163: Factors associated with neuropathic pain medication effectiveness: a cross-sectional study

Topic: Clinical Science
Subtopic: Pain category (ICD-11), Chronic neuropathic pain, Pharmacological treatments, Epidemiology

Background and Aims: Neuropathic pain (NeuP) is common, debilitating, and notoriously difficult to treat.[1,2] The first-line medications – including gabapentinoids, tricyclic antidepressants, and serotonin-norepinephrine reuptake inhibitors – only provide clinically meaningful pain relief to a subset of people.[3] Despite substantial inter-individual variability in treatment response, current clinical guidelines lack evidence to support personalised prescribing. As a result, many people undergo prolonged trial-and-error treatment and experience avoidable adverse effects. Identifying predictors of treatment response could enable more targeted prescribing, improve treatment outcomes, and reduce treatment burden. Therefore, this study aimed to identify factors associated with the effectiveness of first-line NeuP medications.

Methods: We analysed data from the PAINSTORM study questionnaire, which included people with diabetes and people who had undergone chemotherapy.[4,5] We identified participants with current chronic NeuP at the time of questionnaire collection based on current pain or pain medication, pain duration of ≥3 months, and a Douleur Neuropathique en 4 Questions (DN4) interview score ≥3/7.[6] Participants who had received at least one self-reported first-line NeuP medication were classified as reporting either ≥50% or <50% pain relief in the previous 24 hours. We conducted multivariable logistic regression where variables were selected using the R package Elastic-Net.[7]

Results: Of 2,072 participants, 871 were identified as having chronic NeuP. Among these, 281 had at least one first-line NeuP medication and had reported treatment effectiveness. Clinically meaningful pain relief (≥50%) was reported by 149/281 (53%) participants. Factors associated with ≥50% pain relief included lower sleep disturbance (OR: 1.08, 95% CI: 1.01 to 1.15), lower pain intensity (OR: 1.33, 95% CI: 1.06 to 1.67), and lower DN4 scores indicating fewer neuropathic features (OR: 4.00, 95% CI: 1.30 to 12.50). There were no differences based on age, sex, working status, depression, anxiety, Chronic Pain Grade, or pain-related worrying.

Conclusions: Our findings highlight the limited and heterogeneous effectiveness of first-line NeuP medications. Treatment response appeared to be primarily related to pain intensity and the number of different NeuP characteristics, rather than sociodemographic factors or psychological comorbidities. Similar to our results, a study building predictive models from 27 randomised controlled trials (pregabalin vs placebo) found that baseline sleep interference score and pain intensity were predictive of ≥30% pain relief at week 5.[8] Thus, optimising sleep may play a critical role in improving treatment outcomes for NeuP. However, longer-term studies are needed to determine whether these observations persist over time.

References: [1] Torrance N, Lawson KD, Afolabi E, Bennett MI, Serpell MG, Dunn KM, et al. Estimating the burden of disease in chronic pain with and without neuropathic characteristics: Does the choice between the EQ-5D and SF-6D matter? Pain. 2014 Oct;155(10):1996–2004.
[2] van Hecke O, Austin SK, Khan RA, Smith BH, Torrance N. Neuropathic pain in the general population: A systematic review of epidemiological studies. PAIN. 2014 Apr;155(4):654.
[3] Soliman N, Moisset X, Ferraro MC, Andrade DC de, Baron R, Belton J, et al. Pharmacotherapy and non-invasive neuromodulation for neuropathic pain: a systematic review and meta-analysis. Lancet Neurol. 2025 May 1;24(5):413–28.
[4] Hébert HL, Pascal MMV, Smith BH, Wynick D, Bennett DLH. Big data, big consortia, and pain: UK Biobank, PAINSTORM, and DOLORisk. PAIN Rep. 2023 Sep;8(5):e1086.
[5] The PAINSTORM project [Internet]. [cited 2026 Feb 11]. Available from: https://www.painstorm.co.uk/home
[6] Bouhassira D, Attal N, Alchaar H, Boureau F, Brochet B, Bruxelle J, et al. Comparison of pain syndromes associated with nervous or somatic lesions and development of a new neuropathic pain diagnostic questionnaire (DN4). Pain. 2005 Mar;114(1–2):29–36.
[7] Zou H, Hastie T. Regularization and Variable Selection Via the Elastic Net. J R Stat Soc Ser B Stat Methodol. 2005 Apr 1;67(2):301–20.
[8] Almas M, Parsons B, Whalen E. Prediction of therapeutic response to pregabalin in subjects with neuropathic pain. Curr Med Res Opin. 2018 Dec 2;34(12):2041–52.

Presenting Author: Ms Mia Koponen
Submitting Author: Mia Koponen

Senior Author: Prof Lesley Colvin

IASP 2026 Global Year theme of Neuropathic Pain: Yes

• Ms Natalia Dygas - University of Dundee
• Dr Harry Hebert - University of Dundee
• Prof David Bennett - University of Oxford
• Prof Blair H. Smith - University of Dundee
• Prof Lesley Colvin - University of Dundee