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Abstract Functional assessment is a key element in evaluating adult spinal deformity (ASD) patients. The multitude of 3D kinematic parameters provided by movement analysis can be confusing for spine surgeons. The aim was to investigate movement patterns of ASD based on key kinematic parameters. 115 primary ASD and 36 controls underwent biplanar radiographs and 3D movement analysis during walking, sit-to-stand and stair ascent to calculate joint and segment kinematics. Principal component analysis was applied to identify the most relevant kinematic parameters that define movement strategies adopted by ASD. Pelvis and head relative to pelvis kinematics were the most relevant parameters. ASD patients adopted four different movement strategies. Class 1: normative head and pelvis kinematics. Class 2: persistent pelvic backward tilt. Class 3: persistent forward shift of the head. Class 4: both pelvic backward tilt and forward shift of the head. Patients in class 3 and 4 presented sagittal malalignment on static radiographs with increased pelvic tilt, pelvic incidence-lumbar lordosis mismatch and sagittal vertical axis. Surprisingly, patients in class 3 had normal pelvic kinematics during movement, showing the importance of functional evaluation. In addition to being key segments in maintaining static global posture, head and pelvis were found to define movement patterns.

IntroductionAdult 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.MethodsASD 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.ResultsIn 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