Tools to assess lung aeration in neonates with respiratory distress syndrome

Review on the Advancements of Stethoscope Types in Chest Auscultation

Stethoscopes were originally designed for the auscultation of a patient's chest for the purpose of listening to lung and heart sounds. These aid medical professionals in their evaluation of the cardiovascular and respiratory systems, as well as in other applications, such as listening to bowel sounds in the gastrointestinal system or assessing for vascular bruits. Listening to internal sounds during chest auscultation aids healthcare professionals in their diagnosis of a patient's illness. We performed an extensive literature review on the currently available stethoscopes specifically for use in chest auscultation. By understanding the specificities of the different stethoscopes available, healthcare professionals can capitalize on their beneficial features, to serve both clinical and educational purposes. Additionally, the ongoing COVID-19 pandemic has also highlighted the unique application of digital stethoscopes for telemedicine. Thus, the advantages and limitations of digital stethoscopes are reviewed. Lastly, to determine the best available stethoscopes in the healthcare industry, this literature review explored various benchmarking methods that can be used to identify areas of improvement for existing stethoscopes, as well as to serve as a standard for the general comparison of stethoscope quality. The potential use of digital stethoscopes for telemedicine amidst ongoing technological advancements in wearable sensors and modern communication facilities such as 5G are also discussed. Based on the ongoing trend in advancements in wearable technology, telemedicine, and smart hospitals, understanding the benefits and limitations of the digital stethoscope is an essential consideration for potential equipment deployment, especially during the height of the current COVID-19 pandemic and, more importantly, for future healthcare crises when human and resource mobility is restricted.

Predicting the need for intubation within 3 h in the neonatal intensive care unit using a multimodal deep neural network

Respiratory distress is a common chief complaint in neonates admitted to the neonatal intensive care unit. Despite the increasing use of non-invasive ventilation in neonates with respiratory difficulty, some of them require advanced airway support. Delayed intubation is associated with increased morbidity, particularly in urgent unplanned cases. Early and accurate prediction of the need for intubation may provide more time for preparation and increase safety margins by avoiding the late intubation at high-risk infants. This study aimed to predict the need for intubation within 3 h in neonates initially managed with non-invasive ventilation for respiratory distress during the first 48 h of life using a multimodal deep neural network. We developed a multimodal deep neural network model to simultaneously analyze four time-series data collected at 1-h intervals and 19 variables including demographic, physiological and laboratory parameters. Evaluating the dataset of 128 neonates with respiratory distress who underwent non-invasive ventilation, our model achieved an area under the curve of 0.917, sensitivity of 85.2%, and specificity of 89.2%. These findings demonstrate promising results for the multimodal model in predicting neonatal intubation within 3 h.

The newborn delivery room of tomorrow: emerging and future technologies

Advances in neonatal care have resulted in improved outcomes for high-risk newborns with technologies playing a significant part although many were developed for the neonatal intensive care unit. The care provided in the delivery room (DR) during the first few minutes of life can impact short- and long-term neonatal outcomes. Increasingly, technologies have a critical role to play in the DR particularly with monitoring and information provision. However, the DR is a unique environment and has major challenges around the period of foetal to neonatal transition that need to be overcome when developing new technologies. This review focuses on current DR technologies as well as those just emerging and further over the horizon. We identify what key opinion leaders in DR care think of current technologies, what the important DR measures are to them, and which technologies might be useful in the future. We link these with key technologies including respiratory function monitors, electoral impedance tomography, videolaryngoscopy, augmented reality, video recording, eye tracking, artificial intelligence, and contactless monitoring. Encouraging funders and industry to address the unique technological challenges of newborn care in the DR will allow the continued improvement of outcomes of high-risk infants from the moment of birth. IMPACT: Technological advances for newborn delivery room care require consideration of the unique environment, the variable patient characteristics, and disease states, as well as human factor challenges. Neonatology as a speciality has embraced technology, allowing its rapid progression and improved outcomes for infants, although innovation in the delivery room often lags behind that in the intensive care unit. Investing in new and emerging technologies can support healthcare providers when optimising care and could improve training, safety, and neonatal outcomes.

The Associations Between Lung Ultrasonography Scores in the First Day of Life and Clinical Outcomes

OBJECTIVES

To investigate the role of lung ultrasonography (LU) in predicting noninvasive ventilation (NIV) failure and the relationship between lung ultrasonography scores (LUS) with clinical outcomes in neonatal respiratory failure (NRF).

METHODS

A prospective, cross-sectional study was conducted in newborns with NRF who needed NIV and were evaluated by LU. The first LUS (LUS1) was calculated at 2-6 hours and the second (LUS2) at 12-24 hours of life. The patients were divided into NIV failure and NIV non-failure groups. The relationship between LUS and clinical outcomes was evaluated.

RESULTS

Among 157 neonates, the median (interquartile range) of gestational week and birth weight were 37 weeks (34-39), and 2890 grams (2045-3435), respectively. The reasons for NRF were transient tachypnea of the newborn (n = 92, 58.6%), congenital pneumonia (n = 58, 36.9%), and respiratory distress syndrome (n = 7, 4.5%). The rate of NIV failure was 17.8% (n = 28). Both LUS1 and LUS2 were significantly higher in neonates with NIV failure compared to neonates with NIV non-failure (P = .001). A cutoff value of LUS1 ≥ 4 predicted NIV failure with 96% sensitivity and 63% specificity. There were positive correlations between LUS and PEEP values, IMV and total MV days, carbon dioxide values, length of hospital stay, and antibiotic days (ρ_LUS1 , P = .843, <.001; .474, <.001; .444, <.001; .258, .001; .212, .008; .270, <.001, respectively).

CONCLUSIONS

Lung ultrasound scores were higher in neonates with NIV failure than with NIV non-failure group, and strongly correlated with end-expiratory pressure values. Lung ultrasound scores were found to be related with some of the clinical outcomes of the NRF, and this suggested that LUS could provide information about the prognosis of NRF.

The Role of Lung Ultrasound as an Early Diagnostic Tool for Need of Surfactant Therapy in Preterm Infants with Respiratory Distress Syndrome

OBJECTIVE

This study aimed to determine the accuracy of neonatal lung ultrasound (LUS) in predicting the need for surfactant therapy compared with chest X-ray (CXR) in preterm infants.

STUDY DESIGN

A prospective double-blind study was conducted in infants with a gestational age <34 weeks with respiratory distress syndrome (RDS) by evaluation with LUS and CXR on admission.

RESULTS

Among 45 preterm infants, the median (interquartile range [IQR]) LUS score was 4 (2-8) in the mild RDS group, whereas it was 10 (IQR: 9-12) in the severe RDS group (p < 0.01). The LUS score showed a significant correlation with the need for total surfactant doses (ρ = 0.855; 95% confidence interval [CI]: 0.801-0.902; p < 0.001). A cut-off LUS score of four predicted the need for surfactant with 96% sensitivity and 100% specificity (area under the curve [AUC]: 1.00; 95% CI: 0.97-1.00; p < 0.01). LUS scores predicted continuous positive airway pressure (CPAP) failure accurately (AUC: 0.804; 95% CI: 0.673-0.935; p = 0.001). A significant correlation was observed between LUS scores and positive end-expiratory pressure levels (ρ = 0.782; p < 0.001). During the study period, the CXR number per infant with RDS decreased significantly when compared with preceding months (p < 0.001). The LUS score in the first day of life did not predict the development of bronchopulmonary dysplasia (AUC: 0.274; 95% CI: 0.053-0.495; p = 0.065).

CONCLUSION

The LUS score in preterm infants accurately predicts the severity of RDS, the need for surfactant and CPAP failure. The routine use of LUS can decrease the frequency of CXRs in the neonatal intensive care units.

KEY POINTS

· LUS is a nonhazardous bedside technique.. · LUS predicts the need for surfactant in preterm infants.. · LUS predicts the severity of RDS better than CXR..

A New Non-Negative Matrix Co-Factorisation Approach for Noisy Neonatal Chest Sound Separation

Obtaining high quality heart and lung sounds enables clinicians to accurately assess a newborns cardio-respiratory health and provide timely care. However, noisy chest sound recordings are common, hindering timely and accurate assessment. A new Non-negative Matrix Co-Factorisation based approach is proposed to separate noisy chest sound recordings into heart, lung and noise components to address this problem. This method is achieved through training with 20 high quality heart and lung sounds, in parallel with separating the sounds of the noisy recording. The method was tested on 68 10-second noisy recordings containing both heart and lung sounds and compared to the current state of the art Non-negative Matrix Factorisation methods. Results show significant improvements in heart and lung sound quality scores respectively, and improved accuracy of 3.6bpm and 1.2bpm in heart and breathing rate estimation respectively, when compared to existing methods.

The Research Progress of Electrical Impedance Tomography for Lung Monitoring

Medical imaging can intuitively show people the internal structure, morphological information, and organ functions of the organism, which is one of the most important inspection methods in clinical medical diagnosis. Currently used medical imaging methods can only be applied to some diagnostic occasions after qualitative lesions have been generated, and the general imaging technology is usually accompanied by radiation and other conditions. However, electrical impedance tomography has the advantages of being noninvasive and non-radiative. EIT (Electrical Impedance Tomography) is also widely used in the early diagnosis and treatment of some diseases because of these advantages. At present, EIT is relatively mature and more and more image reconstruction algorithms are used to improve imaging resolution. Hardware technology is also developing rapidly, and the accuracy of data collection and processing is continuously improving. In terms of clinical application, EIT has also been used for pathological treatment of lungs, the brain, and the bladder. In the future, EIT has a good application prospect in the medical field, which can meet the needs of real-time, long-term monitoring and early diagnosis. Aiming at the application of EIT in the treatment of lung pathology, this article reviews the research progress of EIT, image reconstruction algorithms, hardware system design, and clinical applications used in the treatment of lung diseases. Through the research and introduction of several core components of EIT technology, it clarifies the characteristics of EIT system complexity and its solutions, provides research ideas for subsequent research, and once again verifies the broad development prospects of EIT technology in the future.

Usefulness of Lung Ultrasound in Paediatric Respiratory Diseases

Respiratory infection diseases are among the major causes of morbidity and mortality in children. Diagnosis is focused on clinical presentation, yet signs and symptoms are not specific and there is a need for new non-radiating diagnostic tools. Among these, lung ultrasound (LUS) has recently been included in point-of-care protocols showing interesting results. In comparison to other imaging techniques, such as chest X-ray and computed tomography, ultrasonography does not use ionizing radiations. Therefore, it is particularly suitable for clinical follow-up of paediatric patients. LUS requires only 5-10 min and allows physicians to make quick decisions about the patient's management. Nowadays, LUS has become an early diagnostic tool to detect pneumonia during the COVID-19 pandemic. In this narrative review, we show the most recent scientific literature about advantages and limits of LUS performance in children. Furthermore, we discuss the major paediatric indications separately, with a paragraph fully dedicated to COVID-19. Finally, we mention potential future perspectives about LUS application in paediatric respiratory diseases.