IntroductionChronic pelvic pain affects up to 24% of women worldwide and for up to 55% of these there is no associated pathology. Despite this there are no established treatments in this cohort. This is a secondary analysis of a randomised-controlled trial (GaPP2) to explore if there are measures which enable us to predict treatment outcome.MethodsGaPP2 recruited women with chronic pelvic pain and no identified pathology and compared the response to gabapentin and placebo. This analysis used variables collected at baseline including validated questionnaires. Binary logistic regression was used to create models to explore whether baseline variables predicted treatment response. Treatment response was determined using 30% reduction in average pain intensity, 30% reduction in worst pain intensity and the Patient Global Impression of Change (‘marked’ or ‘very marked’ improvement) individually. We also explored whether baseline variables predicted the occurrence of side-effects (dizziness, visual disturbances and drowsiness).ResultsUsing the Patient Global Impression of Change questionnaire, we found a significant binary logistic regression (p = 0.029, explaining 31% of the variance), with those with lower worst pain intensity (odds ratio (OR) of 0.393, 95% CI [0.217, 0.712]), lower bladder symptom score (OR = 0.788, CI [0.628, 0.989]), and higher mental component quality of life score (OR = 0.911, CI [0.840, 0.988]), more likely to have ‘marked’ or ‘very marked’ improvement when treated with gabapentin. We could not identify predictors of experiencing side-effects to gabapentin. However, we did find predictors of these in the placebo group (binary logistic regression (p = 0.009) and explained 33% of the variance). Worse mental health (OR = 1.247, CI [1.019, 1.525]) and lower baseline pain interference (OR = 0.687, CI [0.483, 0.978]) were associated with having side effects, whilst the use of hormones reduced the risk of experiencing side effects (OR = 0.239, CI [0.084, 0.676]).DiscussionResearchers and clinicians are increasingly aware of the importance of personalised medicine and treatment decisions being driven by knowledge of what treatments work for whom. Our data suggests an important role of the Patient Global Impression of Change in clinical trials as it may better reflect balance between symptoms reduction and side-effects and therefore be more useful in clinician-patients joint decision making.
