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

Risultato della ricerca: Contributo in rivistaArticolo in rivista

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.
Lingua originaleEnglish
pagine (da-a)2629-2638
Numero di pagine10
RivistaEuropean Spine Journal
Volume31
DOI
Stato di pubblicazionePubblicato - 2022

Keywords

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

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