SUN-070 European Registries for Rare Endocrine Conditions (EuRRECa): Results from the Platform for E-reporting of Rare Endocrine Conditions (e-REC)

摘要

Background EuRRECa (European Registries for Rare Endocrine Conditions) is a group of web-based projects that work closely with the European Reference Network (ERN) for Rare Endocrine Conditions (endo-ern.eu) and helps the ERN with inventorying its clinical activity. To understand the number of new encounters of rare conditions within this network, it has launched an e-reporting programme for rare endocrine conditions (e-REC) that are covered within this network. Methods 46 endocrine centres within 18 countries volunteered to participate in e-REC from July 2018 to June 2019. An electronic reporting ‘card’ developed through REDcap was issued monthly to enquire whether clinicians had encountered a new case of any condition within the 8 Endo-ERN main thematic groups (MTGs). Results On a monthly basis over 1 year, a median of 14 (range 11, 21) paediatric centres and 13 (11, 25) adult centres actively reported cases. A median of 53 (22, 80) paediatric cases and 96 (42, 250) adult cases were reported monthly. Amongst paediatric cases, conditions within the Sex Development and Maturation (SDM) theme were most commonly reported comprising 38% of all reported conditions, with XY, DSD being the most commonly reported condition (24% of cases). Amongst adults, Pituitary and Thyroid conditions were most commonly reported, 34% and 26% of all conditions, respectively. Amongst conditions within the Pituitary group, pituitary adenoma was the most commonly reported condition (74% of cases) and non-metastatic thyroid carcinoma was the most commonly reported condition (95% of cases) amongst the Thyroid group. In children, the median number of cases reported per centre was 21 (9, 32) for conditions affecting SDM. In adults, a median of 37 (6, 75) Pituitary and 22 (5, 80) Thyroid cases were reported per centre. Conclusion e-REC is a simple, yet effective, platform that can be used to capture information on new encounters with patients with several rare conditions and can be adapted to serve the needs of other discrete networks that are interested in understanding the occurrence of rare conditions.