Classifying Patients Operated for Spondylolisthesis: A K-Means Clustering Analysis of Clinical Presentation Phenotypes

BACKGROUND Trials of lumbar spondylolisthesis are difficult to compare because of the heterogeneity in the populations studied. OBJECTIVE To define patterns of clinical presentation. METHODS This is a study of the prospective Quality Outcomes Database spondylolisthesis registry, including patients who underwent single-segment surgery for grade 1 degenerative lumbar spondylolisthesis. Twenty-four-month patient-reported outcomes (PROs) were collected. A k-means clustering analysis-an unsupervised machine learning algorithm-was used to identify clinical presentation phenotypes. RESULTS Overall, 608 patients were identified, of which 507 (83.4%) had 24-mo follow-up. Clustering revealed 2 distinct cohorts. Cluster 1 (high disease burden) was younger, had higher body mass index (BMI) and American Society of Anesthesiologist (ASA) grades, and globally worse baseline PROs. Cluster 2 (intermediate disease burden) was older and had lower BMI and ASA grades, and intermediate baseline PROs. Baseline radiographic parameters were similar (P > .05). Both clusters improved clinically (P