Developing a Standardised Dataset for Natural History Studies in Fibrous Dysplasia/McCune-Albright Syndrome.
Priego Zurita AL., Bulaicon OO., Bryce J., Arrieta N., Caballero Campos M., Cherenko M., Doxiadis G., Grasemann C., Javaid MK., McDevitt H., van der Meeren SW., Ovejero Crespo D., de Sanctis L., Seefried L., Verrijn Stuart AA., Tessaris D., de Witte PB., Chapurlat R., Ahmed SF., Appelman-Dijkstra NM.
Fibrous dysplasia/McCune-Albright syndrome (FD/MAS) is a rare and complex condition caused by somatic variants in the GNAS gene that lead to a wide clinical spectrum. The diagnostic process and therapeutic pathway vary per centre and therefore international harmonisation of data collection should be pursued. To understand the diagnostic pathways and clinical outcomes of patients with FD/MAS reported on an electronic-reporting tool (e-REC) across European centres to guide the develop a condition-specific module within the European Registries for Rare Endocrine and Bone conditions. Centres that reported new cases on e-REC between October 2019 and May 2021 were approached to complete a survey in May 2021. Fifty-eight cases were included. Median age at presentation was 20 years (range, 0, 72). Of the 58 included cases, the presentation type was isolated craniofacial FD in 19 (33%), monostotic FD in 15 (26%), polyostotic FD in 10 (17%), and MAS in 13 (13%). Standardised questionnaires to assess pain and quality of life were used routinely in 21/58 patients (36%). The majority of patients had more than one healthcare provider, with great diversity in the specialty of the coordinating physician. A standardised dataset module for FD/MAS was developed through collaboration with the FD/MAS study group, incorporating expert consensus and clinical insights. Key variables were identified to capture essential diagnostic, clinical, and patient-reported outcomes. The diagnostic path for patients with FD/MAS across European expert centres is variable. The outcomes of this study allowed the building of the first international FD/MAS-specific data collection.