Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Oct;31(10):2629-2638.
doi: 10.1007/s00586-022-07135-9. Epub 2022 Feb 21.

FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease

Victor E Staartjes  1   2   3 , Vittorio Stumpo  4 , Luca Ricciardi  5 , Nicolai Maldaner  4 , Hubert A J Eversdijk  6 , Moira Vieli  4 , Olga Ciobanu-Caraus  4 , Antonino Raco  5 , Massimo Miscusi  5 , Andrea Perna  7   8 , Luca Proietti  7   8 , Giorgio Lofrese  9 , Michele Dughiero  9 , Francesco Cultrera  9 , Nicola Nicassio  9 , Seong Bae An  10 , Yoon Ha  10 , Aymeric Amelot  11   12 , Irene Alcobendas  13 , Jose M Viñuela-Prieto  13 , Maria L Gandía-González  13 , Pierre-Pascal Girod  14 , Sara Lener  14 , Nikolaus Kögl  14 , Anto Abramovic  14 , Nico Akhavan Safa  15 , Christoph J Laux  15 , Mazda Farshad  15 , Dave O'Riordan  16 , Markus Loibl  17 , Anne F Mannion  16 , Alba Scerrati  18 , Granit Molliqaj  19 , Enrico Tessitore  19 , Marc L Schröder  6 , W Peter Vandertop  20 , Martin N Stienen  21 , Luca Regli  4 , Carlo Serra  4
Affiliations
Free article

FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease

Victor E Staartjes et al. Eur Spine J. 2022 Oct.
Free article

Abstract

Background: Indications and outcomes in lumbar spinal fusion for degenerative disease are notoriously heterogenous. Selected subsets of patients show remarkable benefit. However, their objective identification is often difficult. Decision-making may be improved with reliable prediction of long-term outcomes for each individual patient, improving patient selection and avoiding ineffective procedures.

Methods: Clinical prediction models for long-term functional impairment [Oswestry Disability Index (ODI) or Core Outcome Measures Index (COMI)], back pain, and leg pain after lumbar fusion for degenerative disease were developed. Achievement of the minimum clinically important difference at 12 months postoperatively was defined as a reduction from baseline of at least 15 points for ODI, 2.2 points for COMI, or 2 points for pain severity.

Results: Models were developed and integrated into a web-app ( https://neurosurgery.shinyapps.io/fuseml/ ) based on a multinational cohort [N = 817; 42.7% male; mean (SD) age: 61.19 (12.36) years]. At external validation [N = 298; 35.6% male; mean (SD) age: 59.73 (12.64) years], areas under the curves for functional impairment [0.67, 95% confidence interval (CI): 0.59-0.74], back pain (0.72, 95%CI: 0.64-0.79), and leg pain (0.64, 95%CI: 0.54-0.73) demonstrated moderate ability to identify patients who are likely to benefit from surgery. Models demonstrated fair calibration of the predicted probabilities.

Conclusions: Outcomes after lumbar spinal fusion for degenerative disease remain difficult to predict. Although assistive clinical prediction models can help in quantifying potential benefits of surgery and the externally validated FUSE-ML tool may aid in individualized risk-benefit estimation, truly impacting clinical practice in the era of "personalized medicine" necessitates more robust tools in this patient population.

Keywords: Clinical prediction model; Machine learning; Neurosurgery; Outcome prediction; Predictive analytics; Spinal fusion.

PubMed Disclaimer

References

    1. Ravindra VM, Senglaub SS, Rattani A et al (2018) Degenerative lumbar spine disease: estimating global incidence and worldwide volume. Glob Spine J 8:784–794. https://doi.org/10.1177/2192568218770769 - DOI
    1. Manchikanti L, Abdi S, Atluri S et al (2013) An update of comprehensive evidence-based guidelines for interventional techniques in chronic spinal pain. Part II: guidance and recommendations. Pain Physician 16:S49-283 - PubMed
    1. Bono CM, Lee CK (2004) Critical analysis of trends in fusion for degenerative disc disease over the past 20 years: influence of technique on fusion rate and clinical outcome. Spine 29:455–463. https://doi.org/10.1097/01.brs.0000090825.94611.28 - DOI - PubMed
    1. Mannion AF, Brox J-I, Fairbank JC (2016) Consensus at last! long-term results of all randomized controlled trials show that fusion is no better than non-operative care in improving pain and disability in chronic low back pain. Spine J Off J North Am Spine Soc 16:588–590. https://doi.org/10.1016/j.spinee.2015.12.001 - DOI
    1. Staartjes VE, Vergroesen P-PA, Zeilstra DJ, Schröder ML (2018) Identifying subsets of patients with single-level degenerative disc disease for lumbar fusion: the value of prognostic tests in surgical decision making. Spine J 18:558–566. https://doi.org/10.1016/j.spinee.2017.08.242 - DOI - PubMed

Publication types

LinkOut - more resources