Artificial Intelligence Based Hierarchical Clustering of Patient Types and Intervention Categories in Adult Spinal Deformity Surgery

作者：Christopher P. Ames, Justin S. Smith, Ferrán Pellisé, Michael P. Kelly, Ahmet Alanay, Emre Acaroğlu, Francisco Javier Sánchez Pérez-Grueso, Frank Kleinstück, Ibrahim Obeid, Alba Vila-Casademunt, Christopher I. Shaffrey, Douglas C. Burton, Virginie Lafage, Frank J. Schwab, Christopher I. Shaffrey, Shay Bess, Miquel Serra‐Burriel · 发表于：Spine · 年份：2019 · DOI：10.1097/brs.0000000000002974 · 被引用次数：147 · 研究领域：Scoliosis diagnosis and treatment、Spine and Intervertebral Disc Pathology、Cervical and Thoracic Myelopathy

STUDY DESIGN: Retrospective review of prospectively-collected, multicenter adult spinal deformity (ASD) databases. OBJECTIVE: To apply artificial intelligence (AI)-based hierarchical clustering as a step toward a classification scheme that optimizes overall quality, value, and safety for ASD surgery. SUMMARY OF BACKGROUND DATA: Prior ASD classifications have focused on radiographic parameters associated with patient reported outcomes. Recent work suggests there are many other impactful preoperative data points. However, the ability to segregate patient patterns manually based on hundreds of data points is beyond practical application for surgeons. Unsupervised machine-based clustering of patient types alongside surgical options may simplify analysis of ASD patient types, procedures, and outcomes. METHODS: Two prospective cohorts were queried for surgical ASD patients with baseline, 1-year, and 2-year SRS-22/Oswestry Disability Index/SF-36v2 data. Two dendrograms were fitted, one with surgical features and one with patient characteristics. Both were built with Ward distances and optimized with the gap method. For each possible n patient cluster by m surgery, normalized 2-year improvement and major complication rates were computed. RESULTS: Five hundred-seventy patients were included. Three optimal patient types were identified: young with coronal plane deformity (YC, n = 195), older with prior spine surgeries (ORev, n = 157), and older without prior spine surgeries (OPrim, n =...