Techniques to evaluate surfactant activity for a personalized therapy of RDS neonates

Gastric Fluid Metabolomics Predicting the Need for Surfactant Replacement Therapy in Very Preterm Infants Results of a Case-Control Study

Respiratory distress syndrome (RDS) is a major morbidity of prematurity. In this case-control study, we prospectively evaluated whether untargeted metabolomic analysis (gas chromatography-mass spectrometry) of the gastric fluid could predict the need for surfactant in very preterm neonates. 43 infants with RDS necessitating surfactant (cases) were compared with 30 infants who were not treated with surfactant (controls). Perinatal-neonatal characteristics were recorded. Significant differences in gastric fluid metabolites (L-proline, L-glycine, L-threonine, acetyl-L-serine) were observed between groups, but none could solely predict surfactant administration with high accuracy. Univariate analysis revealed significant predictors of surfactant administration involving gastric fluid metabolites (L-glycine, acetyl-L-serine) and clinical parameters (gestational age, Apgar scores, intubation in the delivery room). Multivariable models were constructed for significant clinical variables as well as for the combination of clinical variables and gastric fluid metabolites. The AUC value of the first model was 0.69 (95% CI 0.57-0.81) and of the second, 0.76 (95% CI 0.64-0.86), in which acetyl-L-serine and intubation in the delivery room were found to be significant predictors of surfactant therapy. This investigation adds to the current knowledge of biomarkers in preterm neonates with RDS, but further research is required to assess the predictive value of gastric fluid metabolomics in this field.

Surfactant status assessment and personalized therapy for surfactant deficiency or dysfunction

Surfactant is a pivotal neonatal drug used both for respiratory distress syndrome due to surfactant deficiency and for more complex surfactant dysfunctions (such as in case of neonatal acute respiratory distress syndrome). Despite its importance, indications for surfactant therapy are often based on oversimplified criteria. Lung biology and modern monitoring provide several diagnostic tools to assess the patient surfactant status and they can be used for a personalized surfactant therapy. This is desirable to improve the efficacy of surfactant treatment and reduce associated costs and side effects. In this review we will discuss these diagnostic tools from a pathophysiological and multi-disciplinary perspective, focusing on the quantitative or qualitative surfactant assays, lung mechanics or aeration measurements, and gas exchange metrics. Their biological and technical characteristics are described with practical information for clinicians. Finally, available evidence-based data are reviewed, and the diagnostic accuracy of the different tools is compared. Lung ultrasound seems the most suitable tool for assessing the surfactant status, while some other promising tests require further research and/or development.

Artificial Intelligence-Driven Respiratory Distress Syndrome Prediction for Very Low Birth Weight Infants: Korean Multicenter Prospective Cohort Study

BACKGROUND

Respiratory distress syndrome (RDS) is a disease that commonly affects premature infants whose lungs are not fully developed. RDS results from a lack of surfactant in the lungs. The more premature the infant is, the greater is the likelihood of having RDS. However, even though not all premature infants have RDS, preemptive treatment with artificial pulmonary surfactant is administered in most cases.

OBJECTIVE

We aimed to develop an artificial intelligence model to predict RDS in premature infants to avoid unnecessary treatment.

METHODS

In this study, 13,087 very low birth weight infants who were newborns weighing less than 1500 grams were assessed in 76 hospitals of the Korean Neonatal Network. To predict RDS in very low birth weight infants, we used basic infant information, maternity history, pregnancy/birth process, family history, resuscitation procedure, and test results at birth such as blood gas analysis and Apgar score. The prediction performances of 7 different machine learning models were compared, and a 5-layer deep neural network was proposed in order to enhance the prediction performance from the selected features. An ensemble approach combining multiple models from the 5-fold cross-validation was subsequently developed.

RESULTS

Our proposed ensemble 5-layer deep neural network consisting of the top 20 features provided high sensitivity (83.03%), specificity (87.50%), accuracy (84.07%), balanced accuracy (85.26%), and area under the curve (0.9187). Based on the model that we developed, a public web application that enables easy access for the prediction of RDS in premature infants was deployed.

CONCLUSIONS

Our artificial intelligence model may be useful for preparations for neonatal resuscitation, particularly in cases involving the delivery of very low birth weight infants, as it can aid in predicting the likelihood of RDS and inform decisions regarding the administration of surfactant.

Bile acids-induced lung injury: update of reverse translational biology

The presence of bile acids in lung tissue is associated with some clinical features observed in various medical specialties, but it took time to understand that these are due to a "bile acid-induced lung injury" since specific translational studies and cross-disciplinary awareness were lacking. We used a reverse translational approach to update and summarize the current knowledge about the mechanisms of bile acid-induced lung injury. This has been done in a cross-disciplinary fashion since these conditions may occur in patients of various age and in different medical fields. We here define these clinical conditions, then we review the physiopathology of these conditions and the animal models used to mimic them and, finally, their pathobiology. Mechanisms of bile acid-induced lung injury have been partially clarified overtime and are represented by: 1) the interaction with secretory phospholipase A2 pathway, 2) the effect on surfactant function and structure, 3) the biological effects on inflammation and local immunity, 4) the direct cellular toxicity. These mechanisms are schematically illustrated and histological comparisons between ARDS induced by bile acids and other triggers are also provided. Based on these mechanisms we propose possible direct therapeutic applications and, finally, we discuss further research steps to improve the understanding of processes that generate pathological clinical conditions.