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

Predictive analytics in bronchopulmonary dysplasia: past, present, and future

Bronchopulmonary dysplasia (BPD) remains a significant complication of prematurity, impacting approximately 18,000 infants annually in the United States. Advances in neonatal care have not reduced BPD, and its management is challenged by the rising survival of extremely premature infants and the variability in clinical practices. Leveraging statistical and machine learning techniques, predictive analytics can enhance BPD management by utilizing large clinical datasets to predict individual patient outcomes. This review explores the foundations and applications of predictive analytics in the context of BPD, examining commonly used data sources, modeling techniques, and metrics for model evaluation. We also highlight bioinformatics' potential role in understanding BPD's molecular basis and discuss case studies demonstrating the use of machine learning models for risk prediction and prognosis in neonates. Challenges such as data bias, model complexity, and ethical considerations are outlined, along with strategies to address these issues. Future directions for advancing the integration of predictive analytics into clinical practice include improving model interpretability, expanding data sharing and interoperability, and aligning predictive models with precision medicine goals. By overcoming current challenges, predictive analytics holds promise for transforming neonatal care and providing personalized interventions for infants at risk of BPD.

Predictive modeling of bronchopulmonary dysplasia in premature infants: the impact of new diagnostic standards

Aim

To provide a risk prediction for bronchopulmonary dysplasia (BPD) in premature infants under the new diagnostic criteria and establish a prediction model.

Methods

In this study, we retrospectively collected case data on preterm infants admitted to the NICU from August 2015 to August 2018. A lasso analysis was performed to identify the risk factors associated with the development of BPD. A nomogram predictive model was constructed in accordance with the new diagnostic criteria for BPD.

Result

A total of 276 preterm infants were included in the study.The incidence of BPD under the 2018 diagnostic criteria was 11.2%. Mortality was significantly higher in the BPD group than the non-BPD group under the 2018 diagnostic criteria (P < 0.05). Fourteen possible variables were selected by the Lasso method, with a penalty coefficient λ=0.0154. The factors that eventually entered the logistic regression model included birth weight [BW, OR = 0.9945, 95% CI: 0.9904-0.9979], resuscitation way (OR = 4.8249, 95% CI: 1.3990-19.4752), intrauterine distress (OR = 8.0586, 95% CI: 1.7810-39.5696), score for SNAPPE-II (OR = 1.0880, 95% CI: 1.0210-1.1639), hematocrit (OR = 1.1554, 95% CI: 1.0469-1.2751) and apnea (OR = 7.6916, 95% CI: 1.4180-52.1236). The C-index after adjusting for fitting deviation was 0.894.

Conclusion

This study made a preliminary exploration of the risk model for early prediction of BPD and indicated good discrimination and calibration in premature infants.

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.