Background A consistent approach to cervical spine injury (CSI) clearance for patients 65 and older remains a challenge. Clinical clearance algorithms like the National Emergency X-Radiography Utilisation Study (NEXUS) criteria have variable accuracy and the Canadian C-spine rule excludes older patients. Routine CT of the cervical spine is performed to rule out CSI but at an increased cost and low yield. Herein, we aimed to identify predictive clinical variables to selectively screen older patients for CSI.
Methods The University of Iowa’s trauma registry was interrogated to retrospectively identify all patients 65 years and older who presented with trauma from a ground-level fall from January 2012 to July 2017. The relationship between predictive variables (demographics, NEXUS criteria and distracting injuries) and presence of CSI was examined using the generalised linear modelling (GLM) framework. A training set was used to build the statistical models to identify clinical variables that can be used to predict CSI and a validation set was used to assess the reliability and consistency of the model coefficients estimated from the training set.
Results Overall, 2312 patients ≥65 admitted for ground-level falls were identified; 253 (10.9%) patients had a CSI. Using the GLM framework, the best predictive model for CSI included midline tenderness, focal neurological deficit and signs of trauma to the head/face, with midline tenderness highly predictive of CSI (OR=22.961 (15.178–34.737); p<0.001). The negative predictive value (NPV) for this model was 95.1% (93.9%–96.3%). In the absence of midline tenderness, the best model included focal neurological deficit (OR=2.601 (1.340–5.049); p=0.005) and signs of trauma to the head/face (OR=3.024 (1.898–4.815); p<0.001). The NPV was 94.3% (93.1%–95.5%).
Conclusion Midline tenderness, focal neurological deficit and signs of trauma to the head/face were significant in this older population. The absence of all three variables indicates lower likelihood of CSI for patients≥65. Future observational studies are warranted to prospectively validate this model.
- clinical assessment
- fractures and dislocations
Data availability statement
Data are available upon reasonable request. Requests for data should be send to the listed corresponding author.
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