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Does Magnetic Resonance Imaging Improve the Predictive Performance of a Validated Clinical Prediction Rule Developed to Evaluate Surgical Outcome in Patients With Degenerative Cervical Myelopathy?

Nouri, Aria, MD*,†,‡; Tetreault, Lindsay, HBSc*,†,‡; Côté, Pierre, DC, PhD§; Zamorano, Juan J., MD*,†; Dalzell, Kristian, MBChB, FRACS¶,‖; Fehlings, Michael G., MD, PhD, FRCSC, FACS*,†,‡

doi: 10.1097/BRS.0000000000000919

Study Design. Ambispective study.

Objective. To determine whether MRI parameters improve the predictive performance of a validated clinical prediction rule used to assess functional outcomes in surgical patients with DCM.

Summary of Background Data. Degenerative cervical myelopathy (DCM) is the most common cause of spinal cord dysfunction in the elderly worldwide. A clinical prediction rule was developed to discriminate between patients with mild myelopathy postoperatively (mJOA ≥ 16) and those with substantial residual neurological impairment (mJOA < 16). Recently, a separate magnetic resonance imaging (MRI)-based prediction model was created. However, a model exploring the combined predictive value of imaging and clinical variables does not exist.

Methods. One hundred and fourteen patients with MRIs were examined from a cohort of 278 patients enrolled in the AOSpine CSM-North America Study. Ninety-nine patients had complete preoperative imaging and postoperative outcome data. MRIs were evaluated for the presence/absence of signal change on T2- and T1-weighted images. Quantitative analysis of the T2 signal change was conducted and maximum canal compromise and cord compression were calculated. The added predictive performance of each MRI parameter to the clinical model was evaluated using receiver operator characteristic curves.

Results. The model developed on our subsample yielded an area under the receiver operator curve (AUC) of 0.811 (95% CI: 0.726–0.896). The addition of imaging variables did not significantly improve the predictive performance. Small improvements in prediction were obtained when sagittal extent of T2 hyperintensity (AUC: 0.826, 95% CI: 0.743–0.908, 1.35% increase) or Wang ratio (AUC: 0.823, 95% CI: 0.739–0.907, 1.21%) was added. Anatomic characteristics, such as maximum canal compromise and maximum cord compression, did not improve the discriminative ability of the clinical prediction model.

Conclusion. In our sample of surgical patients, with clinical and image-evidence of DCM, MRI parameters do not significantly add to the predictive performance of a previously published clinical prediction rule. It remains plausible that combinations of the strongest clinical and MRI predictors may yield a similar or a superior prediction model.

Level of Evidence: 3

This study determines whether magnetic resonance imaging parameters improve the predictive performance of a validated clinical prediction rule used to evaluate functional status in surgical patients with degenerative cervical myelopathy. Based on our results, the addition of imaging variables did not significantly improve the discriminative ability of the original model.

*Division of Neurosurgery and Spine Program, Toronto Western Hospital, Toronto, Ontario, Canada;

Toronto Western Research Institute, University Health Network, Toronto, Canada;

Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada;

§Faculty of Health Sciences, University of Ontario Institute of Technology (UOIT), Director, UOIT-CMCC Centre for the Study of Disability Prevention and Rehabilitation, Toronto, Ontario, Canada;

Christchurch Public Hospital, Christchurch, New Zealand; and

Burwood Spinal Unit, Christchurch, New Zealand.

Address correspondence and reprint requests to Michael G. Fehlings, MD, PhD, FRCSC, FACS, Suite 4W449, Toronto Western Hospital, 399 Bathurst St., Toronto, M5T 2S8 Ontario, Canada; E-mail:

Acknowledgment date: December 15, 2014; Revision date: February 20, 2015; Acceptance date: March 18, 2015.

The manuscript submitted does not contain information about medical device(s)/drug(s).

AO Spine funded data collection for this work.

Relevant financial activities outside the submitted work: expert testimony, grants, payment for lectures, travel/accommodations/meeting expenses.

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