Artificial Intelligence Based Hierarchical Clustering of Patient Types and Intervention Categories in Adult Spinal Deformity Surgery: Towards a New Classification Scheme that Predicts Quality and Value.
Abstract
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 = 218). Osteotomy type, instrumentation and interbody
fusion were combined to define four surgical clusters. The intersection of patient-based
and surgery-based clusters yielded 12 subgroups, with major complication rates ranging
from 0% to 51.8% and 2-year normalized improvement ranging from -0.1% for SF36v2 MCS
in cluster [1,3] to 100.2% for SRS self-image score in cluster [2,1]. CONCLUSION:Unsupervised
hierarchical clustering can identify data patterns that may augment preoperative decision-making
through construction of a 2-year risk-benefit grid. In addition to creating a novel
AI-based ASD classification, pattern identification may facilitate treatment optimization
by educating surgeons on which treatment patterns yield optimal improvement with lowest
risk. LEVEL OF EVIDENCE:4.
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https://hdl.handle.net/10161/19126Published Version (Please cite this version)
10.1097/brs.0000000000002974Publication Info
Ames, Christopher P; Smith, Justin S; Pellisé, Ferran; Kelly, Michael; Alanay, Ahmet;
Acaroğlu, Emre; ... European Spine Study Group, International Spine Study Group (2019). Artificial Intelligence Based Hierarchical Clustering of Patient Types and Intervention
Categories in Adult Spinal Deformity Surgery: Towards a New Classification Scheme
that Predicts Quality and Value. Spine, 44(13). pp. 915-926. 10.1097/brs.0000000000002974. Retrieved from https://hdl.handle.net/10161/19126.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Christopher Ignatius Shaffrey
Professor of Orthopaedic Surgery
Christopher Ignatius Shaffrey
Professor of Orthopaedic Surgery
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