Abstract
Image-based subject-specific models and simulations are recently being introduced to complement current state-of-the-art mostly static insights of the adult spinal deformity (ASD) pathology and improve the often poor surgical outcomes. Although the accuracy of a recently developed subject-specific modeling and simulation framework has already been quantified, its reliability to perform marker-driven kinematic analyses has not yet been investigated. The aim of this work was to evaluate the reliability of this subject-specific framework to measure spine kinematics in ASD patients, in terms of 1) the overall test-retest repeatability; 2) the inter-operator agreement of spine kinematic estimates; and, 3) the uncertainty of those spine kinematics to operator-dependent parameters of the framework. To evaluate the overall repeatability 1], four ASD subjects and one control subject participated in a test-retest study with a 2-week interval. At both time instances, subject-specific spino-pelvic models were created by one operator to simulate a recorded forward trunk flexion motion. Next, to evaluate inter-operator agreement 2], three trained operators each created a model for three ASD subjects to simulate the same forward trunk flexion motion. Intraclass correlation coefficients (ICC’s) of the range of motion (ROM) of conventional spino-pelvic parameters [lumbar lordosis (LL), sagittal vertical axis (SVA), thoracic kyphosis (TK), pelvic tilt (PT), T1-and T9-spino-pelvic inclination (T1/T9-SPI)] were used to evaluate kinematic reliability 1] and inter-operator agreement 2]. Lastly, a Monte-Carlo probabilistic simulation was used to evaluate the uncertainty of the intervertebral joint kinematics to operator variability in the framework, for three ASD subjects 3]. LL, SVA, and T1/T9-SPI had an excellent test-retest reliability for the ROM, while TK and PT did not. Inter-operator agreement was excellent, with ICC values higher than test-retest reliability. These results indicate that operator-induced uncertainty has a limited impact on kinematic simulations of spine flexion, while test-retest reliability has a much higher variability. The definition of the intervertebral joints in the framework was identified as the most sensitive operator-dependent parameter. Nevertheless, intervertebral joint estimations had small mean 90% confidence intervals (1.04°–1.75°). This work will contribute to understanding the limitations of kinematic simulations in ASD patients, thus leading to a better evaluation of future hypotheses.
Highlights
Musculoskeletal (MS) models and associated simulations of motion are used to provide a better understanding of the complex biomechanics of, primarily, the healthy spine (Bruno et al, 2015; Ignasiak et al, 2018; Beaucage-Gauvreau et al, 2019)
High Standard error of measurement (SEM) and smallest detectable difference (SDD) were noted for TK, which presented with a poor reliability (ICC
This study aimed at evaluating the kinematic variability associated with both intrinsic and extrinsic sources of error (Schwartz et al, 2004), of a subject-specific spino-pelvic modeling method previously developed to quantify intervertebral joint motion in adult spinal deformity (ASD) subjects (Overbergh et al, 2020)
Summary
Musculoskeletal (MS) models and associated simulations of motion are used to provide a better understanding of the complex biomechanics of, primarily, the healthy spine (Bruno et al, 2015; Ignasiak et al, 2018; Beaucage-Gauvreau et al, 2019). In healthy subjects these MS models have shown excellent test-retest reliability in terms of spine curvature estimation (expressed as lumbar lordosis and thoracic kyphosis) (Burkhart et al, 2020) These MS models and simulation-based approaches were introduced in pathological spine populations, such as adult spinal deformity (ASD) (Overbergh et al, 2020) and adolescent idiopathic scoliosis (AIS) (Schmid et al, 2016), to complement the current state-ofthe-art mostly static assessments and on the longer term improve the often poor outcomes of surgical treatments (Smith et al, 2016). This novel modeling method circumvents the traditional marker-based scaling step (Delp et al, 2007; Burkhart et al, 2020), which is applicable to healthy subjects, but not suitable for subjects with a spinal malalignment due to the lack of sufficient a priori information on the specific spinal deformity
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