OBJECTIVES: The aim of this study was to gain insights into the real-world experience of metastatic melanoma patients. Important Quality of life (QoL) aspects, specific disease burden and treatments experiences of melanoma patients have been investigated.

METHODS: Real-world data from eight online health communities in four countries (US, UK, France, Germany) was collected from the time-period May 2017-August 2021, resulting in a data set with n=2,426 algorithmically identified patients and n=28,261 documents. Melanoma-specific concepts of the patient experience (e.g., QoL topics, symptoms, side effects) were algorithmically coded using machine translation, supervised machine learning, natural language processing and knowledge graph tagging. Average precision of coding algorithms across concepts was high with 0.83. Data were collected and processed in an anonymised, secure, and reliable way to ensure privacy and ethical aspects.

RESULTS: Among n=1,492 patients identifiable by melanoma stage, 49% (n=724) were in advanced stages III and IV. Among n=401 patients identifiable by age, 47% (n=188) were >40 years. Aspects of psychological wellbeing were the most frequently discussed QoL dimensions (>50% of patients). Total population of Melanoma patients, negative and positive feelings were assigned high importance by 37% (n=912) and 15% (n=381) of patients, respectively. On the 1083 mentions of symptoms, Joint pain (33% (n=362) and fatigue (19% (n=207)) were overrepresented. In 8% (n=2,265) of documents a drug therapy was mentioned, and treatment experiences with the most used melanoma drugs could be investigated. Medication discontinuation was reported in n=127 documents, facilitating the identification of causing side effects, or measures adopted to continue an interrupted therapy.

CONCLUSIONS: Certain limitations of social listening approaches notwithstanding, this study has reliably reproduced and complemented findings from clinical trials on disease burdens and treatment experience reported by melanoma patients from a large online population in an unprompted real-world setting.