Cost-utility analysis of palivizumab for preventing respiratory syncytial virus in preterm neonates and infants in Colombia.

AIM: Palivizumab has proven effective in reducing hospitalizations, preventing severe illness, improving health outcomes, and reducing healthcare costs for infants at risk of respiratory syncytial virus (RSV) infection. We aim to assess the value of palivizumab in preventing RSV infection in high-risk infants in Colombia, where RSV poses a significant threat, causing severe respiratory illness and hospitalizations. METHODS: We conducted a decision tree analysis to compare five doses of palivizumab with no palivizumab. The study considered three population groups: preterm neonates (≤ 35 weeks gestational age), infants with bronchopulmonary dysplasia (BPD), and infants with hemodynamically significant congenital heart disease (CHD). We obtained clinical efficacy data from IMpact-RSV and Cardiac Synagis trials, while we derived neonatal hospitalization risks from the SENTINEL-1 study. We based hospitalization and recurrent wheezing management costs on Colombian analyses and validated them by experts. We estimated incremental cost-effectiveness ratios and performed 1,000 Monte Carlo simulations for probabilistic sensitivity analyses. RESULTS: Palivizumab is a dominant strategy for preventing RSV infection in preterm neonates and infants with BPD and CHD. Its high efficacy (78% in preventing RSV in preterm infants), the substantial risk of illness and hospitalization, and the high costs associated with hospitalization, particularly in neonatal intensive care settings, support this finding. The scatter plots and willingness-to-pay curves align with these results. CONCLUSION: Palivizumab is a cost-saving strategy in Colombia, effectively preventing RSV infection in preterm neonates and infants with BPD and CHD by reducing hospitalizations and lowering healthcare costs.

Clinical features of audible upper airway secretions ("death rattle") in patients with cancer in the last days of life.

PURPOSE: Audible upper airway secretions ("death rattle") is a common problem in cancer patients at the end-of-life. However, there is little information about its clinical features. METHODS: This is a secondary analysis of a cluster randomised trial of clinically-assisted hydration in cancer patients in the last days of life. Patients were assessed 4 hourly for end-of-life problems (including audible secretions), which were recorded as present or absent, excepting restlessness/agitation, which was scored using the modified Richmond Agitation and Sedation Scale. Patients were followed up until death. RESULTS: 200 patients were recruited, and 186 patients died during the study period. Overall, 54.5% patients developed audible secretions at some point during the study, but only 34.5% patients had audible secretions at the time of death. The prevalence of audible secretions increased the closer to death, with a marked increase in the last 12-16 h of life (i.e. the prevalence of audible secretions was highest at the time of death). Of those with audible secretions at the time of death, 24 had had a previous episode that had resolved. Development of audible secretions was not associated with use of clinically-assisted hydration, but there was an association between audible secretions and restlessness/agitation, and audible secretions and pain. However, most patients with audible secretions were not restless/agitated, or in pain, when assessed. CONCLUSION: Audible secretions ("death rattle") are common in cancer patients at the end-of-life, but their natural history is extremely variable, with some patients experiencing multiple episodes during the terminal phase (although not necessarily experiencing an episode at the time of death).

Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis.

BACKGROUND: The interpretation of lung sounds plays a crucial role in the appropriate diagnosis and management of pediatric asthma. Applying artificial intelligence (AI) to this task has the potential to better standardize assessment and may even improve its predictive potential. OBJECTIVE: This study aims to objectively review the literature on AI-assisted lung auscultation for pediatric asthma and provide a balanced assessment of its strengths, weaknesses, opportunities, and threats. METHODS: A scoping review on AI-assisted lung sound analysis in children with asthma was conducted across 4 major scientific databases (PubMed, MEDLINE Ovid, Embase, and Web of Science), supplemented by a gray literature search on Google Scholar, to identify relevant studies published from January 1, 2000, until May 23, 2023. The search strategy incorporated a combination of keywords related to AI, pulmonary auscultation, children, and asthma. The quality of eligible studies was assessed using the ChAMAI (Checklist for the Assessment of Medical Artificial Intelligence). RESULTS: The search identified 7 relevant studies out of 82 (9%) to be included through an academic literature search, while 11 of 250 (4.4%) studies from the gray literature search were considered but not included in the subsequent review and quality assessment. All had poor to medium ChAMAI scores, mostly due to the absence of external validation. Identified strengths were improved predictive accuracy of AI to allow for prompt and early diagnosis, personalized management strategies, and remote monitoring capabilities. Weaknesses were the heterogeneity between studies and the lack of standardization in data collection and interpretation. Opportunities were the potential of coordinated surveillance, growing data sets, and new ways of collaboratively learning from distributed data. Threats were both generic for the field of medical AI (loss of interpretability) but also specific to the use case, as clinicians might lose the skill of auscultation. CONCLUSIONS: To achieve the opportunities of automated lung auscultation, there is a need to address weaknesses and threats with large-scale coordinated data collection in globally representative populations and leveraging new approaches to collaborative learning.

Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study.

BACKGROUND: Interstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence-assisted lung auscultation and ultrasound (LUS) could constitute an alternative to conventional, subjective, operator-related methods for the accurate and earlier diagnosis of these diseases. This protocol describes the standardised collection of digitally-acquired lung sounds and LUS images of adult outpatients with IPF, NSIP or COPD and a deep learning diagnostic and severity-stratification approach. METHODS: A total of 120 consecutive patients (≥ 18 years) meeting international criteria for IPF, NSIP or COPD and 40 age-matched controls will be recruited in a Swiss pulmonology outpatient clinic, starting from August 2022. At inclusion, demographic and clinical data will be collected. Lung auscultation will be recorded with a digital stethoscope at 10 thoracic sites in each patient and LUS images using a standard point-of-care device will be acquired at the same sites. A deep learning algorithm (DeepBreath) using convolutional neural networks, long short-term memory models, and transformer architectures will be trained on these audio recordings and LUS images to derive an automated diagnostic tool. The primary outcome is the diagnosis of ILD versus control subjects or COPD. Secondary outcomes are the clinical, functional and radiological characteristics of IPF, NSIP and COPD diagnosis. Quality of life will be measured with dedicated questionnaires. Based on previous work to distinguish normal and pathological lung sounds, we estimate to achieve convergence with an area under the receiver operating characteristic curve of > 80% using 40 patients in each category, yielding a sample size calculation of 80 ILD (40 IPF, 40 NSIP), 40 COPD, and 40 controls. DISCUSSION: This approach has a broad potential to better guide care management by exploring the synergistic value of several point-of-care-tests for the automated detection and differential diagnosis of ILD and COPD and to estimate severity. Trial registration Registration: August 8, 2022. CLINICALTRIALS: gov Identifier: NCT05318599.

Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation.

BACKGROUND: The growing public interest and awareness regarding the significance of sleep is driving the demand for sleep monitoring at home. In addition to various commercially available wearable and nearable devices, sound-based sleep staging via deep learning is emerging as a decent alternative for their convenience and potential accuracy. However, sound-based sleep staging has only been studied using in-laboratory sound data. In real-world sleep environments (homes), there is abundant background noise, in contrast to quiet, controlled environments such as laboratories. The use of sound-based sleep staging at homes has not been investigated while it is essential for practical use on a daily basis. Challenges are the lack of and the expected huge expense of acquiring a sufficient size of home data annotated with sleep stages to train a large-scale neural network. OBJECTIVE: This study aims to develop and validate a deep learning method to perform sound-based sleep staging using audio recordings achieved from various uncontrolled home environments. METHODS: To overcome the limitation of lacking home data with known sleep stages, we adopted advanced training techniques and combined home data with hospital data. The training of the model consisted of 3 components: (1) the original supervised learning using 812 pairs of hospital polysomnography (PSG) and audio recordings, and the 2 newly adopted components; (2) transfer learning from hospital to home sounds by adding 829 smartphone audio recordings at home; and (3) consistency training using augmented hospital sound data. Augmented data were created by adding 8255 home noise data to hospital audio recordings. Besides, an independent test set was built by collecting 45 pairs of overnight PSG and smartphone audio recording at homes to examine the performance of the trained model. RESULTS: The accuracy of the model was 76.2% (63.4% for wake, 64.9% for rapid-eye movement [REM], and 83.6% for non-REM) for our test set. The macro F1-score and mean per-class sensitivity were 0.714 and 0.706, respectively. The performance was robust across demographic groups such as age, gender, BMI, or sleep apnea severity (accuracy 73.4%-79.4%). In the ablation study, we evaluated the contribution of each component. While the supervised learning alone achieved accuracy of 69.2% on home sound data, adding consistency training to the supervised learning helped increase the accuracy to a larger degree (+4.3%) than adding transfer learning (+0.1%). The best performance was shown when both transfer learning and consistency training were adopted (+7.0%). CONCLUSIONS: This study shows that sound-based sleep staging is feasible for home use. By adopting 2 advanced techniques (transfer learning and consistency training) the deep learning model robustly predicts sleep stages using sounds recorded at various uncontrolled home environments, without using any special equipment but smartphones only.

EMERGENCY TRACHEOTOMY IN TRACHEAL STENOSIS - A CASE REPORT.

Airway management in an emergency department is the first step in critical care of an urgent patient. When orotracheal intubation is not possible due to upper airway obstruction, such an emergency is known as a 'cannot intubate - cannot ventilate' situation. Then, emergency tracheotomy is indicated. We present a case of a 70-year-old patient complaining of progressive dyspnea. The patient was conscious, highly tachydyspneic, and tachycardic. Loud stridor and a scar from previous tracheostomy suggested upper airway obstruction. Patient history confirmed previous partial laryngectomy and temporary tracheostomy due to laryngeal cancer 10 months before. Differential diagnosis of tracheal stenosis was set, and an ENT specialist was requested. Flexible fiberoptic laryngoscopy demonstrated a 1-mm subglottic tracheal stenosis. Emergency surgical tracheotomy below the obstruction in awake state using local anesthesia was performed to secure the airway. Early postoperative care was complicated by incipient right-sided pneumonia, which may have provoked narrowing of the existing subglottic stenosis in the first place. Tracheal stenosis is an important differential diagnosis of airway obstruction in patients with previous malignant diseases of the upper respiratory system. Emergency physicians should promptly recognize these situations based on clinical examination to secure appropriate airway management.

A novel system that continuously visualizes and analyzes respiratory sounds to promptly evaluate upper airway abnormalities: a pilot study.

Although respiratory sounds are useful indicators for evaluating abnormalities of the upper airway and lungs, the accuracy of their evaluation may be limited. The continuous evaluation and visualization of respiratory sounds has so far been impossible. To resolve these problems, we developed a novel continuous visualization system for assessing respiratory sounds. Our novel system was used to evaluate respiratory abnormalities in two patients. The results were not known until later. The first patient was a 23-year-old man with chronic granulomatous disease and persistent anorexia. During his hospital stay, he exhibited a consciousness disorder, bradypnea, and hypercapnia requiring tracheal intubation. After the administration of muscle relaxant, he suddenly developed acute airway stenosis. Because we could not intubate and ventilate, we performed cricothyroidotomy. Subsequent review of our novel system revealed mild stridor before the onset of acute airway stenosis, which had not been recognized clinically. The second patient was a 74-year-old woman who had been intubated several days earlier for tracheal burn injury, and was extubated after alleviation of her laryngeal edema. After extubation, she gradually developed inspiratory stridor. We re-intubated her after diagnosing post-extubation laryngeal edema. Subsequent review of our novel system revealed serially increased stridor after the extubation, at an earlier time than was recognized by healthcare providers. This unique continuous monitoring and visualization system for respiratory sounds could be an objective tool for improving patient safety regarding airway complications.

Regional respiratory sound abnormalities in pneumothorax and pleural effusion detected via respiratory sound visualization and quantification: case report.

Assessment of respiratory sounds by auscultation with a conventional stethoscope is subjective. We developed a continuous monitoring and visualization system that enables objectively and quantitatively visualizing respiratory sounds. We herein present two cases in which the system showed regional differences in the respiratory sounds. We applied our novel continuous monitoring and visualization system to evaluate respiratory abnormalities in patients with acute chest disorders. Respiratory sounds were continuously recorded to assess regional changes in respiratory sound volumes. Because we used this system as a pilot study, the results were not shown in real time and were retrospectively analyzed. Case 1 An 89-year-old woman was admitted to our hospital for sudden-onset respiratory distress and hypoxia. Chest X-rays revealed left pneumothorax; thus, we drained the thorax. After confirming that the pneumothorax had improved, we attached the continuous monitoring and visualization system. Chest X-rays taken the next day showed exacerbation of the pneumothorax. Visual and quantitative findings showed a decreased respiratory volume in the left lung after 3 h. Case 2 A 94-year-old woman was admitted to our hospital for dyspnea. Chest X-rays showed a large amount of pleural effusion on the right side. The continuous monitoring and visualization system visually and quantitatively revealed a decreased respiratory volume in the lower right lung field compared with that in the lower left lung field. Our newly developed continuous monitoring and visualization system enabled quantitatively and visually detecting regional differences in respiratory sounds in patients with pneumothorax and pleural effusion.

Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case-control and prospective cohort study.

BACKGROUND: Lung auscultation is fundamental to the clinical diagnosis of respiratory disease. However, auscultation is a subjective practice and interpretations vary widely between users. The digitization of auscultation acquisition and interpretation is a particularly promising strategy for diagnosing and monitoring infectious diseases such as Coronavirus-19 disease (COVID-19) where automated analyses could help decentralise care and better inform decision-making in telemedicine. This protocol describes the standardised collection of lung auscultations in COVID-19 triage sites and a deep learning approach to diagnostic and prognostic modelling for future incorporation into an intelligent autonomous stethoscope benchmarked against human expert interpretation. METHODS: A total of 1000 consecutive, patients aged ≥ 16 years and meeting COVID-19 testing criteria will be recruited at screening sites and amongst inpatients of the internal medicine department at the Geneva University Hospitals, starting from October 2020. COVID-19 is diagnosed by RT-PCR on a nasopharyngeal swab and COVID-positive patients are followed up until outcome (i.e., discharge, hospitalisation, intubation and/or death). At inclusion, demographic and clinical data are collected, such as age, sex, medical history, and signs and symptoms of the current episode. Additionally, lung auscultation will be recorded with a digital stethoscope at 6 thoracic sites in each patient. A deep learning algorithm (DeepBreath) using a Convolutional Neural Network (CNN) and Support Vector Machine classifier will be trained on these audio recordings to derive an automated prediction of diagnostic (COVID positive vs negative) and risk stratification categories (mild to severe). The performance of this model will be compared to a human prediction baseline on a random subset of lung sounds, where blinded physicians are asked to classify the audios into the same categories. DISCUSSION: This approach has broad potential to standardise the evaluation of lung auscultation in COVID-19 at various levels of healthcare, especially in the context of decentralised triage and monitoring. TRIAL REGISTRATION: PB_2016-00500, SwissEthics. Registered on 6 April 2020.

Lung Auscultation Using the Smartphone-Feasibility Study in Real-World Clinical Practice.

Conventional lung auscultation is essential in the management of respiratory diseases. However, detecting adventitious sounds outside medical facilities remains challenging. We assessed the feasibility of lung auscultation using the smartphone built-in microphone in real-world clinical practice. We recruited 134 patients (median[interquartile range] 16[11-22.25]y; 54% male; 31% cystic fibrosis, 29% other respiratory diseases, 28% asthma; 12% no respiratory diseases) at the Pediatrics and Pulmonology departments of a tertiary hospital. First, clinicians performed conventional auscultation with analog stethoscopes at 4 locations (trachea, right anterior chest, right and left lung bases), and documented any adventitious sounds. Then, smartphone auscultation was recorded twice in the same four locations. The recordings (n = 1060) were classified by two annotators. Seventy-three percent of recordings had quality (obtained in 92% of the participants), with the quality proportion being higher at the trachea (82%) and in the children's group (75%). Adventitious sounds were present in only 35% of the participants and 14% of the recordings, which may have contributed to the fair agreement between conventional and smartphone auscultation (85%; k = 0.35(95% CI 0.26-0.44)). Our results show that smartphone auscultation was feasible, but further investigation is required to improve its agreement with conventional auscultation.

Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes.

BACKGROUND: Manual auscultation to detect abnormal breath sounds has poor inter-observer reliability. Digital stethoscopes with artificial intelligence (AI) could improve reliable detection of these sounds. We aimed to independently test the abilities of AI developed for this purpose. METHODS: One hundred and ninety two auscultation recordings collected from children using two different digital stethoscopes (Clinicloud™ and Littman™) were each tagged as containing wheezes, crackles or neither by a pediatric respiratory physician, based on audio playback and careful spectrogram and waveform analysis, with a subset validated by a blinded second clinician. These recordings were submitted for analysis by a blinded AI algorithm (StethoMe AI) specifically trained to detect pathologic pediatric breath sounds. RESULTS: With optimized AI detection thresholds, crackle detection positive percent agreement (PPA) was 0.95 and negative percent agreement (NPA) was 0.99 for Clinicloud recordings; for Littman-collected sounds PPA was 0.82 and NPA was 0.96. Wheeze detection PPA and NPA were 0.90 and 0.97 respectively (Clinicloud auscultation), with PPA 0.80 and NPA 0.95 for Littman recordings. CONCLUSIONS: AI can detect crackles and wheeze with a reasonably high degree of accuracy from breath sounds obtained from different digital stethoscope devices, although some device-dependent differences do exist.

Automatic Classification of Adventitious Respiratory Sounds: A (Un)Solved Problem?

(1) Background: Patients with respiratory conditions typically exhibit adventitious respiratory sounds (ARS), such as wheezes and crackles. ARS events have variable duration. In this work we studied the influence of event duration on automatic ARS classification, namely, how the creation of the Other class (negative class) affected the classifiers' performance. (2) Methods: We conducted a set of experiments where we varied the durations of the other events on three tasks: crackle vs. wheeze vs. other (3 Class); crackle vs. other (2 Class Crackles); and wheeze vs. other (2 Class Wheezes). Four classifiers (linear discriminant analysis, support vector machines, boosted trees, and convolutional neural networks) were evaluated on those tasks using an open access respiratory sound database. (3) Results: While on the 3 Class task with fixed durations, the best classifier achieved an accuracy of 96.9%, the same classifier reached an accuracy of 81.8% on the more realistic 3 Class task with variable durations. (4) Conclusion: These results demonstrate the importance of experimental design on the assessment of the performance of automatic ARS classification algorithms. Furthermore, they also indicate, unlike what is stated in the literature, that the automatic classification of ARS is not a solved problem, as the algorithms' performance decreases substantially under complex evaluation scenarios.

Wheezing Sound Separation Based on Informed Inter-Segment Non-Negative Matrix Partial Co-Factorization.

Wheezing reveals important cues that can be useful in alerting about respiratory disorders, such as Chronic Obstructive Pulmonary Disease. Early detection of wheezing through auscultation will allow the physician to be aware of the existence of the respiratory disorder in its early stage, thus minimizing the damage the disorder can cause to the subject, especially in low-income and middle-income countries. The proposed method presents an extended version of Non-negative Matrix Partial Co-Factorization (NMPCF) that eliminates most of the acoustic interference caused by normal respiratory sounds while preserving the wheezing content needed by the physician to make a reliable diagnosis of the subject's airway status. This extension, called Informed Inter-Segment NMPCF (IIS-NMPCF), attempts to overcome the drawback of the conventional NMPCF that treats all segments of the spectrogram equally, adding greater importance for signal reconstruction of repetitive sound events to those segments where wheezing sounds have not been detected. Specifically, IIS-NMPCF is based on a bases sharing process in which inter-segment information, informed by a wheezing detection system, is incorporated into the factorization to reconstruct a more accurate modelling of normal respiratory sounds. Results demonstrate the significant improvement obtained in the wheezing sound quality by IIS-NMPCF compared to the conventional NMPCF for all the Signal-to-Noise Ratio (SNR) scenarios evaluated, specifically, an SDR, SIR and SAR improvement equals 5.8 dB, 4.9 dB and 7.5 dB evaluating a noisy scenario with SNR = -5 dB.

Associations of ultra-processed food and drink products with asthma and wheezing among Brazilian adolescents.

BACKGROUND: Although both consumptions of ultra-processed products and asthma are common during adolescence, the epidemiological evidence in regarding their association is unclear. We investigated the associations of ultra-processed products consumption with asthma and wheezing in a representative sample of Brazilian adolescents. METHODS: We used data from a representative sample of 109 104 Brazilian adolescents enrolled in the National Survey of School Health, 2012. The consumption of ultra-processed products was based on the weekly consumption (0-2, 3-4, ≥5 d/wk) of sweet biscuits, salty biscuits, ultra-processed meats, sweets/candies, soft drinks, and packaged snacks over the previous 7 days. We also calculated an ultra-processed consumption score by adding partial scores corresponding to weekly frequency intake of each ultra-processed product. The ultra-processed consumption score ranged from 0 to 42, the higher score, the higher the intake of these products. The presence of wheezing in the previous 12 months and asthma at any time in the past was self-reported. RESULTS: The adjusted odds ratios of asthma comparing the extreme categories ranged from 1.08 (95% CI 1.03-1.13) for sweets/candies to 1.30 (1.21-1.40) for ultra-processed meats. Similar magnitude of associations was found for wheezing outcome. The ultra-processed consumption score was positively associated with the presence of asthma and wheezing in a dose-response manner. The adjusted OR of asthma and wheezing comparing highest to lowest quintile of ultra-processed consumption score was 1.27 (95% CI 1.15-1.41) and 1.42 (1.35-1.50), respectively. CONCLUSIONS: The consumption of ultra-processed products was positively associated with the presence of asthma and wheezing in adolescents.

A retrospective medical records review of risk factors for the development of respiratory tract secretions (death rattle) in the dying patient.

AIM: Identification of risk factors predicting the development of death rattle. BACKGROUND: Respiratory tract secretions, often called death rattle, are among the most common symptoms in dying patients around the world. It is unknown whether death rattle causes distress in patients, but it has been globally reported that distress levels can be high in family members. Although there is a poor evidence base, treatment with antimuscarinic medication is standard practice worldwide and prompt intervention is recognized as crucial for effectiveness. The identification of risk factors for the development of death rattle would allow for targeted interventions. DESIGN: A case-control study was designed to retrospectively review two hundred consecutive medical records of mainly cancer patients who died in a hospice inpatient setting between 2009-2011. Fifteen potential risk factors including the original factors weight, smoking, final opioid dose and final midazolam dose were investigated. METHODS: Binary logistic regression to identify risk factors for death rattle development. RESULTS: Univariate analysis showed death rattle was significantly associated with final Midazolam doses and final opioid doses, length of dying phase and anticholinergic drug load in the pre-terminal phase. In the final logistic regression model only Midazolam was statistically significant and only at final doses of 20 mg/24 hrs or over (OR 3.81 CI 1.41-10.34). CONCLUSIONS: Dying patients with a requirement for a high dose of Midazolam have an increased likelihood of developing death rattle.

A Smartphone-Based System for Automated Bedside Detection of Crackle Sounds in Diffuse Interstitial Pneumonia Patients.

In this work, we present a mobile health system for the automated detection of crackle sounds comprised by an acoustical sensor, a smartphone device, and a mobile application (app) implemented in Android. Although pulmonary auscultation with traditional stethoscopes had been used for decades, it has limitations for detecting discontinuous adventitious respiratory sounds (crackles) that commonly occur in respiratory diseases. The proposed app allows the physician to record, store, reproduce, and analyze respiratory sounds directly on the smartphone. Furthermore, the algorithm for crackle detection was based on a time-varying autoregressive modeling. The performance of the automated detector was analyzed using: (1) synthetic fine and coarse crackle sounds randomly inserted to the basal respiratory sounds acquired from healthy subjects with different signal to noise ratios, and (2) real bedside acquired respiratory sounds from patients with interstitial diffuse pneumonia. In simulated scenarios, for fine crackles, an accuracy ranging from 84.86% to 89.16%, a sensitivity ranging from 93.45% to 97.65%, and a specificity ranging from 99.82% to 99.84% were found. The detection of coarse crackles was found to be a more challenging task in the simulated scenarios. In the case of real data, the results show the feasibility of using the developed mobile health system in clinical no controlled environment to help the expert in evaluating the pulmonary state of a subject.

[Study on Respiratory Rate Monitoring Method Based on Breath Sounds].

The article aims to discuss the feasibility of using respiratory sounds to monitor respiratory rate. The average power of respiratory sounds was created firstly, the autocorrelation algorithm was used to calculate the respiratory cycle. The respiratory cycle of nasal flow pressure signal was calculated simultaneously, and the result was taken as a reference standard, then, two groups of respiratory cycle data were analyzed by correlation analysis and Bland-Altman analysis. The respiratory rate is relatively stable, using respiratory sounds monitor respiratory rate is feasible, the respiratory rate changes obviously, the existing methods and algorithm using respiratory sounds are temporarily unable to accurately reflect the changes of respiratory rate, further research is needed.

Exercise inducible laryngeal obstruction: diagnostics and management.

Obstruction of the central airways is an important cause of exercise-induced inspiratory symptoms (EIIS) in young and otherwise healthy individuals. This is a large, heterogeneous and vastly understudied group of patients. The symptoms are too often confused with those of asthma. Laryngoscopy performed as symptoms evolve during increasing exercise is pivotal, since the larynx plays an important role in symptomatology for the majority. Abnormalities vary between patients, and laryngoscopic findings are important for correct treatment and handling. The simplistic view that all EIIS is due to vocal cord dysfunction [VCD] still hampers science and patient management. Causal mechanisms are poorly understood. Most treatment options are based on weak evidence, but most patients seem to benefit from individualised information and guidance. The place of surgery has not been settled, but supraglottoplasty may cure well-defined severe cases. A systematic clinical approach, more and better research and randomised controlled treatment trials are of utmost importance in this field of respiratory medicine.

Digital stethoscopes compared to standard auscultation for detecting abnormal paediatric breath sounds.

Our study aimed to objectively describe the audiological characteristics of wheeze and crackles in children by using digital stethoscope (DS) auscultation, as well as assess concordance between standard auscultation and two different DS devices in their ability to detect pathological breath sounds. Twenty children were auscultated by a paediatric consultant doctor and digitally recorded using the Littman™ 3200 Digital Electronic Stethoscope and a Clinicloud™ DS with smart device. Using spectrographic analysis, we found those with clinically described wheeze had prominent periodic waveform segments spanning expiration for a period of 0.03-1.2 s at frequencies of 100-1050 Hz, and occasionally spanning shorter inspiratory segments; paediatric crackles were brief discontinuous sounds with a distinguishing waveform. There was moderate concordance with respect to wheeze detection between digital and standard binaural stethoscopes, and 100% concordance for crackle detection. Importantly, DS devices were more sensitive than clinician auscultation in detecting wheeze in our study. CONCLUSION: Objective definition of audio characteristics of abnormal paediatric breath sounds was achieved using DS technology. We demonstrated superiority of our DS method compared to traditional auscultation for detection of wheeze. What is Known: • The audiological characteristics of abnormal breath sounds have been well-described in adult populations but not in children. • Inter-observer agreement for detection of pathological breath sounds using standard auscultation has been shown to be poor, but the clinical value of now easily available digital stethoscopes has not been sufficiently examined. What is New: • Digital stethoscopes can objectively define the nature of pathological breath sounds such as wheeze and crackles in children. • Paediatric wheeze was better detected by digital stethoscopes than by standard auscultation performed by an expert paediatric clinician.

[Acoustic information in snoring noises]. / Akustische Informationen von Schnarchgeräuschen.

BACKGROUND: More than one third of all people snore regularly. Snoring is a common accompaniment of obstructive sleep apnea (OSA) and is often disruptive for the bed partner. OBJECTIVE: This work gives an overview of the history of and state of research on acoustic analysis of snoring for classification of OSA severity, detection of obstructive events, measurement of annoyance, and identification of the sound excitation location. MATERIALS AND METHODS: Based on these objectives, searches were conducted in the literature databases PubMed and IEEE Xplore. Publications dealing with the respective objectives according to title and abstract were selected from the search results. RESULTS: A total of 48 publications concerning the above objectives were considered. The limiting factor of many studies is the small number of subjects upon which the analyses are based. CONCLUSION: Recent research findings show promising results, such that acoustic analysis may find a place in the framework of sleep diagnostics, thus supplementing the recognized standard methods.