Pain Severity Trajectories and Patient Profiles in Chronic Pain Patients Treated with Cannabis-Based Medicinal Products: A k-Means Clustering Analysis of the UK Medical Cannabis Registry

Abstract

Cannabis-based medicinal products (CBMPs) are increasingly prescribed for chronic pain, yet the supporting evidence remains limited and the extent to which patients differ in their response is poorly understood. This study applied longitudinal k-means clustering to pain-severity trajectories in patients treated with CBMPs for chronic pain to distinguish patterns of response and identify the baseline characteristics predicting them over 24 months. A cohort study was undertaken of UK Medical Cannabis Registry participants whose primary indication was chronic pain. Pain, health-related quality of life, anxiety, and sleep were assessed with patient-reported outcome measures, including the Brief Pain Inventory Short Form (BPI-SF), EuroQol 5-Dimension 5-Level (EQ-5D-5L), Generalised Anxiety Disorder-7, and Single-Item Sleep Quality Scale, at baseline up to 24 months. BPI-SF pain-severity trajectories were grouped by longitudinal k-means clustering. The number of clusters was chosen using the gap statistic and predictors of cluster membership were examined by logistic regression. Of 3744 patients included, every outcome improved from baseline to 24 months, including BPI-SF pain severity (d = 0.57), pain interference (d = 0.59), and the EQ-5D-5L index value (d = −0.76; all P <  .001). More than half of patients achieved a minimal clinically important difference in each pain measure at 24 months. Five distinct trajectory clusters emerged. All ended below their baseline severity, but the magnitude and pattern of change varied markedly. Adverse events were reported by 14.37% (n = 538) of patients, predominantly mild or moderate. Baseline sleep quality, anxiety, and treatment indication were more strongly associated with response than product- or dose-related factors.