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Study Design: Retrospective analysis., Objective: Analysis of a standardized, pre-surgical psychological evaluation program for complex spine surgery. Adult spinal deformity (ASD) patients have a high rate of comorbid mental health conditions. Although there is a body of literature demonstrating the impact of psychological factors, including anxiety and depression, on spine surgery outcome, it is estimated that spine surgeons utilize a psychological assessment only about one third of the time prior to a patient's spine surgery. At this time, there is not a widely reported pre-surgical psychological evaluation program for ASD patients., Methods: 129 consecutive complex spine surgery candidates receiving a pre-surgical psychological evaluation were analyzed between January 1st 2014 and December 31st 2018. Based on the available literature and professional experience in our facility, a color code for patients was developed from Green (low psychological or psychosocial co-morbidity) to Red (high psychological or psychosocial co-morbidity). Univariate analysis was used to evaluate between color grades and demographics, mental health disorders and outcomes., Results: 83% of complex spine patients had at least one psychological disorder or psychosocial barrier. Only 17% had a combination of realistic expectations for surgery, a good support plan, and were without a history of mental illness. The pre-surgical psychological color criteria were validated in showing higher rates of major depression, anxiety disorder, and bipolar disorder in moderate to severe color grades (p < .001) in addition to higher PHQ-9 and GAD-7 scores (p < .001). Patients having a more severe color grade had lower rates of a discharge home and were taking higher morphine equivalent dosages (MEDs) at their six-month follow-up, though both did not reach statistical significance (p = .07 and p = .08; respectively)., Conclusion: A comprehensive pre-surgical psychological evaluation may be beneficial to risk stratify and counsel patients being evaluated for surgical reconstruction of adult spinal deformities., Level of Evidence: 3.

Background: Complication rates in complex spine surgery range from 25% to 80% in published studies. Numerous studies have shown that surgeons are not able to accurately predict whether patients are likely to face post-operative complications, in part due to biases based on individual experience. The purpose of this study was to develop and evaluate a predictive risk model and decision support system that could accurately predict the likelihood of 30-day postoperative complications in complex spine surgery based on routinely measured preoperative variables., Methods: Preoperative and postoperative data were collected for 136 patients by reviewing medical records. Logistic regression analysis (LRA) was applied to develop the predictive algorithm based on patient demographic parameters, including age, gender, and co-morbidities, including obesity, diabetes, hypertension and anemia. We additionally compared the performance of the predictive model to a spine surgeon's ability to predict patient complications using signal detection theory statistics representing sensitivity and response bias (A' and B″ respectively). We developed a decision support system tool, based on the LRA predictive algorithm, that was able to provide a numeric probabilistic likelihood statistic representing an individual patient's risk of developing a complication within the first 30days after surgery., Results: The predictive model was significant (χ 2 =16.242, p<0.05), showed good fit, and was calibrated by using area under the receiver operating characteristics curve analysis (AUROC=0.712, p<0.01). The model yielded a predictive accuracy of 75.0%. It was validated by splitting the data set, comparing subset models, and testing them with unknown data. Validation also involved comparing the classification of cases by experts with the classification of cases by the model. The model significantly improved the classification accuracy of physicians involved in the delivery of complex spine surgical care., Conclusions: The application of technology and data-driven tools to advanced surgical practice has the potential to improve decision making quality, service quality and patient safety., (Copyright © 2017 Elsevier Ltd. All rights reserved.)