Antonio Bognanni

1. Allergen immunotherapy (AIT) is the only established treatment for IgE-mediated respiratory allergies that can modify the natural history of the disease. Unlike conventional therapy, which prima...

Objective: To demonstrate an evaluation of the use of artificial intelligence (AI) in supporting tasks in evidence synthesis (particularly search for primary studies) in the allergy or respirology fields. Methods: We queried three AI‐based platforms specialised on identifying scientific publications using several strategies (i.e., prompting strategies and search approaches) in November 2024 to identify primary studies related to health‐related case studies in the allergy or respirology field. We compared how many eligible primary studies we identified using AI‐based platforms versus in a systematic search in multiple electronic bibliographic databases. We also compared meta‐analytical results obtained with the primary studies identified by querying AI‐based platforms versus in the context of a systematic review. Finally, we developed a structured methodological framework and a reporting checklist for using these AI‐based platforms. Results: In our main case study, a systematic review of randomised controlled trials (RCTs) informing the Allergic Rhinitis and its Impact on Asthma (ARIA) guidelines, a strategy involving searching multiple AI‐based platforms identified 85.7% of all full articles with DOI, but failed to identify unpublished trials registered in trial databases, resulting in an overall identification of 56.3% eligible RCTs. Meta‐analytical estimates were similar when considering only the primary studies identified using AI‐based strategies versus those identified by the systematic review. We observed a lower performance in the case study of systematic reviews of observational studies. Conclusions: We provide an example on how it is possible to evaluate AI‐based platforms in the support of evidence synthesis, with a focus on the allergy and respirology fields.

Allergic Rhinitis and its Impact on Asthma (ARIA) was, up until 2017, a guideline using the best evidence (Grading of Recommendations, Assessment, Development and Evaluation, GRADE) and developed as a change management strategy. A second change management strategy—in collaboration with the European Academy of Allergy and Clinical Immunology (ARIA‐EAACI)—was developed as a person‐centred, digitally enabled, artificial intelligence‐assisted care (person‐centred care) with strong political involvement. The digital tools of ARIA are mainly based on MASK‐air, an Organisation for Economic Co‐operation and Development (OECD) Best Practice for integrated care for chronic diseases. Artificial intelligence was used, in particular, to approach the patients' views and expectations. The current paper describes the steps to build and achieve a new change management strategy. The future of the Change Management strategy is (i) a collaboration between ARIA and EAACI, (ii) the development of ARIA 2024‐2025 guidelines, (iii) the new ARIA‐MeDALL classification of multimorbid airway diseases and (iv) embedding MASK‐air in a registry for severe allergic diseases. The ultimate goals of the ARIA‐EAACI change management strategy will be (i) the transformation of health and care in rhinitis and asthma multimorbidity and (ii) the development of novel guidelines and policies in a cost‐effective manner, improving shared‐decision‐making.

Background: Allergic rhinitis is the most common chronic disease in children. Information on efficacy and safety of oral allergic rhinitis treatments in children is scarce. We aimed to comprehensively evaluate the efficacy and safety of oral medications for allergic rhinitis in children. Methods: We performed a systematic review, searching three bibliographic databases and clinicaltrials.gov for randomized controlled trials assessing the use of oral antihistamines (OAH) or leukotriene receptor antagonists (LTRA) in children (<12 years old) with seasonal or perennial allergic rhinitis. Evaluated outcomes included the Total Nasal Symptom Score (TNSS), the Total Ocular Symptom Score (TOSS), the Rhinoconjunctivitis Quality‐of‐Life Questionnaire (RQLQ), development of adverse or serious adverse events, and withdrawals due to adverse events. We performed network meta‐analysis at both class and individual treatment levels. Certainty in the body of evidence was assessed using the GRADE approach for network meta‐analysis. Results: We included eight primary studies with 906 participants. Overall, OAH were more effective against placebo in improving the TNSS (mean difference (MD) = −1.60; 95%CI = −2.25 to −0.95). On the other hand, OAH+LTRA did not demonstrate additional benefit over OAH in improving the TNSS (MD = ‐0.15; 95% CI = −1.18 to 0.88), and there were no important differences between individual OAH. Evidence on the TOSS was not found. No significant differences in the frequency of adverse events were observed. Certainty of evidence was low or very low for most comparisons. Conclusions: Oral treatments for allergic rhinitis appear to be effective and safe in children. However, evidence is scarce and the quality of evidence is mostly low. We performed a systematic review of randomised controlled trials assessing oral medications for allergic rhinitis in children and reporting results on nasal symptoms, ocular symptoms, quality of life and adverse events. Oral antihistamines (OAH) were found to be better than placebo, and OAH + oral leukotriene receptor antagonists (OLTRA) were not found to be better than OAH. We identified trivial differences between different OAH. image

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Background: Allergic rhinitis displays a relevant impact on quality of life. Medications used in the treatment of rhinitis have been assessed on their impact on rhinoconjunctivitis‐related quality of life, but not on generic health‐related quality of life metrics, such as utilities or EQ‐5D visual analogue scale (VAS) levels. This study aimed to compare different medication classes and individual medications on utilities and EQ‐5D VAS levels using data from a mobile app. Methods: We conducted an observational study using direct patient data from the MASK‐air mobile application, collected between May 2015 and December 2024. We compared rhinitis medication classes and individual medications on health utilities (computed from the EQ‐5D‐5L questionnaire) and the EQ‐5D VAS. To account for confounding, we employed inverse probability treatment weighting based on propensity scores, adjusting for demographics, baseline symptom control, and asthma status. Results: The study analysed 69,973 observations with EQ‐5D VAS data and 842 observations with utility data. At the medication class level, fixed combinations of intranasal antihistamines and corticosteroids were associated with improvements in EQ‐5D VAS (mean difference = 1.900; 95% CI = 1.316–2.484) and utilities (mean difference = 0.022; 95% CI = −0.015 to 0.059) compared with oral antihistamines (OAH). Intranasal antihistamines were associated with lower EQ‐5D VAS and utility scores than other intranasal treatments. For individual medications, mometasone was associated with a lower EQ‐5D VAS than budesonide and fluticasone furoate, while fexofenadine and levocetirizine tended to be associated with lower VAS values than other OAH. Conclusion: Fixed combinations of intranasal antihistamines and corticosteroids were associated with better quality‐of‐life than oral antihistamines and intranasal antihistamines. These findings could support future cost‐effectiveness analyses.