Development and Validation of Cervical Prediction Models for Patient-Reported Outcomes at 1 Year After Cervical Spine Surgery for Radiculopathy and Myelopathy.


Study design

Retrospective analysis of prospectively collected registry data.


To develop and validate prediction models for 12-month patient-reported outcomes of disability, pain, and myelopathy in patients undergoing elective cervical spine surgery.

Summary of background data

Predictive models have the potential to be utilized preoperatively to set expectations, adjust modifiable characteristics, and provide a patient-centered model of care.


This study was conducted using data from the cervical module of the Quality Outcomes Database. The outcomes of interest were disability (Neck Disability Index:), pain (Numeric Rating Scale), and modified Japanese Orthopaedic Association score for myelopathy. Multivariable proportional odds ordinal regression models were developed for patients with cervical radiculopathy and myelopathy. Patient demographic, clinical, and surgical covariates as well as baseline patient-reported outcomes scores were included in all models. The models were internally validated using bootstrap resampling to estimate the likely performance on a new sample of patients.


Four thousand nine hundred eighty-eight patients underwent surgery for radiculopathy and 2641 patients for myelopathy. The most important predictor of poor postoperative outcomes at 12-months was the baseline Neck Disability Index score for patients with radiculopathy and modified Japanese Orthopaedic Association score for patients with myelopathy. In addition, symptom duration, workers' compensation, age, employment, and ambulatory and smoking status had a statistically significant impact on all outcomes (P < 0.001). Clinical and surgical variables contributed very little to predictive models, with posterior approach being associated with higher odds of having worse 12-month outcome scores in both the radiculopathy and myelopathy cohorts (P < 0.001). The full models overall discriminative performance ranged from 0.654 to 0.725.


These predictive models provide individualized risk-adjusted estimates of 12-month disability, pain, and myelopathy outcomes for patients undergoing spine surgery for degenerative cervical disease. Predictive models have the potential to be used as a shared decision-making tool for evidence-based preoperative counselling.

Level of evidence






Published Version (Please cite this version)


Publication Info

Archer, Kristin R, Mohamad Bydon, Inamullah Khan, Hui Nian, Jacquelyn S Pennings, Frank E Harrell, Ahilan Sivaganesan, Silky Chotai, et al. (2020). Development and Validation of Cervical Prediction Models for Patient-Reported Outcomes at 1 Year After Cervical Spine Surgery for Radiculopathy and Myelopathy. Spine, 45(22). pp. 1541–1552. 10.1097/brs.0000000000003610 Retrieved from

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Christopher Ignatius Shaffrey

Professor of Orthopaedic Surgery

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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