No abstract

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 < 0.05). Similar results were reported when the 3 simulations were tested on ASD after treatment.DiscussionThis 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.

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. ResultsThe 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-R 2 = 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.

No abstract

No abstract