Prediction of therapeutic response to pregabalin in subjects with neuropathic pain.

Abstract:

OBJECTIVE:To evaluate four models based on potential predictors for achieving a response to pregabalin treatment for neuropathic pain (NeP). METHODS:In total, 46 pain studies were screened, of which 27 NeP studies met the criteria for inclusion in this analysis. Data were pooled from these 27 placebo-controlled randomized trials to assess if baseline characteristics (including mean pain and pain-related sleep interference [PRSI] scores), early clinical response during weeks 1-3 of treatment (change from baseline in pain and PRSI scores), and presence of treatment-emergent adverse events (AEs) were predictive of therapeutic response. Therapeutic response was defined as a ≥30% reduction from baseline in either pain and/or PRSI scores at week 5 with supplemental analyses to predict pain outcomes at weeks 8 and 12. Predictors of Patient Global Impression of Change (PGIC) were also evaluated. Four models were assessed: Random Forest, Logistic Regression, Naïve Bayes, and Partial Least Squares. RESULTS:The number of pregabalin-treated subjects in the training/test datasets, respectively, were 2818/1407 (30% pain analysis), 2812/1405 (30% sleep analysis), and 2693/1345 (PGIC analysis). All four models demonstrated consistent results, and the most important predictors of treatment outcomes at week 5 and pain outcomes at weeks 8 and 12 were the reduction in pain score and sleep score in the first 1-3 weeks. The presence or absence of the most common AEs in the first 1-3 weeks was not correlated with any treatment outcome. CONCLUSIONS:Subjects with an early response to pregabalin are more likely to experience an end-of-treatment response.

journal_name

Curr Med Res Opin

authors

Almas M,Parsons B,Whalen E

doi

10.1080/03007995.2018.1520694

subject

Has Abstract

pub_date

2018-12-01 00:00:00

pages

2041-2052

issue

12

eissn

0300-7995

issn

1473-4877

journal_volume

34

pub_type

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