Evidence map›Paper›PMID 42635684›Full record

ReviewCurrent pain and headache reports2026

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

Abstract readReview
In one paragraph

Review in Current pain and headache reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Matteo Luigi Giuseppe LeoniDepartment of Medical and Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, 00189, Italy. matteolg.leoni@gmail.com.ORCID http://orcid.org/0000-0001-5228-3733
Marco MercieriDepartment of Medical and Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, 00189, Italy.
Sandra MagnoniDepartment of Medicine, Surgery and Pharmacy, University of Sassari, Sassari, 07100, Italy.
Giuliano Lo BiancoAnesthesiology and Pain Department, Foundation G. Giglio Cefalù, Palermo, 90015, Italy.
Jamal HasoonDepartment of Anesthesiology, Critical Care, and Pain Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Omar ViswanathDepartment of Anesthesiology, Critical Care, and Pain Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Giacomo FarìDepartment of Experimental Medicine (Di.Me.S), University of Salento, Lecce, 73100, Italy.
Giuseppina CheloPain Unit, Anesthesia and General Intensive Care Unit, AOU Sassari, Sassari, Italy.
Giustino VarrassiVisionary International Board for Research and Analgesia (VIBRA), Fondazione Paolo Procacci, Rome, 00193, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPulsed 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.

methodsThis 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.

resultsTreatment 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%.

conclusionsThis 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.

Indexed as

Chronic PainGanglia, SpinalLow Back PainNomogramsPulsed Radiofrequency TreatmentRadiculopathyAdultFemaleHumansLumbar VertebraeMaleMiddle AgedPain MeasurementPrediction AlgorithmsPrognosisRetrospective StudiesChronic radicular painDorsal root ganglionLASSO regressionNomogramPrediction modelPulsed radiofrequency

Identifiers

PMID42635684
PMCPMC13503403

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.