Identifying Predictors of Failed Back Surgery Syndrome Following Lumbar Spine Surgery: A Machine Learning Approach

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: AUROC 0.715 (lumbar decompression), 0.701 (lumbar fusion)

• Primary Outcome: Failed Back Surgery Syndrome within 1 year postoperatively

• Nnt Or Sensitivity: Top predictors: absence of microdiscectomy, medication usage patterns, prior FBSS diagnosis, operative duration

• Confidence Interval: Not reported in abstract

Clinical Application

Requires implementation of machine learning infrastructure and standardized data collection. Moderate discriminative ability limits immediate clinical adoption Relevant to Australian spine surgery practice. Could inform PBS funding decisions and patient counselling. Requires validation in Australian healthcare setting with different patient populations and surgical practices Patients being considered for lumbar decompression or fusion surgery in tertiary care settings

Abstract

STUDY DESIGN: Retrospective cohort study from a tertiary academic medical center. OBJECTIVE: To build a prognostic machine learning model to predict 1-year FBSS incidence after lumbar spine surgery. SUMMARY OF BACKGROUND DATA: A minority of patients who undergo degenerative lumbar spine surgery will have persistent postoperative pain, characterized as "Failed Back Surgery Syndrome" (FBSS). Adequate preoperative identification of patients at risk of having an undesirable outcome after surgery is an essential part of a spine surgeon's workflow. Although several studies have proposed mechanisms and risk factors for FBSS, no studies have developed a prognostic machine learning model to quantify and functionalize predictions. METHODS: A cohort of lumbar fusion and lumbar decompression surgeries was queried from a tertiary academic medical center from 2002 to 2022. Patient and operative characteristics were systematically extracted for each surgery. Several machine learning algorithms were used and optimized to predict FBSS occurrence within 1 year of surgery. SHAP feature importance values were computed for the top-performing model. RESULTS: A total of 10,128 unique lumbar decompression surgeries and 2890 unique lumbar fusion surgeries were included. The Random Forest model had the highest performance of tested models (AUROC of 0.715 for lumbar decompression, 0.701 for lumbar fusion). For lumbar decompression, the top three predictors of FBSS were absence of microdiscectomy, lack of preoperative immunosuppressant usage, and preoperative benzodiazepine usage. For lumbar fusion, prior FBSS diagnosis, lack of preoperative immunosuppressant usage, and operating room duration were the most important predictors. Other key variables spanned several domains, including preoperative medication usage, patient demographics, and operative indications and characteristics. CONCLUSION: This study demonstrates the successful creation of a prognostic machine learning model for prediction of FBSS within one year postoperatively. These models, after external validation, have the potential to be instrumental aspects of a spine surgeon's workflow. LEVEL OF EVIDENCE: Level III.

References

1. 1.Khazanchi, R., Kumar, D., Oris, R. J., Bajaj, A., Herrera, D. E., Chen, A. R., Shah, R. M., Asthana, S., Reyes, S. G., Bajaj, P., Hsu, W. K., Patel, A. A., & Divi, S. N. (2026). Identifying predictors of failed back surgery syndrome following lumbar spine surgery: A machine learning approach. Spine. https://doi.org/10.1097/BRS.0000000000005411