Cervical Spine Injury Severity Score
Assessment of Reliability
; Paul A. Anderson, MD; Timothy A. Moore, MD; Kirkland W. Davis, MD; Robert W. Molinari, MD; Daniel K. Resnick, MD; Alexander R. Vaccaro, MD; Christopher M. Bono, MD; John R. Dimar II, MD; Bizhan Aarabi, MD, FRCSC; Glen Leverson, PhD

Abstract

Background: Systems for classifying cervical spine injury most commonly use mechanistic or morphologic terms and do not quantify the degree of stability. Along with neurologic function, stability is a major determinant of treatment and prognosis. The goal of our study was to investigate the reliability of a method of quantifying the stability of subaxial (C3-C7) cervical spine injuries.

Methods: A quantitative system was developed in which an analog score of 0 to 5 points is assigned, on the basis of fracture displacement and severity of ligamentous injury, to each of four spinal columns (anterior, posterior, right pillar, and left pillar). The total possible score thus ranges from 0 to 20 points. Fifteen examiners assigned scores after reviewing the plain radiographs and computed tomography images of thirty-four consecutive patients with cervical spine injuries. The scores were then evaluated for interobserver and intraobserver reliability with use of intraclass correlation coefficients.

Results: The mean intraobserver and interobserver intraclass correlation coefficients for the fifteen reviewers were 0.977 and 0.883, respectively. Association between the scores and clinical data was also excellent, as all patients who had a score of ≥7 points had surgery. Similarly, eleven of the fourteen patients with a score of ≥7 points had a neurologic deficit compared with only three of the twenty with a score of <7 points.

Conclusions: The Cervical Spine Injury Severity Score had excellent intraobserver and interobserver reliability. We believe that quantifying stability on the basis of fracture morphology will allow surgeons to better characterize these injuries and ultimately lead to the development of treatment algorithms that can be tested in clinical trials.

Footnotes

  • Disclosure: In support of their research for or preparation of this work, one or more of the authors received, in any one year, outside funding or grants of less than $10,000 from Medtronic. Neither they nor a member of their immediate families received payments or other benefits or a commitment or agreement to provide such benefits from a commercial entity. A commercial entity (Medtronic) paid or directed in any one year, or agreed to pay or direct, benefits of less than $10,000 to a research fund, foundation, division, center, clinical practice, or other charitable or nonprofit organization with which one or more of the authors, or a member of his or her immediate family, is affiliated or associated.

  • Investigation performed at the University of Wisconsin, Madison, Wisconsin


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