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Spinal sagittal alignment goals based on statistical modelling and musculoskeletal simulations


Caprara, Sebastiano; Moschini, Greta; Snedeker, Jess G; Farshad, Mazda; Senteler, Marco (2020). Spinal sagittal alignment goals based on statistical modelling and musculoskeletal simulations. Journal of Biomechanics, 102:109621-109628.

Abstract

The definition of target alignment for spinal fusion surgery follows anatomical criteria and strongly relies on surgical experience. However, the optimal patient-specific alignment often remains unknown. Statistical models could provide information about physiological alignments, and musculoskeletal models are powerful tools to investigate biomechanics. We aimed to statistically predict alignments and hypothesized they would be biomechanically favorable. A statistical model was trained with 60 annotated radiographs to predict physiological sagittal alignment based on position of femoral heads and sacrum. Predicted alignments for 11 back pain patients were clinically evaluated in terms of balance and compared to Original alignments. The normative ranges for spinal balance parameters were obtained from Surgimap™. Musculoskeletal loads were furthermore simulated in upright standing and 30° forward flexion, using alignment-specific musculoskeletal models. For the majority of Predicted alignments (n = 9) at least two of three investigated balance parameters were within the normative range, as opposed to the minority of the Original alignments (n = 4). Predicted alignments resulted in significantly lowered overall muscle activity and compressive loads (all levels, both postures). Shear force magnitudes in upright standing decreased significantly at levels L1L2 (-68 N) and L2L3 (-69 N) and clearly yet not significantly at L3L4 (-39 N) and L4L5 (-152 N). Shear loads at level L5S1 remained the same. In flexed postures identical trends were observed. The statistical model was able to predict spinal alignments that led to both improved balance and reduced musculoskeletal loads. Further studies are needed to investigate clinical validity of such models.

Abstract

The definition of target alignment for spinal fusion surgery follows anatomical criteria and strongly relies on surgical experience. However, the optimal patient-specific alignment often remains unknown. Statistical models could provide information about physiological alignments, and musculoskeletal models are powerful tools to investigate biomechanics. We aimed to statistically predict alignments and hypothesized they would be biomechanically favorable. A statistical model was trained with 60 annotated radiographs to predict physiological sagittal alignment based on position of femoral heads and sacrum. Predicted alignments for 11 back pain patients were clinically evaluated in terms of balance and compared to Original alignments. The normative ranges for spinal balance parameters were obtained from Surgimap™. Musculoskeletal loads were furthermore simulated in upright standing and 30° forward flexion, using alignment-specific musculoskeletal models. For the majority of Predicted alignments (n = 9) at least two of three investigated balance parameters were within the normative range, as opposed to the minority of the Original alignments (n = 4). Predicted alignments resulted in significantly lowered overall muscle activity and compressive loads (all levels, both postures). Shear force magnitudes in upright standing decreased significantly at levels L1L2 (-68 N) and L2L3 (-69 N) and clearly yet not significantly at L3L4 (-39 N) and L4L5 (-152 N). Shear loads at level L5S1 remained the same. In flexed postures identical trends were observed. The statistical model was able to predict spinal alignments that led to both improved balance and reduced musculoskeletal loads. Further studies are needed to investigate clinical validity of such models.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Balgrist University Hospital, Swiss Spinal Cord Injury Center
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Life Sciences > Biophysics
Health Sciences > Orthopedics and Sports Medicine
Physical Sciences > Biomedical Engineering
Health Sciences > Rehabilitation
Language:English
Date:26 March 2020
Deposited On:22 Feb 2021 16:02
Last Modified:23 Feb 2021 21:00
Publisher:Elsevier
ISSN:0021-9290
OA Status:Hybrid
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1016/j.jbiomech.2020.109621
PubMed ID:31959392

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