• Matthew J McGirt, Ahilan Sivaganesan, Anthony L Asher, and Clinton J Devin.
• Carolina Neurosurgery & Spine Associates, University of North Carolina School of Medicine, Charlotte, North Carolina; and 
• Neurosurg Focus. 2015 Dec 1; 39 (6): E13.

AbstractOBJECT Lumbar spine surgery has been demonstrated to be efficacious for many degenerative spine conditions. However, there is wide variability in outcome after spine surgery at the individual patient level. All stakeholders in spine care will benefit from identification of the unique patient or disease subgroups that are least likely to benefit from surgery, are prone to costly complications, and have increased health care utilization. There remains a large demand for individual patient-level predictive analytics to guide decision support to optimize outcomes at the patient and population levels. METHODS One thousand eight hundred three consecutive patients undergoing spine surgery for various degenerative lumbar diagnoses were prospectively enrolled and followed for 1 year. A comprehensive patient interview and health assessment was performed at baseline and at 3 and 12 months after surgery. All predictive covariates were selected a priori. Eighty percent of the sample was randomly selected for model development, and 20% for model validation. Linear regression was performed with Bayesian model averaging to model 12-month ODI (Oswestry Disability Index). Logistic regression with Bayesian model averaging was used to model likelihood of complications, 30-day readmission, need for inpatient rehabilitation, and return to work. Goodness-of-fit was assessed via R(2) for 12-month ODI and via the c-statistic, area under the receiver operating characteristic curve (AUC), for the categorical endpoints. Discrimination (predictive performance) was assessed, using R(2) for the ODI model and the c-statistic for the categorical endpoint models. Calibration was assessed using a plot of predicted versus observed values for the ODI model and the Hosmer-Lemeshow test for the categorical endpoint models. RESULTS On average, all patient-reported outcomes (PROs) were improved after surgery (ODI baseline vs 12 month: 50.4 vs 29.5%, p < 0.001). Complications occurred in 121 patients (6.6%), 108 (5.9%) were readmitted within 30 days of surgery, 188 (10.3%) required discharge to inpatient rehabilitation, 1630 (88.9%) returned to work, and 449 (24.5%) experienced an unplanned outcome (no improvement in ODI, a complication, or readmission). There were 45 unique baseline variable inputs, derived from 39 clinical variables and 38 questionnaire items (ODI, SF-12, MSPQ, VAS-BP, VAS-LP, VAS-NP), included in each model. For prediction of 12-month ODI, R(2) was 0.51 for development and 0.47 for the validation study. For prediction of a complication, readmission, inpatient rehabilitation, and return to work, AUC values ranged 0.72-0.84 for development and 0.79-0.84 for validation study. CONCLUSIONS A novel prediction model utilizing both clinical data and patient interview inputs explained the majority of variation in outcome observed after lumbar spine surgery and reliably predicted 12-month improvement in physical disability, return to work, major complications, readmission, and need for inpatient rehabilitation for individual patients. Application of these models may allow clinicians to offer spine surgery specifically to those who are most likely to benefit and least likely to incur complications and excess costs.

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