Facilitating the Identification of Appropriate Patient-Centered Outcome Measures (PCOMS) in Rare Disease (RD) Clinical Research Using Functional Impacts Matching


Barbier V1, Desvignes-Gleizes C2, Mc Donough G3, Rath A3, Bothorel S4, de Bock E1
1Icon, Lyon, France, 2Mapi Research Trust, Lyon, 69, France, 3Inserm | US14 - Orphanet, Paris, France, 4Mapi Research Trust, Lyon, France

OBJECTIVES: Disease heterogeneity, short-term studies, lack of natural history knowledge and scarcity of RD-specific PCOMs are strong limitations in patient centric clinical research and practice. The development of disease-specific PCOMs for more than 7,000 RDs is not realistic. Using and adapting existing PCOM to turn RD patient experience into meaningful and comparable data is an alternative solution. Mapi Research Trust (MRT)/ICON and Orphanet collaborated to identify existing PCOMs that could efficiently measure RD-specific functional impacts.

METHODS: Orphanet conducted a systematic documentation of the functional impacts of 551 RD, by interviewing clinicians and patient representatives, using questionnaire derived from the International Classification of Functioning (ICF). To identify PCOMs matching RD functional impact profiles, MRT/ICON applied the same ICF coding rule to a selection of PCOMs identified in MRT PROQOLID™ database. Natural Language Processing (NLP) was used to code PCOM items with ICF code. NLP pre-processing steps, including tokenisation, normalization, stop-words list and lemmatisation were conducted prior to coding.

RESULTS: 551 RD were indexed and ICF-coded by Orphanet, generating the first entry of the matrix (RD-entry). The other part of the matrix (PCOMs-entry) is under development: 1/ around 1,000 RD-specific, function-specific and disease area-specific PCOMs were selected for coding; 2/ Collection of selected PCOMs domains and items is on-going; 3/ NLP pre-processing steps were completed, NLP algorithm finalized and successfully applied to a sample of 20 PCOMs items. Quality check of the NLP-allocated ICF codes of PCOMs sample was performed by two reviewers in two steps. 10 PCOMs were first reviewed, discrepancies discussed and quality check rules established. Following similar review process, 10 additional ICF-coded PCOMs were reviewed and quality check rules refined.

CONCLUSIONS: We have tested and confirmed the robustness of the NLP-generated ICF coding rules on a sample of PCOMs. This work is a promising step for the development of a PCOMs-RD database.

Conference/Value in Health Info

2023-11, ISPOR Europe 2023, Copenhagen, Denmark

Value in Health, Volume 26, Issue 11, S2 (December 2023)




Clinical Outcomes, Methodological & Statistical Research, Patient-Centered Research

Topic Subcategory

Clinical Outcomes Assessment, Patient-reported Outcomes & Quality of Life Outcomes, PRO & Related Methods


No Additional Disease & Conditions/Specialized Treatment Areas, Rare & Orphan Diseases

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