Development of Risk Stratification Predictive Models for Cervical Deformity Surgery.
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2022-12
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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
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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.
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Scholars@Duke
Peter Passias
Christopher Ignatius Shaffrey
I have more than 25 years of experience treating patients of all ages with spinal disorders. I have had an interest in the management of spinal disorders since starting my medical education. I performed residencies in both orthopaedic surgery and neurosurgery to gain a comprehensive understanding of the entire range of spinal disorders. My goal has been to find innovative ways to manage the range of spinal conditions, straightforward to complex. I have a focus on managing patients with complex spinal disorders. My patient evaluation and management philosophy is to provide engaged, compassionate care that focuses on providing the simplest and least aggressive treatment option for a particular condition. In many cases, non-operative treatment options exist to improve a patient’s symptoms. I have been actively engaged in clinical research to find the best ways to manage spinal disorders in order to achieve better results with fewer complications.
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