Unsupervised clustering analysis of data from an online community to identify lupus patient profiles with regards to treatment preferences

Abstract

Objective Lupus is a chronic complex autoimmune disease. Non-adherence to treatment can affect patient outcomes. Considering patients’ preferences into medical decisions may increase acceptance to their medication. The PREFERLUP study used unsupervised clustering analysis to identify profiles of patients with similar treatment preferences in an online community of French lupus patients. Methods An online survey was conducted in adult lupus patients from the Carenity community between August 2018 and April 2019. Multiple Correspondence Analysis (MCA) was used with three unsupervised clustering methods (hierarchical, kmeans and partitioning around medoids). Several indicators (measure of connectivity, Dunn index and Silhouette width) were used to select the best clustering algorithm and choose the number of clusters. Results The 268 participants were mostly female (96%), with a mean age of 44.3 years 83% fulfilled the American College of Rheumatology (ACR) self-reported diagnostic criteria for systemic lupus erythematosus. Overall, the preferred route of administration was oral (62%) and the most important feature of an ideal drug was a low risk of side-effects (32%). Hierarchical clustering identified three clusters. Cluster 1 (59%) comprised patients with few comorbidities and a poor ability to identify oncoming flares; 84% of these patients desired oral treatments with limited side-effects. Cluster 2 (13%) comprised younger patients, who had already participated in a clinical trial, were willing to use implants and valued the compatibility of treatments with pregnancy. Cluster 3 (28%) comprised patients with a longer lupus duration, poorer control of the disease and more comorbidities; these patients mainly valued implants and injections and expected a reduction of corticosteroid intake. Conclusions Different profiles of lupus patients were identified according to their drug preferences. These clusters could help physicians tailor their therapeutic proposals to take into account individual patient preferences, which could have a positive impact on treatment acceptance and then adherence. The study highlights the value of data acquired directly from patient communities.

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