Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls

Funding Information: This study was funded by Nordforsk (NordSleep, no. 90458) via Business Finland (no. 5133/31/2018) and via the Icelandic Centre for Research, the Icelandic Research Fund (no. 174067), the Landspitali University Hospital Science Fund 2019 (no. 893831), the European Union’s Horizon 2020 Research and Innovation Programme (grant no. 965417), the National Health and Medical Research Council (NHMRC) of Australia (project nos. 2001729 and 2007001), the Academy of Finland (project no. 323536), the Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding (project nos. 5041794 and 5041803), and the Finnish Anti-Tuberculosis Association and the Research Foundation of the Pulmonary Diseases. The birth cohort study was funded by the European Commission: (a) under the 6th Framework Program (FOOD-CT-2005-514000) within the collaborative research initiative “EuroPrevall” and (b) under the 7th Framework Program (FP7-KBBE-2012-6; grant agreement no. 312147) within the collaborative project “iFAAM.” Additional funds were received by the Icelandic birth cohort center from Landspitali University Hospital Science Fund, and GlaxoSmithKline Iceland. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication. Publisher Copyright: Copyright © 2023 Somaskandhan, Leppänen, Terrill, Sigurdardottir, Arnardottir, Ólafsdóttir, Serwatko, Sigurðardóttir, Clausen, Töyräs and Korkalainen.INTRODUCTION: Visual sleep scoring has several shortcomings, including inter-scorer inconsistency, which may adversely affect diagnostic decision-making. Although automatic sleep staging in adults has been extensively studied, it is uncertain whether such sophisticated algorithms generalize well to different pediatric age groups due to distinctive EEG characteristics. The preadolescent age group (10-13-year-olds) is relatively understudied, and thus, we aimed to develop an automatic deep learning-based sleep stage classifier specifically targeting this cohort. METHODS: A dataset (n = 115) containing polysomnographic recordings of Icelandic preadolescent children with sleep-disordered breathing (SDB) symptoms, and age and sex-matched controls was utilized. We developed a combined convolutional and long short-term memory neural network architecture relying on electroencephalography (F4-M1), electrooculography (E1-M2), and chin electromyography signals. Performance relative to human scoring was further evaluated by analyzing intra- and inter-rater agreements in a subset (n = 10) of data with repeat scoring from two manual scorers. RESULTS: The deep learning-based model achieved an overall cross-validated accuracy of 84.1% (Cohen's kappa κ = 0.78). There was no meaningful performance difference between SDB-symptomatic (n = 53) and control subgroups (n = 52) [83.9% (κ = 0.78) vs. 84.2% (κ = 0.78)]. The inter-rater reliability between manual scorers was 84.6% (κ = 0.78), and the automatic method reached similar agreements with scorers, 83.4% (κ = 0.76) and 82.7% (κ = 0.75). CONCLUSION: The developed algorithm achieved high classification accuracy and substantial agreements with two manual scorers; the performance metrics compared favorably with typical inter-rater reliability between manual scorers and performance reported in previous studies. These suggest that our algorithm may facilitate less labor-intensive and reliable automatic sleep scoring in preadolescent children.Peer reviewe

Food hypersensitivity : an examination of factors influencing symptoms and temporal changes in the prevalence of sensitization in an adult sample

Funding Information: HCYL is supported by the Medical Research Council Centre for Environment and Health. NSI is supported by the Centre for Food and Allergy Research (NHMRC Centre of Research Excellence) PhD scholarship and the Melbourne Children’s LifeCourse top-up PhD scholarship (Royal Children’s Hospital Foundation grant #2018-984). ISGlobal acknowledges support from the Spanish Ministry of Science and Innovation through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S), and the Generalitat de Catalunya through the CERCA Program. Publisher Copyright: © 2023, The Author(s).Background/Objectives: Food hypersensitivity (FHS) is common, but little is known about the factors associated with severe reactions, age of onset and whether sensitization persists. This study examines the factors associated with self-reported severe food reactions, onset age and the changes in prevalence of sensitization to foods over time in an adult sample. Subjects/Methods: We used data from adults taking part in the European Community Respiratory Health Survey (ECRHS) III (2010–2014) who provided information on food hypersensitivity, including symptoms, suspected culprit food and onset age (n = 4865). A subsample from six countries had serum food-specific IgE tested for 25 core foods and also in 10 years earlier (ECRHS II). We applied logistic regression and McNemar’s test for analyses. Results: The prevalence of self-reported FHS was 13.5% at ECRHS III. Of those providing information on symptoms (n = 611), 26.4% reported severe reactions. About 80% of 1033 reported food-specific reactions (reported by 596 participants) began after age 15. History of asthma (odds ratio OR 2.12 95% confidence interval CI 1.13–3.44) and a younger age of onset of FHS (OR 1.02, 95% CI 1.01–1.03, per year) were associated with higher risks of a lifetime experience of severe food reactions. In the subsample with IgE tested in both surveys (n = 1612), the overall prevalence of sensitization to foods did not change over 10 years. Conclusion: Our findings support previous observations of more severe food reactions in people with asthma and that most FHS reported by this sample started after age 15. We found no evidence of changes in the prevalence of sensitization to food in adults followed for 10 years.Peer reviewe

A European-Japanese study on peach allergy : IgE to Pru p 7 associates with severity

Funding Information: M. Fernández‐Rivas received grants or contracts from Instituto de Salud Carlos III, Spanish Government, Aimmune Therapeutics, Diater, and Novartis; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Aimmune Therapeutics, Ediciones Mayo S.A., Diater, Ga2LEN, HAL Allergy, GSK, MEDSCAPE, NOVARTIS, and EPG Health; is member of the Data Safety Monitoring Board at DBV and advisory board at Aimmune Therapeutics, Novartis, Reacta Healthcare, and SPRIM. B. Ballmer‐Weber received consulting fees from ALK, Allergopharma, Menarini, Sanofi, Novartis, Thermofisher and Aimune and payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from ALK, Menarini, Sanofi, Novartis, and Thermofisher. F. De Blay received grants or contract from Aimmune, Stallergenes Greer, GSK, ALK, Chiesi, and Regeneron. Y. Fukutomi received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Thermo Fisher Diagnostics KK. K. Hoffmann‐Sommergruber received funding from Danube Allergy Research Cluster funded by the Country of Lower Austria to (P07) KHS; was Member of the EAACI board until 2022/07. J. Lidholm is employee at Thermo Fisher Scientific. E.N.C Mills received grants or has contracts from Food Standards Agency Patterns and prevalence of adult food allergy (FS101174), European Food Safety Authority (ThRAll; allergenicity prediction [with EuroFIR]) and from Innovate (ML for food allergy); has applied for a patent on oral food challenge meal formulations for diagnosis of food allergy; is member of the Advisory Board of Novartis and Advisory Committee on Novel Foods and Processes; and is shareholder of Reacta Healthcare Ltd. N.G. Papadopoulos received grants or contracts from Capricare, Nestle, Numil, Vianex; received consultancy fees from Abbott, Abbvie, Astra Zeneca, GSK, HAL, Medscape, Menarini/Faes Farma, Mylan, Novartis, Nutricia, OM Pharma, and Regeneron/Sanofi. S. Vieths received royalties or licenses from Schattauer Allergologie Handbuch, Elsevier Nahrungsmittelallergien and Intoleranzen and Karger Food Allergy: Molecular Basis and Clinical Practice; support for attending meetings and/or travel as Associate Editor of the Journal of Allergy and Clinical Immunology. R. van Ree received consulting fees from HAL Allergy, Citeq, Angany, Reacta Healthcare, Mission MightyMe, and Ab Enzymes; received payment of honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from HAL Allergy, Thermo Fisher Scientific and ALK; received payment for expert testimony from AB Enzymes; has stock option at Angany. The rest of the authors declare that they have no relevant conflicts of interest. Funding Information: This work was funded by the European Commission under the 6th Framework Programme through EuroPrevall (FP6‐FOOD‐CT‐2005‐514000), and the 7th Framework Programme iFAAM (grant agreement no. 31214). Funding Information: We thank all the patients for their participation in the study. We would like to thank ALK Abello (Madrid, Spain) for their generous gift of SPT reagents. We thank Angelica Ehrenberg, Jonas Östling and Lars Mattsson (Uppsala) for preparing recombinant Cup s 7 and custom ImmunoCAP tests for this study. We acknowledge the support by the 6th and 7th Framework Programmes of the EU, for EuroPrevall (FP6‐FOOD‐CT‐2005‐514000) and iFAAM (Grant agreement no. 312147), respectively. We thank Alejandro Gonzalo Fernández (Hospital Clinico San Carlos, IdISSC, Madrid) for implementing the FASS in the data set. Publisher Copyright: © 2023 The Authors. Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.BACKGROUND: Pru p 3 and Pru p 7 have been implicated as risk factors for severe peach allergy. This study aimed to establish sensitization patterns to five peach components across Europe and in Japan, to explore their relation to pollen and foods and to predict symptom severity. METHODS: In twelve European (EuroPrevall project) and one Japanese outpatient clinic, a standardized clinical evaluation was conducted in 1231 patients who reported symptoms to peach and/or were sensitized to peach. Specific IgE against Pru p 1, 2, 3, 4 and 7 and against Cup s 7 was measured in 474 of them. Univariable and multivariable Lasso regression was applied to identify combinations of parameters predicting severity. RESULTS: Sensitization to Pru p 3 dominated in Southern Europe but was also quite common in Northern and Central Europe. Sensitization to Pru p 7 was low and variable in the European centers but very dominant in Japan. Severity could be predicted by a model combining age of onset of peach allergy, probable mugwort, Parietaria pollen and latex allergy, and sensitization to Japanese cedar pollen, Pru p 4 and Pru p 7 which resulted in an AUC of 0.73 (95% CI 0.73-0.74). Pru p 3 tended to be a risk factor in South Europe only. CONCLUSIONS: Pru p 7 was confirmed as a significant risk factor for severe peach allergy in Europe and Japan. Combining outcomes from clinical and demographic background with serology resulted in a model that could better predict severity than CRD alone.Peer reviewe

Anomaly detection in sleep : detecting mouth breathing in children

Publisher Copyright: © 2023, The Author(s).Identifying mouth breathing during sleep in a reliable, non-invasive way is challenging and currently not included in sleep studies. However, it has a high clinical relevance in pediatrics, as it can negatively impact the physical and mental health of children. Since mouth breathing is an anomalous condition in the general population with only 2% prevalence in our data set, we are facing an anomaly detection problem. This type of human medical data is commonly approached with deep learning methods. However, applying multiple supervised and unsupervised machine learning methods to this anomaly detection problem showed that classic machine learning methods should also be taken into account. This paper compared deep learning and classic machine learning methods on respiratory data during sleep using a leave-one-out cross validation. This way we observed the uncertainty of the models and their performance across participants with varying signal quality and prevalence of mouth breathing. The main contribution is identifying the model with the highest clinical relevance to facilitate the diagnosis of chronic mouth breathing, which may allow more affected children to receive appropriate treatment.Peer reviewe