Artificial Intelligence Models Predict Operative Versus Nonoperative Management of Patients with Adult Spinal Deformity with 86% Accuracy.


Journal Article

OBJECTIVE: Patients with ASD show complex and highly variable disease. The decision to manage patients operatively is largely subjective and varies based on surgeon training and experience. We sought to develop models capable of accurately discriminating between patients receiving operative versus nonoperative treatment based only on baseline radiographic and clinical data at enrollment. METHODS: This study was a retrospective analysis of a multicenter consecutive cohort of patients with ASD. A total of 1503 patients were included, divided in a 70:30 split for training and testing. Patients receiving operative treatment were defined as those undergoing surgery up to 1 year after their baseline visit. Potential predictors included available demographics, past medical history, patient-reported outcome measures, and premeasured radiographic parameters from anteroposterior and lateral films. In total, 321 potential predictors were included. Random forest, elastic net regression, logistic regression, and support vector machines (SVMs) with radial and linear kernels were trained. RESULTS: Of patients in the training and testing sets, 69.0% (n = 727) and 69.1% (n = 311), respectively, received operative management. On evaluation with the testing dataset, performance for SVM linear (area under the curve =0.910), elastic net (0.913), and SVM radial (0.914) models was excellent, and the logistic regression (0.896) and random forest (0.830) models performed very well for predicting operative management of patients with ASD. The SVM linear model showed 86% accuracy. CONCLUSIONS: This study developed models showing excellent discrimination (area under the curve >0.9) between patients receiving operative versus nonoperative management, based solely on baseline study enrollment values. Future investigations may evaluate the implementation of such models for decision support in the clinical setting.

Full Text

Duke Authors

Cited Authors

  • Durand, WM; Daniels, AH; Hamilton, DK; Passias, P; Kim, HJ; Protopsaltis, T; LaFage, V; Smith, JS; Shaffrey, C; Gupta, M; Klineberg, E; Schwab, F; Burton, D; Bess, S; Ames, C; Hart, R; International Spine Study Group,

Published Date

  • September 2020

Published In

Volume / Issue

  • 141 /

Start / End Page

  • e239 - e253

PubMed ID

  • 32434029

Pubmed Central ID

  • 32434029

Electronic International Standard Serial Number (EISSN)

  • 1878-8769

Digital Object Identifier (DOI)

  • 10.1016/j.wneu.2020.05.099


  • eng

Conference Location

  • United States