Global Sagittal Angle and T9-tilt seem to be the most clinically and functionally relevant global alignment parameters in patients with Adult Spinal Deformity.

Introduction: Radiographic analysis is necessary for the assessment and the surgical planning in adults with spinal deformity (ASD). Restoration of global alignment is key to improving patient's quality of life. However, the large number of existing global alignment parameters can be confusing for surgeons. Research question: To determine the most clinically and functionally relevant global alignment parameters in ASD. Material and methods: ASD and controls underwent full body biplanar X-ray to calculate global alignment parameters: odontoid to hip axis angle (OD-HA), global sagittal angle (GSA), global tilt (GT), SVA, center of auditory meatus to hip axis (CAM-HA), SSA, T1-tilt and T9-tilt. All subjects filled HRQoL questionnaires: ODI, SF-36, VAS for pain and BDI (Beck's Depression Inventory). 3D gait analysis was performed to calculate kinematic and spatio-temporal parameters. A machine learning model predicted gait parameters and HRQoL scores from global alignment parameters. Results: 124 primary ASD and 47 controls were enrolled. T9 tilt predicted the most BDI (31%), hip flexion/extension during gait (36%), and double support time (39%). GSA predicted the most ODI (26%), thorax flexion/extension during gait (33%), and cadence (36%). Discussion and conclusion: Among all global alignment parameters, GSA, evaluating both trunk shift and knee flexion, and T9 tilt, evaluating the shift of the center of mass, were the best predictors for most of HRQoL scores and gait kinematics. Therefore, we recommend using GSA and T9 tilt in clinical practice when evaluating ASD because they represent the most quality of life and functional kinematic of these patients.

Kinematic adaptations from self-selected to fast speed walking in patients with adult spinal deformity.

PURPOSE: To investigate kinematic adaptations from self-selected to fast speed walking in ASD patients. METHODS: 115 primary ASD and 66 controls underwent biplanar radiographic X-rays and 3D gait analysis to calculate trunk, segmental spine and lower limb kinematics during self-selected and fast speed walking. Kinematic adaptation was calculated as the difference (Δ) between fast and self-selected speed walking. ASD with 7 or more limited kinematic adaptation parameters were classified as ASD-limited-KA, while those with less than 7 limited kinematic adaptation parameters were classified as ASD-mild-KA. RESULTS: 25 patients were classified as ASD-limited-KA and 90 as ASD-mild-KA. ASD-limited-KA patients walked with a lesser increase of pelvic rotation (Δ = 1.7 vs 5.5°), sagittal hip movement (Δ = 3.1 vs 7.4°) and shoulder-pelvis axial rotation (Δ = 3.4 vs 6.4°) compared to controls (all p < 0.05). ASD-limited-KA had an increased SVA (60.6 vs - 5.7 mm), PT (23.7 vs 11.9°), PI-LL (9.7 vs - 11.7°), knee flexion (9.2 vs - 0.4°) and a decreased LL (44.0 vs 61.4°) compared to controls (all p < 0.05). Kinematic and radiographic alterations were less pronounced in ASD-mild-KA. The limited increase of walking speed was correlated to the deteriorated physical component summary score of SF-36 (r = 0.37). DISCUSSION: Kinematic limitations during adaptation from self-selected to fast speed walking highlight an alteration of a daily life activity in ASD patients. ASD with limited kinematic adaptations showed more severe sagittal malalignment with an increased SVA, PT, PI-LL, and knee flexion, a decreased LL and the most deteriorated quality of life. This highlights the importance of 3D movement analysis in the evaluation of ASD.

Functional assessment using 3D movement analysis can better predict health-related quality of life outcomes in patients with adult spinal deformity: a machine learning approach.

Introduction: Adult spinal deformity (ASD) is classically evaluated by health-related quality of life (HRQoL) questionnaires and static radiographic spino-pelvic and global alignment parameters. Recently, 3D movement analysis (3DMA) was used for functional assessment of ASD to objectively quantify patient's independence during daily life activities. The aim of this study was to determine the role of both static and functional assessments in the prediction of HRQoL outcomes using machine learning methods. Methods: ASD patients and controls underwent full-body biplanar low-dose x-rays with 3D reconstruction of skeletal segment as well as 3DMA of gait and filled HRQoL questionnaires: SF-36 physical and mental components (PCS&MCS), Oswestry Disability Index (ODI), Beck's Depression Inventory (BDI), and visual analog scale (VAS) for pain. A random forest machine learning (ML) model was used to predict HRQoL outcomes based on three simulations: (1) radiographic, (2) kinematic, (3) both radiographic and kinematic parameters. Accuracy of prediction and RMSE of the model were evaluated using 10-fold cross validation in each simulation and compared between simulations. The model was also used to investigate the possibility of predicting HRQoL outcomes in ASD after treatment. Results: In total, 173 primary ASD and 57 controls were enrolled; 30 ASD were followed-up after surgical or medical treatment. The first ML simulation had a median accuracy of 83.4%. The second simulation had a median accuracy of 84.7%. The third simulation had a median accuracy of 87%. Simulations 2 and 3 had comparable accuracies of prediction for all HRQoL outcomes and higher predictions compared to Simulation 1 (i.e., accuracy for PCS = 85 ± 5 vs. 88.4 ± 4 and 89.7% ± 4%, for MCS = 83.7 ± 8.3 vs. 86.3 ± 5.6 and 87.7% ± 6.8% for simulations 1, 2 and 3 resp., p < 0.05). Similar results were reported when the 3 simulations were tested on ASD after treatment. Discussion: This study showed that kinematic parameters can better predict HRQoL outcomes than stand-alone classical radiographic parameters, not only for physical but also for mental scores. Moreover, 3DMA was shown to be a good predictive of HRQoL outcomes for ASD follow-up after medical or surgical treatment. Thus, the assessment of ASD patients should no longer rely on radiographs alone but on movement analysis as well.

Global malalignment in adolescent idiopathic scoliosis: the axial deformity is the main driver.

PURPOSE: To evaluate the global alignment of non-operated subjects with adolescent idiopathic scoliosis. METHOD: A total of 254 subjects with AIS and 64 controls underwent low dose biplanar X-rays and had their spine, pelvis, and rib cage reconstructed in 3D. Global alignment was measured in the sagittal and frontal planes by calculating the OD-HA angle (between C2 dens to hip axis with the vertical). Subjects with AIS were classified as malaligned if the OD-HA was > 95th percentile relative to controls. RESULTS: The sagittal OD-HA in AIS remained within the normal ranges. In the frontal plane, 182 AIS were normally aligned (Group 1, OD-HA = 0.9°) but 72 were malaligned (Group 2, OD-HA = 2.9°). Group 2 had a more severe spinal deformity in the frontal and horizontal planes compared to Group 1 (Cobb: 42 ± 16° vs. 30 ± 18°; apical vertebral rotation AVR: 19 ± 10° vs. 12 ± 7°, all p < 0.05). Group 2 subjects were mainly classified as Lenke 5 or 6. 19/72 malaligned subjects had a mild deformity (Cobb < 30°) but a progressive scoliosis (severity index ≥ 0.6). The frontal OD-HA angle was found to be mainly determined (adjusted-R2 = 0.22) by the apical vertebral rotation and secondarily by the Lenke type. CONCLUSIONS: This study showed that frontal malalignment is more common in distal major structural scoliosis and its main driver is the apical vertebral rotation. This highlights the importance of monitoring the axial plane deformity in order to avoid worsening of the frontal global alignment.