Listening panel agreement and characteristics of lung sounds digitally recorded from children aged 1-59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) case-control study

A MEMS seismometer respiratory monitor for work of breathing assessment and adventitious lung sounds detection via deep learning

Physicians evaluate a patient's respiratory health during a physical examination by visual assessment of the work of breathing (WoB) to determine respiratory stability, and by detecting abnormal lung sounds via lung auscultation using a stethoscope to identify common pathological lung diseases, such as chronic obstructive pulmonary disease (COPD) and pneumonia. Since these assessment methods are subjective, a low-profile device used for an accurate and quantitative monitoring approach could provide valuable preemptive insights into respiratory health, proving to be clinically beneficial. To achieve this goal, we have developed a miniature patch consisting of a sensitive wideband multi-axis seismometer that can be placed on the anatomical areas of a patient's lungs to enable an effective quantification of a patient's WoB and lung sounds. When used on a patch, the seismometer captures chest wall vibrations due to respiratory muscle effort, known as high-frequency mechanomyogram (MMG), during tidal breathing as well as seismic pulmonary-induced vibrations (PIVs) during deep breathing due to normal and/or adventitious lung sounds like crackles, while simultaneously recording respiration rate and phase. A system comprised of multiple patches was evaluated on 124 patients in the hospital setting and shown to accurately assess and quantify a patent's physical signs of WoB by measuring the average respiratory effort extracted from high-frequency MMG signals, demonstrating statistical significance of this method in comparison to clinical bedside observation of WoB and respiration rate. A data fusion deep learning model was developed which combined the inputs of PIVs lung sounds and the corresponding respiration phase to detect crackle, wheeze and normal breath sound features. The model exhibited high accuracy, sensitivity, specificity, precision and F1 score of 93%, 93%, 97%, 93% and 93% respectively, with area under the curve (AUC) of precision recall (PR) of 0.97 on the test set. Additionally, the PIVs with corresponding respiration phase captured from each auscultation point generated an acoustic map of the patient's lung, which correlated with traditional lung radiographic findings.

Breaking ICD Codes: Identifying Ambiguous Respiratory Infection Codes via Regional Diagnosis Heterogeneity

PURPOSE

We aimed to analyze regional variations in the assignment of International Classification of Diseases, 10th Revision (ICD-10) codes to acute respiratory infections, seeking to identify notable anomalies that suggest diverse diagnoses of the same condition.

METHODS

We analyzed national weekly diagnosis data for acute respiratory infections (ICD-10 codes J00-J22) in Poland from 2010 to 2019, covering all 380 county-equivalent administrative regions and encompassing 292 million consultations. Data were aggregated into age brackets. We calculated the Kendall tau correlations between shares of particular diagnoses.

RESULTS

We found staggering differences across regions in applied diagnoses that persisted even after disaggregating the data into age groups. The differences did not seem to stem from different levels of health care use, as there was no consistent pattern suggesting variability in milder diagnoses. Instead, there were numerous pairs of strongly negatively correlated codes implying classification ambiguity, with the most problematic diagnosis being J06 (acute upper respiratory infections of multiple and unspecified sites), which was used almost interchangeably with a diverse range of others, especially J00 (common cold) and J20 (bronchitis).

CONCLUSIONS

To the best of our knowledge, this is the first study using observable anomalies to analyze regional coding variability for the same respiratory infection. Although some of these discrepancies may raise concerns about misdiagnosis, the majority of cases involving interchangeably used codes did not seem to substantially impact treatment or prognosis. This suggests that ICD codes may have clinical ambiguities and could face challenges not only in fulfilling their intended purpose of generating internationally comparable health data but also in their use for comprehensive government health planning.

Robust Anomaly Detection of Adventitious Auscultation Signals using Bayesian Belief Tracking

Stethoscope screening serves as a primary method for diagnosing pulmonary infections, with medical professionals actively listening for signs of pathologies in breathing sounds like wheezing and crackling, which carry different clinical interpretations. Environmental conditions during auscultation recordings often share similarities with these abnormal lung sounds, and can mask or confound their presence making their detection highly sensitive to surrounding factors. To automate this process, a robust anomaly detection scheme with resilience to ambient backgrounds and high precision is essential. In this study, we propose an unsupervised framework for anomaly detection where statistics of a deep neural network embeddings are tracked using a Bayesian belief model in order to flag variations that are deemed anomalous, hence facilitating detection of adventitious auscultation events. The proposed scheme leverages two key principles: (1) learning of statistics of normal auscultation patterns using variational constraints, and (2) tracking changes in the statistics using Bayesian beliefs that interpret anomalies as deviations from normal statistics. This approach is shown to be very effective in detecting adventitious auscultations under various noise levels hence ensuring its resilience to environmental conditions.

Feasibility and Acceptability of Pediatric Smartphone Lung Auscultation by Parents: Cross-Sectional Study

Background

The use of a smartphone built-in microphone for auscultation is a feasible alternative to the use of a stethoscope, when applied by physicians.

Objective

This cross-sectional study aims to assess the feasibility of this technology when used by parents-the real intended end users.

Methods

Physicians recruited 46 children (male: n=33, 72%; age: mean 11.3, SD 3.1 y; children with asthma: n=24, 52%) during medical visits in a pediatric department of a tertiary hospital. Smartphone auscultation using an app was performed at 4 locations (trachea, right anterior chest, and right and left lung bases), first by a physician (recordings: n=297) and later by a parent (recordings: n=344). All recordings (N=641) were classified by 3 annotators for quality and the presence of adventitious sounds. Parents completed a questionnaire to provide feedback on the app, using a Likert scale ranging from 1 ("totally disagree") to 5 ("totally agree").

Results

Most recordings had quality (physicians' recordings: 253/297, 85.2%; parents' recordings: 266/346, 76.9%). The proportions of physicians' recordings (34/253, 13.4%) and parents' recordings (31/266, 11.7%) with adventitious sounds were similar. Parents found the app easy to use (questionnaire: median 5, IQR 5-5) and were willing to use it (questionnaire: median 5, IQR 5-5).

Conclusions

Our results show that smartphone auscultation is feasible when performed by parents in the clinical context, but further investigation is needed to test its feasibility in real life.

Reference free auscultation quality metric and its trends

Stethoscopes are used ubiquitously in clinical settings to 'listen' to lung sounds. The use of these systems in a variety of healthcare environments (hospitals, urgent care rooms, private offices, community sites, mobile clinics, etc.) presents a range of challenges in terms of ambient noise and distortions that mask lung signals from being heard clearly or processed accurately using auscultation devices. With advances in technology, computerized techniques have been developed to automate analysis or access a digital rendering of lung sounds. However, most approaches are developed and tested in controlled environments and do not reflect real-world conditions where auscultation signals are typically acquired. Without a priori access to a recording of the ambient noise (for signal-to-noise estimation) or a reference signal that reflects the true undistorted lung sound, it is difficult to evaluate the quality of the lung signal and its potential clinical interpretability. The current study proposes an objective reference-free Auscultation Quality Metric (AQM) which incorporates low-level signal attributes with high-level representational embeddings mapped to a nonlinear quality space to provide an independent evaluation of the auscultation quality. This metric is carefully designed to solely judge the signal based on its integrity relative to external distortions and masking effects and not confuse an adventitious breathing pattern as low-quality auscultation. The current study explores the robustness of the proposed AQM method across multiple clinical categorizations and different distortion types. It also evaluates the temporal sensitivity of this approach and its translational impact for deployment in digital auscultation devices.

A machine learning approach to the development and prospective evaluation of a pediatric lung sound classification model

Auscultation, a cost-effective and non-invasive part of physical examination, is essential to diagnose pediatric respiratory disorders. Electronic stethoscopes allow transmission, storage, and analysis of lung sounds. We aimed to develop a machine learning model to classify pediatric respiratory sounds. Lung sounds were digitally recorded during routine physical examinations at a pediatric pulmonology outpatient clinic from July to November 2019 and labeled as normal, crackles, or wheezing. Ensemble support vector machine models were trained and evaluated for four classification tasks (normal vs. abnormal, crackles vs. wheezing, normal vs. crackles, and normal vs. wheezing) using K-fold cross-validation (K = 10). Model performance on a prospective validation set (June to July 2021) was compared with those of pediatricians and non-pediatricians. Total 680 clips were used for training and internal validation. The model accuracies during internal validation for normal vs. abnormal, crackles vs. wheezing, normal vs. crackles, and normal vs. wheezing were 83.68%, 83.67%, 80.94%, and 90.42%, respectively. The prospective validation (n = 90) accuracies were 82.22%, 67.74%, 67.80%, and 81.36%, respectively, which were comparable to pediatrician and non-pediatrician performance. An automated classification model of pediatric lung sounds is feasible and maybe utilized as a screening tool for respiratory disorders in this pandemic era.

Implications of clinical variability on computer-aided lung auscultation classification

Thanks to recent advances in digital stethoscopes and rapid adoption of deep learning techniques, there has been tremendous progress in the field of Computerized Auscultation Analysis (CAA). Despite these promising leaps, the deploy-ment of these technologies in real-world applications remains limited due to inherent challenges with properly interpreting clinical data, particularly auscultations. One of the limiting factors is the inherent ambiguity that comes with variability in clinical opinion, even from highly trained experts. The lack of unanimity in expert opinions is often ignored in developing machine learning techniques to automatically screen normal from abnormal lung signals, with most algorithms being developed and tested on highly curated datasets. To better understand the potential pitfalls this selective analysis could cause in deployment, the current work explores the impact of clinical opinion variability on algorithms to detect adventitious patterns in lung sounds when trained on gold-standard data. The study shows that uncertainty in clinical opinion introduces far more variability and performance drop than dissidence in expert judgments. The study also explores the feasibility of automatically flagging auscultation signals based on their estimated uncertainty, thereby recommending further reassessment as well as improving computer-aided analysis.

Digitally recorded and remotely classified lung auscultation compared with conventional stethoscope classifications among children aged 1-59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) case-control study

BACKGROUND

Diagnosis of pneumonia remains challenging. Digitally recorded and remote human classified lung sounds may offer benefits beyond conventional auscultation, but it is unclear whether classifications differ between the two approaches. We evaluated concordance between digital and conventional auscultation.

METHODS

We collected digitally recorded lung sounds, conventional auscultation classifications and clinical measures and samples from children with pneumonia (cases) in low-income and middle-income countries. Physicians remotely classified recordings as crackles, wheeze or uninterpretable. Conventional and digital auscultation concordance was evaluated among 383 pneumonia cases with concurrently (within 2 hours) collected conventional and digital auscultation classifications using prevalence-adjusted bias-adjusted kappa (PABAK). Using an expanded set of 737 cases that also incorporated the non-concurrently collected assessments, we evaluated whether associations between auscultation classifications and clinical or aetiological findings differed between conventional or digital auscultation using χ2 tests and logistic regression adjusted for age, sex and site.

RESULTS

Conventional and digital auscultation concordance was moderate for classifying crackles and/or wheeze versus neither crackles nor wheeze (PABAK=0.50), and fair for crackles-only versus not crackles-only (PABAK=0.30) and any wheeze versus no wheeze (PABAK=0.27). Crackles were more common on conventional auscultation, whereas wheeze was more frequent on digital auscultation. Compared with neither crackles nor wheeze, crackles-only on both conventional and digital auscultation was associated with abnormal chest radiographs (adjusted OR (aOR)=1.53, 95% CI 0.99 to 2.36; aOR=2.09, 95% CI 1.19 to 3.68, respectively); any wheeze was inversely associated with C-reactive protein >40 mg/L using conventional auscultation (aOR=0.50, 95% CI 0.27 to 0.92) and with very severe pneumonia using digital auscultation (aOR=0.67, 95% CI 0.46 to 0.97). Crackles-only on digital auscultation was associated with mortality compared with any wheeze (aOR=2.70, 95% CI 1.12 to 6.25).

CONCLUSIONS

Conventional auscultation and remotely-classified digital auscultation displayed moderate concordance for presence/absence of wheeze and crackles among cases. Conventional and digital auscultation may provide different classification patterns, but wheeze was associated with decreased clinical severity on both.

Derivation and validation of a novel risk assessment tool to identify children aged 2–59 months at risk of hospitalised pneumonia-related mortality in 20 countries

Introduction

Existing risk assessment tools to identify children at risk of hospitalised pneumonia-related mortality have shown suboptimal discriminatory value during external validation. Our objective was to derive and validate a novel risk assessment tool to identify children aged 2–59 months at risk of hospitalised pneumonia-related mortality across various settings.

Methods

We used primary, baseline, patient-level data from 11 studies, including children evaluated for pneumonia in 20 low-income and middle-income countries. Patients with complete data were included in a logistic regression model to assess the association of candidate variables with the outcome hospitalised pneumonia-related mortality. Adjusted log coefficients were calculated for each candidate variable and assigned weighted points to derive the Pneumonia Research Partnership to Assess WHO Recommendations (PREPARE) risk assessment tool. We used bootstrapped selection with 200 repetitions to internally validate the PREPARE risk assessment tool.

Results

A total of 27 388 children were included in the analysis (mean age 14.0 months, pneumonia-related case fatality ratio 3.1%). The PREPARE risk assessment tool included patient age, sex, weight-for-age z-score, body temperature, respiratory rate, unconsciousness or decreased level of consciousness, convulsions, cyanosis and hypoxaemia at baseline. The PREPARE risk assessment tool had good discriminatory value when internally validated (area under the curve 0.83, 95% CI 0.81 to 0.84).

Conclusions

The PREPARE risk assessment tool had good discriminatory ability for identifying children at risk of hospitalised pneumonia-related mortality in a large, geographically diverse dataset. After external validation, this tool may be implemented in various settings to identify children at risk of hospitalised pneumonia-related mortality.

Digital auscultation as a novel childhood pneumonia diagnostic tool for community clinics in Sylhet, Bangladesh: protocol for a cross-sectional study

INTRODUCTION

The WHO's Integrated Management of Childhood Illnesses (IMCI) algorithm for diagnosis of child pneumonia relies on counting respiratory rate and observing respiratory distress to diagnose childhood pneumonia. IMCI case defination for pneumonia performs with high sensitivity but low specificity, leading to overdiagnosis of child pneumonia and unnecessary antibiotic use. Including lung auscultation in IMCI could improve specificity of pneumonia diagnosis. Our objectives are: (1) assess lung sound recording quality by primary healthcare workers (HCWs) from under-5 children with the Feelix Smart Stethoscope and (2) determine the reliability and performance of recorded lung sound interpretations by an automated algorithm compared with reference paediatrician interpretations.

METHODS AND ANALYSIS

In a cross-sectional design, community HCWs will record lung sounds of ~1000 under-5-year-old children with suspected pneumonia at first-level facilities in Zakiganj subdistrict, Sylhet, Bangladesh. Enrolled children will be evaluated for pneumonia, including oxygen saturation, and have their lung sounds recorded by the Feelix Smart stethoscope at four sequential chest locations: two back and two front positions. A novel sound-filtering algorithm will be applied to recordings to address ambient noise and optimise recording quality. Recorded sounds will be assessed against a predefined quality threshold. A trained paediatric listening panel will classify recordings into one of the following categories: normal, crackles, wheeze, crackles and wheeze or uninterpretable. All sound files will be classified into the same categories by the automated algorithm and compared with panel classifications. Sensitivity, specificity and predictive values, of the automated algorithm will be assessed considering the panel's final interpretation as gold standard.

ETHICS AND DISSEMINATION

The study protocol was approved by the National Research Ethics Committee of Bangladesh Medical Research Council, Bangladesh (registration number: 09630012018) and Academic and Clinical Central Office for Research and Development Medical Research Ethics Committee, Edinburgh, UK (REC Reference: 18-HV-051). Dissemination will be through conference presentations, peer-reviewed journals and stakeholder engagement meetings in Bangladesh.

TRIAL REGISTRATION NUMBER

NCT03959956.

Diagnostic Errors Are Common in Acute Pediatric Respiratory Disease: A Prospective, Single-Blinded Multicenter Diagnostic Accuracy Study in Australian Emergency Departments

Background: Diagnostic errors are a global health priority and a common cause of preventable harm. There is limited data available for the prevalence of misdiagnosis in pediatric acute-care settings. Respiratory illnesses, which are particularly challenging to diagnose, are the most frequent reason for presentation to pediatric emergency departments. Objective: To evaluate the diagnostic accuracy of emergency department clinicians in diagnosing acute childhood respiratory diseases, as compared with expert panel consensus (reference standard). Methods: Prospective, multicenter, single-blinded, diagnostic accuracy study in two well-resourced pediatric emergency departments in a large Australian city. Between September 2016 and August 2018, a convenience sample of children aged 29 days to 12 years who presented with respiratory symptoms was enrolled. The emergency department discharge diagnoses were reported by clinicians based upon standard clinical diagnostic definitions. These diagnoses were compared against consensus diagnoses given by an expert panel of pediatric specialists using standardized disease definitions after they reviewed all medical records. Results: For 620 participants, the sensitivity and specificity (%, [95% CI]) of the emergency department compared with the expert panel diagnoses were generally poor: isolated upper respiratory tract disease (64.9 [54.6, 74.4], 91.0 [88.2, 93.3]), croup (76.8 [66.2, 85.4], 97.9 [96.2, 98.9]), lower respiratory tract disease (86.6 [83.1, 89.6], 92.9 [87.6, 96.4]), bronchiolitis (66.9 [58.6, 74.5], 94.3 [80.8, 99.3]), asthma/reactive airway disease (91.0 [85.8, 94.8], 93.0 [90.1, 95.3]), clinical pneumonia (63·9 [50.6, 75·8], 95·0 [92·8, 96·7]), focal (consolidative) pneumonia (54·8 [38·7, 70·2], 86.2 [79.3, 91.5]). Only 59% of chest x-rays with consolidation were correctly identified. Between 6.9 and 14.5% of children were inappropriately prescribed based on their eventual diagnosis. Conclusion: In well-resourced emergency departments, we have identified a previously unrecognized high diagnostic error rate for acute childhood respiratory disorders, particularly in pneumonia and bronchiolitis. These errors lead to the potential of avoidable harm and the administration of inappropriate treatment.

Comprehensive Analysis System for Automated Respiratory Cycle Segmentation and Crackle Peak Detection

Digital auscultation is a well-known method for assessing lung sounds, but remains a subjective process in typical practice, relying on the human interpretation. Several methods have been presented for detecting or analyzing crackles but are limited in their real-world application because few have been integrated into comprehensive systems or validated on non-ideal data. This work details a complete signal analysis methodology for analyzing crackles in challenging recordings. The procedure comprises five sequential processing blocks: (1) motion artifact detection, (2) deep learning denoising network, (3) respiratory cycle segmentation, (4) separation of discontinuous adventitious sounds from vesicular sounds, and (5) crackle peak detection. This system uses a collection of new methods and robustness-focused improvements on previous methods to analyze respiratory cycles and crackles therein. To validate the accuracy, the system is tested on a database of 1000 simulated lung sounds with varying levels of motion artifacts, ambient noise, cycle lengths and crackle intensities, in which ground truths are exactly known. The system performs with average F-score of 91.07% for detecting motion artifacts and 94.43% for respiratory cycle extraction, and an overall F-score of 94.08% for detecting the locations of individual crackles. The process also successfully detects healthy recordings. Preliminary validation is also presented on a small set of 20 patient recordings, for which the system performs comparably. These methods provide quantifiable analysis of respiratory sounds to enable clinicians to distinguish between types of crackles, their timing within the respiratory cycle, and the level of occurrence. Crackles are one of the most common abnormal lung sounds, presenting in multiple cardiorespiratory diseases. These features will contribute to a better understanding of disease severity and progression in an objective, simple and non-invasive way.

Real-World Verification of Artificial Intelligence Algorithm-Assisted Auscultation of Breath Sounds in Children

Objective: Lung auscultation plays an important role in the diagnosis of pulmonary diseases in children. The objective of this study was to evaluate the use of an artificial intelligence (AI) algorithm for the detection of breath sounds in a real clinical environment among children with pulmonary diseases. Method: The auscultations of breath sounds were collected in the respiratory department of Shanghai Children's Medical Center (SCMC) by using an electronic stethoscope. The discrimination results for all chest locations with respect to a gold standard (GS) established by 2 experienced pediatric pulmonologists from SCMC and 6 general pediatricians were recorded. The accuracy, sensitivity, specificity, precision, and F1-score of the AI algorithm and general pediatricians with respect to the GS were evaluated. Meanwhile, the performance of the AI algorithm for different patient ages and recording locations was evaluated. Result: A total of 112 hospitalized children with pulmonary diseases were recruited for the study from May to December 2019. A total of 672 breath sounds were collected, and 627 (93.3%) breath sounds, including 159 crackles (23.1%), 264 wheeze (38.4%), and 264 normal breath sounds (38.4%), were fully analyzed by the AI algorithm. The accuracy of the detection of adventitious breath sounds by the AI algorithm and general pediatricians with respect to the GS were 77.7% and 59.9% (p < 0.001), respectively. The sensitivity, specificity, and F1-score in the detection of crackles and wheeze from the AI algorithm were higher than those from the general pediatricians (crackles 81.1 vs. 47.8%, 94.1 vs. 77.1%, and 80.9 vs. 42.74%, respectively; wheeze 86.4 vs. 82.2%, 83.0 vs. 72.1%, and 80.9 vs. 72.5%, respectively; p < 0.001). Performance varied according to the age of the patient, with patients younger than 12 months yielding the highest accuracy (81.3%, p < 0.001) among the age groups. Conclusion: In a real clinical environment, children's breath sounds were collected and transmitted remotely by an electronic stethoscope; these breath sounds could be recognized by both pediatricians and an AI algorithm. The ability of the AI algorithm to analyze adventitious breath sounds was better than that of the general pediatricians.

Digital auscultation in PERCH: Associations with chest radiography and pneumonia mortality in children

BACKGROUND

Whether digitally recorded lung sounds are associated with radiographic pneumonia or clinical outcomes among children in low-income and middle-income countries is unknown. We sought to address these knowledge gaps.

METHODS

We enrolled 1 to 59monthold children hospitalized with pneumonia at eight African and Asian Pneumonia Etiology Research for Child Health sites in six countries, recorded digital stethoscope lung sounds, obtained chest radiographs, and collected clinical outcomes. Recordings were processed and classified into binary categories positive or negative for adventitial lung sounds. Listening and reading panels classified recordings and radiographs. Recording classification associations with chest radiographs with World Health Organization (WHO)-defined primary endpoint pneumonia (radiographic pneumonia) or mortality were evaluated. We also examined case fatality among risk strata.

RESULTS

Among children without WHO danger signs, wheezing (without crackles) had a lower adjusted odds ratio (aOR) for radiographic pneumonia (0.35, 95% confidence interval (CI): 0.15, 0.82), compared to children with normal recordings. Neither crackle only (no wheeze) (aOR: 2.13, 95% CI: 0.91, 4.96) or any wheeze (with or without crackle) (aOR: 0.63, 95% CI: 0.34, 1.15) were associated with radiographic pneumonia. Among children with WHO danger signs no lung recording classification was independently associated with radiographic pneumonia, although trends toward greater odds of radiographic pneumonia were observed among children classified with crackle only (no wheeze) or any wheeze (with or without crackle). Among children without WHO danger signs, those with recorded wheezing had a lower case fatality than those without wheezing (3.8% vs. 9.1%, p = .03).

CONCLUSIONS

Among lower risk children without WHO danger signs digitally recorded wheezing is associated with a lower odds for radiographic pneumonia and with lower mortality. Although further research is needed, these data indicate that with further development digital auscultation may eventually contribute to child pneumonia care.

Diagnosis and management of community-acquired pneumonia in children: South African Thoracic Society guidelines

BACKGROUND

Pneumonia remains a major cause of morbidity and mortality amongst South African children. More comprehensive immunisation regimens, strengthening of HIV programmes, improvement in socioeconomic conditions and new preventive strategies have impacted on the epidemiology of pneumonia. Furthermore, sensitive diagnostic tests and better sampling methods in young children improve aetiological diagnosis.

OBJECTIVES

To produce revised guidelines for pneumonia in South African children under 5 years of age.

METHODS

The Paediatric Assembly of the South African Thoracic Society and the National Institute for Communicable Diseases established seven expert subgroups to revise existing South African guidelines focusing on: (i) epidemiology; (ii) aetiology; (iii) diagnosis; (iv) antibiotic management and supportive therapy; (v) management in intensive care; (vi) prevention; and (vii) considerations in HIV-infected or HIVexposed, uninfected (HEU) children. Each subgroup reviewed the published evidence in their area; in the absence of evidence, expert opinion was accepted. Evidence was graded using the British Thoracic Society (BTS) grading system. Sections were synthesized into an overall guideline which underwent peer review and revision.

RECOMMENDATIONS

Recommendations include a diagnostic approach, investigations, management and preventive strategies. Specific recommendations for HIV infected and HEU children are provided.

VALIDATION

The guideline is based on available published evidence supplemented by the consensus opinion of SA paediatric experts. Recommendations are consistent with those in published international guidelines.

An objective measure of signal quality for pediatric lung auscultations

A stethoscope is a ubiquitous tool used to 'listen' to sounds from the chest in order to assess lung and heart conditions. With advances in health technologies including digital devices and new wearable sensors, access to these sounds is becoming easier and abundant; yet proper measures of signal quality do not exist. In this work, we develop an objective quality metric of lung sounds based on low-level and high-level features in order to independently assess the integrity of the signal in presence of interference from ambient sounds and other distortions. The proposed metric outlines a mapping of auscultation signals onto rich low-level features extracted directly from the signal which capture spectral and temporal characteristics of the signal. Complementing these signal-derived attributes, we propose high-level learnt embedding features extracted from a generative auto-encoder trained to map auscultation signals onto a representative space that best captures the inherent statistics of lung sounds. Integrating both low-level (signal-derived) and high-level (embedding) features yields a robust correlation of 0.85 to infer the signal-to-noise ratio of recordings with varying quality levels. The method is validated on a large dataset of lung auscultation recorded in various clinical settings with controlled varying degrees of noise interference. The proposed metric is also validated against opinions of expert physicians in a blind listening test to further corroborate the efficacy of this method for quality assessment.

Factors affecting airway compliance and resistance in children receiving general anesthesia during adenotonsillectomy

Airway compliance is an important index in the surgery of pediatric patients. This study aimed to explore factors affecting dynamic airway compliance (Cdyn) and airway resistance (Raw) after general anesthesia endotracheal intubation for adenotonsillectomy of pediatric patients.A prospective study was undertaken of 107 children who underwent adenotonsillectomy in Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine between January and June 2018. The values of Cdyn and Raw were recorded at 5, 10, and 15 minute during general anesthesia endotracheal intubation. Univariate analysis and multiple linear regression analysis were performed for factors that affected Cdyn and Raw.Of the 107 patients aged 56.67 ± 18.28 months, 69 (64%) patients were male, and 26 (24%) and 12 (11%) had an upper respiratory infection in the past week and 1 to 2 weeks, respectively. During anesthesia, Cdyn showed a decreasing trend (P < .001) while Raw showed an increasing trend (P < .001). Multivariate analysis revealed that height (β=0.177-0.193) had the strongest correlation with Cdyn; rales during pulmonary auscultation (β= -2.727 to -1.363) and sputum suction (β= -1.670 to -0.949) were also associated with Cdyn (all P < .05). Height was the factor with the strongest negative correlation with Raw (β= -0.382 to -0.305). Rales during pulmonary auscultation (β=10.063-11.326) and sputum suction (β=3.863-9.003) were also associated with Raw (All P < .05).Height, rales during preoperative auscultation and sputum suction were all associated with intraoperative Cydn and Raw for pediatric patients undergoing adenotonsillectomy and should be considered before the surgery.

Challenges in the diagnosis of paediatric pneumonia in intervention field trials: recommendations from a pneumonia field trial working group

Pneumonia is a leading killer of children younger than 5 years despite high vaccination coverage, improved nutrition, and widespread implementation of the Integrated Management of Childhood Illnesses algorithm. Assessing the effect of interventions on childhood pneumonia is challenging because the choice of case definition and surveillance approach can affect the identification of pneumonia substantially. In anticipation of an intervention trial aimed to reduce childhood pneumonia by lowering household air pollution, we created a working group to provide recommendations regarding study design and implementation. We suggest to, first, select a standard case definition that combines acute (≤14 days) respiratory symptoms and signs and general danger signs with ancillary tests (such as chest imaging and pulse oximetry) to improve pneumonia identification; second, to prioritise active hospital-based pneumonia surveillance over passive case finding or home-based surveillance to reduce the risk of non-differential misclassification of pneumonia and, as a result, a reduced effect size in a randomised trial; and, lastly, to consider longitudinal follow-up of children younger than 1 year, as this age group has the highest incidence of severe pneumonia.

Digital stethoscopes in paediatric medicine

AIM

To explore, synthesise and discuss currently available digital stethoscopes (DS) and the evidence for their use in paediatric medicine.

METHODS

Systematic review and narrative synthesis of digital stethoscope use in paediatrics following searches of OVID Medline, Embase, Scopus, PubMed and Google Scholar databases.

RESULTS

Six digital stethoscope makes were identified to have been used in paediatric focused studies so far. A total of 25 studies of DS use in paediatrics were included. We discuss the use of digital stethoscope technology in current paediatric medicine, comment on the technical properties of the available devices, the effectiveness and limitations of this technology, and potential uses in the fields of paediatrics and neonatology, from telemedicine to computer-aided diagnostics.

CONCLUSION

Further validation and testing of available DS devices is required. Comparison studies between different types of DS would be useful in identifying strengths and flaws of each DS as well as identifying clinical situations for which each may be most appropriately suited.

International perception of lung sounds: a comparison of classification across some European borders

Introduction

Lung auscultation is helpful in the diagnosis of lung and heart diseases; however, the diagnostic value of lung sounds may be questioned due to interobserver variation. This situation may also impair clinical research in this area to generate evidence-based knowledge about the role that chest auscultation has in a modern clinical setting. The recording and visual display of lung sounds is a method that is both repeatable and feasible to use in large samples, and the aim of this study was to evaluate interobserver agreement using this method.

Methods

With a microphone in a stethoscope tube, we collected digital recordings of lung sounds from six sites on the chest surface in 20 subjects aged 40 years or older with and without lung and heart diseases. A total of 120 recordings and their spectrograms were independently classified by 28 observers from seven different countries. We employed absolute agreement and kappa coefficients to explore interobserver agreement in classifying crackles and wheezes within and between subgroups of four observers.

Results

When evaluating agreement on crackles (inspiratory or expiratory) in each subgroup, observers agreed on between 65% and 87% of the cases. Conger's kappa ranged from 0.20 to 0.58 and four out of seven groups reached a kappa of ≥0.49. In the classification of wheezes, we observed a probability of agreement between 69% and 99.6% and kappa values from 0.09 to 0.97. Four out of seven groups reached a kappa ≥0.62.

Conclusions

The kappa values we observed in our study ranged widely but, when addressing its limitations, we find the method of recording and presenting lung sounds with spectrograms sufficient for both clinic and research. Standardisation of terminology across countries would improve international communication on lung auscultation findings.