Early prediction of pulmonary outcomes in preterm infants using electrical impedance tomography

Chest Radiography Scores for Predicting the Severity of Respiratory Status in Newborns Weighing More Than 1,500 g

Background Acute respiratory failure (ARF) may occur in neonates. Chest radiography is commonly used to evaluate the severity of ARF; however, the application of quantitative scales in clinical practice in neonatal intensive care units is uncommon. This study aimed to assess the usefulness of two semi-quantitative radiographical scales, the Brixia and radiographic assessment of lung edema (RALE) scores, in newborns weighing more than 1,500 g. Methods Newborns weighing > 1,500 g who received invasive respiratory support with arterial lines between January 2010 and October 2023 were enrolled in this study (n = 98; gestational age, 35.6 ± 3.1 weeks; birthweight, 2,321 ± 600 g). We investigated the correlation between the Brixia or RALE scores and the oxygen index (OI), alveolar-arterial oxygen gradient (A-aDO2), and ventilation index (VI). Furthermore, the cut-off points of the two radiographic scores for the prediction of these respiratory indices were determined. Results All respiratory indices correlated with the Brixia (OI: r = 0.71, p < 0.001; A-aDO2: r = 0.74, p < 0.001; VI: r = 0.56, p < 0.001) and RALE scores (OI: r = 0.78, p < 0.001; A-aDO2: r = 0.82, p < 0.001; VI: r = 0.60, p < 0.001). Additionally, the receiver operating characteristic curve showed that the radiographical scores had a statistically significant ability to predict respiratory index values. Conclusion Brixia and RALE scores are useful predictive markers of acute respiratory failure in infants weighing >1,500 g. These chest radiography scores may be good predictors of respiratory status and have wider clinical applications in neonatal care.

Regional Differences in Lung Ventilation During the Early Transition Period in Late Preterm and Term Neonates Assessed by Electrical Impedance Tomography

BACKGROUND

Changes in lung ventilation are well documented in term neonates while in late preterm neonates these patterns are poorly understood despite their increased risk of respiratory morbidity.

OBJECTIVES

The study aimed to compare and clarify the differences in regional lung ventilation of late preterm and term neonates during the early adaptation period using electrical impedance tomography (EIT).

MATERIAL AND METHODS

The case-control study was conducted in the years 2020-2022. It included 51 late preterm neonates (LPN, Study group) and 45 term neonates (TN, Control) born by normal vaginal delivery (NVD). EIT examinations were performed with a Swisstom BB2 (Switzerland) equipment. The data recordings were performed no later than 30 (I Record), 60 (II), and 90 (III) minutes after the birth.

RESULTS

Statistically significant differences between LPN and TN were observed in the non-dependent lung areas at I record, with more silent spaces observed in the LPN (p < 0.001). Differences in the dependent lung regions were observed across all recordings, with LPN demonstrating more silent spaces (p < 0.001). LPN demonstrated greater stretch-related changes in the 10% and 20% stretch categories across all recordings, while TN showed greater changes in the 50%, 70%, and 90% categories. Tidal volumes in the right lung of TN are distributed more towards the ventral and central ventral regions. In contrast, tidal volumes of LPN are distributed to the central dorsal and dorsal regions of the right lung.

CONCLUSIONS

LPN during the first 90 min after the birth show reduced lung ventilation assessed by EIT, suggesting a possible impairment of early postnatal adaptation.

Advances in Pediatric Lung Function Testing Techniques

For decades spirometry has been the benchmark test for capturing lung function in children but its recognized limitations required the development of other techniques. This article introduces novel techniques in lung function assessment for pediatric patients, including multiple breath washout, impulse oscillometry, structured light plethysmography, and electrical impedance tomography, and common themes in interpreting the results. Challenges include standardization, reference data, and clinical integration of these innovative tools. Further research is ongoing to optimize these tests for clinical use, especially in diverse populations and pediatric settings.

Bronchopulmonary Dysplasia

Bronchopulmonary dysplasia (BPD) is a chronic lung disease, associated with premature birth, that arises during the infantile period. It is an evolving disease process with an unchanged incidence due to advancements in neonatal care which allow for the survival of premature infants of lower gestational ages and birth weights. Currently, there are few effective interventions to prevent BPD. However, careful attention to BPD phenotypes and comprehensive care provided by an interdisciplinary team have improved care. Interventions early in the disease course hold promise for improving long-term survival and outcomes in adulthood for this high-risk population.

The Usefulness of Continuous Respiratory Sound Monitoring for the Detection of Pulmonary Atelectasis in a Ventilated Extremely Low Birth Weight Infant

The assessment of auscultation using a stethoscope is unsuitable for continuous monitoring. Therefore, we developed a novel acoustic monitoring system that continuously, objectively, and visually evaluates respiratory sounds. In this report, we assess the usefulness of our revised system in a ventilated extremely low birth weight infant (ELBWI) for the diagnosis of pulmonary atelectasis and evaluation of treatment by lung lavage. A female infant was born at 24 weeks of age with a birth weight of 636 g after emergency cesarean section. The patient received invasive mechanical ventilation immediately after birth in our neonatal (NICU). After obtaining informed consent, we monitored her respiratory status using the respiratory-sound monitoring system by attaching a sound collection sensor to the right anterior chest wall. On day 26, lung-sound spectrograms showed that the breath sounds were attenuated simultaneously as hypoxemia progressed. Finally, chest radiography confirmed the diagnosis as pulmonary atelectasis. To relieve atelectasis, surfactant lavage was performed, after which the lung-sound spectrograms returned to normal. Hypoxemia and chest radiographic findings improved significantly. On day 138, the patient was discharged from the NICU without complications. The continuous respiratory-sound monitoring system enabled the visual, quantitative, and noninvasive detection of acute regional lung abnormalities at the bedside. We, therefore, believe that this system can resolve several problems associated with neonatal respiratory management and save lives.

Pediatric pulmonology year in review 2023: Physiology

Application of the principles of pulmonary physiology and lung development to the care and management of respiratory disease in children is a distinguishing feature of pediatric pulmonology. In 2023, this was evident in numerous publications in Pediatric Pulmonology and other journals. This review will highlight some of the papers in this area.

Identification of potential biomarkers in the peripheral blood of neonates with bronchopulmonary dysplasia using WGCNA and machine learning algorithms

Bronchopulmonary dysplasia (BPD) is often seen as a pulmonary complication of extreme preterm birth, resulting in persistent respiratory symptoms and diminished lung function. Unfortunately, current diagnostic and treatment options for this condition are insufficient. Hence, this study aimed to identify potential biomarkers in the peripheral blood of neonates affected by BPD. The Gene Expression Omnibus provided the expression dataset GSE32472 for BPD. Initially, using this database, we identified differentially expressed genes (DEGs) in GSE32472. Subsequently, we conducted gene set enrichment analysis on the DEGs and employed weighted gene co-expression network analysis (WGCNA) to screen the most relevant modules for BPD. We then mapped the DEGs to the WGCNA module genes, resulting in a gene intersection. We conducted detailed functional enrichment analyses on these overlapping genes. To identify hub genes, we used 3 machine learning algorithms, including SVM-RFE, LASSO, and Random Forest. We constructed a diagnostic nomogram model for predicting BPD based on the hub genes. Additionally, we carried out transcription factor analysis to predict the regulatory mechanisms and identify drugs associated with these biomarkers. We used differential analysis to obtain 470 DEGs and conducted WGCNA analysis to identify 1351 significant genes. The intersection of these 2 approaches yielded 273 common genes. Using machine learning algorithms, we identified CYYR1, GALNT14, and OLAH as potential biomarkers for BPD. Moreover, we predicted flunisolide, budesonide, and beclomethasone as potential anti-BPD drugs. The genes CYYR1, GALNT14, and OLAH have the potential to serve as diagnostic biomarkers for BPD. This may prove beneficial in clinical diagnosis and prevention of BPD.