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1.
Pediatr Pulmonol ; 56(7): 1946-1950, 2021 07.
Article in English | MEDLINE | ID: covidwho-1525483

ABSTRACT

INTRODUCTION: Preschool wheezers are at high risk of recurrent attacks triggered by respiratory viruses, sometimes exacerbated by exposure to allergens and pollution. Because of the COVID-19 infection, the lockdown was introduced, but the effects on preschool wheezers are unknown. We hypothesized that there would be an improvement in outcomes during the lockdown, and these would be lost when the lockdown was eased. MATERIALS AND METHODS: Patients underwent medical visits before and after the COVID-19 lockdown. We recorded the childhood Asthma Control Test (cACT) and a clinical questionnaire. Data on symptoms, the need for medications and the use of healthcare resources were recorded. We compared these data with retrospective reports from the preceding year and prospectively acquired questionnaires after lockdown. RESULTS: We studied 85 preschool wheezers, mean age 4.9 years. During the lockdown, cACT score was significantly higher (median 25 vs. 23); families reported a dramatic drop in wheezing episodes (51 vs. none), significant reductions in the day and nighttime symptoms, including episodes of shortness of breath (p < .0001); the use of salbutamol and oral corticosteroids (OCS) dropped significantly (p < .0001) and 79 (95%) patients needed no OCS bursts during the lockdown. Finally, patients had significantly fewer extra medical examinations, as well as fewer Emergency Room visits (p < .0001). All were improved compared with the same time period from the previous year, but outcomes worsened significantly again after lockdown (cACT median: 22). CONCLUSIONS: During the national lockdown, children with persistent preschool wheeze showed a significant clinical improvement with reduction of respiratory symptoms, medication use for exacerbations, and use of healthcare resources. This trend reversed when lockdown restrictions were eased.


Subject(s)
COVID-19/epidemiology , Pandemics , Respiratory Sounds , Adrenal Cortex Hormones , Allergens , COVID-19/physiopathology , COVID-19/virology , Child, Preschool , Communicable Disease Control , Female , Humans , Male , Recurrence , Retrospective Studies , SARS-CoV-2/isolation & purification , Surveys and Questionnaires
3.
Sensors (Basel) ; 21(16)2021 Aug 18.
Article in English | MEDLINE | ID: covidwho-1376960

ABSTRACT

Intelligent systems are transforming the world, as well as our healthcare system. We propose a deep learning-based cough sound classification model that can distinguish between children with healthy versus pathological coughs such as asthma, upper respiratory tract infection (URTI), and lower respiratory tract infection (LRTI). To train a deep neural network model, we collected a new dataset of cough sounds, labelled with a clinician's diagnosis. The chosen model is a bidirectional long-short-term memory network (BiLSTM) based on Mel-Frequency Cepstral Coefficients (MFCCs) features. The resulting trained model when trained for classifying two classes of coughs-healthy or pathology (in general or belonging to a specific respiratory pathology)-reaches accuracy exceeding 84% when classifying the cough to the label provided by the physicians' diagnosis. To classify the subject's respiratory pathology condition, results of multiple cough epochs per subject were combined. The resulting prediction accuracy exceeds 91% for all three respiratory pathologies. However, when the model is trained to classify and discriminate among four classes of coughs, overall accuracy dropped: one class of pathological coughs is often misclassified as the other. However, if one considers the healthy cough classified as healthy and pathological cough classified to have some kind of pathology, then the overall accuracy of the four-class model is above 84%. A longitudinal study of MFCC feature space when comparing pathological and recovered coughs collected from the same subjects revealed the fact that pathological coughs, irrespective of the underlying conditions, occupy the same feature space making it harder to differentiate only using MFCC features.


Subject(s)
Asthma , Cough , Asthma/diagnosis , Child , Cough/diagnosis , Humans , Longitudinal Studies , Neural Networks, Computer , Respiratory Sounds/diagnosis , Sound
4.
J Allergy Clin Immunol Pract ; 9(7): 2635-2637, 2021 07.
Article in English | MEDLINE | ID: covidwho-1300838
5.
PLoS One ; 16(7): e0254134, 2021.
Article in English | MEDLINE | ID: covidwho-1290687

ABSTRACT

A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm for breath phase detection and adventitious sound detection at the recording level has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchus labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests using long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.


Subject(s)
COVID-19/physiopathology , Lung/physiopathology , Respiratory Sounds/physiopathology , Adult , Aged , Aged, 80 and over , Benchmarking , COVID-19/diagnosis , Databases, Factual , Disease Progression , Female , Humans , Male , Middle Aged , Neural Networks, Computer , Respiration
6.
Ann Biomed Eng ; 49(9): 2481-2490, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1270519

ABSTRACT

This paper focuses on an important issue of disease progression of COVID-19 (coronavirus disease 2019) through processing COVID-19 cough sounds by proposing a fully-automated method. The new method is based on time-domain exploiting only phase 1 data which is always available for any cough events. The proposed approach generates plausible click sequences consist of clicks for various cough samples from covid-19 patients. The click sequence, which is extracted from the phase slope function of an input signal, is used to calculate inter-click intervals (ICIs), and thereby a scoring index (SI) is derived based on coefficient of variation(CV) of the extracted ICIs. Moreover, probability density function (pdf) of the output click sequence is obtained. The method does not need to adjust any parameters. The experimental results achieved from real-recorded COVID-19 cough data using the medically annotated Novel Coronavirus Cough Database (NoCoCoDa) reveal that the proposed time-domain method can be a very useful tool for automatic cough sound processing to determine the disease progression of coronavirus patients.


Subject(s)
Algorithms , COVID-19 , Cough/physiopathology , Databases, Factual , Respiratory Sounds , SARS-CoV-2 , Signal Processing, Computer-Assisted , COVID-19/diagnosis , COVID-19/physiopathology , Cough/virology , Female , Humans , Male
7.
Proc Natl Acad Sci U S A ; 118(19)2021 05 11.
Article in English | MEDLINE | ID: covidwho-1203480

ABSTRACT

Capabilities in continuous monitoring of key physiological parameters of disease have never been more important than in the context of the global COVID-19 pandemic. Soft, skin-mounted electronics that incorporate high-bandwidth, miniaturized motion sensors enable digital, wireless measurements of mechanoacoustic (MA) signatures of both core vital signs (heart rate, respiratory rate, and temperature) and underexplored biomarkers (coughing count) with high fidelity and immunity to ambient noises. This paper summarizes an effort that integrates such MA sensors with a cloud data infrastructure and a set of analytics approaches based on digital filtering and convolutional neural networks for monitoring of COVID-19 infections in sick and healthy individuals in the hospital and the home. Unique features are in quantitative measurements of coughing and other vocal events, as indicators of both disease and infectiousness. Systematic imaging studies demonstrate correlations between the time and intensity of coughing, speaking, and laughing and the total droplet production, as an approximate indicator of the probability for disease spread. The sensors, deployed on COVID-19 patients along with healthy controls in both inpatient and home settings, record coughing frequency and intensity continuously, along with a collection of other biometrics. The results indicate a decaying trend of coughing frequency and intensity through the course of disease recovery, but with wide variations across patient populations. The methodology creates opportunities to study patterns in biometrics across individuals and among different demographic groups.


Subject(s)
COVID-19/physiopathology , Heart Rate , Respiratory Rate , Respiratory Sounds , SARS-CoV-2 , Wireless Technology , Biomarkers , Humans , Monitoring, Physiologic
8.
J Biol Phys ; 47(2): 103-115, 2021 06.
Article in English | MEDLINE | ID: covidwho-1202797

ABSTRACT

The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation. The thirty-five sound signals of bronchial (BB) and pulmonary crackle (PC) analysed by fast Fourier transform and wavelet not only give the details of number, nature, and time of occurrence of the frequency components but also throw light onto the embedded air flow during breathing. Fractal dimension, phase portrait, and sample entropy help in divulging the greater randomness, antipersistent nature, and complexity of airflow dynamics in BB than PC. The potential of principal component analysis through the spectral feature extraction categorises BB, fine crackles, and coarse crackles. The phase portrait feature-based supervised classification proves to be better compared to the unsupervised machine learning technique. The present work elucidates phase portrait features as a better choice of classification, as it takes into consideration the temporal correlation between the data points of the time series signal, and thereby suggesting a novel surrogate method for the diagnosis in pulmonology. The study suggests the possible application of the techniques in the auscultation of coronavirus disease 2019 seriously affecting the respiratory system.


Subject(s)
Auscultation , Machine Learning , Respiratory Sounds/diagnosis , Signal Processing, Computer-Assisted , COVID-19/physiopathology , Fourier Analysis , Humans , Principal Component Analysis
9.
F1000Res ; 9: 1286, 2020.
Article in English | MEDLINE | ID: covidwho-1110755

ABSTRACT

Background: Available data suggest that case fatality rate of COVID-19 patients in Surabaya is higher than global cases. Thus, it is important to identify risk factors to prevent the mortality. This study aimed to assess the factors associated with hospital mortality of COVID-19 patients, and develop a prediction score based on these findings. Methods: We analyzed 111 patients, who were diagnosed with COVID-19 based on reverse-transcriptase polymerase chain reaction. The following patient characteristics were obtained from records: age, gender, type of symptoms, onset of symptoms, neutrophil lymphocyte ratio (NLR), absolute lymphocyte count, chest x-ray abnormalities, lung involvement, type of lesion, radiographic assessment of the quantity of lung edema (RALE) score, and mortality. Data were analyzed using SPSS 25.0. Results Multivariate analysis showed that age >50 years ( p=0.043), NLR score >5.8 ( p=0.016) and RALE score >2 ( p=0.002) can predict the mortality of COVID-19 patients in the hospital. ROC curve analysis of the score ability to predict mortality showed an area under the curve of 0.794. The cut-off point is 4.5, with a sensitivity of 96.7% and specificity of 49.4% to predict the mortality of COVID-19 patient in the hospital. Conclusions Age, NLR score and RALE score were associated with mortality of COVID-19 patients in the hospital and might be used as a predictor for mortality of COVID-19 patients in health care centre where radiologists are available. The prediction score may be useful for frontline physicians to effectively manage patients with a higher score to prevent mortality.


Subject(s)
Age Factors , COVID-19/mortality , Edema/diagnostic imaging , Hospital Mortality , Lymphocytes/cytology , Neutrophils/cytology , Adult , Aged , COVID-19/diagnosis , Female , Humans , Lung/diagnostic imaging , Lung/physiopathology , Male , Middle Aged , Respiratory Sounds , Retrospective Studies
10.
PLoS One ; 15(12): e0243735, 2020.
Article in English | MEDLINE | ID: covidwho-1067396

ABSTRACT

INTRODUCTION: Wheezing is a major problem in children, and respiratory viruses are often believed to be the causative agent. While molecular detection tools enable identification of respiratory viruses in wheezing children, it remains unclear if and how these viruses are associated with wheezing. The objective of this systematic review is to clarify the prevalence of different respiratory viruses in children with wheezing. METHODS: We performed an electronic in Pubmed and Global Index Medicus on 01 July 2019 and manual search. We performed search of studies that have detected common respiratory viruses in children ≤18 years with wheezing. We included only studies using polymerase chain reaction (PCR) assays. Study data were extracted and the quality of articles assessed. We conducted sensitivity, subgroup, publication bias, and heterogeneity analyses using a random effects model. RESULTS: The systematic review included 33 studies. Rhinovirus, with a prevalence of 35.6% (95% CI 24.6-47.3, I2 98.4%), and respiratory syncytial virus, at 31.0% (95% CI 19.9-43.3, I2 96.4%), were the most common viruses detected. The prevalence of other respiratory viruses was as follows: human bocavirus 8.1% (95% CI 5.3-11.3, I2 84.6%), human adenovirus 7.7% (95% CI 2.6-15.0, I2 91.0%), influenza virus6.5% (95% CI 2.2-12.6, I2 92.4%), human metapneumovirus5.8% (95% CI 3.4-8.8, I2 89.0%), enterovirus 4.3% (95% CI 0.1-12.9, I2 96.2%), human parainfluenza virus 3.8% (95% CI 1.5-6.9, I2 79.1%), and human coronavirus 2.2% (95% CI 0.6-4.4, I2 79.4%). CONCLUSIONS: Our results suggest that rhinovirus and respiratory syncytial virus may contribute to the etiology of wheezing in children. While the clinical implications of molecular detection of respiratory viruses remains an interesting question, this study helps to illuminate the potential of role respiratory viruses in pediatric wheezing. REVIEW REGISTRATION: PROSPERO, CRD42018115128.


Subject(s)
Respiratory Sounds/etiology , Respiratory Sounds/genetics , Respiratory Tract Infections/diagnosis , Bocavirus/genetics , Bocavirus/isolation & purification , Bocavirus/pathogenicity , Child , Child, Preschool , Coronavirus/isolation & purification , Coronavirus/pathogenicity , Humans , Orthomyxoviridae/genetics , Orthomyxoviridae/isolation & purification , Orthomyxoviridae/pathogenicity , Parainfluenza Virus 1, Human/genetics , Parainfluenza Virus 1, Human/isolation & purification , Parainfluenza Virus 1, Human/pathogenicity , Polymerase Chain Reaction , Respiratory Sounds/physiopathology , Respiratory System/pathology , Respiratory System/virology , Respiratory Tract Infections/genetics , Respiratory Tract Infections/virology
11.
J Acoust Soc Am ; 149(1): 66, 2021 01.
Article in English | MEDLINE | ID: covidwho-1035286

ABSTRACT

During the COVID-19 outbreak, the auscultation of heart and lung sounds has played an important role in the comprehensive diagnosis and real-time monitoring of confirmed cases. With clinicians wearing protective clothing in isolation wards, a potato chip tube stethoscope, which is a secure and flexible substitute for a conventional stethoscope, has been used by Chinese medical workers in the first-line treatment of COVID-19. In this study, an optimal design for this simple cylindrical stethoscope is proposed based on the fundamental theory of acoustic waveguides. Analyses of the cutoff frequency, sound power transmission coefficient, and sound wave propagation in the uniform lossless tube provide theoretical guidance for selecting the geometric parameters for this simple cylindrical stethoscope. A basic investigation into the auscultatory performances of the original tube and the optimal tube with proposed dimensions was conducted both in a semi-anechoic chamber and in a quiet laboratory. Both experimental results and front-line doctors' clinical feedback endorse the proposed theoretical optimization.


Subject(s)
Acoustics , Auscultation/standards , COVID-19/diagnosis , Equipment Design/standards , Stethoscopes/standards , Acoustics/instrumentation , Auscultation/instrumentation , Auscultation/methods , COVID-19/epidemiology , COVID-19/physiopathology , Equipment Design/instrumentation , Equipment Design/methods , Humans , Respiratory Sounds/physiology , Respiratory Sounds/physiopathology
12.
J Acoust Soc Am ; 148(6): 3385, 2020 12.
Article in English | MEDLINE | ID: covidwho-991716

ABSTRACT

Forced expiratory (FE) noise is a powerful bioacoustic signal containing information on human lung biomechanics. FE noise is attributed to a broadband part and narrowband components-forced expiratory wheezes (FEWs). FE respiratory noise is composed by acoustic and hydrodynamic mechanisms. An origin of the most powerful mid-frequency FEWs (400-600 Hz) is associated with the 0th-3rd levels of bronchial tree in terms of Weibel [(2009). Swiss Med. Wkly. 139(27-28), 375-386], whereas high-frequency FEWs (above 600 Hz) are attributed to the 2nd-6th levels of bronchial tree. The laboratory prototype of the apparatus is developed, which includes the electret microphone sensor with stethoscope head, a laptop with external sound card, and specially developed software. An analysis of signals by the new method, including FE time in the range from 200 to 2000 Hz and band-pass durations and energies in the 200-Hz bands evaluation, is applied instead of FEWs direct measures. It is demonstrated experimentally that developed FE acoustic parameters correspond to basic indices of lung function evaluated by spirometry and body plethysmography and may be even more sensitive to some respiratory deviations. According to preliminary experimental results, the developed technique may be considered as a promising instrument for acoustic monitoring human lung function in extreme conditions, including diving and space flights. The developed technique eliminates the contact of the sensor with the human oral cavity, which is characteristic for spirometry and body plethysmography. It reduces the risk of respiratory cross-contamination, especially during outpatient and field examinations, and may be especially relevant in the context of the COVID-19 pandemic.


Subject(s)
Acoustics/instrumentation , COVID-19 , Exhalation/physiology , Respiratory Sounds/diagnosis , Humans , Noise , SARS-CoV-2
14.
BMJ Case Rep ; 13(12)2020 Dec 13.
Article in English | MEDLINE | ID: covidwho-975661

ABSTRACT

A 59-year-old man presented to the emergency department with recent onset biphasic stridor, dyspnoea and increased work of breathing on the background of prolonged intubation for the novel COVID-19 2 months previously. Flexible laryngoscopy revealed bilateral vocal fold immobility with a soft tissue mass in the interarytenoid region. The patient's symptoms improved with oxygen therapy, nebulised epinephrine (5 mL; 1:10 000) and intravenous dexamethasone (3.3 mg). The following morning, the patient was taken to theatre, underwent suspension microlaryngoscopy and found to have bilateral fixation of the cricoarytenoid joints and a large granuloma in the interarytenoid area. He underwent cold steel resection of the granuloma and balloon dilatation between the arytenoids, with the hope of mobilising the joints. This failed and CO2 laser arytenoidectomy was performed on the left side. The stridor had resolved postoperatively, with normalisation of work of breathing and the patient was discharged home on the first postoperative day.


Subject(s)
COVID-19/therapy , Granuloma/surgery , Intubation, Intratracheal/adverse effects , Laryngeal Diseases/surgery , Constriction, Pathologic/etiology , Constriction, Pathologic/surgery , Dyspnea/etiology , Emergencies , Granuloma/etiology , Humans , Laryngeal Diseases/etiology , Larynx/pathology , Male , Middle Aged , Respiratory Sounds , SARS-CoV-2 , Work of Breathing
15.
Respiration ; 99(9): 755-763, 2020.
Article in English | MEDLINE | ID: covidwho-910309

ABSTRACT

BACKGROUND: Effective auscultations are often hard to implement in isolation wards. To date, little is known about the characteristics of pulmonary auscultation in novel coronavirus (COVID-19) pneumonia. OBJECTIVES: The aim of this study was to explore the features and clinical significance of pulmonary auscultation in COVID-19 pneumonia using an electronic stethoscope in isolation wards. METHODS: This cross-sectional, observational study was conducted among patients with laboratory-confirmed COVID-19 at Wuhan Red-Cross Hospital during the period from January 27, 2020, to February 12, 2020. Standard auscultation with an electronic stethoscope was performed and electronic recordings of breath sounds were analyzed. RESULTS: Fifty-seven patients with average age of 60.6 years were enrolled. The most common symptoms were cough (73.7%) during auscultation. Most cases had bilateral lesions (96.4%) such as multiple ground-glass opacities (69.1%) and fibrous stripes (21.8%). High-quality auscultation recordings (98.8%) were obtained, and coarse breath sounds, wheezes, coarse crackles, fine crackles, and Velcro crackles were identified. Most cases had normal breath sounds in upper lungs, but the proportions of abnormal breath sounds increased in the basal fields where Velcro crackles were more commonly identified at the posterior chest. The presence of fine and coarse crackles detected 33/39 patients with ground-glass opacities (sensitivity 84.6% and specificity 12.5%) and 8/9 patients with consolidation (sensitivity 88.9% and specificity 15.2%), while the presence of Velcro crackles identified 16/39 patients with ground-glass opacities (sensitivity 41% and specificity 81.3%). CONCLUSIONS: The abnormal breath sounds in COVID-19 pneumonia had some consistent distributive characteristics and to some extent correlated with the radiologic features. Such evidence suggests that electronic auscultation is useful to aid diagnosis and timely management of the disease. Further studies are indicated to validate the accuracy and potential clinical benefit of auscultation in detecting pulmonary abnormalities in COVID-19 infection.


Subject(s)
Auscultation , COVID-19/physiopathology , Lung/physiopathology , Respiratory Sounds/physiopathology , Adult , Aged , Aged, 80 and over , Anti-Bacterial Agents/therapeutic use , Antiviral Agents/therapeutic use , COVID-19/diagnosis , COVID-19/diagnostic imaging , COVID-19/drug therapy , COVID-19/therapy , China , Cough/physiopathology , Cross-Sectional Studies , Electrical Equipment and Supplies , Female , Glucocorticoids/therapeutic use , Humans , Lung/diagnostic imaging , Male , Middle Aged , Oxygen Inhalation Therapy , Respiration, Artificial , SARS-CoV-2 , Sensitivity and Specificity , Severity of Illness Index , Smartphone , Sound Spectrography , Sputum , Stethoscopes , Tomography, X-Ray Computed , Young Adult
16.
Intern Med ; 59(24): 3213-3216, 2020 Dec 15.
Article in English | MEDLINE | ID: covidwho-902224

ABSTRACT

A 60-year-old woman was admitted to our hospital due to coronavirus disease 2019 (COVID-19) pneumonia with a chief complaint of persistent low-grade fever and dry cough for two weeks. Thoracic computed tomography demonstrated a crazy paving pattern in the bilateral lower lobes. In a COVID-19 ward, we used a novel wireless stethoscope with a telemedicine system and successfully recorded and shared the lung sounds in real-time between the red and green zones. The fine crackles at the posterior right lower lung fields changed from mid-to-late (day 1) to late inspiratory crackles (day 3), which disappeared at day 5 along with an improvement in both the clinical symptoms and thoracic CT findings.


Subject(s)
Auscultation/instrumentation , COVID-19/diagnosis , Respiratory Sounds/diagnosis , SARS-CoV-2 , Stethoscopes , Telemedicine/methods , COVID-19/epidemiology , Equipment Design , Female , Humans , Middle Aged , Tomography, X-Ray Computed/methods
17.
Phys Eng Sci Med ; 43(4): 1339-1347, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-871576

ABSTRACT

Since the outbreak of the pandemic Coronavirus Disease 2019, the world is in search of novel non-invasive methods for safer and early detection of lung diseases. The pulmonary pathological symptoms reflected through the lung sound opens a possibility of detection through auscultation and of employing spectral, fractal, nonlinear time series and principal component analyses. Thirty-five signals of vesicular and expiratory wheezing breath sound, subjected to spectral analyses shows a clear distinction in terms of time duration, intensity, and the number of frequency components. An investigation of the dynamics of air molecules during respiration using phase portrait, Lyapunov exponent, sample entropy, fractal dimension, and Hurst exponent helps in understanding the degree of complexity arising due to the presence of mucus secretions and constrictions in the respiratory airways. The feature extraction of the power spectral density data and the application of principal component analysis helps in distinguishing vesicular and expiratory wheezing and thereby, giving a ray of hope in accomplishing an early detection of pulmonary diseases through sound signal analysis.


Subject(s)
Fractals , Respiratory Sounds/physiopathology , Humans , Principal Component Analysis , Respiration , Signal Processing, Computer-Assisted , Time Factors , Wavelet Analysis
20.
J Allergy Clin Immunol Pract ; 8(2): 588-595.e4, 2020 02.
Article in English | MEDLINE | ID: covidwho-822716

ABSTRACT

BACKGROUND: Respiratory syncytial virus (RSV)- and rhinovirus (RV)-induced bronchiolitis are associated with an increased risk of asthma, but more detailed information is needed on virus types. OBJECTIVE: To study whether RSV or RV types are differentially associated with the future use of asthma control medication. METHODS: Over 2 consecutive winter seasons (2008-2010), we enrolled 408 children hospitalized for bronchiolitis at age less than 24 months into a prospective, 3-center, 4-year follow-up study in Finland. Virus detection was performed by real-time reverse transcription PCR from nasal wash samples. Four years later, we examined current use of asthma control medication. RESULTS: A total of 349 (86%) children completed the 4-year follow-up. At study entry, the median age was 7.5 months, and 42% had RSV, 29% RV, 2% both RSV and RV, and 27% non-RSV/-RV etiology. The children with RV-A (adjusted hazard ratio, 2.3; P = .01), RV-C (adjusted hazard ratio, 3.5; P < .001), and non-RSV/-RV (adjusted hazard ratio, 2.0; P = .004) bronchiolitis started the asthma control medication earlier than did children with RSV bronchiolitis. Four years later, 27% of patients used asthma control medication; both RV-A (adjusted odds ratio, 3.0; P = .03) and RV-C (adjusted odds ratio, 3.7; P < .001) etiology were associated with the current use of asthma medication. The highest risk was found among patients with RV-C, atopic dermatitis, and fever (adjusted odds ratio, 5.0; P = .03). CONCLUSIONS: Severe bronchiolitis caused by RV-A and RV-C was associated with earlier initiation and prolonged use of asthma control medication. The risk was especially high when bronchiolitis was associated with RV-C, atopic dermatitis, and fever.


Subject(s)
Asthma , Bronchiolitis , Influenza A Virus, H1N1 Subtype , Picornaviridae Infections , Rhinovirus , Asthma/drug therapy , Asthma/epidemiology , Asthma/virology , Bronchiolitis/drug therapy , Bronchiolitis/epidemiology , Child , Child, Preschool , Finland/epidemiology , Follow-Up Studies , Humans , Infant , Male , Picornaviridae Infections/complications , Prospective Studies , Respiratory Sounds , Rhinovirus/classification , Rhinovirus/pathogenicity
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