Pregabalin in the Management of Painful Diabetic Neuropathy: A Narrative Review

Pregabalin is a first-line treatment in all major international guidelines on the management of painful diabetic neuropathy (pDPN). Treatment with pregabalin leads to a clinically meaningful improvement in pain scores, offers consistent relief of pain and has an acceptable tolerance level. Despite its efficacy in relieving neuropathic pain, more robust methods and comprehensive studies are required to evaluate its effects in relation to co-morbid anxiety and sleep interference in pDPN. The sustained benefits of modulating pain have prompted further exploration of other potential target sites and the development of alternative GABAergic agents such as mirogabalin. This review evaluates the role of pregabalin in the management of pDPN as well as its potential adverse effects, such as somnolence and dizziness, which can lead to withdrawal in ~ 30% of long-term use. Recent concern about misuse and an increase in deaths linked to its use has led to demands for reclassification of pregabalin as a class C controlled substance in the UK. We believe these demands need to be tempered in relation to the difficulties it would create for repeat prescriptions for the many millions of patients with pDPN for whom pregabalin provides benefit.Plain Language Summary: Plain language summary available for this article.

Integrating data from randomized controlled trials and observational studies to predict the response to pregabalin in patients with painful diabetic peripheral neuropathy

BACKGROUND

More 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).

METHODS

We 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.

RESULTS

Cluster 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.

CONCLUSION

The 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.