Machine learning clustering of adult spinal deformity patients identifies four prognostic phenotypes: a multicenter prospective cohort analysis with single surgeon external validation

Mohanty, S., Hassan, F. M., Lenke, L. G., Lewerenz, E., Passias, P. G., Klineberg, E. O., Lafage, V., Smith, J. S., Hamilton, D. K., Gum, J. L., Lafage, R., Mullin, J., Diebo, B., Buell, T. J., Kim, H. J., Kebaish, K., Eastlack, R., Daniels, A. H., Mundis, G., … Bess, S. (2024). Machine learning clustering of adult spinal deformity patients identifies four prognostic phenotypes: a multicenter prospective cohort analysis with single surgeon external validation. The Spine Journal, 24(6), 1095–1108. https://doi.org/10.1016/j.spinee.2024.02.010
Authors:
Sarthak Mohanty
Fthimnir M Hassan
Lawrence G Lenke
Erik Lewerenz
Peter G Passias
Eric O Klineberg
Virginie Lafage
Justin S Smith
D Kojo Hamilton
Jeffrey L Gum
Renaud Lafage
Jeffrey Mullin
Bassel Diebo
Thomas J Buell
Han Jo Kim
Khalid Kebaish
Robert Eastlack
Alan H Daniels
Gregory Mundis
Richard Hostin
Themistocles S Protopsaltis
Robert A Hart
Munish Gupta
Frank J Schwab
Christopher I Shaffrey
Christopher P Ames
Douglas Burton
Shay Bess
Affiliated Authors:
Sarthak Mohanty
Fthimnir M Hassan
Lawrence G Lenke
Erik Lewerenz
Columbia Affiliation:
Subjects:
Machine Learning (MeSH)
Author Keywords:
adult spinal deformity
classifications
frailty
machine learning
mental health
patient reported outcomes
phenotypes
spinal deformity surgery
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Publication Type:
Article
Unique ID:
10.1016/j.spinee.2024.02.010
PMID:
38365004
DOI:
10.1016/j.spinee.2024.02.010
Journal:
Spine Journal
Publication Date:
Data Source:
PubMed
Source Link:
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Record Created: