Comparison of two clustering methods on surgical patients with adult spinal deformity: Importance of the variable choice on the obtained results and their interpretation.

Introduction: Clustering techniques can reveal patterns in complex datasets and enable further statistical analysis, but outcomes may vary based on variable selection. Research question: Does the choice of input variables affect clustering results in patients undergoing surgery for adult spinal deformity (ASD)? Materials and methods: Hierarchical clustering was applied using two variable sets: C16 (16 variables including demographic, radiographic and quality-of-life metrics) and C12 (12 primarily radiographic variables). Results: Data from 784 patients were analyzed. Both C16 and C12 identified three clusters. In Cluster 1, C16 included younger idiopathic scoliosis patients (age 29.42 ± 11.69 years), while C12 grouped slightly older patients (35.77 ± 15.44 years) with similar sagittal alignment and Cobb angles, but C16 had better quality of life (inverse ODI: 82.24 ± 11.90 vs 74.50 ± 16.90). Cluster 2 included patients with sagittal malalignment and moderate disability, showing similar demographics and ODI, but differing in radiographic features such as Cobb angle (41.39° vs 36.40°), coronal balance (22.12 mm vs 18.66 mm), and lumbar lordosis index (0.77 vs 0.71). Cluster 3 captured patients with severe sagittal malalignment and greater disability. Here, C12 showed more pronounced malalignment (global tilt: 47.45° vs 39.81°), but better quality of life (inverse ODI: 45.94 vs 41.41). The PCA revealed that clustering was driven by quality-of-life metrics in C16 and by radiological parameters in C12. Discussion and conclusion: both algorithms identified similar cluster numbers and profiles, but the dominant clustering variables differed, highlighting the need to align variable selection with specific study goals.