Recurrent Severe Preschool Wheeze: From Prespecified Diagnostic Labels to Underlying Endotypes.

Rationale: Preschool wheezing is heterogeneous, but the underlying mechanisms are poorly understood.Objectives: To investigate lower airway inflammation and infection in preschool children with different clinical diagnoses undergoing elective bronchoscopy and BAL.Methods: We recruited 136 children aged 1-5 years (105 with recurrent severe wheeze [RSW]; 31 with nonwheezing respiratory disease [NWRD]). Children with RSW were assigned as having episodic viral wheeze (EVW) or multiple-trigger wheeze (MTW). We compared lower airway inflammation and infection in different clinical diagnoses and undertook data-driven analyses to determine clusters of pathophysiological features, and we investigated their relationships with prespecified diagnostic labels.Measurements and Main Results: Blood eosinophil counts and percentages and allergic sensitization were significantly higher in children with RSW than in children with a NWRD. Blood neutrophil counts and percentages, BAL eosinophil and neutrophil percentages, and positive bacterial culture and virus detection rates were similar between groups. However, pathogen distribution differed significantly, with higher detection of rhinovirus in children with RSW and higher detection of Moraxella in sensitized children with RSW. Children with EVW and children with MTW did not differ in terms of blood or BAL-sample inflammation, or bacteria or virus detection. The Partition around Medoids algorithm revealed four clusters of pathophysiological features: 1) atopic (17.9%), 2) nonatopic with a low infection rate and high use of inhaled corticosteroids (31.3%), 3) nonatopic with a high infection rate (23.1%), and 4) nonatopic with a low infection rate and no use of inhaled corticosteroids (27.6%). Cluster allocation differed significantly between the RSW and NWRD groups (RSW was evenly distributed across clusters, and 60% of the NWRD group was assigned to cluster 4; P < 0.001). There was no difference in cluster membership between the EVW and MTW groups. Cluster 1 was dominated by Moraxella detection (P = 0.04), and cluster 3 was dominated by Haemophilus or Staphylococcus or Streptococcus detection (P = 0.02).Conclusions: We identified four clusters of severe preschool wheeze, which were distinguished by using sensitization, peripheral eosinophilia, lower airway neutrophilia, and bacteriology.

Childhood wheezing phenotypes and their association to perinatal factors: a longitudinal study.

INTRODUCTION: Multiple gestational and early life factors have been described as the variables that increase the risk for each phenotype of infantile wheezing. Our objective was to study the evolution of wheezing in a cohort of children followed up to 9-10 years of age and its relationship with different perinatal risk factors. METHODS: A longitudinal study was made on the evolution of wheezing, over time, in 1164 children from Salamanca (Spain) included in the International Study of Wheezing in Infants, when the children were 12 months old. They were classified into three phenotypes: transient early wheezing (last episode before 3 years of age), early persistent wheezing (start before 3 years age and persisting thereafter), and late-onset wheezing (first episode after 3 years of age). Univariate and multivariable analyses were performed to establish associations between the different phenotypes and perinatal factors. RESULTS: Data were obtained corresponding to a total of 531 children. Of these, 169 (31.8%) had experienced transient early wheezing, 100 (18.8%) early persistent wheezing, 28 (5.3%) late-onset wheezing, and 234 (44.1%) had never experienced wheezing. Cesarean delivery, early exposure to infections, the presence of atopic eczema, and a smoking father were associated with transient early wheezing. Early persistent wheezing was associated with a family history of allergy, smoking, and obstetric diseases. Exclusive breastfeeding was identified as a protective factor in both transient and persistent early wheezing. Late-onset wheezing was associated with the male gender and with maternal history of rhinitis and eczema. CONCLUSIONS: Wheezing phenotypes were associated with different risk perinatal factors. Knowledge in the field is essential in order to influence the modifiable factors.

[Lung auscultation in the 21th century]. / La auscultación pulmonar en el siglo 21.

Lung auscultation is an essential part of the physical examination for diagnosing respiratory diseases. The terminology standardization for lung sounds, in addition to advances in their analysis through new technologies, have improved the use of this technique. However, traditional auscultation has been questioned due to the limited concordance among health professionals. Despite the revolu tionary use of new diagnostic tools of imaging and lung function tests allowing diagnostic accuracy in respiratory diseases, no technology can replace lung auscultation to guide the diagnostic process. Lung auscultation allows identifying those patients who may benefit from a specific test. Moreover, this technique can be performed many times to make clinical decisions, and often with no need for- complicated and sometimes unavailable tests. This review describes the current state-of-the-art of lung auscultation and its efficacy based on the current respiratory sound terminology. In addition, it describes the main evidence on respiratory sound concordance studies among health professionals and its objective analysis through new technology.

Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination.

Lung auscultation is an important part of a physical examination. However, its biggest drawback is its subjectivity. The results depend on the experience and ability of the doctor to perceive and distinguish pathologies in sounds heard via a stethoscope. This paper investigates a new method of automatic sound analysis based on neural networks (NNs), which has been implemented in a system that uses an electronic stethoscope for capturing respiratory sounds. It allows the detection of auscultatory sounds in four classes: wheezes, rhonchi, and fine and coarse crackles. In the blind test, a group of 522 auscultatory sounds from 50 pediatric patients were presented, and the results provided by a group of doctors and an artificial intelligence (AI) algorithm developed by the authors were compared. The gathered data show that machine learning (ML)-based analysis is more efficient in detecting all four types of phenomena, which is reflected in high values of recall (also called as sensitivity) and F1-score.Conclusions: The obtained results suggest that the implementation of automatic sound analysis based on NNs can significantly improve the efficiency of this form of examination, leading to a minimization of the number of errors made in the interpretation of auscultation sounds. What is Known: • Auscultation performance of average physician is very low. AI solutions presented in scientific literature are based on small data bases with isolated pathological sounds (which are far from real recordings) and mainly on leave-one-out validation method thus they are not reliable. What is New: • AI learning process was based on thousands of signals from real patients and a reliable description of recordings was based on multiple validation by physicians and acoustician resulting in practical and statistical prove of AI high performance.

Temporal stability of multitrigger and episodic viral wheeze in early childhood.

The distinction between episodic viral wheeze (EVW) and multitrigger wheeze (MTW) is used to guide management of preschool wheeze. It has been questioned whether these phenotypes are stable over time. We examined the temporal stability of MTW and EVW in two large population-based cohorts.We classified children from the Avon Longitudinal Study of Parents and Children (n=10 970) and the Leicester Respiratory Cohorts ((LRCs), n=3263) into EVW, MTW and no wheeze at ages 2, 4 and 6 years based on parent-reported symptoms. Using multinomial regression, we estimated relative risk ratios for EVW and MTW at follow-up (no wheeze as reference category) with and without adjusting for wheeze severity.Although large proportions of children with EVW and MTW became asymptomatic, those that continued to wheeze showed a tendency to remain in the same phenotype: among children with MTW at 4 years in the LRCs, the adjusted relative risk ratio was 15.6 (95% CI 8.3-29.2) for MTW (stable phenotype) compared to 7.0 (95% CI 2.6-18.9) for EVW (phenotype switching) at 6 years. The tendency to persist was weaker for EVW and from 2-4 years. Results were similar across cohorts.This suggests that MTW, and to a lesser extent EVW, tend to persist regardless of wheeze severity.

Associations of wheezing phenotypes with late asthma outcomes in the Avon Longitudinal Study of Parents and Children: A population-based birth cohort.

BACKGROUND: Variable patterns of childhood wheezing might indicate differences in the cause and prognosis of respiratory illnesses. Better understanding of these patterns could facilitate identification of modifiable factors related to development of asthma. OBJECTIVES: We characterized childhood wheezing phenotypes from infancy to adolescence and their associations with asthma outcomes. METHODS: Latent class analysis was used to derive phenotypes based on patterns of wheezing recorded at up to 14 time points from birth to 16½ years among 12,303 participants from the Avon Longitudinal Study of Parents and Children. Measures of lung function (FEV1, forced vital capacity [FVC], and forced expiratory flow between 25% and 75% [FEF25-75]) and fraction of exhaled nitric oxide (Feno) were made at 14 to 15 years of age. RESULTS: Six wheezing phenotypes were identified: never/infrequent, preschool-onset remitting, midchildhood-onset remitting, school age-onset persisting, late childhood-onset persisting, and continuous wheeze. The 3 persistent phenotypes were associated with bronchodilator reversibility of 12% or greater (BDR) from baseline (odds ratio [OR] range, 2.14-3.34), a Feno value of 35 ppb or greater (OR range, 3.82-6.24), and lung function decrements (mean range of differences: -0.22 to -0.27 SD units (SDU) for FEV1/FVC ratio and -0.21 to -0.33 SDU for FEF25-75) compared with never/infrequent wheeze. Midchildhood-onset (4½ years) remitting wheeze was associated with BDR (OR, 1.77; 95% CI, 1.11-2.82), a Feno value of 35 ppb or greater (OR, 1.72; 95% CI, 1.14-2.59), FEV1/FVC ratio decrements (OR, -0.22 SDU; 95% CI, -0.36 to -0.08 SDU), and FEF25-75 decrements (OR, -0.16 SDU; 95% CI, -0.30 to -0.01 SDU). Preschool-onset (18 months) remitting wheeze was only associated with FEV1/FVC ratio decrements (OR, -0.15 SDU; 95% CI, -0.25 to -0.05 SDU) and FEF25-75 decrements (OR, -0.14 SDU; 95% CI, -0.24 to -0.04 SDU). The persisting phenotypes showed evidence of sex stratification during adolescence. CONCLUSIONS: Early childhood-onset wheezing that persists into adolescence represents the clearest target group for interventions to maximize lung function outcomes.

Towards the standardisation of lung sound nomenclature.

Auscultation of the lung remains an essential part of physical examination even though its limitations, particularly with regard to communicating subjective findings, are well recognised. The European Respiratory Society (ERS) Task Force on Respiratory Sounds was established to build a reference collection of audiovisual recordings of lung sounds that should aid in the standardisation of nomenclature. Five centres contributed recordings from paediatric and adult subjects. Based on pre-defined quality criteria, 20 of these recordings were selected to form the initial reference collection. All recordings were assessed by six observers and their agreement on classification, using currently recommended nomenclature, was noted for each case. Acoustical analysis was added as supplementary information. The audiovisual recordings and related data can be accessed online in the ERS e-learning resources. The Task Force also investigated the current nomenclature to describe lung sounds in 29 languages in 33 European countries. Recommendations for terminology in this report take into account the results from this survey.

[Normal and Adventitious Breath Sounds]. / Atemgeräusche und Atem-Nebengeräusche.

Auscultation of the lung is an inexpensive, noninvasive and easy-to-perform tool. It is an important part of the physical examination and is help ful to distinguish physiological respiratory sounds from pathophysiological events. Computerized lung sound analysis is a powerful tool for optimizing and quantifying electronic auscultation based on the specific lung sound spectral characteristics. The automatic analysis of respiratory sounds assumes that physiological and pathological sounds are reliably analyzed based on special algorithms. The development of automated long-term lungsound monitors enables objective assessment of different respiratory symptoms.

Using K-Nearest Neighbor Classification to Diagnose Abnormal Lung Sounds.

A reported 30% of people worldwide have abnormal lung sounds, including crackles, rhonchi, and wheezes. To date, the traditional stethoscope remains the most popular tool used by physicians to diagnose such abnormal lung sounds, however, many problems arise with the use of a stethoscope, including the effects of environmental noise, the inability to record and store lung sounds for follow-up or tracking, and the physician's subjective diagnostic experience. This study has developed a digital stethoscope to help physicians overcome these problems when diagnosing abnormal lung sounds. In this digital system, mel-frequency cepstral coefficients (MFCCs) were used to extract the features of lung sounds, and then the K-means algorithm was used for feature clustering, to reduce the amount of data for computation. Finally, the K-nearest neighbor method was used to classify the lung sounds. The proposed system can also be used for home care: if the percentage of abnormal lung sound frames is > 30% of the whole test signal, the system can automatically warn the user to visit a physician for diagnosis. We also used bend sensors together with an amplification circuit, Bluetooth, and a microcontroller to implement a respiration detector. The respiratory signal extracted by the bend sensors can be transmitted to the computer via Bluetooth to calculate the respiratory cycle, for real-time assessment. If an abnormal status is detected, the device will warn the user automatically. Experimental results indicated that the error in respiratory cycles between measured and actual values was only 6.8%, illustrating the potential of our detector for home care applications.

Classification and pharmacological treatment of preschool wheezing: changes since 2008.

Since the publication of the European Respiratory Society Task Force report in 2008, significant new evidence has become available on the classification and management of preschool wheezing disorders. In this report, an international consensus group reviews this new evidence and proposes some modifications to the recommendations made in 2008. Specifically, the consensus group acknowledges that wheeze patterns in young children vary over time and with treatment, rendering the distinction between episodic viral wheeze and multiple-trigger wheeze unclear in many patients. Inhaled corticosteroids remain first-line treatment for multiple-trigger wheeze, but may also be considered in patients with episodic viral wheeze with frequent or severe episodes, or when the clinician suspects that interval symptoms are being under reported. Any controller therapy should be viewed as a treatment trial, with scheduled close follow-up to monitor treatment effect. The group recommends discontinuing treatment if there is no benefit and taking favourable natural history into account when making decisions about long-term therapy. Oral corticosteroids are not indicated in mild-to-moderate acute wheeze episodes and should be reserved for severe exacerbations in hospitalised patients. Future research should focus on better clinical and genetic markers, as well as biomarkers, of disease severity.

Distinguishing snoring sounds from breath sounds: a straightforward matter?

BACKGROUND: Although snoring is a common problem, no unequivocal definition yet exists for this acoustic phenomenon. The primary study objective was to investigate whether snoring sounds can be distinguished at all clearly from breath sounds. Our secondary objective was to evaluate whether the sound pressure level in common use and psychoacoustic parameters are suitable for making this distinction. METHODS: Twenty-five subjects exposed to 55 sound sequences were asked to decide whether these were breath sounds or snoring sounds, and to indicate how certain they were about their decision. The sound pressure level and the psychoacoustic parameters of loudness, sharpness, roughness, and fluctuation strength were then analyzed, and psychoacoustic annoyance was calculated from these parameters. RESULTS: Sixteen percent of the sound sequences could not be classified unequivocally, although the individual raters stated that they were still moderately certain about their decision. The sound pressure level and psychoacoustic parameters were capable of distinguishing between breath sounds and snoring sounds. The optimum for sensitivity and specificity was 76.9 and 78.8 %, respectively. CONCLUSIONS: Because snoring appears to be a subjective impression, at least in part, a generally valid acoustic definition therefore seems to be impossible. The sound pressure level and psychoacoustic parameters are suitable for distinguishing between breath sounds and snoring sounds. Nevertheless, when interpreting results, the only moderate validity of these parameters due to the absence of a universally valid definition of snoring should be taken into account.

Joint modeling of parentally reported and physician-confirmed wheeze identifies children with persistent troublesome wheezing.

BACKGROUND: Previous studies have suggested the presence of different childhood wheeze phenotypes through statistical modeling based on parentally reported wheezing. OBJECTIVE: We sought to investigate whether joint modeling of observations from both medical records and parental reports helps to more accurately define wheezing disorders during childhood and whether incorporating information from medical records better characterizes severity. METHODS: In a population-based birth cohort (n = 1184), we analyzed data from 2 sources (parentally reported current wheeze at 4 follow-ups and physician-confirmed wheeze from medical records in each year from birth to age 8 years) to determine classes of children who differ in wheeze trajectories. We tested the validity of these classes by examining their relationships with objective outcomes (lung function, airway hyperreactivity, and atopy), asthma medication, and severe exacerbations. RESULTS: Longitudinal latent class modeling identified a 5-class model that best described the data. We assigned classes as follows: no wheezing (53.3%), transient early wheeze (13.7%), late-onset wheeze (16.7%), persistent controlled wheeze (13.1%), and persistent troublesome wheeze (PTW; 3.2%). Longitudinal trajectories of atopy and lung function differed significantly between classes. Patients in the PTW class had diminished lung function and more hyperreactive airways compared with all other classes. We observed striking differences in exacerbations, hospitalizations, and unscheduled visits, all of which were markedly higher in patients in the PTW class compared with those in the other classes. For example, the risk of exacerbation was much higher in patients in the PTW class compared with patients with persistent controlled wheeze (odds ratio [OR], 3.58; 95% CI, 1.27-10.09), late-onset wheeze (OR, 15.92; 95% CI, 5.61-45.15), and transient early wheeze (OR, 12.24; 95% CI, 4.28-35.03). CONCLUSION: We identified a novel group of children with persistent troublesome wheezing, who have markedly different outcomes compared with persistent wheezers with controlled disease.

[LEOSound, an innovative procedure for acoustic long-term monitoring of asthma symptoms (wheezing and coughing) in children and adults]. / LEOSound - Ein innovatives Verfahren zum akustischen Langzeit-Monitoring von asthmatischen Symptomen (Wheezing und Husten) bei Kindern und Erwachsenen.

Particularly in young children the diagnosis of asthma is difficult and mostly based on clinical symptoms like wheezing, cough and dyspnea. Children with nocturnal wheezing often suffer from a low quality of sleep and impaired sense of well-being during the day. Physicians recommend that parents record the frequency of asthma attacks or symptoms to help manage their children's disease. The lack of an appropriate method for standardized and objective monitoring makes asthma management difficult. The aim of this paper is to present a new method for automated wheeze and cough detection and analysis. The mobile LEOSound recording and analysing system described here should help improve diagnosis and monitoring of asthma symptoms in children.

Wheeze in a pre-school child: is it asthma?

Pre-school wheeze is a challenging condition and can cause anxiety in many parents. Hopefully, this article will empower you to support worried families. Education is key and health professionals should support families to get the answers that deal with their worries. Advice should be reinforced or a review sought if the child's symptoms are not improving.

[Laryngomalacia: principal cause of stridor in infants and small children]. / Laryngomalacie: cause principale de stridor chez le nourrisson et le petit enfant.

Laryngomalacia (LM) is the most common cause of congenital stridor. It is caused by obstruction of the upper airway by collapse of redundant supraglottic tissues during inspiration. In the management of a child with congenital stridor, it is important to rule out other malformations of the upper airway that could mimic or be synchronous with LM. Symptoms of LM are usually mild and disappear spontaneously by 2 years. About 20% of patients with LM may have extreme symptoms (severe stridor, feeding difficulties and growth retardation) requiring treatment by endoscopic surgery (supraglottoplasty), which has an excellent success rate with little risk of recurrence and complications.

An open auscultation dataset for machine learning-based respiratory diagnosis studies.

Machine learning enabled auscultating diagnosis can provide promising solutions especially for prescreening purposes. The bottleneck for its potential success is that high-quality datasets for training are still scarce. An open auscultation dataset that consists of samples and annotations from patients and healthy individuals is established in this work for the respiratory diagnosis studies with machine learning, which is of both scientific importance and practical potential. A machine learning approach is examined to showcase the use of this new dataset for lung sound classifications with different diseases. The open dataset is available to the public online.

Tunable Q-factor wavelet transform based lung signal decomposition and statistical feature extraction for effective lung disease classification.

The auscultation is a non-invasive and cost-effective method used for the diagnosis of lung diseases, which are one of the leading causes of death worldwide. However, the efficacy of the auscultation suffers from the limitations of the analog stethoscopes and the subjective nature of human interpretation. To overcome these limitations, the accurate diagnosis of these diseases by employing the computer based automated algorithms applied to the digitized lung sounds has been studied for the last decades. This study proposes a novel approach that uses a Tunable Q-factor Wavelet Transform (TQWT) based statistical feature extraction followed by individual and ensemble learning model training with the aim of lung disease classification. During the learning stage various machine learning algorithms are utilized as the individual learners as well as the hard and soft voting fusion approaches are employed for performance enhancement with the aid of the predictions of individual models. For an objective evaluation of the proposed approach, the study was structured into two main tasks that were investigated in detail by using several sub-tasks to comparison with state-of-the-art studies. Among the sub-tasks which investigates patient-based classification, the highest accuracy obtained for the binary classification was achieved as 97.63% (healthy vs. non-healthy), while accuracy values up to 66.32% for three-class classification (obstructive-related, restrictive-related, and healthy), and 53.42% for five-class classification (asthma, chronic obstructive pulmonary disease, interstitial lung disease, pulmonary infection, and healthy) were obtained. Regarding the other sub-task, which investigates sample-based classification, the proposed approach was superior to almost all previous findings. The proposed method underscores the potential of TQWT based signal decomposition that leverages the power of its adaptive time-frequency resolution property satisfied by Q-factor adjustability. The obtained results are very promising and the proposed approach paves the way for more accurate and automated digital auscultation techniques in clinical settings.

ConvLSNet: A lightweight architecture based on ConvLSTM model for the classification of pulmonary conditions using multichannel lung sound recordings.

Characterization of lung sounds (LS) is indispensable for diagnosing respiratory pathology. Although conventional neural networks (NNs) have been widely employed for the automatic diagnosis of lung sounds, deep neural networks can potentially be more useful than conventional NNs by allowing accurate classification without requiring preprocessing and feature extraction. Utilizing the long short-term memory (LSTM) layers to reveal the sequence-based properties of the LS time series, a novel architecture consisting of a cascade of convolutional long short-term memory (ConvLSTM) and LSTM layers, namely ConvLSNet is developed, which permits highly accurate diagnosis of pulmonary disease states. By modeling the multichannel lung sounds through the ConvLSTM layer, the proposed ConvLSNet architecture can concurrently deal with the spatial and temporal properties of the six-channel LS recordings without heavy preprocessing or data transformation. Notably, the proposed model achieves a classification accuracy of 97.4 % based on LS data corresponding to three pulmonary conditions, namely asthma, COPD, and the healthy state. Compared with architectures consisting exclusively of CNN or LSTM layers, as well as those employing a cascade integration of 2DCNN and LSTM layers, the proposed ConvLSNet architecture exhibited the highest classification accuracy, while imposing the lowest computational cost as quantified by the number of parameters, training time, and learning rate.

[Research progress of adventitious respiratory sound signal processing].

Adventitious respiratory sound signal processing has been an important researching topic in the field of computerized respiratory sound analysis system. In recent years, new progress has been achieved in adventitious respiratory sound signal analysis due to the applications of techniques of non-stationary random signal processing. Algorithm progress of adventitious respiratory sound detections is discussed in detail in this paper. Then the state of art of adventitious respiratory sound analysis is reviewed, and development directions of next phase are pointed out.

Using latent class growth analysis to identify childhood wheeze phenotypes in an urban birth cohort.

BACKGROUND: To advance asthma cohort research, we need a method that can use longitudinal data, including when collected at irregular intervals, to model multiple phenotypes of wheeze and identify both time-invariant (eg, sex) and time-varying (eg, environmental exposure) risk factors. OBJECTIVE: To demonstrate the use of latent class growth analysis (LCGA) in defining phenotypes of wheeze and examining the effects of causative factors, using repeated questionnaires in an urban birth cohort study. METHODS: We gathered repeat questionnaire data on wheeze from 689 children ages 3 through 108 months (n = 7,048 questionnaires) and used LCGA to identify wheeze phenotypes and model the effects of time-invariant (maternal asthma, ethnicity, prenatal environmental tobacco smoke, and child sex) and time-varying (cold/influenza [flu] season) risk factors on prevalence of wheeze in each phenotype. RESULTS: LCGA identified four wheezing phenotypes: never/infrequent (47.1%), early-transient (37.5%), early-persistent (7.6%), and late-onset (7.8%). Compared with children in the never/infrequent phenotype, maternal asthma was a risk factor for the other 3 phenotypes; Dominican versus African American ethnicity was a risk factor for the early-transient phenotype; and male sex was a risk factor for the early-persistent phenotype. The prevalence of wheeze was higher during the cold/flu season than otherwise among children in the early-persistent phenotype (P = .08). CONCLUSION: This is the first application of LCGA to identify wheeze phenotypes in asthma research. Unlike other methods, this modeling technique can accommodate questionnaire data collected at irregularly spaced age intervals and can simultaneously identify multiple trajectories of health outcomes and associations with time-invariant and time-varying causative factors.