Machine learning–based identification and ranking of risk factors for lumbar paraspinal muscle atrophy - Summary - MDSpire

Machine learning–based identification and ranking of risk factors for lumbar paraspinal muscle atrophy

  • By

  • Lukas Schönnagel

  • Tom Folkerts

  • Ali Guven

  • Erika Chiapparelli

  • Jiaqi Zhu

  • Gaston Camino-Willhuber

  • Thomas Caffard

  • Artine Arzani

  • Paul Köhli

  • Marco D. Burkhard

  • Jennifer Shue

  • Andrew A. Sama

  • Federico P. Girardi

  • Frank P. Cammisa

  • Alexander P. Hughes

  • March 28, 2026

  • 0 min

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Objective:

To assess and rank the association of established and novel factors associated with atrophy of the multifidus muscle (MF) in patients undergoing spinal surgery due to degenerative pathologies, with a clear definition of what constitutes established versus novel factors.

Key Findings:
  • Established and novel risk factors for MF atrophy were identified and ranked, with implications for improving patient assessment and management strategies.
Interpretation:

The study highlights the importance of integrating multiple risk factors into predictive models to better understand and anticipate MF atrophy in patients undergoing spinal surgery.

Limitations:
  • Retrospective design may introduce selection bias.
  • Exclusion of patients with lumbar spinal fusion limits generalizability.
  • Potential confounding factors not accounted for in the analysis.
Conclusion:

This research underscores the potential of machine learning in identifying and prioritizing risk factors for MF atrophy, which could enhance patient assessment and management strategies.

Original Source(s)

Machine learning–based identification and ranking of risk factors for lumbar paraspinal muscle atrophy

  • by Lukas Schönnagel, Tom Folkerts, Ali Guven, Erika Chiapparelli, Jiaqi Zhu, Gaston Camino-Willhuber, Thomas Caffard, Artine Arzani, Paul Köhli, Marco D. Burkhard, Jennifer Shue, Andrew A. Sama, Federico P. Girardi, Frank P. Cammisa, Alexander P. Hughes

  • March 28, 2026

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