Time course of nocturnal cough and wheezing in children with acute bronchitis monitored by lung sound analysis

Real-time counting of wheezing events from lung sounds using deep learning algorithms: Implications for disease prediction and early intervention

This pioneering study aims to revolutionize self-symptom management and telemedicine-based remote monitoring through the development of a real-time wheeze counting algorithm. Leveraging a novel approach that includes the detailed labeling of one breathing cycle into three types: break, normal, and wheeze, this study not only identifies abnormal sounds within each breath but also captures comprehensive data on their location, duration, and relationships within entire respiratory cycles, including atypical patterns. This innovative strategy is based on a combination of a one-dimensional convolutional neural network (1D-CNN) and a long short-term memory (LSTM) network model, enabling real-time analysis of respiratory sounds. Notably, it stands out for its capacity to handle continuous data, distinguishing it from conventional lung sound classification algorithms. The study utilizes a substantial dataset consisting of 535 respiration cycles from diverse sources, including the Child Sim Lung Sound Simulator, the EMTprep Open-Source Database, Clinical Patient Records, and the ICBHI 2017 Challenge Database. Achieving a classification accuracy of 90%, the exceptional result metrics encompass the identification of each breath cycle and simultaneous detection of the abnormal sound, enabling the real-time wheeze counting of all respirations. This innovative wheeze counter holds the promise of revolutionizing research on predicting lung diseases based on long-term breathing patterns and offers applicability in clinical and non-clinical settings for on-the-go detection and remote intervention of exacerbated respiratory symptoms.

Efficacy, Safety and Mechanism of Jinzhen Oral Liquid in the Treatment of Acute Bronchitis in Children: A Randomized, Double-Blind, Multicenter Clinical Trial Protocol

Background: Acute bronchitis (AB) is a common disease in pediatrics. Prolonged AB may develop into chronic bronchitis. Bronchitis caused by the influenza virus can lead to severe hypoxia or insufficient ventilation, causing great harm to patients and increasing the burden on children and society. Presently, there is no specific treatment for AB except symptomatic supportive treatment. It is urgent to find an effective treatment for AB. Jinzhen Oral Liquid (JZOL) has been found to have a broad spectrum of anti-inflammatory and antiviral effects in previous clinical and basic studies and has a good effect on AB in children. However, the large-sample, randomized, double-blind, head-to-head, evidence-based studies are lacking. The purpose of this protocol is to evaluate the efficacy, safety, and mechanism of JZOL in the treatment of AB in children.

Methods: This is a randomized, double-blind, parallel-controlled multi-center clinical trial. The sample size is 500 participants in the intervention group and the control group respectively, with a total of 1000 participants. They will be recruited by 10 hospitals in China. The Intervention group takes JZOL and Ambroxol Hydrochloride and Clenbuterol Hydrochloride Oral Solution (AHCHOS) placebo, while the control group receives AHCHOS and JZOL placebo. The dosage of the two drugs varies according to age and weight. The medication lasts for 7 days. The disappearance time of cough is adopted as the primary outcome. Quality control will be carried out at every stage of data management and processing to ensure that all data are reliable and processed correctly. SAS is used for statistical analysis. Intention-to-treat analysis will be carried out in this trial. All statistical tests are conducted using a two-sided test, and p <0.05 would be considered statistically significant.

Discussion: We hypothesized that children with AB could get good health benefits from JZOL. This study not only evaluates the clinical efficacy and safety of JZOL but also conducts metagenomics analysis and metabolomics analysis of feces and saliva of participants to study the mechanism of JZOL against AB. Therefore, this protocol evaluates the efficacy, safety, and mechanism of JZOL from a comprehensive perspective, so as to obtain a more solid evidence chain, which will enhance the credibility of the evidence. If successful, this study will provide a high-level evidence-based reference for the treatment of AB in children and future relevant studies.

The association between short-term exposure to ambient carbon monoxide and hospitalization costs for bronchitis patients: A hospital-based study

Ambient carbon monoxide (CO) is associated with bronchitis morbidity, but there is no evidence concerning its correlation with hospitalization costs for bronchitis patients. This study aimed to investigate the relationship between short-term ambient CO exposure and hospitalization costs for bronchitis patients in Chongqing, China. Baseline data for 3162 hospitalized bronchitis patients from November 2013 to December 2019 were collected. Multiple linear regression analysis was used to determine the association, delayed and cumulative, between short-term CO exposure and hospitalization costs. Additionally, subgroup analyses were performed by gender, age, season, and comorbidity. Positive association between CO and hospitalization costs for bronchitis patients was observed. The strongest association was observed at lag 015 days, with per 1 mg/m³ increase of CO concentrations corresponded to 5834.40 Chinese Yuan (CNY) (95% CI: 2318.71, 9350.08; P < 0.001) (845.97 US dollars) increment in hospitalization costs. Stratified analysis results showed that the association was more obvious among those males, elderly, with comorbidities, and in warm seasons. More importantly, there was strongest correlation between CO and bronchitis patients with coronary heart disease. In summary, short-term exposure to ambient CO, even lower than Chinese and WHO standards, can be associated with increased hospitalization costs for bronchitis. Controlling CO exposure can be helpful to reduce medical burden associated with bronchitis patients. The results also suggest that when setting air quality standards and formulating preventive measures, susceptible subpopulations ought to be considered.

Validation of the LEOSound® monitor for standardized detection of wheezing and cough in children

BACKGROUND

A hallmark of many respiratory conditions is the presence of nocturnal symptoms. Nevertheless, especially in children there is currently still a huge diagnostic gap in detecting nighttime symptoms, which leads to an underestimation of the frequency in clinical practise.

METHODS

We evaluated the clinical applicability and determined the formal test characteristics of the LEOSound ® system, a device for digital long-time recording and automated detection of acoustic airway events. Airway sounds were recorded overnight in 115 children and adolescents (1-17 years) with and without respiratory conditions. The automated classification for "cough" and "wheezing" was subsequently validated against the manual acoustic reassessment by an expert physician.

RESULTS

The general acceptance was good across all age groups and a technically successful recording was obtained in 98 children, corresponding to 92,976 sound epochs (à 30 s) or a total of 774 h of lung sounds. We found a sensitivity of 89% and a specificity of 99% for the automated detection of cough. For detection of wheezing, sensitivity and specificity were both 98%. The cough index and the wheeze index (events per hour) of individual patients showed a strong positive correlation (cough: r_S  = 0.85, wheeze: r_S  = 0.95) and a sufficient agreement of the two assessment methods in the Bland-Altman analysis.

CONCLUSION

Our data show that the LEOSound® is a suitable device for standardized detection of cough and wheezing and hence a promising diagnostic tool to detect nocturnal respiratory symptoms, especially in children. However, a validation process to reduce false positive classifications is essential in clinical routine use.

Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI

Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability.

Wheeze and cough measurements at night in children with respiratory symptoms

Background

Nocturnal cough and wheeze are important symptoms when diagnosing any respiratory disease in a child, but objective measurements of these symptoms are not performed.

Methods

The aim of our study was to analyze the use of an automated detection system to assess breath sounds objectively in comparison to cough and wheeze questionnaires and to evaluate its feasibility in clinical practice.

Results

Forty-nine recordings of thirty-nine children were processed (asthma n = 13; cystic fibrosis n = 2; pneumonia n = 5; suspicion of habit cough n = 7; prolonged, recurrent or chronic cough n = 13), and cough and asthma scores were compared to the objective nocturnal recordings. Time for audio-validation of recordings took between 2 and 40 min (mean: 14.22 min, (SD): 10.72). Accuracy of the automated measurement was higher for cough than for wheezing sounds. Nocturnal cough readings but not wheeze readings correlated with some of the corresponding scores.

Conclusion

To our knowledge this is the first study using a new device to assess nocturnal cough and obstructive breath sounds objectively in children with a wide variety of respiratory diseases. The assessment proved user friendly. We obtained additional information on nighttime symptoms, which would otherwise have remained obscure. Further studies to assess possible diagnostic and therapeutic benefits of this device are needed.

Disease control in patients with asthma and respiratory symptoms (wheezing, cough) during sleep

INTRODUCTION

The Global Initiative for Asthma (GINA)-defined criteria for asthma control include questions about daytime symptoms, limitation of activity, nocturnal symptoms, need for reliever treatment and patients' satisfaction. Patients with nocturnal symptoms like wheezing and cough often suffer from lower sleep quality and impaired daytime performance. The lack of an appropriate method for standardized and objective monitoring of respiratory symptoms leads to difficulties in asthma management. The aim of this study is to present a new method for automated wheeze and cough detection during sleep and to assess the actual level of asthma control by the Asthma Control Test (ACT).

METHODS

Respiratory symptoms like wheezing and cough were recorded with the LEOSound-Monitor for one night in 55 asthmatic patients in their individual domestic setting. Patients were asked to assess their level of asthma subjectively with the ACT. The study consisted of 37 women and 18 men, with a mean age of 41 years, and a mean BMI of 27 kg/m2. Most of the patients had been taking an ICS/LABA combination and would resort to a SABA as their rescue medication.

RESULTS

60% of the participants were classed as having controlled, and 40% were classed as having partially- or uncontrolled asthma. During sleep wheezing was found in 8 of the 55 asthma patients (14.5%) and coughing was found in 30 patients (54.5%). The median ACT score in wheezing-patients was 14, while in non-wheezing patients it was 21. Uncontrolled asthma was found in 6 of the 8 wheezing-patients. Coughing versus non-coughing patients did not show a significant difference in the ACT-score (20, 22 respectively).

CONCLUSION

Wheezing is a sign of uncontrolled asthma. The ACT-score in wheezing patients is worse compared to patients without wheezing. LEOSound proofed to be a useful tool in providing an objective evaluation of respiratory symptoms, like coughing and wheezing. In clinical practice, this may allow an improvement in asthma therapy.