Improved allergen immunotherapy prescription for seasonal allergic
rhinitis: an innovative algorithm
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.