Recommendations for the inclusion of Fabry disease as a rare febrile condition in existing algorithms for fever of unknown origin

Manna R., Cauda R., Feriozzi S., Gambaro G., Gasbarrini A., Lacombe D., ...Daha Fazla

INTERNAL AND EMERGENCY MEDICINE, cilt.12, ss.1059-1067, 2017 (SCI İndekslerine Giren Dergi) identifier identifier identifier

  • Cilt numarası: 12 Konu: 7
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1007/s11739-017-1704-y
  • Sayfa Sayıları: ss.1059-1067


Fever of unknown origin (FUO) is a rather rare clinical syndrome representing a major diagnostic challenge. The occurrence of more than three febrile attacks with fever-free intervals of variable duration during 6 months of observation has recently been proposed as a subcategory of FUO, Recurrent FUO (RFUO). A substantial number of patients with RFUO have auto-inflammatory genetic fevers, but many patients remain undiagnosed. We hypothesize that this undiagnosed subgroup may be comprised of, at least in part, a number of rare genetic febrile diseases such as Fabry disease. We aimed to identify key features or potential diagnostic clues for Fabry disease as a model of rare genetic febrile diseases causing RFUO, and to develop diagnostic guidelines for RFUO, using Fabry disease as an example of inserting other rare diseases in the existing FUO algorithms. An international panel of specialists in recurrent fevers and rare diseases, including internists, infectious disease specialists, rheumatologists, gastroenterologists, nephrologists, and medical geneticists convened to review the existing diagnostic algorithms, and to suggest recommendations for arriving at accurate diagnoses on the basis of available literature and clinical experience. By combining specific features of rare diseases with other diagnostic considerations, guidelines have been designed to raise awareness and identify rare diseases among other causes of FUO. The proposed guidelines may be useful for the inclusion of rare diseases in the diagnostic algorithms for FUO. A wide spectrum of patients will be needed to validate the algorithm in different clinical settings.