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

Abstract

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. 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. 2.