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Improved allergen immunotherapy prescription for seasonal allergic rhinitis: an innovative algorithm
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  • Stefania Arasi,
  • Sveva Castelli,
  • Marco Di Fraia,
  • Danilo Villalta,
  • Salvatore Tripodi,
  • Serena Perna,
  • Stephanie Dramburg,
  • Maria Antonia Brighetti,
  • Mariaelisabetta Conte,
  • Paola Martelli,
  • Ifigenia Sfika,
  • Alessandro Travaglini,
  • Pier Luigi Verardo,
  • Valeria Villella,
  • Paolo Matricardi
Stefania Arasi
Bambino Gesu Pediatric Hospital, Bambino Gesù Children Research Hospital (IRCCS)
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Sveva Castelli
Charite Universitatsmedizin Berlin
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Marco Di Fraia
Sapienza, University of Rome
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Danilo Villalta
Pordenone Hospital
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Salvatore Tripodi
Sandro Pertini Hospital
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Serena Perna
Charitè University Medical Center
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Stephanie Dramburg
Charitè University Medical Center
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Maria Antonia Brighetti
University of Rome Tor Vergata
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Mariaelisabetta Conte
Pordenone Hospital
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Paola Martelli
Pordenone Hospital
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Ifigenia Sfika
Sandro Pertini Hospital
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Alessandro Travaglini
University of Rome Tor Vergata
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Pier Luigi Verardo
ARPA
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Valeria Villella
Sandro Pertini Hospital
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Paolo Matricardi
Charitè University Medical Center
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Abstract

Background: Allergen immunotherapy(AIT) is the only disease-modifying treatment with long-term effects in patients with seasonal allergic rhinoconjunctivitis(SAR). Its efficacy depends on the precise identification of the pollen triggering symptoms. However, a diagnostic approach based on retrospective clinical history and sensitization to extracts often does not lead to unequivocal results. Objectives: To assess the usability and impact of a recently established algorithm for a potential clinical decision support system (@IT.2020-DSS) for pollen allergy and its diagnostic steps (including anamnesis, SPT, component resolved diagnosis, CRD, and real-time digital symptom recording, eDiary) on doctor’s AIT prescription decisions. Methods: After a concise educational training on the @IT.2020-DSS algorithm, 46 doctors (18allergy specialists, AS, and 28general practitioners, GP) expressed their hypothetical AIT prescription for 10 clinical index cases. Decisions were recorded repeatedly based on different steps of the support algorithm. The usability and perceived impact of the algorithm on individual clinical performance were evaluated. Results: The combined use of CRD and an eDiary increased the hypothetical AIT prescriptions, both among AS and GP (p<.01). AIT prescription based on anamnesis and SPT were heterogeneous but converged towards a consensus after the integration of CRD and eDiary information. Doctors considered the algorithm useful and recognized its potential in enhancing traditional diagnostics. Conclusions: The implementation of CRD and eDiary in the @IT2020-DSS algorithm improved consensus on hypothetical AIT prescription for SAR among AS and GP. The hypothesis, that a CDSS for etiological SAR diagnosis and AIT prescription may be useful in real-life clinical practice deserves further investigations.