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, Vittorio Stumpo, Luca Ricciardi, Nicolai Maldaner, Hubert A. J. Eversdijk, Moira Vieli, Olga Ciobanu-Caraus, Antonino Raco, Massimo Miscusi, Andrea Perna, Luca Proietti, Giorgio Lofrese, Michele Dughiero, Francesco Cultrera, Nicola Nicassio, Seong Bae An, Yoon Ha, Aymeric Amelot, Irene Alcobendas, Jose M. Viñuela-PrietoMaria L. Gandía-González, Pierre-Pascal Girod, Sara Lener, Nikolaus Kögl, Anto Abramovic, Nico Akhavan Safa, Christoph J. Laux, Mazda Farshad, Dave O’Riordan, Markus Loibl, Anne F. Mannion, Alba Scerrati, Granit Molliqaj, Enrico Tessitore, Marc L. Schröder, W. Peter Vandertop, Martin N. Stienen, Luca Regli, Carlo Serra

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)2629-2638
Number of pages10
JournalEuropean Spine Journal
Volume31
Issue number10
Early online date2022
DOIs
Publication statusPublished - Oct 2022

Keywords

  • Clinical prediction model
  • Machine learning
  • Neurosurgery
  • Outcome prediction
  • Predictive analytics
  • Spinal fusion

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