Identifying definite patterns of unmet needs in patients with multiple sclerosis using unsupervised machine learning.

INTRODUCTION: People with multiple sclerosis (PwMS) exhibit a spectrum of needs that extend beyond solely disease-related determinants. Investigating unmet needs from the patient perspective may address daily difficulties and optimize care. Our aim was to identify patterns of unmet needs among PwMS and their determinants. METHODS: We conducted a cross-sectional multicentre study. Data were collected through an anonymous, self-administered online form. To cluster PwMS according to their main unmet needs, we performed agglomerative hierarchical clustering algorithm. Principal component analysis (PCA) was applied to visualize cluster distribution. Pairwise comparisons were used to evaluate demographics and clinical distribution among clusters. RESULTS: Out of 1764 mailed questionnaires, we received 690 responses. Access to primary care was the main contributor to the overall unmet need burden. Four patterns were identified: cluster C1, 'information-seekers with few unmet needs'; cluster C2, 'high unmet needs'; cluster C3, 'socially and assistance-dependent'; cluster C4, 'self-sufficient with few unmet needs'. PCA identified two main components in determining the patterns: the 'public sphere' (access to information and care) and the 'private sphere' (need for assistance and social life). Older age, lower education, longer disease duration and higher disability characterized clusters with more unmet needs in the private sphere. However, demographic and clinical factors failed in explaining the four identified patterns. CONCLUSION: Our study identified four unmet need patterns among PwMS, emphasizing the importance of personalized care. While clinical and demographic factors provide some insight, additional variables warrant further investigation to fully understand unmet needs in PwMS.

Sativex in resistant multiple sclerosis spasticity: Discontinuation study in a large population of Italian patients (SA.FE. study).

BACKGROUND: The approval of Sativex for the management of multiple sclerosis (MS) spasticity opened a new opportunity to many patients. In Italy, the healthcare payer can be fully reimbursed by the involved pharma company with the cost of treatment for patients not responding after a 4 week (28 days) trial period (Payment by Results, PbR), and 50% reimbursed with the cost of 6 weeks (42 days) treatment for other patients discontinuing (Cost Sharing, CS). The aim of our study was to describe the Sativex discontinuation profile from a large population of spasticity treated Italian MS patients. METHODS: We collected data of patients from 30 MS centres across the country starting Sativex between January 2014 and February 2015. Data were collected from the mandatory Italian Medicines Agency (AIFA) web-registry. Predictors of treatment discontinuation were assessed using a multivariate Cox proportional regression analysis. RESULTS: During the observation period 631 out of 1597 (39.5%) patients discontinued Sativex. The Kaplan-Meier estimates curve showed that 333 patients (20.8%) discontinued treatment at 4 weeks while 422 patients (26.4%) discontinued at 6 weeks. We found after adjusted modeling that a higher NRS score at T1 (adjHR 2.23, 95% 2.07-2.41, p<0.001) and a lower baseline NRS score (adjHR 0.51 95% CI 0.46-0.56, p<0.001) were predictive of treatment discontinuation. CONCLUSION: These data show that the first 6 weeks are useful in identifying those patients in which Sativex could be effective, thus avoiding the cost of longer term evaluation.