Abstract
The choice of the best management for Adult Spine Deformity (ASD) is challenging. Health-related quality of life (HRQoL), comorbidities, symptoms and spine geometry, along with surgical risk and potential residual disabilityplay a role, and adefinite algorithm for patient management is lacking. Machine learning allows to analyse complex settingsmore efficiently than other available statistical tools. Aim of this study was to develop a machine-learning algorithm that, based on baseline data, would be able to predict whether an ASD patient would undergo surgery or not. Retrospective evaluation of prospectively collected data.Demographic data, HRQoL and radiographic parameters were collected. Two clustering methodswereperformed to differentiate groups of patients with similar characteristics. Threemodels were then used to identify the most relevant variables for management prediction. Data from 1319 patients were available. Three clusters were identified:older subjects with sagittal imbalance and high PI, younger patientswith greater coronal deformity and no sagittal imbalance,older patients with moderate sagittal imbalance and lower PI.The group of younger patients showed the highest error rate for the prediction (37%), which was lower for the other two groups (20-27%). For all groups, quality of life parameters such as the ODI and the SRS 22 and the Cobb angle of the major curve were the strongest predictors of surgical indication, albeit with different odds ratios in each group. Threeclusters could be identifiedalong with the variables that, in each, are most likely to drive the choice of management.
Published Version
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