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May 8, 2026Frontiers in Radiology0 citationsOpen Access

A preoperative nomogram for predicting 2-year postoperative recurrence after percutaneous transforaminal endoscopic decompression in degenerative lumbar spinal stenosis

XLXinyi LuoAnhui Medical UniversityYWYing WangHebei Medical UniversityLXLele XueThird People's Hospital of Hefei

Key Points

  • This study aims to create and validate a preoperative nomogram for predicting 2-year recurrence risk after PTED in degenerative lumbar spinal stenosis patients.
  • Conducted a retrospective cohort study with 206 patients who underwent single-level PTED from August 2021 to August 2023.
  • Developed a multivariable logistic regression model using preoperative clinical and imaging variables as predictors.
  • Evaluated model performance through discrimination, calibration, and clinical utility, with internal validation using bootstrap resampling and leave-one-out cross-validation.
  • 29 out of 206 patients (14.08%) experienced postoperative recurrence within 2 years.
  • The nomogram includes five predictors: body mass index, diabetes mellitus, lumbosacral transitional vertebrae, senior grade facet degeneration, and paraspinal skeletal muscle index.
  • The model achieved an AUC of 0.845 (95% CI, 0.778–0.912) for prediction accuracy, with satisfactory calibration.

Abstract

Background and objectives Postoperative recurrence after percutaneous transforaminal endoscopic decompression (PTED) for degenerative lumbar spinal stenosis (DLSS) remains a clinically relevant challenge, complicating preoperative counseling and long-term management. Reliable tools for predicting individual 2-year recurrence risk using routinely available preoperative data are currently lacking. This study aimed to develop and internally validate a practical preoperative nomogram for individualized recurrence risk prediction after PTED. Methods We conducted a retrospective cohort study including 206 patients with DLSS who underwent single-level PTED between August 2021 and August 2023. Preoperative clinical and imaging variables were extracted to construct a multivariable logistic regression model. Candidate predictors were prespecified based on clinical relevance and routine availability. Model performance was evaluated in terms of discrimination, calibration, and clinical utility. Internal validation was performed using 1000 bootstrap resamples and leave-one-out cross-validation (LOOCV). Results During the 2-year follow-up period, 29 patients (14.08%) experienced postoperative recurrence. The final nomogram incorporated five preoperative predictors: body mass index, diabetes mellitus, lumbosacral transitional vertebrae, number of levels with senior grade facet degeneration, and paraspinal skeletal muscle index. The model showed good discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.845 (95% CI, 0.778–0.912). Bootstrap validation showed a mean AUC of 0.842 (95% CI, 0.772–0.912), and LOOCV yielded an AUC of 0.797 (95% CI, 0.716–0.878). Calibration was satisfactory, and decision curve analysis demonstrated net clinical benefit across a wide range of threshold probabilities. Conclusions We developed a clinically interpretable preoperative nomogram that reliably predicts 2-year postoperative recurrence after PTED in patients with DLSS. By integrating routinely assessed clinical and imaging factors, this tool may facilitate individualized risk stratification, support informed preoperative counseling, and guide risk-adapted perioperative management. External validation in independent cohorts is warranted.

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Cite This Study

Luo et al. (2026) studied this question.

synapsesocial.com/papers/69fd7cd4bfa21ec5bbf05ac4https://doi.org/10.3389/fradi.2026.1821920
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