Introduction
Seasonal rhinoconjunctivitis due to pollen allergy (SAR) affects
millions of people around the globe and is particularly prevalent among
children1. Symptom-relieving drugs can control the
disease, but the only disease-modifying treatment with long-term effects
is an allergen-specific immunotherapy (AIT)2,3. The
efficacy of AIT depends on the precise identification of the eliciting
pollen inducing IgE sensitization and triggering the patient’s
symptoms4-6. Unfortunately, pinning down the causing
allergen is often difficult, especially in Southern European countries,
as patients are frequently sensitized to multiple, often cross-reactive,
allergenic sources with overlapping pollination
seasons7.
This diagnostic challenge can be confronted with the use of component
resolved diagnostics (CRD) in order to identify the eliciting allergen
and thereby choose the proper agent for an allergen-specific
immunotherapy. Corresponding algorithms on the molecular diagnosis of
allergies have been published8-10. However, a
traditional approach, based exclusively on anamnesis and the use of
pollen extracts, is still the most frequently used
worldwide3 and the implementation of molecular
diagnostic algorithms – still considered a complex matter by most
doctors - is infrequent10. Expert systems and software
solutions have been proposed as tools to make the adoption of diagnostic
algorithms for CRD easier11. However, to our
knowledge, no informatics tool dedicated to support the implementation
of internationally validated algorithms is yet available.
In contrast, a great variety of mobile phone applications has flooded
the market, aiming at an improved disease control and quality of life
for allergic patients. Unfortunately, only a small number of
applications has been clinically validated, but especially in the area
of real-time symptom monitoring, the usefulness of mobile devices has
been proven12-16. Though in the daily clinical
practice still most of the patient´s history is being assessed
retrospectively, the electronic clinical diary (eDiary) enables the
doctor to evaluate individual symptoms and the need for medication
prospectively. With the help of software systems, clinical scores can be
automatically generated, graphically matching patients´ SMS trajectories
with those of the local pollen counts16,17.
The opportunity of mobile Health (mHealth) technology is being used not
only to record patients´ data, but also as part of clinical decision
support systems (CDSS), created to assist patients, clinicians and
pharmacists at the point of care18-21. In allergology,
several support systems have been created, related to symptom monitoring
and a facilitated diagnosis of respiratory
allergies21-22. In order to create a support tool for
the precise prescription of AIT, we identified a diagnostic algorithm
based on the use of CRD and eDiaries in combination with local pollen
counts as potentially efficient and user-friendly tools to be included
in a future clinical decision support system. The purpose of the present
study is to assess the effectiveness and usability of this algorithm and
its individual tools between two groups of allergy specialists (AS) and
general practitioners (GP) in order to facilitate their clinical
decision taking with regard to AIT prescription.