Ventilator flow data predict bronchopulmonary dysplasia in extremely premature neonates

Development and external validation of a machine learning model to predict bronchopulmonary dysplasia using dynamic factors

We hypothesized that incorporating postnatal dynamic factors would enhance the prediction accuracy of bronchopulmonary dysplasia in preterm infants. This retrospective cohort study included neonates born before 32 weeks of gestation at Seoul National University Hospital between 2013 and 2022. The primary outcome was moderate or severe bronchopulmonary dysplasia. We assessed both static perinatal risk factors and dynamic factors, such as respiratory support type, inspired oxygen concentration, and blood gas analysis results within the first 7 days. The model was developed using data from 546 infants born between 2013 and 2021, with internal validation on 75 infants born in 2022. External validation was based on 105 infants recruited at the Boramae Medical Center. The integrated prediction model, combining static and dynamic factors, showed superior predictive performance, with an area under the receiver operating characteristic curve (AUROC) of 0.841 in the development set, outperforming the static perinatal factor model. Internal validation confirmed the robustness of the integrated model (AUROC: 0.912 vs. 0.805, p < 0.0001). The performance was maintained in the external validation (AUROC: 0.814). Incorporating early respiratory support and blood gas analysis into predictive models substantially improved the accuracy of bronchopulmonary dysplasia prediction in preterm infants.

Effects of bradycardia, hypoxemia and early intubation on bronchopulmonary dysplasia in very preterm infants: An observational study

BACKGROUND

Bronchopulmonary dysplasia (BPD) is the most common pulmonary complication in preterm infants.

OBJECTIVES

The study aimed to explore the effects of bradycardia, hypoxemia, and early intubation on BPD in very preterm infants.

METHODS

This is a prospective observational cohort study. Preterm infants with a mean gestational age of 28.67 weeks were recruited from two level III neonatal intensive care units (NICUs) in Taiwan. Continuous electrocardiography was used to monitor heart rates and oxygen saturation (SpO2). Infants were monitored for heart rates of <100 beats per minute and SpO2 levels of <90 % lasting for 30 s. Generalized estimating equations were used to analyze the effects of bradycardia, hypoxemia, and early intubation on BPD in very preterm infants. Model fit was visually assessed using receiver operating characteristic curve analysis.

RESULTS

Bradycardia, hypoxemia, and early intubation significantly increased the odds of BPD among the preterm infants (N = 39) during NICU stay; the odds ratios for bradycardia, hypoxemia, and early intubation for BPD versus non-BPD were 1.058, 1.013, and 29.631, respectively (all p < 0.05). A model combining bradycardia, hypoxemia, and early intubation accurately predicted BPD development (area under the curve = 0.919).

CONCLUSIONS

Bradycardia, hypoxemia, and early intubation significantly increased the odds of BPD among very preterm infants during NICU stay. The model combining bradycardia, hypoxemia, and early intubation accurately predicted BPD development.

Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants: A Systematic Review and Meta-Analysis

OBJECTIVE

To review systematically and assess the accuracy of prediction models for bronchopulmonary dysplasia (BPD) at 36 weeks of postmenstrual age.

STUDY DESIGN

Searches were conducted in MEDLINE and EMBASE. Studies published between 1990 and 2022 were included if they developed or validated a prediction model for BPD or the combined outcome death/BPD at 36 weeks in the first 14 days of life in infants born preterm. Data were extracted independently by 2 authors following the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (ie, CHARMS) and PRISMA guidelines. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (ie, PROBAST).

RESULTS

Sixty-five studies were reviewed, including 158 development and 108 externally validated models. Median c-statistic of 0.84 (range 0.43-1.00) was reported at model development, and 0.77 (range 0.41-0.97) at external validation. All models were rated at high risk of bias, due to limitations in the analysis part. Meta-analysis of the validated models revealed increased c-statistics after the first week of life for both the BPD and death/BPD outcome.

CONCLUSIONS

Although BPD prediction models perform satisfactorily, they were all at high risk of bias. Methodologic improvement and complete reporting are needed before they can be considered for use in clinical practice. Future research should aim to validate and update existing models.

Corticosteroid response predicts bronchopulmonary dysplasia status at 36 weeks in preterm infants treated with dexamethasone: A pilot study

IMPORTANCE

A major barrier to therapeutic development in neonates is a lack of standardized drug response measures that can be used as clinical trial endpoints. The ability to quantify treatment response in a way that aligns with relevant downstream outcomes may be useful as a surrogate marker for new therapies, such as those for bronchopulmonary dysplasia (BPD).

OBJECTIVE

To construct a measure of clinical response to dexamethasone that was well aligned with the incidence of severe BPD or death at 36 weeks' postmenstrual age.

DESIGN

Retrospective cohort study.

SETTING

Level IV Neonatal Intensive Care Unit.

PARTICIPANTS

Infants treated with dexamethasone for developing BPD between 2010 and 2020.

MAIN OUTCOME(S) AND MEASURE(S)

Two models were built based on demographics, changes in ventilatory support, and partial pressure of carbon dioxide (pCO₂ ) after dexamethasone administration. An ordinal logistic regression and regularized binary logistic model for the composite outcome were used to associate response level to BPD outcomes defined by both the 2017 BPD Collaborative and 2018 Neonatal Research Network definitions.

RESULTS

Ninety-five infants were treated with dexamethasone before 36 weeks. Compared to the baseline support and demographic data at the time of treatment, changes in ventilatory support improved ordinal model sensitivity and specificity. For the binary classification, BPD incidence was well aligned with risk levels, increasing from 16% to 59%.

CONCLUSIONS AND RELEVANCE

Incorporation of response variables as measured by changes in ventilatory parameters and pCO₂ following dexamethasone administration were associated with downstream outcomes. Incorporating drug response phenotype into a BPD model may enable more rapid development of future therapeutics.

Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants: A Systematic Review

OBJECTIVE

To provide an overview and critical appraisal of prediction models for bronchopulmonary dysplasia (BPD) in preterm infants.

METHODS

We searched PubMed, Embase, and the Cochrane Library to identify relevant studies (up to November 2021). We included studies that reported prediction model development and/or validation of BPD in preterm infants born at ≤32 weeks and/or ≤1,500 g birth weight. We extracted the data independently based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). We assessed risk of bias and applicability independently using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

RESULTS

Twenty-one prediction models from 13 studies reporting on model development and 21 models from 10 studies reporting on external validation were included. Oxygen dependency at 36 weeks' postmenstrual age was the most frequently reported outcome in both development studies (71%) and validation studies (81%). The most frequently used predictors in the models were birth weight (67%), gestational age (62%), and sex (52%). Nearly all included studies had high risk of bias, most often due to inadequate analysis. Small sample sizes and insufficient event patients were common in both study types. Missing data were often not reported or were discarded. Most studies reported on the models' discrimination, while calibration was seldom assessed (development, 19%; validation, 10%). Internal validation was lacking in 69% of development studies.

CONCLUSION

The included studies had many methodological shortcomings. Future work should focus on following the recommended approaches for developing and validating BPD prediction models.

Bronchopulmonary Dysplasia Predicted by Developing a Machine Learning Model of Genetic and Clinical Information

Background

An early and accurate evaluation of the risk of bronchopulmonary dysplasia (BPD) in premature infants is pivotal in implementing preventive strategies. The risk prediction models nowadays for BPD risk that included only clinical factors but without genetic factors are either too complex without practicability or provide poor-to-moderate discrimination. We aim to identify the role of genetic factors in BPD risk prediction early and accurately.

Methods

Exome sequencing was performed in a cohort of 245 premature infants (gestational age <32 weeks), with 131 BPD infants and 114 infants without BPD as controls. A gene burden test was performed to find risk genes with loss-of-function mutations or missense mutations over-represented in BPD and severe BPD (sBPD) patients, with risk gene sets (RGS) defined as BPD-RGS and sBPD-RGS, respectively. We then developed two predictive models for the risk of BPD and sBPD by integrating patient clinical and genetic features. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUROC).

Results

Thirty and 21 genes were included in BPD-RGS and sBPD-RGS, respectively. The predictive model for BPD, which combined the BPD-RGS and basic clinical risk factors, showed better discrimination than the model that was only based on basic clinical features (AUROC, 0.915 vs. AUROC, 0.814, P = 0.013, respectively) in the independent testing dataset. The same was observed in the predictive model for sBPD (AUROC, 0.907 vs. AUROC, 0.826; P = 0.016).

Conclusion

This study suggests that genetic information contributes to susceptibility to BPD. The predictive model in this study, which combined BPD-RGS with basic clinical risk factors, can thus accurately stratify BPD risk in premature infants.

Risk Identification of Bronchopulmonary Dysplasia in Premature Infants Based on Machine Learning

Bronchopulmonary dysplasia (BPD) is one of the most common complications in premature infants. This disease is caused by long-time use of supplemental oxygen, which seriously affects the lung function of the child and imposes a heavy burden on the family and society. This research aims to adopt the method of ensemble learning in machine learning, combining the Boruta algorithm and the random forest algorithm to determine the predictors of premature infants with BPD and establish a predictive model to help clinicians to conduct an optimal treatment plan. Data were collected from clinical records of 996 premature infants treated in the neonatology department of Liuzhou Maternal and Child Health Hospital in Western China. In this study, premature infants with congenital anomaly, premature infants who died, and premature infants with incomplete data before the diagnosis of BPD were excluded from the data set. After exclusion, we included 648 premature infants in the study. The Boruta algorithm and 10-fold cross-validation were used for feature selection in this study. Six variables were finally selected from the 26 variables, and the random forest model was established. The area under the curve (AUC) of the model was as high as 0.929 with excellent predictive performance. The use of machine learning methods can help clinicians predict the disease so as to formulate the best treatment plan.

Predicting the likelihood of bronchopulmonary dysplasia in premature neonates

Introduction: Bronchopulmonary dysplasia (BPD) is the most common serious pulmonary morbidity in premature infants. Despite ongoing advances in neonatal care, the incidence of BPD has not improved. A potential explanation for this phenomenon is the limited ability for accurate early prediction of the risk of BPD. BPD continues to represent a therapeutic challenge and no single effective therapy exists for this condition. Areas covered: Here, we review risk factors of BPD derived from clinical data, biological fluid biomarkers, respiratory management data, and scientific advancements using 'omics' technologies, and their ability to predict the pathogenesis of BPD in preterm neonates. Risk factors and biomarkers were identified via literature search with a focus on the last 5 years of data. Expert opinion: The most accurate predictive tools utilize risk factors that encompass a variety of categories. Numerous predictive models have been proposed but suffer from a lack of adequate validation. An ideal model should include multiple, easily measurable variables validated across a heterogeneous population. In addition to evaluating recent BPD prediction models, we suggest approaches to enhance future models.