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
Retrospective review of prospectively-collected, multicenter adult spinal deformity (ASD) databases. 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. Prior ASD classificatio...
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| Vydané v: | Spine (Philadelphia, Pa. 1976) Ročník 44; číslo 13; s. 915 |
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| Hlavní autori: | , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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01.07.2019
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| ISSN: | 1528-1159, 1528-1159 |
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| Abstract | Retrospective review of prospectively-collected, multicenter adult spinal deformity (ASD) databases.
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.
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.
Two prospective cohorts were queried for surgical ASD patients with baseline, 1-year, and 2-year SRS-22/ODI/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.
570 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 4 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]).
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.
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| AbstractList | Retrospective review of prospectively-collected, multicenter adult spinal deformity (ASD) databases.STUDY DESIGNRetrospective review of prospectively-collected, multicenter adult spinal deformity (ASD) databases.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.OBJECTIVETo apply artificial intelligence (AI)-based hierarchical clustering as a step toward a classification scheme that optimizes overall quality, value, and safety for ASD surgery.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.SUMMARY OF BACKGROUND DATAPrior 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.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.METHODSTwo 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.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].RESULTSFive 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].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.CONCLUSIONUnsupervised 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.4.LEVEL OF EVIDENCE4. Retrospective review of prospectively-collected, multicenter adult spinal deformity (ASD) databases. 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. 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. Two prospective cohorts were queried for surgical ASD patients with baseline, 1-year, and 2-year SRS-22/ODI/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. 570 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 4 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]). 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. 4. |
| Author | Pérez-Grueso, Francisco Javier Sánchez Shaffrey, Jr, Christopher I Burton, Douglas Lafage, Virginie Schwab, Frank Bess, Shay Alanay, Ahmet Kleinstück, Frank Vila-Casademunt, Alba Obeid, Ibrahim Shaffrey, Sr, Christopher I Ames, Christopher P Serra-Burriel, Miquel Pellisé, Ferran Kelly, Michael Smith, Justin S Acaroğlu, Emre |
| Author_xml | – sequence: 1 givenname: Christopher P surname: Ames fullname: Ames, Christopher P organization: Department of Neurosurgery, University of California San Francisco, San Francisco, CA, USA – sequence: 2 givenname: Justin S surname: Smith fullname: Smith, Justin S organization: Department of Neurosurgery, University of Virginia Medical Center, Charlottesville, VA, USA – sequence: 3 givenname: Ferran surname: Pellisé fullname: Pellisé, Ferran organization: Spine Surgery Unit, Hospital Vall d'Hebron, Barcelona, Spain – sequence: 4 givenname: Michael surname: Kelly fullname: Kelly, Michael organization: Department of Orthopaedic Surgery, Washington University, St Louis, MO, USA – sequence: 5 givenname: Ahmet surname: Alanay fullname: Alanay, Ahmet organization: Department of Orthopedics and Traumatology, Acibadem University, Istanbul, Turkey – sequence: 6 givenname: Emre surname: Acaroğlu fullname: Acaroğlu, Emre organization: Ankara ARTES Spine Center, Ankara, Turkey – sequence: 7 givenname: Francisco Javier Sánchez surname: Pérez-Grueso fullname: Pérez-Grueso, Francisco Javier Sánchez organization: Spine Surgery Unit, Hospital Universitario La Paz, Madrid, Spain – sequence: 8 givenname: Frank surname: Kleinstück fullname: Kleinstück, Frank organization: Spine Center Division, Department of Orthopedics and Neurosurgery, Schulthess Klinik, Zurich, Switzerland – sequence: 9 givenname: Ibrahim surname: Obeid fullname: Obeid, Ibrahim organization: Spine Surgery Unit, Bordeaux University Hospital, Bordeaux, France – sequence: 10 givenname: Alba surname: Vila-Casademunt fullname: Vila-Casademunt, Alba organization: Vall d'Hebron Institute of Research (VHIR) Barcelona, Spain – sequence: 11 givenname: Christopher I surname: Shaffrey, Jr fullname: Shaffrey, Jr, Christopher I organization: Vall d'Hebron Institute of Research (VHIR) Barcelona, Spain – sequence: 12 givenname: Douglas surname: Burton fullname: Burton, Douglas organization: Department of Orthopaedic Surgery, University of Kansas Medical Center, Kansas City, KS, USA – sequence: 13 givenname: Virginie surname: Lafage fullname: Lafage, Virginie organization: Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA – sequence: 14 givenname: Frank surname: Schwab fullname: Schwab, Frank organization: Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA – sequence: 15 givenname: Christopher I surname: Shaffrey, Sr fullname: Shaffrey, Sr, Christopher I organization: Department of Neurosurgery, University of Virginia Medical Center, Charlottesville, VA, USA – sequence: 16 givenname: Shay surname: Bess fullname: Bess, Shay organization: Denver International Spine Center, Presbyterian St. Luke's/Rocky Mountain Hospital for Children, Denver, CO, USA – sequence: 17 givenname: Miquel surname: Serra-Burriel fullname: Serra-Burriel, Miquel organization: Center for Research in Health and Economics, Universitat Pompeu Fabra, Barcelona, Spain |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30633115$$D View this record in MEDLINE/PubMed |
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| Snippet | Retrospective review of prospectively-collected, multicenter adult spinal deformity (ASD) databases.
To apply artificial intelligence (AI)-based hierarchical... Retrospective review of prospectively-collected, multicenter adult spinal deformity (ASD) databases.STUDY DESIGNRetrospective review of... |
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| 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 |
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