A Comparative Study on the Efficacy, Safety and Cost Effectiveness of Gabapentin and Pregabalin in the Treatment of Neuropathic Pain
Bibliographic record
Abstract
Introduction Neuropathic pain distinguishes itself from nociceptive pain not only in its etiology and clinical presentation but also in its limited responsiveness to non-steroidal anti-inflammatory drugs (NSAIDs) and opioid analgesics. Gabapentin and pregabalin, collectively referred to as gabapentinoids, are anticonvulsant agents that have demonstrated efficacy in managing neuropathic pain. Evidence supports their use in comparison to placebo or antidepressants, such as tricyclic antidepressants, for the treatment of neuropathic pain. However, despite extensive investigation, there remains a paucity of conclusive data and often contradictory findings regarding the relative superiority of one drug over the other, particularly in resource-constrained settings and including the practicality of the money spent. Aim The aim of the study is to evaluate and compare the efficacy, safety parameters and cost-effectiveness of gabapentin (GB) and pregabalin (PG) in neuropathic pain. Methods A randomized double-blind comparative study was conducted to evaluate the effectiveness of GB and PG in the treatment of neuropathic pain, involving 60 patients attending the Neurology outpatient department (OPD) at Sri Ramachandra Institute of Higher Education and Research, Chennai. The participants were equally divided into two groups: Group A received GB and Group B received PG. Both groups were followed up regularly at four, eight, and 12 weeks. From day 0, doses were titrated according to the response and side effects, costs were noted down at each visit and pain was assessed using the Visual Analog Scale (VAS) and the McGill Pain Questionnaire. Results Both GB and PG demonstrate significant effectiveness in improving the symptoms of the patients after three months of treatment (2.5±0.9 for PG vs. 4.5±1.3 for GB, p<0.001) according to VAS, with a similar result when analysed with the McGill Pain Questionnaire. PG also has the advantages in terms of statistical results and evidence along with fewer reported adverse effects and better patient compliance.Cost evaluation indicated that the cumulative expense for PG over three months was INR 1,286 compared to INR 3,420 for GB, even with dose adjustments. Moreover, PG demonstrated a 75% reduction in cost per VAS point and a 72% decrease in cost per McGill point, highlighting its greater cost-effectiveness along with improved clinical results. Conclusion This randomized prospective study provides a comparison of the efficacy and safety of GB and PG in neuropathic pain, while throwing light into health economic part by including cost analysis. Such comparisons, including expense analysis, are limited in the literature. Our study plays a role in substantiating PG as a better analgesic in many respects for neuropathic pain while also proposing a possibility for evaluating combination therapies and to provide an optimize healthcare resource allocation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".