The SORG Nomogram showed a predictive ability at 90-days and 1-year with AUC’s of 0.87 and 0.76 respectively. These results showed excellent discriminative ability as compared with the Tokuhashi Original Score which achieved AUC’s of 0.70 and 0.69 and the Tokuhashi Revised Score which had AUC’s of 0.65 and 0.71 for 3-month and 1-year respectively. The predictive ability of the SORG ML algorithm and nomogram were superior to currently used preoperative survival estimation scores for spinal metastatic disease.

show abstract

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

Cited by 2 publications

References 0 publications

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

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

SORG Algorithm to Predict 3- and 12-month Survival in Metastatic Spinal Disease: A Cross-Sectional Population-Based Retrospective Study.

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