Evidence map›Paper›PMID 35925406›Full record

ArticleActa neurochirurgica2022

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

Gregory Zegarek, Enrico Tessitore, Etienne Chaboudez, Aria Nouri, Karl Schaller, Renato Gondar

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Article in Acta neurochirurgica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Gregory ZegarekDepartment of Neurosurgery, Geneva University Hospitals, University of Geneva, Geneva, Switzerland. gzegarek3@gmail.com.
Enrico TessitoreDepartment of Neurosurgery, Geneva University Hospitals, University of Geneva, Geneva, Switzerland.
Etienne ChaboudezDepartment of Neurosurgery, Geneva University Hospitals, University of Geneva, Geneva, Switzerland.
Aria NouriDepartment of Neurosurgery, Geneva University Hospitals, University of Geneva, Geneva, Switzerland.
Karl SchallerDepartment of Neurosurgery, Geneva University Hospitals, University of Geneva, Geneva, Switzerland.
Renato GondarDepartment of Neurosurgery, Geneva University Hospitals, University of Geneva, Geneva, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeIn this study, we wished to compare statistically the novel SORG algorithm in predicting survival in spine metastatic disease versus currently used methods.

methodsWe 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.

resultsThe 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.

conclusionThe predictive ability of the SORG ML algorithm and nomogram was superior to currently used preoperative survival estimation scores for spinal metastatic disease.

Indexed as

Spinal NeoplasmsAlgorithmsCross-Sectional StudiesHumansPrognosisRetrospective StudiesSeverity of Illness IndexHybrid therapyNOMS algorithmOutcome measuresSeparation surgerySORGSpinal metastasesSurvival prediction

Identifiers

PMID35925406

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