Patient specific finite element modelling outputs outperform clinical metrics in predicting fusion cage subsidence
File(s)
Author(s)
Lali, Francis
Raftery, Kay
Levy, Hannah
Freedman, Brett
Newell, Nicolas
Type
Journal Article
Abstract
Study Design.
Finite element (FE) analysis of retrospective clinical cohort.
Objective.
To determine whether preoperative CT-derived FE model outputs can improve subsidence prediction compared to conventional clinical measurements alone in patients undergoing transforaminal lumbar interbody fusion (TLIF).
Summary of Background Data.
Cage subsidence occurs in approximately 20% of spinal fusion patients and can lead to complications requiring reoperation. While individual risk factors are known, no validated tool integrates patient anatomy, bone quality, and implant characteristics to predict subsidence. Finite element models have been hypothesized to predict subsidence but lack clinical validation.
Methods.
Patient-specific FE models were created from preoperative CT scans of 42 TLIF patients: N=22 Severe subsidence (≥4 mm); N=20 Non-severe subsidence (<4 mm). Vertebral geometries were segmented, and bone material properties were assigned based on Hounsfield units (HU). Cage positions from postoperative scans were registered to preoperative anatomy. Endplate and trabecular stresses and strains from FE models were compared to clinical measures using receiver operating characteristic analysis.
Results.
15 Principal stresses and strains of the FE simulations showed significantly higher values in severely subsided patients compared to the Non-Severe group. Average trabecular intermediate strain achieved the highest area under curve score (AUC=0.809), outperforming all clinical metrics. Peak endplate minimum principal stress (AUC=0.775) was the second-best FE classifier. Traditional clinical measures showed lower discriminative ability: cage length (AUC=0.797), cage width (AUC=0.750), and cage height (AUC=0.698).
Conclusion.
Patient-specific FE model outputs significantly correlate with clinical subsidence outcomes and outperform several traditional metrics in classifying severe subsidence. Both endplate and trabecular stresses and strains are important predictors, with average values showing comparable or superior performance to peak values. Integration of FE models into the clinical workflow could provide a comprehensive preoperative subsidence prediction tool.
Finite element (FE) analysis of retrospective clinical cohort.
Objective.
To determine whether preoperative CT-derived FE model outputs can improve subsidence prediction compared to conventional clinical measurements alone in patients undergoing transforaminal lumbar interbody fusion (TLIF).
Summary of Background Data.
Cage subsidence occurs in approximately 20% of spinal fusion patients and can lead to complications requiring reoperation. While individual risk factors are known, no validated tool integrates patient anatomy, bone quality, and implant characteristics to predict subsidence. Finite element models have been hypothesized to predict subsidence but lack clinical validation.
Methods.
Patient-specific FE models were created from preoperative CT scans of 42 TLIF patients: N=22 Severe subsidence (≥4 mm); N=20 Non-severe subsidence (<4 mm). Vertebral geometries were segmented, and bone material properties were assigned based on Hounsfield units (HU). Cage positions from postoperative scans were registered to preoperative anatomy. Endplate and trabecular stresses and strains from FE models were compared to clinical measures using receiver operating characteristic analysis.
Results.
15 Principal stresses and strains of the FE simulations showed significantly higher values in severely subsided patients compared to the Non-Severe group. Average trabecular intermediate strain achieved the highest area under curve score (AUC=0.809), outperforming all clinical metrics. Peak endplate minimum principal stress (AUC=0.775) was the second-best FE classifier. Traditional clinical measures showed lower discriminative ability: cage length (AUC=0.797), cage width (AUC=0.750), and cage height (AUC=0.698).
Conclusion.
Patient-specific FE model outputs significantly correlate with clinical subsidence outcomes and outperform several traditional metrics in classifying severe subsidence. Both endplate and trabecular stresses and strains are important predictors, with average values showing comparable or superior performance to peak values. Integration of FE models into the clinical workflow could provide a comprehensive preoperative subsidence prediction tool.
Date Issued
2026-08-01
Date Acceptance
2026-03-01
Citation
Spine, 2026, 51 (15), pp.E391-E399
ISSN
0362-2436
Publisher
Ovid Technologies (Wolters Kluwer Health)
Start Page
E391
End Page
E399
Journal / Book Title
Spine
Volume
51
Issue
15
Copyright Statement
© 2026 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
Identifier
10.1097/BRS.0000000000005698
Subjects
finite element modelling
fusion cage
fusion cage subsidence
spine
surgery
TLIF
biomechanics
interbody fusion
Publication Status
Published
Date Publish Online
2026-03-24