Risk prediction of bronchopulmonary dysplasia in preterm infants by the nomogram model

Improving early diagnosis of bronchopulmonary dysplasia

INTRODUCTION

Bronchopulmonary disease (BPD) is associated with long-term neurodevelopmental and cardiorespiratory complications, often requiring significant use of resources. To reduce this healthcare burden, it is essential that those at high risk of BPD are identified early so that strategies are introduced to prevent disease progression. Our aim was to discuss potential methods for improving early diagnosis in the first week after birth.

AREAS COVERED

A narrative review was undertaken. The search strategy involved searching PubMed, Embase and Cochrane databases from 1967 to 2024. The results of potential biomarkers and imaging modes are discussed. Furthermore, the value of scoring systems is explored.

EXPERT OPINION

BPD occurs as a result of disruption to pulmonary vascular and alveolar development, thus abnormal levels of factors regulating those processes are promising avenues to explore with regard to early detection of high-risk infants. Data from twin studies suggests genetic factors can be attributed to 82% of the observed difference in moderate to severe BPD, but large genome-wide studies have yielded conflicting results. Comparative studies are required to determine which biomarker or imaging mode may most accurately diagnose early BPD development. Models which include the most predictive factors should be evaluated going forward.

A clinical informatics approach to bronchopulmonary dysplasia: current barriers and future possibilities

Bronchopulmonary dysplasia (BPD) is a complex, multifactorial lung disease affecting preterm neonates that can result in long-term pulmonary and non-pulmonary complications. Current therapies mainly focus on symptom management after the development of BPD, indicating a need for innovative approaches to predict and identify neonates who would benefit most from targeted or earlier interventions. Clinical informatics, a subfield of biomedical informatics, is transforming healthcare by integrating computational methods with patient data to improve patient outcomes. The application of clinical informatics to develop and enhance clinical therapies for BPD presents opportunities by leveraging electronic health record data, applying machine learning algorithms, and implementing clinical decision support systems. This review highlights the current barriers and the future potential of clinical informatics in identifying clinically relevant BPD phenotypes and developing clinical decision support tools to improve the management of extremely preterm neonates developing or with established BPD. However, the full potential of clinical informatics in advancing our understanding of BPD with the goal of improving patient outcomes cannot be achieved unless we address current challenges such as data collection, storage, privacy, and inherent data bias.