Effectiveness and adverse events of topical and allergen immunotherapy for atopic dermatitis: a systematic review and network meta-analysis protocol

Gomez-Escobar, Luis; Mora-Ochoa, Hansel; Villanueva, Andrea Vargas; Spineli, Loukia M.; Sanclemente, Gloria; Couban, Rachel; García, Elizabeth; Chapman, Edgardo; Yepes-Núñez, Juan José

doi:10.1186/s13643-020-01472-w

Cited by 6 publications

(7 citation statements)

References 54 publications

(67 reference statements)

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“…Systemic reactions affecting the entire body can range from anaphylaxis to asthma or urticaria and are generally uncommon in both methods, regardless of SCIT or SLIT [97][98][99]. Similar results regarding the adverse effects of AIT on AD have been noted [100].…”

Section: Attributes Of Aitmentioning

confidence: 55%

Advancements in Allergen Immunotherapy for the Treatment of Atopic Dermatitis

Guo,

Wu,

Chen

et al. 2024

IJMS

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Atopic dermatitis (AD) is a chronic inflammatory skin condition that affects individuals of all age groups, manifesting as a spectrum of symptoms varying from mild to severe. Allergen immunotherapy (AIT) involves the administration of allergen extracts and has emerged as a potential treatment strategy for modifying immune responses. Its pathogenesis involves epidermal barrier dysfunction, microbiome imbalance, immune dysregulation, and environmental factors. Existing treatment strategies encompass topical steroids to systemic agents, while AIT is under investigation as a potential immune-modifying alternative. Several studies have shown reductions in the severity scoring of atopic dermatitis (SCORAD) scores, daily rescue medication use, and visual analog scale (VAS) scores following AIT. Biomarker changes include increased IgG4 levels and decreased eosinophil counts. This review provides valuable insights for future research and clinical practice, exploring AIT as a viable option for the management of AD.

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Section: Attributes Of Aitmentioning

confidence: 55%

Advancements in Allergen Immunotherapy for the Treatment of Atopic Dermatitis

Guo,

Wu,

Chen

et al. 2024

IJMS

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“…Cluster and factor analysis are the two most common methods. Of the 37 BS papers, a manual inspection found that only eight 21 , 33 , 34 , 35 , 36 , 37 , 38 , 39 specifically study AD. Of these eight, half 33 , 34 , 35 , 36 used random‐effects Bayesian network meta‐analysis to compare treatments for AD, and one 21 uses a Bayesian mechanistic machine learning model to predict next‐day AD severity for individual patients.…”

Section: Resultsmentioning

confidence: 99%

Data‐driven research on eczema: Systematic characterization of the field and recommendations for the future

Duverdier

Čustović

Tanaka

2022

Clinical & Translational All

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Background The past decade has seen a substantial rise in the employment of modern data‐driven methods to study atopic dermatitis (AD)/eczema. The objective of this study is to summarise the past and future of data‐driven AD research, and identify areas in the field that would benefit from the application of these methods. Methods We retrieved the publications that applied multivariate statistics (MS), artificial intelligence (AI, including machine learning‐ML), and Bayesian statistics (BS) to AD and eczema research from the SCOPUS database over the last 50 years. We conducted a bibliometric analysis to highlight the publication trends and conceptual knowledge structure of the field, and applied topic modelling to retrieve the key topics in the literature. Results Five key themes of data‐driven research on AD and eczema were identified: (1) allergic co‐morbidities, (2) image analysis and classification, (3) disaggregation, (4) quality of life and treatment response, and (5) risk factors and prevalence. ML&AI methods mapped to studies investigating quality of life, prevalence, risk factors, allergic co‐morbidities and disaggregation of AD/eczema, but seldom in studies of therapies. MS was employed evenly between the topics, particularly in studies on risk factors and prevalence. BS was focused on three key topics: treatment, risk factors and allergy. The use of AD or eczema terms was not uniform, with studies applying ML&AI methods using the term eczema more often. Within MS, papers using cluster and factor analysis were often only identified with the term AD. In contrast, those using logistic regression and latent class/transition models were “eczema” papers. Conclusions Research areas that could benefit from the application of data‐driven methods include the study of the pathogenesis of the condition and related risk factors, its disaggregation into validated subtypes, and personalised severity management and prognosis. We highlight BS as a new and promising approach in AD and eczema research.

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“…Cluster and factor analysis are the two most common methods. Of the 37 BS papers, a manual inspection found that only eight 21,[33][34][35][36][37][38][39] specifically study AD. Of these eight, half [33][34][35][36] used random-effects Bayesian network meta-analysis to compare treatments for AD, and one 21 uses a Bayesian mechanistic machine learning model to predict next-day AD severity for individual patients.…”

Section: Analytical Methods and The Use Of Ad Or Eczema Termsmentioning

confidence: 99%

Data-driven research on eczema: systematic characterization of the field and recommendations for the future

Duverdier

Čustović

Tanaka

2022

Preprint

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Background: The past decade has seen a substantial rise in the employment of modern data-driven methods to study atopic dermatitis (AD) / eczema. Objective: To summarise the past and future of data-driven AD research, and identify areas in the field that would benefit from the application of these methods. Methods: We retrieved the publications that applied multivariate statistics (MS), artificial intelligence (AI, including machine learning-ML), and Bayesian statistics (BS) to AD and eczema research from the SCOPUS database over the last 50 years. We conducted a bibliometric analysis to highlight the publication trends and conceptual knowledge structure of the field, and applied topic modelling to retrieve the key topics in the literature. Results: Five key themes of data-driven research on AD and eczema were identified: (1) allergic co-morbidities, (2) image analysis and classification, (3) disaggregation, (4) quality of life, and (5) risk factors and prevalence. ML&AI methods mapped to studies investigating quality of life, prevalence, risk factors, allergic co-morbidities and disaggregation of AD/eczema, but seldom in studies of therapies. MS was employed evenly between the topics, particularly in studies on risk factors and prevalence. BS was focused on three key topics: treatment, risk factors and allergy. The use of AD or eczema terms was not uniform, with studies applying ML&AI methods using the term eczema more often. Within MS, papers using cluster and factor analysis were often only identified with the term AD. In contrast, those using logistic regression and latent class/transition models were "eczema" papers. Conclusions: Research areas that could benefit from the application of data-driven methods include the study of the pathogenesis of the condition and related risk factors, its disaggregation into validated subtypes, and personalized severity management and prognosis. We highlight Bayesian statistics as a new and promising approach in AD and eczema research.

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Effectiveness and adverse events of topical and allergen immunotherapy for atopic dermatitis: a systematic review and network meta-analysis protocol

Cited by 6 publications

References 54 publications

Advancements in Allergen Immunotherapy for the Treatment of Atopic Dermatitis

Advancements in Allergen Immunotherapy for the Treatment of Atopic Dermatitis

Data‐driven research on eczema: Systematic characterization of the field and recommendations for the future

Data-driven research on eczema: systematic characterization of the field and recommendations for the future

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