The Prediction of Bronchopulmonary Dysplasia in Very Low Birth Weight Infants through Clinical Indicators within 1 Hour of Delivery

Combined gestational age and serum fucose for early prediction of risk for bronchopulmonary dysplasia in premature infants

OBJECTIVE

As the predominant complication in preterm infants, Bronchopulmonary Dysplasia (BPD) necessitates accurate identification of infants at risk and expedited therapeutic interventions for an improved prognosis. This study evaluates the potential of Monosaccharide Composite (MC) enriched with environmental information from circulating glycans as a diagnostic biomarker for early-onset BPD, and, concurrently, appraises BPD risk in premature neonates.

MATERIALS AND METHODS

The study incorporated 234 neonates of ≤32 weeks gestational age. Clinical data and serum samples, collected one week post-birth, were meticulously assessed. The quantification of serum-free monosaccharides and their degraded counterparts was accomplished via High-performance Liquid Chromatography (HPLC). Logistic regression analysis facilitated the construction of models for early BPD diagnosis. The diagnostic potential of various monosaccharides for BPD was determined using Receiver Operating Characteristic (ROC) curves, integrating clinical data for enhanced diagnostic precision, and evaluated by the Area Under the Curve (AUC).

RESULTS

Among the 234 neonates deemed eligible, BPD development was noted in 68 (29.06%), with 70.59% mild (48/68) and 29.41% moderate-severe (20/68) cases. Multivariate analysis delineated several significant risk factors for BPD, including gestational age, birth weight, duration of both invasive mechanical and non-invasive ventilation, Patent Ductus Arteriosus (PDA), pregnancy-induced hypertension, and concentrations of two free monosaccharides (Glc-F and Man-F) and five degraded monosaccharides (Fuc-D, GalN-D, Glc-D, and Man-D). Notably, the concentrations of Glc-D and Fuc-D in the moderate-to-severe BPD group were significantly diminished relative to the mild BPD group. A potent predictive capability for BPD development was exhibited by the conjunction of gestational age and Fuc-D, with an AUC of 0.96.

CONCLUSION

A predictive model harnessing the power of gestational age and Fuc-D demonstrates promising efficacy in foretelling BPD development with high sensitivity (95.0%) and specificity (94.81%), potentially enabling timely intervention and improved neonatal outcomes.

Montelukast Sodium to Prevent and Treat Bronchopulmonary Dysplasia in Very Preterm Infants: A Quasi-Randomized Controlled Trial

Bronchopulmonary dysplasia (BPD) is the most common chronic lung disease in preterm infants and lacks effective methods for prevention and treatment. The aim of this study is to explore the efficacy and safety of montelukast in preventing or treating BPD in preterm infants. The preterm infants with BPD risk factors were divided randomly into a montelukast group and a control group. In the montelukast group, preterm infants were given 1 mg/kg of montelukast sodium daily. There was no placebo in the control group. There was no significant difference in the incidence of moderate or severe BPD between the two groups (31.8% vs. 35%). The duration of respiratory support in the montelukast group was shorter than that in the control group (36.4 ± 12.8 d vs. 43.1 ± 15.9 d, p = 0.037). The pulmonary severity score (PSS) at 21 days of life in the montelukast group was significantly lower than that in the control group (0.56 ± 0.13 vs. 0.62 ± 0.14, p = 0.048). There were no significant differences in the duration of mechanical ventilation, length of stay, hospitalization expenses, or incidence of adverse events. Although montelukast cannot alleviate the severity of BPD, it may shorten the duration of respiratory support and decrease the PSS in very preterm infants. There were no significant adverse drug events associated with montelukast treatment.

An Early Prediction Model for Estimating Bronchopulmonary Dysplasia in Preterm Infants

INTRODUCTION

Accurate assessment of the risk for bronchopulmonary dysplasia (BPD) is critical to determine the prognosis and identify infants who will benefit from preventive therapies. Clinical prediction models can support the identification of high-risk patients. In this study, we investigated the potential risk factors for BPD and compared machine learning models for predicting the outcome of BPD/death on days 1, 7, 14, and 28 in preterm infants. We also developed a local BPD estimator.

METHODS

This study involved 124 infants. We evaluated the composite outcome of BPD/death at a postmenstrual age of 36 weeks and identified risk factors that would improve BPD/death prediction. SPSS for Windows Version 11.5 and Weka 3.9 software were used for the data analysis.

RESULTS

To evaluate the combined effect of all variables, all risk factors were taken into consideration. Gestational age, birth weight, mode of respiratory support, intraventricular hemorrhage, necrotizing enterocolitis, surfactant requirement, and late-onset sepsis were risk factors on postnatal days 7, 14, and 28. In a comparison of four different time points (postnatal days 1, 7, 14, and 28), the day 7 model provided the best prediction. According to this model, when a patient was diagnosed with BPD/death, the accuracy rate was 89.5%.

CONCLUSION

The postnatal day 7 model was the best predictor of BPD or death. Future validation studies will help identify infants who may benefit from preventive therapies and develop individualized care.

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.

Two-stage learning-based prediction of bronchopulmonary dysplasia in very low birth weight infants: a nationwide cohort study

Introduction

The aim of this study is to develop an enhanced machine learning-based prediction models for bronchopulmonary dysplasia (BPD) and its severity through a two-stage approach integrated with the duration of respiratory support (RSd) using prenatal and early postnatal variables from a nationwide very low birth weight (VLBW) infant cohort.

Methods

We included 16,384 VLBW infants admitted to the neonatal intensive care unit (NICU) of the Korean Neonatal Network (KNN), a nationwide VLBW infant registry (2013-2020). Overall, 45 prenatal and early perinatal clinical variables were selected. A multilayer perceptron (MLP)-based network analysis, which was recently introduced to predict diseases in preterm infants, was used for modeling and a stepwise approach. Additionally, we applied a complementary MLP network and established new BPD prediction models (PMbpd). The performances of the models were compared using the area under the receiver operating characteristic curve (AUROC) values. The Shapley method was used to determine the contribution of each variable.

Results

We included 11,177 VLBW infants (3,724 without BPD (BPD 0), 3,383 with mild BPD (BPD 1), 1,375 with moderate BPD (BPD 2), and 2,695 with severe BPD (BPD 3) cases). Compared to conventional machine learning (ML) models, our PMbpd and two-stage PMbpd with RSd (TS-PMbpd) model outperformed both binary (0 vs. 1,2,3; 0,1 vs. 2,3; 0,1,2 vs. 3) and each severity (0 vs. 1 vs. 2 vs. 3) prediction (AUROC = 0.895 and 0.897, 0.824 and 0.825, 0.828 and 0.823, 0.783, and 0.786, respectively). GA, birth weight, and patent ductus arteriosus (PDA) treatment were significant variables for the occurrence of BPD. Birth weight, low blood pressure, and intraventricular hemorrhage were significant for BPD ≥2, birth weight, low blood pressure, and PDA ligation for BPD ≥3. GA, birth weight, and pulmonary hypertension were the principal variables that predicted BPD severity in VLBW infants.

Conclusions

We developed a new two-stage ML model reflecting crucial BPD indicators (RSd) and found significant clinical variables for the early prediction of BPD and its severity with high predictive accuracy. Our model can be used as an adjunctive predictive model in the practical NICU field.

Development and Validation of a Risk Scoring Tool for Bronchopulmonary Dysplasia in Preterm Infants Based on a Systematic Review and Meta-Analysis

Background: Bronchopulmonary dysplasia (BPD) is the most common serious pulmonary morbidity in preterm infants with high disability and mortality rates. Early identification and treatment of BPD is critical. Objective: This study aimed to develop and validate a risk scoring tool for early identification of preterm infants that are at high-risk for developing BPD. Methods: The derivation cohort was derived from a systematic review and meta-analysis of risk factors for BPD. The statistically significant risk factors with their corresponding odds ratios were utilized to construct a logistic regression risk prediction model. By scoring the weights of each risk factor, a risk scoring tool was established and the risk stratification was divided. External verification was carried out by a validation cohort from China. Results: Approximately 83,034 preterm infants with gestational age < 32 weeks and/or birth weight < 1500 g were screened in this meta-analysis, and the cumulative incidence of BPD was about 30.37%. The nine predictors of this model were Chorioamnionitis, Gestational age, Birth weight, Sex, Small for gestational age, 5 min Apgar score, Delivery room intubation, and Surfactant and Respiratory distress syndrome. Based on the weight of each risk factor, we translated it into a simple clinical scoring tool with a total score ranging from 0 to 64. External validation showed that the tool had good discrimination, the area under the curve was 0.907, and that the Hosmer-Lemeshow test showed a good fit (p = 0.3572). In addition, the results of the calibration curve and decision curve analysis suggested that the tool showed significant conformity and net benefit. When the optimal cut-off value was 25.5, the sensitivity and specificity were 0.897 and 0.873, respectively. The resulting risk scoring tool classified the population of preterm infants into low-risk, low-intermediate, high-intermediate, and high-risk groups. This BPD risk scoring tool is suitable for preterm infants with gestational age < 32 weeks and/or birth weight < 1500 g. Conclusions: An effective risk prediction scoring tool based on a systematic review and meta-analysis was developed and validated. This simple tool may play an important role in establishing a screening strategy for BPD in preterm infants and potentially guide early intervention.

Bronchopulmonary dysplasia prediction models: a systematic review and meta-analysis with validation

Prediction models could identify infants at the greatest risk of bronchopulmonary dysplasia (BPD) and allow targeted preventative strategies. We performed a systematic review and meta-analysis with external validation of identified models. Studies using predictors available before day 14 of life to predict BPD in very preterm infants were included. Two reviewers assessed 7628 studies for eligibility. Meta-analysis of externally validated models was followed by validation using 62,864 very preterm infants in England and Wales. A total of 64 studies using 53 prediction models were included totalling 274,407 infants (range 32-156,587/study). In all, 35 (55%) studies predated 2010; 39 (61%) were single-centre studies. A total of 97% of studies had a high risk of bias, especially in the analysis domain. Following meta-analysis of 22 BPD and 11 BPD/death composite externally validated models, Laughon's day one model was the most promising in predicting BPD and death (C-statistic 0.76 (95% CI 0.70-0.81) and good calibration). Six models were externally validated in our cohort with C-statistics between 0.70 and 0.90 but with poor calibration. Few BPD prediction models were developed with contemporary populations, underwent external validation, or had calibration and impact analyses. Contemporary, validated, and dynamic prediction models are needed for targeted preventative strategies. IMPACT: This review aims to provide a comprehensive assessment of all BPD prediction models developed to address the uncertainty of which model is sufficiently valid and generalisable for use in clinical practice and research. Published BPD prediction models are mostly outdated, single centre and lack external validation. Laughon's 2011 model is the most promising but more robust models, using contemporary data with external validation are needed to support better treatments.

Association of Maternal Hypertensive Disorders During Pregnancy With Severe Bronchopulmonary Dysplasia: A Systematic Review and Meta-Analysis

BACKGROUND

This meta-analysis was performed to examine the association between maternal hypertension during pregnancy (HDP) and neonatal bronchopulmonary dysplasia (BPD).

METHODS

We systematically searched PubMed, EMBASE, the Cochrane Library, and the KoreaMed database for relevant studies. We used the Newcastle-Ottawa Scale for quality assessment of all included studies. The meta-analysis was performed using Comprehensive Meta-Analysis software (version 3.3).

RESULTS

We included 35 studies that fulfilled the inclusion criteria; the total number of infants evaluated came to 97,399 through review process. Maternal HDP was not significantly associated with any definition of BPD, i.e., oxygen dependency at 36 weeks of gestation (odds ratio [OR], 1.162; 95% confidence interval [CI], 0.991-1.362; P = 0.064) in pooled analysis of 29 studies or oxygen dependency at 28 days of age (OR, 1.084; 95% CI, 0.660-1.780; P = 0.751) in pooled analysis of 8 studies. Maternal HDP was significantly associated only with severe BPD (OR, 2.341; 95% CI, 1.726-3.174; P < 0.001). BPD was not associated with HDP in the overall analysis (OR, 1.131; 95% CI, 0.977-1.309; P = 0.100) or subgroup analysis according to the definition of HDP.

CONCLUSION

Maternal HDP was not associated with neonatal BPD defined by the duration of oxygen dependency (at either 36 weeks of gestation or 28 days of life) but was associated with severe BPD.

Predictive values of clinical data,molecular biomarkers, and echocardiographic measurements in preterm infants with bronchopulmonary dysplasia

Objective

We aimed to use molecular biomarkers and clinical data and echocardiograms that were collected during admission to predict bronchopulmonary dysplasia (BPD) in preterm infants with gestational age ≤32 weeks.

Methods

Eighty-two patients (40 with BPD, BPD group and 42 healthy as controls, non-BPD group) admitted to the Department of Neonatology of the Children's Hospital of Soochow University between October 1, 2018, and February 29, 2020, were enrolled in this study at the tertiary hospital. Basic clinical data on the perinatal period, echocardiographic measurements, and molecular biomarkers (N-terminal-pro-B-brain natriuretic peptide, NT-proBNP) were collected. We used multiple logistic regression analysis to establish an early predictive model for detecting BPD development in preterm infants of gestational age ≤32 weeks. We also used a receiver operating characteristic curve to assess the sensitivity and specificity of the model.

Results

No significant differences were found between the BPD and non-BPD groups in terms of sex, birth weight, gestational age, incidence of asphyxia, maternal age, gravidity, parity, mode of delivery, premature rupture of membranes >18 h, use of prenatal hormones, placental abruption, gestational diabetes mellitus, amniotic fluid contamination, prenatal infections, and maternal diseases. The use of caffeine, albumin, gamma globulin; ventilation; days of FiO₂ ≥ 40%; oxygen inhalation time; red blood cell suspension infusion volume (ml/kg); and proportion of infants who received total enteral nutrition (120 kcal/kg.d) ≥24 d after birth were higher in the BPD group than in the non-BPD group. The levels of hemoglobin, hematocrit, and albumin in the BPD group were significantly lower than those in the non-BPD group. The total calorie intake was significantly lower in the BPD group on the 3rd, 7th, and 14th day after birth than in the non-BPD group (P < 0.05). The incidence rates of patent ductus arteriosus (PDA), pulmonary hypertension, and tricuspid regurgitation were significantly higher in the BPD group than in the non-BPD group (P < 0.05). The serum level of NT-proBNP 24 h after birth was significantly higher in the BPD group than in the non-BPD group (P < 0.05). Serum NT-proBNP levels were significantly higher in infants with severe BPD than in those with mild or moderate BPD (P < 0.05).

Conclusion

As there were various risk factors for BPD, a combining clinical data, molecular biomarkers, and echocardiogram measurements can be valuable in predicting the BPD. The tricuspid regurgitation flow rate (m/s), NT-proBNP (pg/ml), ventilator-associated pneumonia, days of FiO₂ ≥ 40% (d), red blood cell suspension infusion volume (ml/kg), and proportion of infants who received total enteral nutrition (120 kcal/kg.d) ≥24 d after birth were the most practical factors considered for designing an appropriate model for predicting the risk of BPD.

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.

Construction of early risk prediction models for bronchopulmonary dysplasia in preterm infants

OBJECTIVES

To construct risk prediction models for bronchopulmonary dysplasia (BPD) in preterm infants on postnatal days 3, 7, and 14.

METHODS

A retrospective analysis was performed on the medical data of 414 preterm infants, with a gestational age of <32 weeks and a birth weight (BW) of <1 500 g, who were admitted to the neonatal intensive care unit from July 2019 to April 2021. According to the diagnostic criteria for BPD revised in 2018, they were divided into a BPD group with 98 infants and a non-BPD group with 316 infants. The two groups were compared in terms of general status, laboratory examination results, treatment, and complications. The logistic regression model was used to identify the variables associated with BPD. The receiver operating characteristic (ROC) curve was used to evaluate the predictive value of models.

RESULTS

The logistic regression analysis showed that BW, asphyxia, grade III-IV respiratory distress syndrome (RDS), acute chorioamnionitis, interstitial pneumonia, fraction of inspired oxygen (FiO2), and respiratory support mode were the main risk factors for BPD (P<0.05). The prediction models on postnatal days 7 and 14 were established as logit (P7) =-2.049-0.004×BW (g) +0.686×asphyxia (no=0, yes=1) +1.842×grade III-IV RDS (no=0, yes=1) +0.906×acute chorioamnionitis (no=0, yes=1) +0.506×interstitial pneumonia (no=0, yes=1) +0.116×FiO2 (%) +0.816×respiratory support mode (no=0, nasal tube=1, nasal continuous positive airway pressure=2, conventional mechanical ventilation=3, high-frequency mechanical ventilation=4) and logit (P14) =-1.200-0.004×BW (g) +0.723×asphyxia+2.081×grade III-IV RDS+0.799×acute chorioamnionitis+0.601×interstitial pneumonia+0.074×FiO2 (%) +0.800×respiratory support mode, with an area under the ROC curve (AUC) of 0.876 and 0.880, respectively, which was significantly larger than the AUC of the prediction model on postnatal day 3 (P<0.05).

CONCLUSIONS

BW, asphyxia, grade III-IV RDS, acute chorioamnionitis, interstitial pneumonia, FiO2, and respiratory support mode are the main risk factors for BPD and can be used to construct risk prediction models. The prediction models on postnatal days 7 and 14 can effectively predict BPD.

Surfactant Administration Through Laryngeal or Supraglottic Airways

Noninvasive ventilation is frequently used in the treatment of infants with respiratory distress syndrome. This practice is often effective in higher gestational age neonates, but can be difficult in those with lower gestational ages as surfactant deficiency can be severe. While noninvasive ventilation avoids the negative effects of intubation and ventilator-induced lung injury, failure of this mode of support does occur with relative frequency and is primarily caused by the poorly compliant, surfactant-deficient lung. Because of the potential problems associated with laryngoscopy and intubation, neonatologists have developed various methods to deliver surfactant in minimally invasive ways with the aim of improving the success of noninvasive ventilation. Methods of minimally invasive surfactant administration include various thin catheter techniques, aerosolization/nebulization, and the use of a laryngeal mask airway/supraglottic airway device. The clinician should recognize that currently the only US Food and Drug Administration-approved device to deliver surfactant is an endotracheal tube and all methods reviewed here are considered off-label use. This review will focus primarily on surfactant administration through laryngeal or supraglottic airways, providing a review of the history of this technique, animal and human trials, and comparison with other minimally invasive techniques. In addition, this review provides a step-by-step instruction guide on how to perform this procedure, including a multimedia tutorial to facilitate learning.