Abstract
Clinical and quality of life (QOL) outcomes vary depending on the patient's demographics, comorbidities, presenting symptoms, pathology, and surgical treatment used. While there have been individual predictors identified, no comprehensive method incorporates a patient's complex clinical presentation to predict a specific individual postoperative outcome. To create tool that predicts patient-specific outcomes among those undergoing cervical spine surgery. A total of 952 patients at a single tertiary care institution who underwent anterior or posterior cervical decompression/fusion between 2007 and 2013 were retrospectively reviewed. Outcomes included postoperative emergency department visit or readmission within 30 d, reoperation within 90 d for infection, and changes in QOL outcomes. Nomograms were modeled based on patient demographics and surgical variables. Bootstrap was used for internal validation. Bias-corrected c-index for emergency department visits, readmission, and reoperation were 0.63, 0.78, and 0.91, respectively. For the QOL metrics, the bias-corrected adjusted R-squared was EQ-5D (EuroQOL): 0.43, for PHQ-9 (Patient Health Questionnaire-9): 0.35, and for PDQ (Pain/Disability Questionnaire): 0.47. Variables predicting the clinical outcomes varied, but included race and median income, body mass index, comorbidities, presenting symptoms, indication for surgery, surgery type, and levels. For the QOL nomograms, the predictors included similar variables, but were significantly more affected by the preoperative QOL of the patient. These prediction models enable referring physicians and spine surgeons to provide patients with personalized expectations regarding postoperative clinical and QOL outcomes following a cervical spine surgery. After appropriate validation, use of patient-specific prediction tools, such as nomograms, has the potential to lead to superior spine surgery outcomes and more cost effective care.
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