Special Poster Session 50th International Society for the Study of the Lumbar Spine Annual Meeting 2024

IMPACT OF GENERIC VS. SUBJECT-SPECIFIC MUSCLE PARAMETERS ON SPINAL LOAD PREDICTION IN MUSCULOSKELETAL MODELING (#SP-2f)

Nima Ashjaee 1 , Sidney Fels 2 , John Street 3 , Thomas Oxland 4
  1. Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
  2. Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
  3. University of British Columbia, Vancouver, BC, Canada
  4. Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada

INTRODUCTION

Subject-specific musculoskeletal models show promise in adult spinal deformity prevention and treatment [1–3]. Unfortunately, fully subject-specific models rely on comprehensive 3D imaging that is often unavailable in typical clinical settings. Conversely, generic models are more accessible but potentially less accurate than the subject-specific models. In this context, muscle properties emerge as a potentially important variable influencing the predictive accuracy. This research explores the circumstances under which the generic muscle parameters are adequate, pinpointing situations that necessitate subject-specific modeling. The objective of this study is to identify which muscle parameters, and in which specific simulations, exhibit significant differences when implementing generic versus subject-specific properties.

METHODS

We implemented a musculoskeletal modelling approach (OpenSim 3.3; Figure 1) following established research [4,5] that included generic (average of 250 subjects from the Framingham Heart Study [6,7]) and subject-specific models. Our research focused on four primary muscle parameters: geometry-path (muscle route from origin to insertion), maximum-isometric-force (derived from muscle cross-sectional area), optimal-fiber-length (the length at which a muscle fiber produces its maximum force), and tendon-slack-length. We simulated 11 different static postures, including neutral standing and trunk flexion or extension positions, each chosen based on recommendations of Mokhtarzadeh et al. [7] related to maximum Load-to-Strength ratios. Five subject-specific categories were investigated, with the foremost being a fully subject-specific model. The other four categories evaluated scenarios where one muscle parameter was reverted to generic properties. Variations in axial compression load from L5 to T1 between the fully subject-specific and adjusted models were quantified using the Relative Difference Percentage. A linear mixed-effects model was applied to analyze the influence of muscle parameters on predicted spine compression loads, incorporating covariates such as posture, age, and gender.

RESULTS

Findings showed subject-specific muscle geometry-path and maximum-isometric-force significantly impacted spinal compression loads, with average increases of 13% and 8% respectively (Figure 2).  Further, the differences between the subject-specific and generic model predictions were dependant on the posture as shown in Figure 2 (geometry-path p<0.001; max-isometric-force p=0.005). Conversely, parameters such as optimal-fiber-length (p=0.053) and tendon-slack-length (p=0.68) showed minimal effect on load predictions, with only 2% and 1% change, respectively. Notably, high trunk flexion postures were more affected by generic muscle parameters, underlining a posture-specific sensitivity. In contrast, typical standing postures had negligible differences, typically staying below the 5% variation threshold.

DISCUSSION

Our findings underscored the significant impact of individual muscle parameters, particularly the geometry-path and maximum-isometric-force, on the outcomes of biomechanical simulations. The pronounced variations observed in postures characterized by high trunk flexion emphasized the necessity of subject-specific data in such scenarios. Conversely, the minimal deviations observed in standing and low-flexion postures suggested that, when subject-specific muscle data is unavailable, these postures can be reasonably approximated using generic muscle parameters. To translate our findings into clinical application, it is recommended to prioritize standing and low-flexion postures as primary simulations in musculoskeletal modeling for spinal deformity assessments.

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Figure 1. Musculoskeletal models in various postures.

 

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Figure 2. Deviation in load prediction between generic and fully subject-specific models, showing average deviations of 13% and 8%.

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