Vocal biomarkers correlate with FEV1 variations during methacholine challenge

IF 4.6 2区 医学 Q2 ALLERGY
Giovanni Paoletti, Giovanni Costanzo, Morena Merigo, Francesca Puggioni, Sebastian Ferri, Maria Rita Messina, Fulvio Cordella, Giuseppe Ranieri, Arianna Arienzo, Victor Savevski, Giorgio Walter Canonica, Ayana de Brito Martins, Enrico Heffler
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Abstract

Background

Mobile health applications are increasingly valued for their role in asthma management and the opportunity for large dataset collection. Our study aimed to investigate the feasibility of applying signal-processing and machine-learning technologies to detect alterations in the lower airway caliber and develop a machine-learning algorithm to identify changes in vocal biomarkers and detect bronchoconstriction in patients with airway hyperreactivity.

Methods

This is an explorative observational prospective longitudinal study focused on vocal biomarkers and their association with bronchial constriction and respiratory function. Non-smoker adults with clinical suspicion of asthma were consecutively enrolled from May 2023 to September 2023. At each step of a Methacholine Challenge Test (MCT) performed on these patients, the respiratory sounds were recorded via a smartphone through an app specifically developed. Several biomarkers were extracted and their relationship with the change in Forced Expiratory Volume in the first second (FEV1) was measured.

Results

Forty-two subjects were enrolled. The highest correlation with FEV1 came from exhalation vocal events. No single feature exhibited robust behavior across different subjects, while each subject showed “personal” highly correlated features. All values were strongly statistically significant irrespectively of the result of MCT.

Conclusion

The app’s algorithm is sensitive in correlating specific vocal biomarkers to FEV1 variations during MCT. This feature may assist physicians in diagnosing asthma and its exacerbation and in assessing therapy response and adherence. The socio-economic implications might be significant, and the simplicity of use makes it an ideal tool for research.

Abstract Image

在甲胆碱刺激期间,声音生物标志物与FEV1变化相关
移动健康应用程序因其在哮喘管理中的作用和大型数据集收集的机会而越来越受到重视。我们的研究旨在探讨应用信号处理和机器学习技术检测下气道口径变化的可行性,并开发一种机器学习算法来识别声音生物标志物的变化,并检测气道高反应性患者的支气管收缩。方法本研究是一项探索性观察性前瞻性纵向研究,重点研究声乐生物标志物及其与支气管收缩和呼吸功能的关系。从2023年5月至2023年9月,连续入组临床怀疑有哮喘的非吸烟者成人。在对这些患者进行甲基胆碱激发测试(MCT)的每一步中,通过专门开发的应用程序通过智能手机记录呼吸声音。提取了几种生物标志物,并测量了它们与第一秒用力呼气量(FEV1)变化的关系。结果共纳入42例受试者。呼气声事件与FEV1的相关性最高。没有单一的特征在不同的受试者中表现出强大的行为,而每个受试者都表现出“个人”高度相关的特征。与MCT结果无关,所有值均有显著统计学意义。结论该应用程序的算法在MCT期间特异性声乐生物标志物与FEV1变化的相关性方面是敏感的。这一特征可以帮助医生诊断哮喘及其恶化,并评估治疗反应和依从性。社会经济影响可能是重大的,使用简单使其成为理想的研究工具。
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来源期刊
Clinical and Translational Allergy
Clinical and Translational Allergy Immunology and Microbiology-Immunology
CiteScore
7.50
自引率
4.50%
发文量
117
审稿时长
12 weeks
期刊介绍: Clinical and Translational Allergy, one of several journals in the portfolio of the European Academy of Allergy and Clinical Immunology, provides a platform for the dissemination of allergy research and reviews, as well as EAACI position papers, task force reports and guidelines, amongst an international scientific audience. Clinical and Translational Allergy accepts clinical and translational research in the following areas and other related topics: asthma, rhinitis, rhinosinusitis, drug hypersensitivity, allergic conjunctivitis, allergic skin diseases, atopic eczema, urticaria, angioedema, venom hypersensitivity, anaphylaxis, food allergy, immunotherapy, immune modulators and biologics, animal models of allergic disease, immune mechanisms, or any other topic related to allergic disease.
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