Development of Risk Stratification Predictive Models for Cervical Deformity Surgery.
Date
2022-12
Journal Title
Journal ISSN
Volume Title
Repository Usage Stats
views
downloads
Citation Stats
Abstract
Background
As corrective surgery for cervical deformity (CD) increases, so does the rate of complications and reoperations. To minimize suboptimal postoperative outcomes, it is important to develop a tool that allows for proper preoperative risk stratification.Objective
To develop a prognostic utility for identification of risk factors that lead to the development of major complications and unplanned reoperations.Methods
CD patients age 18 years or older were stratified into 2 groups based on the postoperative occurrence of a revision and/or major complication. Multivariable logistic regressions identified characteristics that were associated with revision or major complication. Decision tree analysis established cutoffs for predictive variables. Models predicting both outcomes were quantified using area under the curve (AUC) and receiver operating curve characteristics.Results
A total of 109 patients with CD were included in this study. By 1 year postoperatively, 26 patients experienced a major complication and 17 patients underwent a revision. Predictive modeling incorporating preoperative and surgical factors identified development of a revision to include upper instrumented vertebrae > C5, lowermost instrumented vertebrae > T7, number of unfused lordotic cervical vertebrae > 1, baseline T1 slope > 25.3°, and number of vertebral levels in maximal kyphosis > 12 (AUC: 0.82). For developing a major complication, a model included a current smoking history, osteoporosis, upper instrumented vertebrae inclination angle < 0° or > 40°, anterior diskectomies > 3, and a posterior Smith Peterson osteotomy (AUC: 0.81).Conclusion
Revisions were predicted using a predominance of radiographic parameters while the occurrence of major complications relied on baseline bone health, radiographic, and surgical characteristics.Type
Department
Description
Provenance
Citation
Permalink
Published Version (Please cite this version)
Publication Info
Passias, Peter G, Waleed Ahmad, Cheongeun Oh, Bailey Imbo, Sara Naessig, Katherine Pierce, Virginie Lafage, Renaud Lafage, et al. (2022). Development of Risk Stratification Predictive Models for Cervical Deformity Surgery. Neurosurgery, 91(6). pp. 928–935. 10.1227/neu.0000000000002136 Retrieved from https://hdl.handle.net/10161/27992.
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
Scholars@Duke
Peter Passias
Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.