Predictors of Treatment Success Following Pulsed Radiofrequency of the Lumbar Dorsal Root Ganglion: A Multivariable Prediction Model with Clinical Nomogram

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.

Authors

Institutions

Publication Details

Journal
Current Pain and Headache Reports
Published
2026-08-24
DOI
https://doi.org/10.1007/s11916-026-01544-x
Primary Topic
Spine and Intervertebral Disc Pathology
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predictors of Treatment Success Following Pulsed Radiofrequency of the Lumbar Dorsal Root Ganglion: A Multivariable Prediction Model with Clinical Nomogram

Omar Viswanath, Giacomo Farì, Matteo Luigi Giuseppe Leoni, Marco Mercieri et al.
Current Pain and Headache Reports
Spine and Intervertebral Disc Pathology
article

Predictors of Treatment Success Following Pulsed Radiofrequency of the Lumbar Dorsal Root Ganglion: A Multivariable Prediction Model with Clinical Nomogram

Omar Viswanath, Giacomo Farì, Matteo Luigi Giuseppe Leoni, Marco Mercieri, Jamal Hasoon, Giuliano Lo Bianco, Giuseppina Chelo, Giustino Varrassi, Sandra Magnoni
article en

Abstract

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.

Current Pain and Headache ReportsVol. 30(1)
University of Baghdad (IQ), University of Salento (IT), University of Sassari (IT), Fondazione Roma (IT), Azienda Ospedaliero Universitaria di Sassari (IT), Fondazione Istituto G. Giglio di Cefalù (IT), Guglielmo da Saliceto Hospital (IT), Sapienza University of Rome (IT), The University of Texas Health Science Center at Houston (US)
Peace, Justice and strong institutions, Reduced inequalities
Openalex Percentile: Top 10%
Spine and Intervertebral Disc Pathology
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.