Predictors of Treatment Success Following Pulsed Radiofrequency of the Lumbar Dorsal Root Ganglion: A Multivariable Prediction Model with Clinical Nomogram
Year: 2026
Type: article
Study design: Observational Study
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.
Institutions: Guglielmo da Saliceto Hospital, Fondazione Paolo Procacci, Sapienza University of Rome, University of Sassari, Fondazione Istituto G. Giglio di Cefalù, The University of Texas Health Science Center at Houston, University of Salento, Azienda Ospedaliero Universitaria di Sassari, University of Baghdad
Language: English
Subfield: Anesthesiology and Pain Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,590
Funder: Sapienza Università di Roma
OpenAlex ID: W7204131659
