Hierarchical Clustering of Rare Diseases Based on Functional Impacts to Support the Identification of Potential Patient-Centered Outcomes Measures for Rare Diseases Research
Vilcot T1, Desvignes-Gleizes C2, Mc Donough G3, Pereira MM4, Pellegrini M5, Sherafat-Kazemzadeh R6, Rath A3, de Bock E1, Bothorel S6
1ICON, Lyon, France, 2Mapi Research Trust, Lyon, 69, France, 3Inserm | US14 - Orphanet, Paris, France, 4Vall d'Hebron Institut de Recerca, Barcelona, Spain, 5AP-HP Hôpital Saint Louis, Paris, France, 6Mapi Research Trust, Lyon, France
OBJECTIVES: The lack of specific validated patient-centered outcomes measures (PCOMs) in Rare Diseases (RDs) is a strong limitation in patient centric clinical research. However, the development of disease specific PCOMs for more than 7,000 RDs is not a realistic objective. Mapi Research Trust/ICON, Orphanet and EuroBloodNet collaborated to perform a clustering of RDs based on functional impacts similarities to identify PCOMs that could be potential candidates for RDs among the same cluster.
METHODS: Orphanet conducted a systematic documentation of the functional consequences of RDs and their impact on quality of life using the Orphanet Disability Questionnaire (ODQ), which is derived and adapted from the WHO Classification of Functioning (ICF). ODQ was administered to clinicians and patient representatives to capture RD functional impacts (Severity, Frequency and Temporality). A Multiple Factorial Analysis (MFA) was performed to summarize information from the 343 ODQ items.
RESULTS: Two consecutive hierarchical clusterings were first performed on 551 RDs. It led to 57 clusters, including 35 with less than 5 RDs and a large cluster of 65 RDs. The size of the clusters made their interpretation difficult. Therefore, MFA was refined according to 3 criteria: forcing cluster size to 10 RDs, applying weight to the frequency and severity and merging disorders and subtypes within the same cluster. 47 clusters were generated, ranging from 10 to 12 RDs. For each cluster, existing PCOMs developed for the clustered RDs were identified and PCOMs domains were documented to allow matches between PCOMs domains and RDs functional impacts.
CONCLUSIONS: This clustering work identified RDs with similar functional impacts as well as existing PCOMs within each cluster. Those PCOMs could be good candidates for RDs within the same cluster and without disease-specific PCOM. Review and validation of the clusters and related PCOMs by clinicians and patient representatives is the next step of this work.
Conference/Value in Health Info
Value in Health, Volume 26, Issue 11, S2 (December 2023)
Clinical Outcomes, Methodological & Statistical Research, Patient-Centered Research
Clinical Outcomes Assessment, Patient-reported Outcomes & Quality of Life Outcomes
No Additional Disease & Conditions/Specialized Treatment Areas, Rare & Orphan Diseases