The Potential of Clinical Decision Support Systems for Prevention, Diagnosis, and Monitoring of Allergic Diseases

Dramburg, Stephanie; Fernández, María Marchante; Potapova, Ekaterina; Matricardi, Paolo Maria

doi:10.3389/fimmu.2020.02116

Cited by 28 publications

(21 citation statements)

References 69 publications

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“…Thus molecular allergy diagnosis may be compared a bit with the switching from previous old telephones to mobile phones which offer many different additional applications that need to be explored by the user. The challenges of interpreting allergen microarray results may be met by machine learning approaches and other diagnostic algorithms in addition to continuous medical education ( 10 , 101 ).…”

Section: Unmet Needs In Microarray Technologymentioning

confidence: 99%

Microarray-Based Allergy Diagnosis: Quo Vadis?

et al. 2021

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More than 30% of the world population suffers from allergy. Allergic individuals are characterized by the production of immunoglobulin E (IgE) antibodies against innocuous environmental allergens. Upon allergen recognition IgE mediates allergen-specific immediate and late-phase allergic inflammation in different organs. The identification of the disease-causing allergens by demonstrating the presence of allergen-specific IgE is the key to precision medicine in allergy because it allows tailoring different forms of prevention and treatment according to the sensitization profiles of individual allergic patients. More than 30 years ago molecular cloning started to accelerate the identification of the disease-causing allergen molecules and enabled their production as recombinant molecules. Based on recombinant allergen molecules, molecular allergy diagnosis was introduced into clinical practice and allowed dissecting the molecular sensitization profiles of allergic patients. In 2002 it was demonstrated that microarray technology allows assembling large numbers of allergen molecules on chips for the rapid serological testing of IgE sensitizations with small volumes of serum. Since then microarrayed allergens have revolutionized research and diagnosis in allergy, but several unmet needs remain. Here we show that detection of IgE- and IgG-reactivity to a panel of respiratory allergens microarrayed onto silicon elements is more sensitive than glass-based chips. We discuss the advantages of silicon-based allergen microarrays and how this technology will allow addressing hitherto unmet needs in microarray-based allergy diagnosis. Importantly, it described how the assembly of silicon microarray elements may create different microarray formats for suiting different diagnostic applications such as quick testing of single patients, medium scale testing and fully automated large scale testing.

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Section: Unmet Needs In Microarray Technologymentioning

confidence: 99%

Microarray-Based Allergy Diagnosis: Quo Vadis?

et al. 2021

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“…16,17 Mobile Health (mHealth) technology is used also as part of clinical decision support systems (CDSS) assisting patients, clinicians and pharmacists at the point of care. [18][19][20][21][22] In preparation of a CDSS for the precise prescription of AIT, we developed @IT2020, a diagnostic algorithm based on CRD and eDiaries. We aimed this study at assessing usability and impact of this algorithm when used by allergy specialists (AS) and general practitioners or non-allergy specialists (GP) examining SAR patients sensitized to pollen and/or Alternaria.…”

Section: Introductionmentioning

confidence: 99%

“…Mobile Health (mHealth) technology is used also as part of clinical decision support systems (CDSS) assisting patients, clinicians and pharmacists at the point of care 18‐22 . In preparation of a CDSS for the precise prescription of AIT, we developed @IT2020, a diagnostic algorithm based on CRD and eDiaries.…”

Section: Introductionmentioning

confidence: 99%

@IT2020: An innovative algorithm for allergen immunotherapy prescription in seasonal allergic rhinitis

Arasi

Fraia

et al. 2021

Clin Experimental Allergy

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Background: Allergen immunotherapy (AIT) is the only disease-modifying treatment in patients with seasonal allergic rhinoconjunctivitis (SAR). Its efficacy depends on the precise identification of the triggering allergen. However, diagnostics based on retrospective clinical history and sensitization to whole extracts (SWE) often leads to equivocal results. Objectives: To assess the usability and impact of a recently established algorithm for a clinical decision support system (@IT2020-CDSS) for SAR and its diagnostic steps [anamnesis, SWE (skin prick test or serum IgE), component resolved diagnosis, CRD, and real-time digital symptom recording, eDiary] on doctor's AIT prescription decisions. Methods: After educational training on the @IT2020-CDSS algorithm, 46 doctors (18 allergy specialists, AS, and 28 general practitioners, GP) expressed their hypothetical AIT prescription for 10 clinical index cases. Decisions were recorded repeatedly based on different steps of the algorithm. The usability and perceived impact of the algorithm 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 for pollen and Alternaria allergy based on anamnesis and SWE was heterogeneous but converged towards a consensus by integrating CRD and eDiary information. Doctors considered the algorithm useful and recognized its potential in enhancing traditional diagnostics. Conclusions and clinical implications:The implementation of CRD and eDiary in the @IT2020-CDSS algorithm improved consensus on AIT prescription for SAR among AS and GP. The potential usefulness of a CDSS for aetiological diagnosis of SAR and AIT prescription in real-world clinical practice deserves further investigation.

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“…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 care [18][19][20][21] . In allergology, several support systems have been created, related to symptom monitoring and a facilitated diagnosis of respiratory allergies [21][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.…”

Section: Introductionmentioning

confidence: 99%

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

Arasi¹,

S²,

Fraia³

et al. 2020

Preprint

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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.

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The Potential of Clinical Decision Support Systems for Prevention, Diagnosis, and Monitoring of Allergic Diseases

Cited by 28 publications

References 69 publications

Microarray-Based Allergy Diagnosis: Quo Vadis?

Microarray-Based Allergy Diagnosis: Quo Vadis?

@IT2020: An innovative algorithm for allergen immunotherapy prescription in seasonal allergic rhinitis

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

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