Evidence map›Paper›PMID 39638884›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2025

Temporal validation of the SORG 90-Day and 1-Year machine learning algorithms for survival of patients with spinal metastatic disease.

Hester Zijlstra, R H Kuijten, Anirudh V Bhimavarapu, Amanda Lans, Rachel E Cross, Ahmad Alnasser, Aditya V Karhade, Jorrit-Jan Verlaan, Olivier Q Groot, Joseph H Schwab

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Hester ZijlstraDepartment of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA. hesterzijlstra@outlook.com.ORCID http://orcid.org/0000-0003-3962-6871
R H KuijtenDepartment of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
Anirudh V BhimavarapuDepartment of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
Amanda LansDepartment of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
Rachel E CrossDepartment of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
Ahmad AlnasserDepartment of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
Aditya V KarhadeDepartment of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
Jorrit-Jan VerlaanDepartment of Orthopedic Surgery, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.
Olivier Q GrootDepartment of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
Joseph H SchwabDepartment of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe SORG-MLA was developed to predict 90-day and 1-year postoperative survival in patients with spinal metastatic disease who underwent surgery between 2000 and 2016. Due to the constant changes in treatment methods, it is essential to perform temporal validation with a recent patient population. Therefore, the purpose of this study was to validate the Skeletal Oncology Research Group machine learning algorithms (SORG-MLA) using a contemporary patient cohort.

methodsThis retrospective cohort study investigated patients who received surgical treatment for spinal metastases between January 2017 and July 2021 in two tertiary care centers in the US. Eighteen input variables needed for the SORG-MLA were collected including primary tumor, Eastern Cooperative Oncology Group (ECOG) Performance Status, and nine preoperative laboratory values. Outcomes were defined as mortality at 90-day and 1-year postoperative. Performance was assessed using calibration, discrimination, overall performance, and decision curve analysis.

resultsIn total, 464 patients were included. The validation cohort varied from the development cohort in multiple variables. Despite these differences, the SORG-MLA continued to perform well on calibration, discrimination (area under the receiver operating characteristic curve [AUC] 0.81 (95% confidence interval [CI], 0.77-0.86) for 90-day, AUC 0.75 (95% CI, 0.71-0.80) for 1-year), Brier score, and decision curve analyses.

conclusionsIn spite of recent progress in treating spinal metastases, SORG-MLA for survival in patients with spinal metastatic disease continued to perform well on temporal validation. However, updating the models using a contemporary patient cohort and stratifying by primary tumor could further improve the performance.

Indexed as

Machine LearningSpinal NeoplasmsAdultAgedAlgorithmsFemaleHumansMaleMiddle AgedRetrospective StudiesMachine learningPostoperative survivalPredictionSpinal metastasisTemporal validation

Identifiers

PMID39638884

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.