Clinical Characteristics of Cough Frequency Patterns in Patients with and without Asthma

Fukuhara, Atsuro; Saito, Junpei; Birring, Surinder S.; Sato, Suguru; Uematsu, Manabu; Suzuki, Yasuhito; Rikimaru, Mami; Watanabe, Nobuhisa; Saito, Mikako; Kawamata, Takaya; Umeda, Takashi; Togawa, Ryuichi; Sato, Yuki; Koizumi, Tatsuhiko; Hirai, Kenichiro; Minemura, Hiroyuki; Nikaido, Takefumi; Kanazawa, Kenya; Tanino, Yoshinori; Munakata, Mitsuru; Shibata, Yoko

doi:10.1016/j.jaip.2019.08.053

Cited by 21 publications

(19 citation statements)

References 37 publications

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“…Nocturnal cough is highly prevalent in adult patients with asthma (27) and a high frequency of nocturnal cough is shown to be significantly related to the presence of AHR (28). While our findings confirm the importance of wheezing and nocturnal cough as predictors of AHR, Yavuz et al (7) found that, in spite of a quite similar patient selection, there is neither a significant association of AHR with wheezing nor with nocturnal cough.…”

Section: Association Of Asthma-like Symptoms With Ahrcontrasting

confidence: 56%

Novel predictors of airway hyperresponsiveness in children with respiratory symptoms without obstructive pattern

Reinsberg

Siebert

Dreher

et al. 2021

Preprint

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Background: Methacholine challenge (MCC) is the most common method to detect airway hyperresponsiveness (AHR). Although MCC is accepted as safe diagnostic tool, it is time consuming and could be exhausting for patients. Thus, it might be helpful to identify predictive factors for AHR. We aimed to develop a diagnostic tool for predicting AHR in children with respiratory symptoms without obstructive pattern. Methods: Data from children who had undergone MCC were analyzed retrospectively. The demographic features of patients along with laboratory results were collected. Results: A total of 123 children with a median age of 10.5 years were enrolled. AHR was detected in 81 children (65.8%). The age of the children with AHR was significantly younger. The prevalences of aeroallergen sensitization, nocturnal cough, wheezing and a baseline forced expiratory flow at 75% of vital capacity (FEF75) <65% were significantly more frequent in children with AHR. Multivariate logistic regression analysis revealed age, ever wheezing, nocturnal cough, tree pollen allergy and FEF75<65% as independent predictors of the AHR. A weighted clinical risk score was developed (range, 0-75 points). At a cutoff point of 35 the presence of AHR is predicted with a specificity of 90.5% and a positive predictive value (PPV) of 91.5%. Conclusion: In children suspected of having asthma, but without an obstructive pattern, combining independent predictors, which can be easily obtained in clinical practice, in a novel prediction rule might be used to identify children with AHR.

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Section: Association Of Asthma-like Symptoms With Ahrcontrasting

confidence: 56%

Novel predictors of airway hyperresponsiveness in children with respiratory symptoms without obstructive pattern

Reinsberg

Siebert

Dreher

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…5 Due to the cross-sectional design, however, information regarding longitudinal trends and cause-effect relationships of nocturnal cough in asthma is lacking. In a recent study, cough in general and even more nocturnal cough improved after appropriate treatment, 6 indicating the potential to serve as a useful parameter for disease self-monitoring.…”

Section: Introductionmentioning

confidence: 99%

<p>Characteristics of Asthma-related Nocturnal Cough: A Potential New Digital Biomarker</p>

Rassouli¹,

Tinschert²,

Barata³

et al. 2020

JAA

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The nature of nocturnal cough is largely unknown. It might be a valid marker for asthma control but very few studies characterized it as a basis for better defining its role and its use as clinical marker. This study investigated prevalence and characteristics of nocturnal cough in asthmatics over the course of four weeks. Methods: In two centers, 94 adult patients with physician-diagnosed asthma were recruited. Patient-reported outcomes and nocturnal sensor data were collected by a smartphone with a chat-based study app. Results: Patients coughed in 53% of 2212 nights (range: 0-345 coughs/night). Median coughs per hour were 0 (IQR 0-1). Nocturnal cough rates showed considerable interindividual variance. The highest counts were measured in the first 30 min in bed (4.5-fold higher than rest of night). Eighty-six percent of coughs were part of a cough cluster. Clusters consisted of a median of two coughs (IQR 2-4). Nocturnal cough was persistent within patient. Conclusion: To the best of the authors' knowledge, this study is the first to describe prevalence and characteristics of nocturnal cough in asthma over a period of one month, demonstrating that it was a prevalent symptom with large variance between patients and high persistence within patients. Cough events in asthmatics were 4.5 times more frequent within the first 30 min in bed indicating a potential role of positional change, and not more frequent during the early morning hours. An important next step will investigate the association between nocturnal cough and asthma control.

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“…Moreover, lower levels of asthma control have been correlated with poor quality of life [ 20 ] and are a predictor for cost-of-illness [ 21 ]. Coughing may also provide valuable information to predict the effects of asthma therapy early on [ 22 ]. Thus, nocturnal cough monitoring may play an important role in the prevention of exacerbations and in the personalization of treatment.…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition, Segmentation, and Sex Assignment of Nocturnal Asthmatic Coughs and Cough Epochs in Smartphone Audio Recordings: Observational Field Study

Barata¹,

Tinschert²,

Rassouli³

et al. 2020

J Med Internet Res

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Background Asthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio recordings remains unsolved. Objective The objective of this study was to evaluate the automatic recognition and segmentation of nocturnal asthmatic coughs and cough epochs in smartphone-based audio recordings that were collected in the field. We also aimed to distinguish partner coughs from patient coughs in contact-free audio recordings by classifying coughs based on sex. Methods We used a convolutional neural network model that we had developed in previous work for automated cough recognition. We further used techniques (such as ensemble learning, minibatch balancing, and thresholding) to address the imbalance in the data set. We evaluated the classifier in a classification task and a segmentation task. The cough-recognition classifier served as the basis for the cough-segmentation classifier from continuous audio recordings. We compared automated cough and cough-epoch counts to human-annotated cough and cough-epoch counts. We employed Gaussian mixture models to build a classifier for cough and cough-epoch signals based on sex. Results We recorded audio data from 94 adults with asthma (overall: mean 43 years; SD 16 years; female: 54/94, 57%; male 40/94, 43%). Audio data were recorded by each participant in their everyday environment using a smartphone placed next to their bed; recordings were made over a period of 28 nights. Out of 704,697 sounds, we identified 30,304 sounds as coughs. A total of 26,166 coughs occurred without a 2-second pause between coughs, yielding 8238 cough epochs. The ensemble classifier performed well with a Matthews correlation coefficient of 92% in a pure classification task and achieved comparable cough counts to that of human annotators in the segmentation of coughing. The count difference between automated and human-annotated coughs was a mean –0.1 (95% CI –12.11, 11.91) coughs. The count difference between automated and human-annotated cough epochs was a mean 0.24 (95% CI –3.67, 4.15) cough epochs. The Gaussian mixture model cough epoch–based sex classification performed best yielding an accuracy of 83%. Conclusions Our study showed longitudinal nocturnal cough and cough-epoch recognition from nightly recorded smartphone-based audio from adults with asthma. The model distinguishes partner cough from patient cough in contact-free recordings by identifying cough and cough-epoch signals that correspond to the sex of the patient. This research represents a step towards enabling passive and scalable cough monitoring for adults with asthma.

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Clinical Characteristics of Cough Frequency Patterns in Patients with and without Asthma

Cited by 21 publications

References 37 publications

Novel predictors of airway hyperresponsiveness in children with respiratory symptoms without obstructive pattern

Novel predictors of airway hyperresponsiveness in children with respiratory symptoms without obstructive pattern

<p>Characteristics of Asthma-related Nocturnal Cough: A Potential New Digital Biomarker</p>

Automatic Recognition, Segmentation, and Sex Assignment of Nocturnal Asthmatic Coughs and Cough Epochs in Smartphone Audio Recordings: Observational Field Study

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