Dataset Information

Efficacy of Pregabalin and Duloxetine in Patients with Painful Diabetic Peripheral Neuropathy (PDPN): A Multi-Centre Phase IV Clinical Trial-BLOSSOM.

ABSTRACT:

Introduction

Our trial (ClinicalTrials.gov Identifier: NCT04246619) evaluates the efficacy of two generic medications, pregabalin and duloxetine, for treating pain in PDPN patients.

Methods

The patients were randomised either into the pregabalin (99) or the duloxetine (102) arm. Pain was evaluated using the DN-4 questionnaire, and visual analogue scales (VASs, 0-100 mm) were used to measure the average pain intensity (API), worst pain intensity (WPI) in the last 24 h and current pain intensity (CPI).

Results

The proportion of patients with a clinically significant improvement in the API at Week 12 was 88.3% [CI 81.7%, 94.8%] in the pregabalin arm and 86.9% [CI 76.7%, 97.1%] in the duloxetine arm. After 12 weeks, the CPI, API, and WPI decreased by -35.3 [-40.5, -30.0], -37.0 [-41.4, -32.6], and -41.6 [-46.6, -36.5] in the pregabalin arm, and by -35.0 [-39.2, -30.7], -36.9 [-41.5, -32.3], and -40.0 [-44.8, -35.2] in the duloxetine arm (all in mm, all p < 0.001).

Conclusion

Our results demonstrate that pregabalin and duloxetine are effective medications for treating pain in PDPN in more than 86% of all randomised patients.

SUBMITTER: Rakusa M

PROVIDER: S-EPMC10386018 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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Publications

Efficacy of Pregabalin and Duloxetine in Patients with Painful Diabetic Peripheral Neuropathy (PDPN): A Multi-Centre Phase IV Clinical Trial-BLOSSOM.

Rakusa Martin M Marolt Iris I Stevic Zorica Z Rebrina Sandra Vuckovic SV Milenkovic Tatjana T Stepien Adam A

Pharmaceuticals (Basel, Switzerland) 20230718 7

<h4>Introduction</h4>Our trial (ClinicalTrials.gov Identifier: NCT04246619) evaluates the efficacy of two generic medications, pregabalin and duloxetine, for treating pain in PDPN patients.<h4>Methods</h4>The patients were randomised either into the pregabalin (99) or the duloxetine (102) arm. Pain was evaluated using the DN-4 questionnaire, and visual analogue scales (VASs, 0-100 mm) were used to measure the average pain intensity (API), worst pain intensity (WPI) in the last 24 h and current p ...[more]

PMID: 37513930

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Project description:BackgroundDuloxetine hydrochloride is a reuptake inhibitor of 5-hydroxytryptamine and norepinephrine used to treat depression, generalized anxiety disorder, neuropathic pain, and stress incontinence in women. We investigated the efficacy of duloxetine in painful diabetic neuropathy and fibromyalgia to allow comparison with other antidepressants.MethodsWe searched PubMed, EMBASE (via Ovid), and Cochrane CENTRAL up to June 2008 for randomised controlled trials using duloxetine to treat neuropathic pain.ResultsWe identified six trials with 1,696 patients: 1,510 were treated with duloxetine and 706 with placebo. All patients had established baseline pain of at least moderate severity. Trial duration was 12 to 13 weeks. Three trials enrolled patients with painful diabetic neuropathy (PDN) and three enrolled patients with fibromyalgia. The number needed to treat (NNT) for at least 50% pain relief at 12 to 13 weeks with duloxetine 60 mg versus placebo (1,211 patients in the total comparison) was 5.8 (95% CI 4.5 to 8.4), and for duloxetine 120 mg (1,410 patients) was 5.7 (4.5 to 5.7). There was no difference in NNTs between PDN and fibromyalgia. With all doses of duloxetine combined (20/60/120 mg) there were fewer withdrawals for lack of efficacy than with placebo (number needed to treat to prevent one withdrawal 20 (13 to 42)), but more withdrawals due to adverse events (number needed to harm (NNH) 15 (11 to 25)). Nausea, somnolence, constipation, and reduced appetite were all more common with duloxetine than placebo (NNH values 6.3, 11, 11, and 18 respectively). The results for duloxetine are compared with published data for other antidepressants in neuropathic pain.ConclusionDuloxetine is equally effective for the treatment of PDN and fibromyalgia, judged by the outcome of at least 50% pain relief over 12 weeks, and is well tolerated. The NNT of 6 for 50% pain relief suggests that this is likely to be a useful drug in these difficult-to-treat conditions, where typically only a minority of patients respond. Comparing duloxetine with antidepressants for pain relief in DPN shows inadequacies in the evidence for efficacy of antidepressants, which are currently recommended in PDN care pathways.

Project description:IntroductionWith the development of new neuroimaging tools it has become possible to assess neurochemical alterations in patients experiencing chronic pain and to determine how these factors change during pharmacological treatment. The goal of this study was to examine the exact neurochemical mechanism underlying pregabalin treatment, utilizing magnetic resonance spectroscopy (1H-MRS), in a population of patients with painful diabetic polyneuropathy (PDN), with the overall aim to ultimately objectify the clinical effect of pregabalin.MethodsA double blind, randomized, placebo-controlled study was conducted. A total of 27 patients with PDN were enrolled in the study, of whom 13 received placebo treatment (control group) and 14 received pregabalin (intervention group). Pregabalin treatment consisted of stepwise dose escalation over the study period from 75 mg daily ultimately to 600 mg daily. 1H-MRS was performed at 3T on four regions of interest in the brain: the rostral anterior cingulate cortex (rACC), left and right thalamus and prefrontal cortex. The absolute concentrations of N-acetyl aspartate, glutamate, glutamine, gamma-amino-butyric-acid (GABA), glucose (Glc) and myo-inositol (mINS) were determined using LCModel.ResultsThe concentration of most neurometabolites in the placebo and pregabalin group did not significantly differ over time, with only a small significant difference in Glc level in the left thalamus (p = 0.049). Comparison of the effects of the different doses revealed significant differences for mINS in the rACC (baseline 2.42 ± 1.21 vs. 450 mg 1.58 ± 0.94; p = 0.022) and dorsolateral prefrontal cortex (75 mg 2.38 ± 0.89 vs. 450 mg 1.59 ± 0.85; p = 0.042) and also for GABA in the rACC (75 mg 0.53 ± 0.51 vs. 225 mg 0.28 ± 0.19; p = 0.014).ConclusionNo differences were found in metabolite concentrations between the placebo (control) and intervention groups, but some differences, although small, were found between the different doses.Trial registrationThis study is registered at ClinicalTrials.gov (NCT01180608).FundingLyrica Independent Investigator Research Award (LIIRA) 2010 (Pfizer) funded the study.

Project description:BackgroundMore patient-specific medical care is expected as more is learned about variations in patient responses to medical treatments. Analytical tools enable insights by linking treatment responses from different types of studies, such as randomized controlled trials (RCTs) and observational studies. Given the importance of evidence from both types of studies, our goal was to integrate these types of data into a single predictive platform to help predict response to pregabalin in individual patients with painful diabetic peripheral neuropathy (pDPN).MethodsWe utilized three pivotal RCTs of pregabalin (398 North American patients) and the largest observational study of pregabalin (3159 German patients). We implemented a hierarchical cluster analysis to identify patient clusters in the Observational Study to which RCT patients could be matched using the coarsened exact matching (CEM) technique, thereby creating a matched dataset. We then developed autoregressive moving average models (ARMAXs) to estimate weekly pain scores for pregabalin-treated patients in each cluster in the matched dataset using the maximum likelihood method. Finally, we validated ARMAX models using Observational Study patients who had not matched with RCT patients, using t tests between observed and predicted pain scores.ResultsCluster analysis yielded six clusters (287-777 patients each) with the following clustering variables: gender, age, pDPN duration, body mass index, depression history, pregabalin monotherapy, prior gabapentin use, baseline pain score, and baseline sleep interference. CEM yielded 1528 unique patients in the matched dataset. The reduction in global imbalance scores for the clusters after adding the RCT patients (ranging from 6 to 63% depending on the cluster) demonstrated that the process reduced the bias of covariates in five of the six clusters. ARMAX models of pain score performed well (R 2 : 0.85-0.91; root mean square errors: 0.53-0.57). t tests did not show differences between observed and predicted pain scores in the 1955 patients who had not matched with RCT patients.ConclusionThe combination of cluster analyses, CEM, and ARMAX modeling enabled strong predictive capabilities with respect to pain scores. Integrating RCT and Observational Study data using CEM enabled effective use of Observational Study data to predict patient responses.

Project description:IntroductionPrediction of final clinical outcomes based on early weeks of treatment can enable more effective patient care for chronic pain. Our goal was to predict, with at least 90% accuracy, 12- to 13-week outcomes for pregabalin-treated painful diabetic peripheral neuropathy (pDPN) patients based on 4 weeks of pain and pain-related sleep interference data.MethodsWe utilized active treatment data from six placebo-controlled randomized controlled trials (n = 939) designed to evaluate efficacy of pregabalin for reducing pain in patients with pDPN. We implemented a three-step, trajectory-focused analytics approach based upon patient responses collected during the first 4 weeks using monotonicity, path length, frequency domain (FD), and k-nearest neighbor (kNN) methods. The first two steps were based on combinations of baseline pain, pain at 4 weeks, weekly monotonicity and path length during the first 4 weeks, and assignment of patients to one of four responder groups (based on presence/absence of 50% or 30% reduction from baseline pain at 4 and at 12/13 weeks). The third step included agreement between prediction of logistic regression of daily FD amplitudes and assignment made from kNN analyses.ResultsStep 1 correctly assigned 520/939 patients from the six studies to a responder group using a 3-metric combination approach based on unique assignment to a 50% responder group. Step 2 (applied to the remaining 419 patients) predicted an additional 121 patients, using a blend of 50% and 30% responder thresholds. Step 3 (using a combination of FD and kNN analyses) predicted 204 of the remaining 298 patients using the 50% responder threshold. Our approach correctly predicted 90.0% of all patients.ConclusionBy correctly predicting 12- to 13-week responder outcomes with 90% accuracy based on responses from the first month of treatment, we demonstrated the value of trajectory measures in predicting pDPN patient response to pregabalin.Trial registrationwww.clinicaltrials.gov identifiers, NCT00156078/NCT00159679/NCT00143156/NCT00553475.FundingPfizer. Plain language summary available for this article.

Dataset Information

Efficacy of Pregabalin and Duloxetine in Patients with Painful Diabetic Peripheral Neuropathy (PDPN): A Multi-Centre Phase IV Clinical Trial-BLOSSOM.

Introduction

Methods

Results

Conclusion

Publications

Efficacy of Pregabalin and Duloxetine in Patients with Painful Diabetic Peripheral Neuropathy (PDPN): A Multi-Centre Phase IV Clinical Trial-BLOSSOM.

Similar Datasets

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