Risk factors and machine learning prediction models for bronchopulmonary dysplasia severity in the Chinese population

Advancements in biomarkers and machine learning for predicting of bronchopulmonary dysplasia and neonatal respiratory distress syndrome in preterm infants

Recent advancements in biomarker identification and machine learning have significantly enhanced the prediction and diagnosis of Bronchopulmonary Dysplasia (BPD) and neonatal respiratory distress syndrome (nRDS) in preterm infants. Key predictors of BPD severity include elevated cytokines like Interleukin-6 (IL-6) and Tumor Necrosis Factor-alpha (TNF-α), as well as inflammatory markers such as the Neutrophil-to-Lymphocyte Ratio (NLR) and soluble gp130. Research into endoplasmic reticulum stress-related genes, differentially expressed genes, and ferroptosis-related genes provides valuable insights into BPD's pathophysiology. Machine learning models like XGBoost and Random Forest have identified important biomarkers, including CYYR1, GALNT14, and OLAH, improving diagnostic accuracy. Additionally, a five-gene transcriptomic signature shows promise for early identification of at-risk neonates, underscoring the significance of immune response factors in BPD. For nRDS, biomarkers such as the lecithin/sphingomyelin (L/S) ratio and oxidative stress indicators have been effectively used in innovative diagnostic methods, including attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and high-content screening for ABCA3 modulation. Machine learning algorithms like Partial Least Squares Regression (PLSR) and C5.0 have shown potential in accurately identifying critical health indicators. Furthermore, advanced feature extraction methods for analyzing neonatal cry signals offer a non-invasive means to differentiate between conditions like sepsis and nRDS. Overall, these findings emphasize the importance of combining biomarker analysis with advanced computational techniques to improve clinical decision-making and intervention strategies for managing BPD and nRDS in vulnerable preterm infants.

Machine learning predictive model for aspiration risk in early enteral nutrition patients with severe acute pancreatitis

Background

The aim of this study was to build and validate a risk prediction model for aspiration in severe acute pancreatitis patients receiving early enteral nutrition (EN) by identifying risk factors for aspiration in these patients.

Methods

The risk factors for aspiration were analyzed to build a prediction model based on the data collected from 339 patients receiving enteral nutrition. Subsequently, we used six machine learning algorithms and the model was validated by the area under the curve.

Results

In this study, the collected data were divided into two groups: a training cohort and a validation cohort. The results showed that 28.31 % (77) of patients had aspiration and 71.69 % (195) of patients had non-aspiration in training cohort. Moreover, age, consciousness, mechanical ventilation, aspiration history, nutritional risk and number of comorbidities were included as predictive factors for aspiration in patients receiving EN. The XGBoost model is the best of all machine learning models, with an AUROC of 0.992 and an F1 value of 0.902. The specificity and accuracy of XGBoost are higher than those of traditional logistic regression.

Conclusion

In accordance with the predictive factors, XGBoost model, characterized by excellent discrimination and high accuracy, can be used to clinically identify severe acute pancreatitis patients with a high risk of enteral nutrition aspiration.

Relevance to clinical practice

This study contributed to the development of a predictive model for early enteral nutrition aspiration in severe acute pancreatitis patients during hospitalization that can be shared with medical staff and patients in the future.

No patient or public contribution

This is a retrospective cohort study, and no patient or public contribution was required to design or undertake this research.

Association between early metabolic acidosis and bronchopulmonary dysplasia/death in preterm infants born at less than 28 weeks' gestation: an observational cohort study

BACKGROUND

Metabolic acidosis occurs frequently during the first postnatal days in extremely preterm infants and is mainly attributed to renal immaturity. Recent studies suggested a link between metabolic acidosis and the development of BPD. The aim of this study was to systematically investigate the association between severe metabolic acidosis during the first two weeks of life and bronchopulmonary dysplasia (BPD) / mortality among preterm infants born before 28 weeks' gestation.

METHODS

Monocentric observational cohort study including 1748 blood gas samples of 138 extremely preterm infants born 2020-2022. Metabolic acidosis was defined as pH < 7.2 with base excess (BE) < -10 mmol/L or standard bicarbonate (SBC) < 12 mmol/L. Primary outcome was BPD and/or death at 36 weeks postmenstrual age.

RESULTS

Fifty-six (40.6%) infants had BPD/death. Metabolic acidosis occurred in 50.0% of infants with BPD/death, compared to 22.0% of BPD-free survivors (p = 0.001) during the first 14 postnatal days. Minimum pH (median 7.12 vs. 7.19, p < 0.001), BE (median -10.9 vs. -9.5 mmol/L, p = 0.005), SBC (median 14.7 vs. 16.1 mmol/L, p < 0.001) were different between the two groups. After adjusting for confounders, pH (postnatal days 2-6), BE (postnatal day 3) and SBC (postnatal days 2-4) were significantly lower in infants with BPD/death. Metabolic acidosis on postnatal days 1-7 was associated with higher odds of BPD (adjusted Odds Ratio (aOR) 3.461, 95% CI 1.325-9.042) and BPD/death (aOR 3.087, 95% CI 1.225-7.778).

CONCLUSIONS

Metabolic acidosis during the first week of life was associated with higher odds of BPD/death in extremely preterm infants.

[Risk factors for bronchopulmonary dysplasia in twin preterm infants: a multicenter study]

OBJECTIVES

To investigate the risk factors for bronchopulmonary dysplasia (BPD) in twin preterm infants with a gestational age of <34 weeks, and to provide a basis for early identification of BPD in twin preterm infants in clinical practice.

METHODS

A retrospective analysis was performed for the twin preterm infants with a gestational age of <34 weeks who were admitted to 22 hospitals nationwide from January 2018 to December 2020. According to their conditions, they were divided into group A (both twins had BPD), group B (only one twin had BPD), and group C (neither twin had BPD). The risk factors for BPD in twin preterm infants were analyzed. Further analysis was conducted on group B to investigate the postnatal risk factors for BPD within twins.

RESULTS

A total of 904 pairs of twins with a gestational age of <34 weeks were included in this study. The multivariate logistic regression analysis showed that compared with group C, birth weight discordance of >25% between the twins was an independent risk factor for BPD in one of the twins (OR=3.370, 95%CI: 1.500-7.568, P<0.05), and high gestational age at birth was a protective factor against BPD (P<0.05). The conditional logistic regression analysis of group B showed that small-for-gestational-age (SGA) birth was an independent risk factor for BPD in individual twins (OR=5.017, 95%CI: 1.040-24.190, P<0.05).

CONCLUSIONS

The development of BPD in twin preterm infants is associated with gestational age, birth weight discordance between the twins, and SGA birth.

Nutritional support during the first week for infants with bronchopulmonary dysplasia and respiratory distress: a multicenter cohort study in China

BACKGROUND

Bronchopulmonary dysplasia (BPD) is a major complication affecting the survival rate and long-term outcomes of preterm infants. A large, prospective, multicenter cohort study was conducted to evaluate early nutritional support during the first week of life for preterm infants with a gestational age < 32 weeks and to verify nutritional risk factors related to BPD development.

METHODS

A prospective multicenter cohort study of very preterm infants was conducted in 40 tertiary neonatal intensive care units across mainland China between January 1, 2020, and December 31, 2021. Preterm infants who were born at a gestational age < 32 weeks, < 72 h after birth and had a respiratory score > 4 were enrolled. Antenatal and postnatal information focusing on nutritional parameters was collected through medical systems. Statistical analyses were also performed to identify BPD risk factors.

RESULTS

The primary outcomes were BPD and severity at 36 weeks postmenstrual age. A total of 1410 preterm infants were enrolled in this study. After applying the exclusion criteria, the remaining 1286 infants were included in this analysis; 614 (47.7%) infants were in the BPD group, and 672 (52.3%) were in the non-BPD group. In multivariate logistic regression model, the following six factors were identified of BPD: birth weight (OR 0.99, 95% CI 0.99-0.99; p = 0.039), day of full enteral nutrition (OR 1.03, 95% CI 1.02-1.04; p < 0.001), parenteral protein > 3.5 g/kg/d during the first week (OR 1.65, 95% CI 1.25-2.17; p < 0.001), feeding type (formula: OR 3.48, 95% CI 2.21-5.49; p < 0.001, mixed feed: OR 1.92, 95% CI 1.36-2.70; p < 0.001; breast milk as reference), hsPDA (OR 1.98, 95% CI 1.44-2.73; p < 0.001), and EUGR ats 36 weeks (OR 1.40, 95% CI 1.02-1.91; p = 0.035).

CONCLUSIONS

A longer duration to achieve full enteral nutrition in very preterm infants was associated with increased BPD development. Breastfeeding was demonstrated to have a protective effect against BPD. Early and rapidly progressive enteral nutrition and breastfeeding should be promoted in very preterm infants.

TRIAL REGISTRATION

The trial was registered in the Chinese Clinical Trial Registry (No. ChiCTR2000030125 on 24/02/2020) and in www.ncrcch.org (No. ISRCTN84167642 on 25/02/2020).

Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities

Artificial intelligence (AI) offers tremendous potential to transform neonatology through improved diagnostics, personalized treatments, and earlier prevention of complications. However, there are many challenges to address before AI is ready for clinical practice. This review defines key AI concepts and discusses ethical considerations and implicit biases associated with AI. Next we will review literature examples of AI already being explored in neonatology research and we will suggest future potentials for AI work. Examples discussed in this article include predicting outcomes such as sepsis, optimizing oxygen therapy, and image analysis to detect brain injury and retinopathy of prematurity. Realizing AI's potential necessitates collaboration between diverse stakeholders across the entire process of incorporating AI tools in the NICU to address testability, usability, bias, and transparency. With multi-center and multi-disciplinary collaboration, AI holds tremendous potential to transform the future of neonatology.

Machine learning predictive models for grading bronchopulmonary dysplasia: umbilical cord blood IL-6 as a biomarker

Objectives

This study aimed to analyze the predictive value of umbilical cord blood Interleukin-6 (UCB IL-6) for the severity-graded BPD and to establish machine learning (ML) predictive models in a Chinese population based on the 2019 NRN evidence-based guidelines.

Methods

In this retrospective analysis, we included infants born with gestational age <32 weeks, who underwent UCB IL-6 testing within 24 h of admission to our NICU between 2020 and 2022. We collected their medical information encompassing the maternal, perinatal, and early neonatal phases. Furthermore, we classified the grade of BPD according to the 2019 NRN evidence-based guidelines. The correlation between UCB IL-6 and the grades of BPD was analyzed. Univariate analysis and ordinal logistic regression were employed to identify risk factors, followed by the development of ML predictive models based on XGBoost, CatBoost, LightGBM, and Random Forest. The AUROC was used to evaluate the diagnostic value of each model. Besides, we generated feature importance distribution plots based on SHAP values to emphasize the significance of UCB IL-6 in the models.

Results

The study ultimately enrolled 414 preterm infants, with No BPD group (n = 309), Grade 1 BPD group (n = 73), and Grade 2-3 BPD group (n = 32). The levels of UCB IL-6 increased with the grades of BPD. UCB IL-6 demonstrated clinical significance in predicting various grades of BPD, particularly in distinguishing Grade 2-3 BPD patients, with an AUROC of 0.815 (95% CI: 0.753-0.877). All four ML models, XGBoost, CatBoost, LightGBM, and Random Forest, exhibited Micro-average AUROC values of 0.841, 0.870, 0.851, and 0.878, respectively. Notably, UCB IL-6 consistently appeared as the most prominent feature across the feature importance distribution plots in all four models.

Conclusion

UCB IL-6 significantly contributes to predicting severity-graded BPD, especially in grade 2-3 BPD. Through the development of four ML predictive models, we highlighted UCB IL-6's importance.

Neonatal sequential organ failure assessment score within 72 h after delivery reliably predicts bronchopulmonary dysplasia in very preterm infants

Background

The neonatal sequential organ failure assessment (nSOFA) score is an operational definition of organ dysfunction employed to predict sepsis-associated mortality. However, the relationship between the nSOFA score and bronchopulmonary dysplasia (BPD) has not been investigated clearly. This study evaluates whether the nSOFA score within 72 h after delivery could be used to predict the occurrence of BPD in very preterm infants.

Methods

In this retrospective, single-center cohort study, preterm infants born between 2019 and 2021 were investigated, the nSOFA score was calculated from medical records after admission to the neonatal intensive care unit (NICU) within 72 h after delivery, and the peak value was used for calculation. A logistic regression model was used to evaluate the relationship between the nSOFA score and BPD. Propensity score matching and subgroup analysis were performed to verify the reliability of the results.

Results

Of 238 infants meeting the inclusion criteria, 93 infants (39.1%) were diagnosed with BPD. The receiver operating characteristic curve of the nSOFA score in predicting BPD was 0.790 [95% confidence interval (CI): 0.731-0.849]. The logistic regression model showed that an increment of one in the nSOFA score was related to a 2.09-fold increase in the odds of BPD (95% CI: 1.57-2.76) and 6.36-fold increase when the nSOFA score was higher than 1.5 (95% CI: 2.73-14.79).

Conclusions

The nSOFA score within 72 h after delivery is independently related to BPD and can be used to identify high-risk infants and implement early interventions.

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.