Unsupervised Clustering of Adult Spinal Deformity Patterns Predicts Surgical and Patient-Reported Outcomes.

Abstract

Study designRetrospective cohort study.ObjectivesTo evaluate whether different radiographic clusters of adult spinal deformity identified using artificial intelligence-based clustering are associated with distinct surgical outcomes.MethodsPatients were classified based on the results of a previously conducted analysis that examined clusters of deformity, including Moderate Sagittal (Mod Sag), Severe Sagittal (Sev Sag), Coronal, and Hyper-Thoracic Kyphosis (Hyper-TK). The surgical data, HRQOL, and complication outcomes of these clusters were then compared.ResultsThe final analysis included 1062 patients. Similar to published results on a different patient sample, Mod Sag and Sev Sag patients were older, more likely to have a history of previous spine surgery, and more disabled. By 2-year, all clusters improved in HRQOL and reached a similar rate of minimal clinically important difference (MCID).The Sev Sag cluster had the highest rate major complications (53% vs 34-40%), and complications leading to reoperation (29% vs 17-23%), implant failures (20% vs 8-11%), and operative complications (27% vs 10-17%). Coronal patients had the highest rate of pulmonary complications (9% vs 3-6%) but the lowest rate of X-ray imbalance (10% vs 19-21%). No significant differences were found in neurological complications, infection rate, gastrointestinal, or cardiac events (all P > .1). Kaplan-Meier survival curves demonstrated a lower time to first complications for the Sev Sag cluster.ConclusionsAll clusters of adult spinal deformity benefit similarly from surgery as they all achieved similar rates of MCID. Although the rates of complications varied among the clusters, the types of complications were not significantly different.