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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.

dc.contributor.author Shaffrey, Christopher
dc.contributor.author Ames, Christopher P
dc.contributor.author Smith, Justin S
dc.contributor.author Pellisé, Ferran
dc.contributor.author Kelly, Michael
dc.contributor.author Alanay, Ahmet
dc.contributor.author Acaroğlu, Emre
dc.contributor.author Pérez-Grueso, Francisco Javier Sánchez
dc.contributor.author Kleinstück, Frank
dc.contributor.author Obeid, Ibrahim
dc.contributor.author Vila-Casademunt, Alba
dc.contributor.author Burton, Douglas
dc.contributor.author Lafage, Virginie
dc.contributor.author Schwab, Frank
dc.contributor.author Shaffrey, Christopher I
dc.contributor.author Bess, Shay
dc.contributor.author Serra-Burriel, Miquel
dc.date.accessioned 2019-08-01T13:28:42Z
dc.date.available 2019-08-01T13:28:42Z
dc.date.issued 2019-07
dc.identifier 00007632-201907010-00005
dc.identifier.issn 0362-2436
dc.identifier.issn 1528-1159
dc.identifier.uri https://hdl.handle.net/10161/19126
dc.description.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.
dc.language eng
dc.publisher Ovid Technologies (Wolters Kluwer Health)
dc.relation.ispartof Spine
dc.relation.isversionof 10.1097/brs.0000000000002974
dc.subject European Spine Study Group, International Spine Study Group
dc.title 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.
dc.type Journal article
dc.date.updated 2019-08-01T13:28:42Z
pubs.begin-page 915
pubs.end-page 926
pubs.issue 13
pubs.organisational-group School of Medicine
pubs.organisational-group Duke
pubs.organisational-group Orthopaedics
pubs.organisational-group Clinical Science Departments
pubs.organisational-group Neurosurgery
pubs.publication-status Published
pubs.volume 44


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