Introduction: Re-alignment surgery for patients with adult spinal deformity has been shown to improve quality of life outcome measures; however, large reconstructive surgery is associated with significant morbidity. We sought to create a preoperative predictive nomogram to determine which patients would benefit from surgery.
Methods: All patients aged 25-years-old with radiographic evidence of ASD and quality of life data that underwent thoracolumbar fusion between 2008 and 2014 were retrospectively identified. Demographic and clinical parameters were obtained. The EuroQol five dimensions questionnaire (EQ-5D) was used to measure health-related quality of life (HRQoL) preoperatively and at 12 months postoperative follow-up. A preoperative to postoperative decline of .04 or greater was used to indicate the presence of clinically relevant decline in HRQoL.
Results: Our sample included data from 191 patients. 63% of patients experienced clinically relevant postoperative decline in HRQoL. Seven variables were included in the final model: preoperative EQ-5D score, sex, dyslipidemia, diagnosis (degenerative, idiopathic, or iatrogenic), race, diabetes mellitus 2, and BMI. Female gender (OR 2.21, p = .036) and preoperative EQ-5D (OR = 1.531, p < .0001) each were independently associated with the poorer postoperative outcome.
Conclusions: Lower preoperative EQ-5D scores and female gender were associated with a clinically significant decrease in postoperative EQ-5D scores, while race, diabetes mellitus type 2, and BMI showed no significant association with post-operative quality of life outcomes. The predictive nomogram that we developed using these data can improve preoperative risk counseling and patient selection for deformity correction surgery.
Patient Care: The predictive nomogram that we developed using these data can improve preoperative risk counseling and patient selection for deformity correction surgery.
Learning Objectives: * Understand the significant predictors of morbidity in patients undergoing surgical correction of ASD
* Understand the utility of predictive modeling for outcomes following ASD surgery
* Consider practice-changing guidelines that reflect findings based on evidence-based modeling