Spine - 2026-05-15 - Journal Article
Identifying Predictors of Failed Back Surgery Syndrome Following Lumbar Spine Surgery: A Machine Learning Approach.
Khazanchi R, Kumar D, Oris RJ, Bajaj A, Herrera DE, Chen AR, Shah RM, Asthana S, Reyes SG, Bajaj P, Hsu WK, Patel AA, Divi SN
Topics
Key Takeaway
A Random Forest machine learning model predicted 1-year Failed Back Surgery Syndrome with AUROC 0.715 for lumbar decompression and 0.701 for lumbar fusion across 13,018 surgeries.
Summary Depth
Choose how much analysis to show on this article page.
Summary
This study queried lumbar decompression and fusion cases from a single tertiary center (2002–2022) to build and validate machine learning models predicting 1-year FBSS incidence. Random Forest outperformed other tested algorithms with AUROC 0.715 (decompression) and 0.701 (fusion). Top predictors for decompression included absence of microdiscectomy, lack of preoperative immunosuppressant use, and benzodiazepine use; for fusion, prior FBSS, lack of immunosuppressant use, and OR duration dominated.
Key Limitation
Single-center retrospective data without external validation means the model's AUROC values (~0.70) and feature importance rankings may not generalize to community or non-academic practice settings.
Original 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.