Skip to main content
Duke University Libraries
DukeSpace Scholarship by Duke Authors
  • Login
  • Ask
  • Menu
  • Login
  • Ask a Librarian
  • Search & Find
  • Using the Library
  • Research Support
  • Course Support
  • Libraries
  • About
View Item 
  •   DukeSpace
  • Duke Scholarly Works
  • Scholarly Articles
  • View Item
  •   DukeSpace
  • Duke Scholarly Works
  • Scholarly Articles
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

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.

Thumbnail
View / Download
2.0 Mb
Date
2019-07
Authors
Ames, Christopher P
Smith, Justin S
Pellisé, Ferran
Kelly, Michael
Alanay, Ahmet
Acaroğlu, Emre
Pérez-Grueso, Francisco Javier Sánchez
Kleinstück, Frank
Obeid, Ibrahim
Vila-Casademunt, Alba
Shaffrey, Christopher I
Burton, Douglas
Lafage, Virginie
Schwab, Frank
Shaffrey, Christopher I
Bess, Shay
Serra-Burriel, Miquel
European Spine Study Group, International Spine Study Group
Show More
(18 total)
Repository Usage Stats
43
views
35
downloads
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.
Type
Journal article
Subject
European Spine Study Group, International Spine Study Group
Permalink
https://hdl.handle.net/10161/19126
Published Version (Please cite this version)
10.1097/brs.0000000000002974
Publication 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.
Collections
  • Scholarly Articles
More Info
Show full item record

Scholars@Duke

Christopher Ignatius Shaffrey

Professor of Orthopaedic Surgery

Christopher Ignatius Shaffrey

Professor of Orthopaedic Surgery
Alphabetical list of authors with Scholars@Duke profiles.
Open Access

Articles written by Duke faculty are made available through the campus open access policy. For more information see: Duke Open Access Policy

Rights for Collection: Scholarly Articles

Make Your Work Available Here

How to Deposit

Browse

All of DukeSpaceCommunities & CollectionsAuthorsTitlesTypesBy Issue DateDepartmentsAffiliations of Duke Author(s)SubjectsBy Submit DateThis CollectionAuthorsTitlesTypesBy Issue DateDepartmentsAffiliations of Duke Author(s)SubjectsBy Submit Date

My Account

LoginRegister

Statistics

View Usage Statistics
Duke University Libraries

Contact Us

411 Chapel Drive
Durham, NC 27708
(919) 660-5870
Perkins Library Service Desk

Digital Repositories at Duke

  • Report a problem with the repositories
  • About digital repositories at Duke
  • Accessibility Policy
  • Deaccession and DMCA Takedown Policy

TwitterFacebookYouTubeFlickrInstagramBlogs

Sign Up for Our Newsletter
  • Re-use & Attribution / Privacy
  • Support the Libraries
Duke University