Adventitious Lung Sounds Research Articles

LungNeXt: A novel lightweight network utilizing enhanced mel-spectrogram for lung sound classification

Lung auscultation is essential for early lung condition detection. Categorizing adventitious lung sounds requires expert discrimination by medical specialists. This paper details the features of LungNeXt, a novel classification model specifically designed for lung sound analysis. Furthermore, we propose two auxiliary methods: RandClipMix (RCM) for data augmentation and Enhanced Mel-Spectrogram for Feature Extraction (EMFE). RCM addresses the issue of data imbalance by randomly mixing clips within the same category to create new adventitious lung sounds. EMFE augments specific frequency bands in spectrograms to highlight adventitious features. These contributions enable LungNeXt to achieve outstanding performance. LungNeXt optimally integrates an appropriate number of NeXtblocks, ensuring superior performance and a lightweight model architecture. The proposed RCM and EMFE methods, along with the LungNeXt classification network, have been evaluated on the SPRSound dataset. Experimental results revealed a commendable score of 0.5699 for the lung sound five-category task on SPRSound. Specifically, the LungNeXt model is characterized by its efficiency, with only 3.804M parameters and a computational complexity of 0.659G FLOPS. This lightweight and efficient model is particularly well-suited for applications in electronic stethoscope back-end processing equipment, providing efficient diagnostic advice to physicians and patients.

Automatic breathing phase identification based on the second derivative of the recorded lung sounds

This paper presents a new method for automatic identification of the inspiratory and expiratory breathing phases in lung sound recordings. Adventitious lung sounds (wheezes and crackles), superimposed on the breath sounds, are generally an early indication of the disease, and their timing in the breathing cycle (early/mid/late inspiratory or expiratory) has clinical significance for monitoring or diagnosing disease. Therefore, the identification of the phases of the breathing cycle is an essential step for clinical interpretation of pulmonary auscultation. The proposed algorithm is designed to be robust in the presence of adventitious lung sounds or where the breath sounds may be noisy compared to healthy lung sounds. The algorithm uses the Savitzky & Golay (SG) filter to estimate the second derivative of the lung sound signal then calculates its normalized absolute value. A threshold value is used to clip any large amplitude peaks and, following low-pas filtering, the breathing phases are visible in the plotted signal. A rule-based approach to locating peaks and troughs is then used to identify inspirations and expirations. The performance of the method is evaluated using four different datasets: (a) a longitudinal dataset recorded from 19 subjects with a diagnosis of idiopathic pulmonary fibrosis (IPF), (b) a cross-sectional dataset recorded from 55 subjects who were referred for high-resolution computed tomography (HRCT) scan of the chest for various clinical indications, (c) a longitudinal dataset recorded from 10 healthy subjects, and (d) an open access lung sounds dataset containing recordings from 41 subjects with wheeze (9 with chronic obstructive pulmonary fibrosis and 32 with asthma). On average for inspiratory phase identification the algorithm had a sensitivity of (mean (standard deviation)) 92.84 (12.88)%, positive predictive value of 90.64(15.96)%, and F1-score of 90.67 (12.53)%. For identification of the expiratory phase, the algorithm had an average sensitivity of 92.19 (13.44)%, an average positive predictive value of 91.55 (15.23)%, and an average F1-score of 90.89 (12.45)%. The method shows good potential for automatic identification of breathing phases in recorded lung sounds.

An efficient new adaptive variational mode decomposition algorithm for extracting adventitious lung sounds

The rapid and accurate extraction of adventitious lung sounds (ALSs) from original lung sounds is greatly significant for diagnosing lung diseases and assessing lung condition. Due to interference from heart sounds and various noises, the empirical mode decomposition (EMD), variational mode decomposition (VMD) and some other classical algorithms have poor performance. Here, an efficient new adaptive variational mode decomposition (AVMD) algorithm is proposed to improve the efficiency for extracting ALSs. For minimizing the bandwidth of each extracted mode and reducing frequency aliasing between modes, the proposed algorithm constructs a frequency-constrained model based on VMD. In order to further improve its performance, the algorithm adopts a strategy of gradually extracting each mode to determine the optimal number of modes for avoiding the under-decomposition or over-decomposition, and uses Pool Adjacent Violators (PAV) to automatically initialize the center frequency of each mode to reduce algorithm iteration steps. Based on the above improved characteristics, the experiments presented in this paper, which conducted on the artificial simulation signal and 102 groups of lung sounds from 20 patients with severe pulmonary diseases, highlight the proposed AVMD's superiority over the other five algorithms when comprehensively considering both decomposition quality and computational efficiency. Furthermore, the above results of 102 groups of lung sounds are statistically analyzed to obtain the characteristic frequency distribution of ALSs, which can provide important references for clinicians and demonstrate the potential application of the proposed algorithm in the diagnosis of pulmonary diseases.

Computational aeroacoustics in flexible conduits with application to biomedical fluid dynamics

Flexible tubes are simple yet powerful tools for the modeling of respiratory and circulatory systems [1, 2]. In the last decade, access to high-performancecomputational resources has allowed the implementation of realistic numerical models of human vessels based on Computational Fluid Dynamics (CFD) and Fluid-Structure Interaction (FSI) simulations. Interestingly, the recently proven correlation [3] between CFD observables (like the pharyngeal airway's resistance) and clinical data (such as the Apnoea Hypopnea Index in Obstructive Sleep Apnoea patients) suggests a possible development of diagnostic methods based on numerical simulations. This work aims to investigate the extension of these correlations to the aeroacoustics characteristics of flexible conduits by means of a fully coupled FSI numerical model based on the Large Eddy Simulation method. These results have relevant applications to studying diseases of the human upper vocal tract, voice production, obstructive sleep apnoea, and adventitious lung sounds, such as wheezing and crackling. [1] Grotberg, J. B., & Jensen, O. E. (2004). Annu. Rev. Fluid Mech., 36, 121–147. [2] Schwartz, A. R., & Smith, P. L. (2013). The Journal of physiology, 591(Pt 9), 2229. [3] Schickhofer L., Malinen J., Mihaescu M., J. Acoust. Soc. Am. - JASA, 145 (4): 2049–2061, 2019. https://doi.org/10.1121/1.5095250

Physicians' ability to recognize adventitious lung sounds.

Lung auscultation is an important tool for diagnosing respiratory diseases. However, the ability of observers to recognize respiratory sounds varies considerably and depends on the sound. The present study aimed to assess the auscultatory skills of healthcare professionals and medical students. A total of 295 physicians (185 pediatricians, 69 pulmonologists, and 41 physicians of general/internal medicine and subspecialties), 55 residents, and 50 medical students participated in the survey. They listened to five audio-recorded respiratory sounds and described them in free-form answers. The rates of correct answers were 55.2% for fine crackles, 74.5% for coarse crackles, 72.2% for wheezes, 18.75% for squawks, and 11.25% for pleural friction rub. The medical specialty was correlated with the correct answers and both pediatricians and physicians of general/internal medicine and subspecialties recognized fewer sounds compared with respiratory physicians (odds ratio [OR]: 0.37; confidence interval [CI]: 0.22-0.62; p < 0.001 and, OR: 0.47; CI: 0.22-0.99, p = 0.048, respectively). Years of experience were negatively correlated with the number of correct answers (OR: 0.73; CI:0.62-0.84; p = 0.001). Gaps remain in both terminology and recognition of lung sounds among a wide population of Greek physicians. Less experienced physicians perform better on lung auscultation, indicating that continuing education with critical feedback should be offered.

Lung Sound Classification Using Co-Tuning and Stochastic Normalization.

Computational methods for lung sound analysis are beneficial for computer-aided diagnosis support, storage and monitoring in critical care. In this paper, we use pre-trained ResNet models as backbone architectures for classification of adventitious lung sounds and respiratory diseases. The learned representation of the pre-trained model is transferred by using vanilla fine-tuning, co-tuning, stochastic normalization and the combination of the co-tuning and stochastic normalization techniques. Furthermore, data augmentation in both time domain and time-frequency domain is used to account for the class imbalance of the ICBHI and our multi-channel lung sound dataset. Additionally, we introduce spectrum correction to account for the variations of the recording device properties on the ICBHI dataset. Empirically, our proposed systems mostly outperform all state-of-the-art lung sound classification systems for the adventitious lung sounds and respiratory diseases of both datasets.

Digital auscultation as a diagnostic aid to detect childhood pneumonia: A systematic review

BackgroundFrontline health care workers use World Health Organization Integrated Management of Childhood Illnesses (IMCI) guidelines for child pneumonia care in low-resource settings. IMCI guideline pneumonia diagnostic criterion performs with low specificity, resulting in antibiotic overtreatment. Digital auscultation with automated lung sound analysis may improve the diagnostic performance of IMCI pneumonia guidelines. This systematic review aims to summarize the evidence on detecting adventitious lung sounds by digital auscultation with automated analysis compared to reference physician acoustic analysis for child pneumonia diagnosis.MethodsIn this review, articles were searched from MEDLINE, Embase, CINAHL Plus, Web of Science, Global Health, IEEExplore database, Scopus, and the ClinicalTrial.gov databases from the inception of each database to October 27, 2021, and reference lists of selected studies and relevant review articles were searched manually. Studies reporting diagnostic performance of digital auscultation and/or computerized lung sound analysis compared against physicians’ acoustic analysis for pneumonia diagnosis in children under the age of 5 were eligible for this systematic review. Retrieved citations were screened and eligible studies were included for extraction. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. All these steps were independently performed by two authors and disagreements between the reviewers were resolved through discussion with an arbiter. Narrative data synthesis was performed.ResultsA total of 3801 citations were screened and 46 full-text articles were assessed. 10 studies met the inclusion criteria. Half of the studies used a publicly available respiratory sound database to evaluate their proposed work. Reported methodologies/approaches and performance metrics for classifying adventitious lung sounds varied widely across the included studies. All included studies except one reported overall diagnostic performance of the digital auscultation/computerised sound analysis to distinguish adventitious lung sounds, irrespective of the disease condition or age of the participants. The reported accuracies for classifying adventitious lung sounds in the included studies varied from 66.3% to 100%. However, it remained unclear to what extent these results would be applicable for classifying adventitious lung sounds in children with pneumonia.ConclusionsThis systematic review found very limited evidence on the diagnostic performance of digital auscultation to diagnose pneumonia in children. Well-designed studies and robust reporting are required to evaluate the accuracy of digital auscultation in the paediatric population.

Analysis of Features based on Wavelet Bi-Spectrum and Power Spectrum for the Detection of Adventitious Lung Sounds

Lung sounds contribute essential information about a patient’s health. Here, for detecting adventitious sounds of lungs, two sets of eight features each (total 16 features), based on wavelet bi-spectrum (WBS) and power spectrum (WPS), respectively, are proposed. The feature sets are analyzed using five classifiers with one to seven sub-classifier types. A matrix (17X14) of seven evaluation parameters compares the feature sets. Results show that Random Forest, Random Tree, Random committee with WPS and WBS features, LMT with WPS, and Randomizable filter classifier with WBS have shown the best results updating the accuracy obtained in previous researches.

Analysis of Normal and Adventitious Lung Sound Signals Using Empirical Mode Decomposition and Central Tendency Measure

Diagnosing chronic obstructive pulmonary disease (COPD) from lung sounds is time consuming, onerous, and subjective to the expertise of pulmonologists. The preliminary diagnosis of COPD is often based on adventitious lung sounds (ALS). This paper proposes to objectively analyze the lung sound signals associated with COPD. Specifically, empirical mode decomposition (EMD), a data adaptive signal decomposition technique suitable for analyzing non-stationary signals, was adopted to decompose non-stationary lung sound signals. The use of EMD on lung sound signal results in intrinsic mode functions (IMFs), which are symmetric and band limited. The analytic IMFs were then computed through the Hilbert transform, which reveals the instantaneous frequency content of each IMF. The Hilbert transformed signal is analytic, and has a complex representation containing real and imaginary parts. Next, the central tendency measure (CTM) was introduced to quantify the circular shape of the analytical IMF plot. The result was taken as a useful feature to distinguish normal lung sound signal with ALS. Simulation results show that the CTM of analytic IMFs has a strong ability to distinguish between normal lung sound signals and ALS.

Detection of pathological mechano-acoustic signatures using precision accelerometer contact microphones in patients with pulmonary disorders

Monitoring pathological mechano-acoustic signals emanating from the lungs is critical for timely and cost-effective healthcare delivery. Adventitious lung sounds including crackles, wheezes, rhonchi, bronchial breath sounds, stridor or pleural rub and abnormal breathing patterns function as essential clinical biomarkers for the early identification, accurate diagnosis and monitoring of pulmonary disorders. Here, we present a wearable sensor module comprising of a hermetically encapsulated, high precision accelerometer contact microphone (ACM) which enables both episodic and longitudinal assessment of lung sounds, breathing patterns and respiratory rates using a single integrated sensor. This enhanced ACM sensor leverages a nano-gap transduction mechanism to achieve high sensitivity to weak high frequency vibrations occurring on the surface of the skin due to underlying lung pathologies. The performance of the ACM sensor was compared to recordings from a state-of-art digital stethoscope, and the efficacy of the developed system is demonstrated by conducting an exploratory research study aimed at recording pathological mechano-acoustic signals from hospitalized patients with a chronic obstructive pulmonary disease (COPD) exacerbation, pneumonia, and acute decompensated heart failure. This unobtrusive wearable system can enable both episodic and longitudinal evaluation of lung sounds that allow for the early detection and/or ongoing monitoring of pulmonary disease.

Scalp Swelling and Headache in a 12-year-old Boy

1. Kathryn Westphal, MD* 2. Respicius Bakalemwa, MD† 3. Elizabeth Groothuis, MD, MPH* 1. *Department of Pediatrics, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 2. †Department of Pediatrics, Bugando Medical Centre, Mwanza, Tanzania A 12-year-old boy with sickle cell disease (SCD) is admitted to a hospital in Mwanza, Tanzania, with acute onset of periorbital and scalp swelling. Associated symptoms include fever, headache, difficulty breathing, and back pain. Because resources in sub-Saharan Africa are limited, access to comprehensive care for patients with SCD is variable. The general principles of management focus on early diagnosis, parental education, prevention of infection with vaccinations and prophylactic medications, prompt treatment of infection and pain, the use of hydroxyurea for stroke prevention, and blood transfusions for life-threatening anemia. This patient has a known diagnosis of SCD but has infrequent pain episodes and no previous transfusions. He is not receiving hydroxyurea or prophylaxis against bacterial infections or malaria. He has no history of stroke and no known head trauma. On presentation he is afebrile and his heart rate is 120 beats/min, respiratory rate is 40 breaths/min, and blood pressure is 105/75 mm Hg. His oxygen saturation is 92% in room air. Physical examination is significant for pallor, periorbital edema, scalp edema, dyspnea without adventitious lung sounds, and splenomegaly with diffuse abdominal tenderness. His scalp edema is fluctuant and extends posteriorly from the periorbital region, encompassing the entire scalp. Swelling is most prominent in the frontal and parietal areas. The overlying skin is nonerythematous and nontender but is warm to the touch. His neurologic examination has no focal findings, although generally he appears tired. Initial laboratory results demonstrate a white blood cell count of 84,000/μL (84×109/L) with a lymphocytic predominance (57%), a hemoglobin level of …

An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis.

Chronic obstructive pulmonary disease (COPD) and pneumonia are two of the few fatal lung diseases which share common adventitious lung sounds. Diagnosing the disease from lung sound analysis to design a noninvasive technique for telemedicine is a challenging task. A novel framework is presented to perform a diagnosis of COPD and Pneumonia via application of the signal processing and machine learning approach. This model will help the pulmonologist to accurately detect disease A and B. COPD, normal and pneumonia lung sound (LS) data from the ICBHI respiratory database is used in this research. The performance analysis is evidence of the improved performance of the quadratic discriminate classifier with an accuracy of 99.70% on selected fused features after experimentation. The fusion of time domain, cepstral, and spectral features are employed. Feature selection for fusion is performed through the back-elimination method whereas empirical mode decomposition (EMD) and discrete wavelet transform (DWT)-based techniques are used to denoise and segment the pulmonic signal. Class imbalance is catered with the implementation of the adaptive synthetic (ADASYN) sampling technique.

Methods for Adventitious Respiratory Sound Analyzing Applications Based on Smartphones: A Survey.

Detection and classification of adventitious acoustic lung sounds plays an important role in diagnosing, monitoring, controlling and, caring the patients with lung diseases. Such systems can be presented as different platforms like medical devices, standalone software or smartphone application. Ubiquity of smartphones and widespread use of the corresponding applications make such a device an attractive platform for hosting the detection and classification systems for adventitious lung sounds. In this paper, the smartphone-based systems for automatic detection and classification of the adventitious lung sounds are surveyed. Such adventitious sounds include cough, wheeze, crackle and, snore. Relevant sounds related to abnormal respiratory activities are considered as well. The methods are shortly described and the analyzing algorithms are explained. The analysis includes detection and/or classification of the sound events. A summary of the main surveyed methods together with the classification parameters and used features for the sake of comparison is given. Existing challenges, open issues and future trends will be discussed as well.

Prevalence and clinical associations of wheezes and crackles in the general population: the Troms\xf8 study

BackgroundWheezes and crackles are well-known signs of lung diseases, but can also be heard in apparently healthy adults. However, their prevalence in a general population has been sparsely described. The objective of this study was to determine the prevalence of wheezes and crackles in a large general adult population and explore associations with self-reported disease, smoking status and lung function.MethodsWe recorded lung sounds in 4033 individuals 40 years or older and collected information on self-reported disease. Pulse oximetry and spirometry were carried out. We estimated age-standardized prevalence of wheezes and crackles and associations between wheezes and crackles and variables of interest were analyzed with univariable and multivariable logistic regressions.ResultsTwenty-eight percent of individuals had wheezes or crackles. The age-standardized prevalence of wheezes was 18.6% in women and 15.3% in men, and of crackles, 10.8 and 9.4%, respectively. Wheezes were mostly found during expiration and crackles during inspiration. Significant predictors of expiratory wheezes in multivariable analyses were age (10 years increase - OR 1.18, 95%CI 1.09–1.30), female gender (1.45, 1.2–1.8), self-reported asthma (1.36, 1.00–1.83), and current smoking (1.70, 1.28–2.23). The most important predictors of inspiratory crackles were age (1.76, 1.57–1.99), current smoking, (1.94, 1.40–2.69), mMRC ≥2 (1.79, 1.18–2.65), SpO2 (0.88, 0.81–0.96), and FEV1 Z-score (0.86, 0.77–0.95).ConclusionsNearly over a quarter of adults present adventitious lung sounds on auscultation. Age was the most important predictor of adventitious sounds, particularly crackles. The adventitious sounds were also associated with self-reported disease, current smoking and measures of lung function. The presence of findings in two or more auscultation sites was associated with a higher risk of decreased lung function than solitary findings.

Breath Sounds: From Basic Science to Clinical Practice

The book Breath Sounds:From Basic Science to Clinical Practice is a literary accomplishment of epic dimensions. Yet, what is the need for a hardcover on lung sounds in the year 2018? The answer, given by the editors Priftis, Hadjileontiadis, and Everard, is that “the stethoscope will remain the first medical device a medical student will invest in (other than their smartphone).” The book takes the reader on a 319-page journey in 4 parts and 19 chapters through the past, present, and future of respiratory sound analysis. An impressive number of 36 international authors have contributed to this state-of-the-art overview of thoracic auscultation and technological advances in breath sound analysis. The book begins with the historical origins of the art of auscultation, which we owe to the invention of the stethoscope—recently celebrating its bicentennial anniversary—by René-Théophile-Hyacinthe Laënnec (1781–1826). This first part underlines the importance of clinical skills to examine and interpret the manifold variations in respiratory sounds. Chapter 3 includes instructive cases from the emergency room. “Let’s get physical,” in the second part, provides insights into the basic principles of sound recording and analysis. Chapter 6, introducing home-based sleep studies to screen for obstructive sleep apnea, is convincing and well written. Due to the development of new tracheal sound sensors and amplification of signals with subsequent inline filtering, we not only detect but also characterize respiratory events in sleep disorders. Extending the transmission through a modified medium brings back the percussion technique (first described by Leopold Auenbrugger in 1761) and may have some value in acute lung injury. Experimental studies to adapt ventilation with positive end-expiratory pressure are presented. Chapters 8 and 9 on breath sound recording and analysis address current techniques for multichannel recording of respiratory sounds followed by analog processing and digitization. Advanced approaches with fractality/lacunarity, swarm decomposition–swarm transform, and deep learning are shown to explain how big data will enhance the diagnostic value of breath sounds. The third part of the book sheds light on pulmonary pathophysiology and exposes the acoustic correlate of various respiratory conditions. Normal and adventitious lung sounds such as wheezing, crackles, rhonchi, and cough sounds are described in detail. Anesthesiologists will appreciate chapter 13 on the anatomy of the upper airway and stridor due to laryngeal obstruction or vocal cord dysfunction. The next chapter describes the fascinating biomedical engineering behind sleep disorder assessment and apnea detection. Deriving the sleep architecture from breath sounds recorded by freestanding microphones can be a medical application of the Internet of Things and refine the monitoring of patients in the intensive care unit. Part 4 of the book gives a short outlook on future prospects for respiratory sound research and the pursuit of a unified nomenclature. The book is complemented by 3 short audio files, accessible via a link in chapter 5. Overall, the online extras are scarce for a book of this scale. Additional samples of breath sounds are welcomed as a clinical guide for the reader or teaching purposes. Modern medicine has definitely entered the digital age. High-tech additions to conventional auscultation such as ultrasonography are now well-established routine investigations. However, the breath sound as a biosignal should not disappear from clinical practice, but be further integrated into innovative diagnostic tools and enriched by a computerized analysis of sound. Training of auscultatory skills remains relevant for junior doctors. The stethoscope will always offer a quick first impression of the patient’s respiratory condition. Moreover, the caring and gestural communication aspect of traditional auscultation plays a meaningful role in the patient–physician relationship. Because this book deals with a particular aspect of clinical diagnostics, its value is certainly highest in respiratory medicine, while critical care, infectious diseases, and pediatrics are marginally touched. In that, the book occupies a niche for a distinctive readership. Whether or not to own a copy of this book as an anesthesiologist comes down to a simple question: do you regularly wear a stethoscope in the operating room or intensive care unit? If yes, this book is for you, and it will precisely inform about the acoustic features of respiratory conditions encountered in clinical practice. If not, the volume’s worst fate is to take a lonely spot in an antiquary collection of books for the nostalgic. Nicolai Goettel, MD, DESA, EDICDepartment of AnesthesiaSurgical Intensive CarePrehospital Emergency Medicine and Pain TherapyUniversity Hospital Basel, University of BaselBasel, Switzerland[email protected] Matthias J. Herrmann, MDClinic of Pulmonary Medicine and Respiratory Cell ResearchUniversity Hospital Basel, University of BaselBasel, Switzerland

Bilateral Cricoarytenoid Joint Ankylosis with a Perplexing Etiology

Bilateral Cricoarytenoid Joint Ankylosis with a Perplexing Etiology

Lung and Heart Sounds Analysis: State-of-the-Art and Future Trends

Lung sounds, which include all sounds that are produced during the mechanism of respiration, may be classified into normal breath sounds and adventitious sounds. Normal breath sounds occur when no respiratory problems exist, whereas adventitious lung sounds (wheeze, rhonchi, crackle, etc.) are usually associated with certain pulmonary pathologies. Heart and lung sounds that are heard using a stethoscope are the result of mechanical interactions that indicate operation of cardiac and respiratory systems, respectively. In this article, we review the research conducted during the last six years on lung and heart sounds, instrumentation and data sources (sensors and databases), technological advances, and perspectives in processing and data analysis. Our review suggests that chronic obstructive pulmonary disease (COPD) and asthma are the most common respiratory diseases reported on in the literature; related diseases that are less analyzed include chronic bronchitis, idiopathic pulmonary fibrosis, congestive heart failure, and parenchymal pathology. Some new findings regarding the methodologies associated with advances in the electronic stethoscope have been presented for the auscultatory heart sound signaling process, including analysis and clarification of resulting sounds to create a diagnosis based on a quantifiable medical assessment. The availability of automatic interpretation of high precision of heart and lung sounds opens interesting possibilities for cardiovascular diagnosis as well as potential for intelligent diagnosis of heart and lung diseases.

Validity of Red Cell Distribution Width as a Predictor of Clinical Outcomes in Pediatric Patients Diagnosed With Pneumonia

SESSION TITLE: Pediatrics SESSION TYPE: Original Investigation Poster PRESENTED ON: Wednesday, November 1, 2017 at 01:30 PM - 02:30 PM PURPOSE: To determine the validity of RDW in predicting clinical outcomes of pediatric patients with pneumonia at Philippine Heart Center METHODS: Medical records of all patients 1-18 years old admitted from January 1, 2013 to December 31, 2015 with pneumonia as one of the final diagnosis were retrieved. Demographic data, laboratory and clinical parameters such as, RDW, white blood cell (WBC) and presence of tachypnea, adventitious lung sounds, or chest in-drawing were recorded. Pneumonia was classified as community-acquired or hospital-acquired. The primary outcome of this study was in-hospital mortality, while the secondary outcome was development of complications that were absent upon initial presentation, such as (1) need for or prolonged mechanical ventilation, (2) need for or increased vasopressors, (3) admission or transfer to or prolonged pediatric intensive care unit (PICU) stay, (4) prolonged hospital stay, or (5) development of atelectasis, empyema thoracis, pleural effusion, sepsis, pneumothorax, or pulmonary abscess. Data analysis was done using Stata SE version 13. The accuracy of RDW in predicting mortality among pediatric patients with pneumonia were presented as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV). Homogeneity of characteristics between two groups were tested using independent t-test for quantitative variables and Fisher’s exact test for qualitative variables. Association between RDW and mortality were determined using logistic regression analysis. The level of significance was set at 0.05. RESULTS: There are 59 (50%) females and 57 (49.1%) males. The coexisting illnesses identified were congenital heart disease (37, 31.9%), bronchial asthma (15, 12.9%), and rheumatic heart disease (7, 6%). Most (84, 72.4%) of the patients were diagnosed with PCAP C. There is a significant association between high RDW value and development of complications (p<0.001) as well as non-survival (p<0.006) from pneumonia. Elevated RDW has a high specificity (94.7%) and sensitivity (60%) in identifying patients with pneumonia who will develop complications with a positive predictive value of 85.7% and a negative predictive value of 81.8%. In predicting in-hospital mortality, high RDW has a sensitivity of 66.7% and specificity of 79.4%, with a positive predictive value of 21.4% and a negative predictive value of 96.6%. CONCLUSIONS: Elevated RDW is significantly associated to mortality and development of complications among pediatric patients with pneumonia. It is both specific and sensitive as a prognostic tool for pneumonia and can be a valid and cost-efficient marker for pneumonia severity. CLINICAL IMPLICATIONS: Diagnostic tests such as C-reactive protein and erythrocyte sedimentation rate are used in the hospital setting in assessing treatment response in pediatric community-acquired pneumonia but these are not readily available in laboratories and would entail additional costs. The RDW, meanwhile, being part of a routine CBC, would be very useful and cost-efficient if proven to have clinical validity in predicting outcomes of pneumonia in children. DISCLOSURE: The following authors have nothing to disclose: Sherryl Jane Miranda No Product/Research Disclosure Information

Markers of Disease Severity in Patients with Spanish Influenza in the Japanese Armed Forces, 1919-1920.

We examined preserved medical charts of 470 Spanish influenza patients (8 with fatal cases) hospitalized at former army hospitals in Japan during 1919–1920. The following factors were associated with longer periods of hospitalization: adventitious discontinuous lung sounds, maximum respiration rate, continuation of high fever after hospital admission, and diphasic fever.

Study of Adventitious Lung Sounds of Paediatric Population using Artificial Neural Network Approach

Study of Adventitious Lung Sounds of Paediatric Population using Artificial Neural Network Approach