Predictors of Treatment Success Following Pulsed Radiofrequency of the Lumbar Dorsal Root Ganglion: A Multivariable Prediction Model with Clinical Nomogram - Matteo Luigi Giuseppe Leoni; Marco Mercieri; Sandra Magnoni; Giuliano Lo Bianco; Jamal Hasoon; Omar Viswanath; Giacomo Farì; Giuseppina Chelo; Giustino Varrassi (Current Pain and Headache Reports, 2026)
Abstract Background Pulsed radiofrequency of the lumbar dorsal root ganglion (DRG-PRF) is a minimally invasive treatment for chronic radicular pain, but outcomes vary substantially and validated prediction models to guide patient selection are lacking. The aim of this study was to develop and internally validate a multivariable prediction model identifying patients most likely to achieve treatment success following lumbar DRG-PRF. Methods This retrospective cohort study included 308 consecutive patients who underwent DRG-PRF for chronic lumbar radicular pain. Positive outcome was defined as ≥ 50% pain reduction on the Numerical Rating Scale at 6-month follow-up. Candidate prognostic variables were selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression and included in the multivariable logistic regression analysis. Model performance was evaluated using area under the receiver operating characteristic curve (AUC-ROC) and calibration plots. Internal validation employed 10,000 bootstrap replications. A clinical nomogram was developed for bedside application. Results Treatment success was achieved in 43.5% of patients (134/308). LASSO identified four independent predictors: baseline pain intensity (OR = 1.558, p = 0.013), daily morphine milligram equivalents (OR = 0.981, p < 0.001), pain duration (OR = 0.816, p = 0.008) and previous fusion/decompression surgery (OR = 0.315, p = 0.004). The model demonstrated moderate discrimination (AUC = 0.734, 95%CI:0.678–0.788), good calibration (Hosmer-Lemeshow p = 0.454), and robust internal validation (optimism-corrected AUC = 0.742). At optimal cutoff (0.478), sensitivity was 67.2% and specificity 69.0%. Conclusions This validated prediction model incorporating opioid consumption, pain duration, baseline pain intensity, and surgical history enables individualized risk stratification for lumbar DRG-PRF. The accompanying nomogram facilitates clinical decision-making and patient counseling.