Validity of a pre-surgical algorithm to predict pain, functional disability, and emotional functioning 1 year after spine surgery.

Psychopathology has been associated with patient reports of poor outcome and an algorithm has been useful in predicting short-term outcomes. The objective of this study is to investigate whether a pre-surgical psychological algorithm could predict 1-year spine surgery outcome reports, including pain, functional disability, and emotional functioning. A total of 1,099 patients consented to participate. All patients underwent spine surgery (e.g., spinal fusion, discectomy, etc.). Pre-operatively, patients completed self-report measures prior to surgery. An algorithm predicting patient prognosis based on data from the pre-surgical psychological evaluation was filled out by the provider for each patient prior to surgery. Post-operatively, patients completed self-report measures at 3- and 12-months after surgery. Longitudinal latent class growth analysis (LCGA) was used to derive patient outcome groups. These outcome groups were then compared to pre-surgical predictions made. LCGA analyses derived three groups of patients from the reported outcome data (entropy = .84): excellent outcomes, good outcomes, and poor outcomes. The excellent and good groups demonstrated improvements over time, but the poor outcome groups, on some measures, reported worsening of pain, functional disability, and emotional functioning over time. The pre-surgical algorithm yielded good concordance with the statistically derived outcome groups (Kendall's = .81). Using a pre-surgical psychological evaluation algorithm for predicting long-term spine surgery outcomes can identify patients who are unlikely to report good outcomes, and point to areas for psychological intervention that can either improve surgery results or to be utilized as alternatives to elective spine surgery. (PsycInfo Database Record (c) 2021 APA, all rights reserved).