Clinical characteristics of bronchopulmonary dysplasia and the risk of sepsis onset prediction via machine learning models
Bronchopulmonary dysplasia (BPD), also known as chronic lung disease, is the most common cause of respiratory morbidity in preterm infants. Sepsis plays a significant role in the pathogenesis of BPD, and the systemic inflammatory response caused by sepsis is associated with lung development, leading...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Pediatrics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fped.2025.1566747/full |
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| author | Yanhua Wang Yanhua Wang Yanhua Wang Yanhua Wang Yi Wang Yi Wang Yi Wang Linhong Song Jun Li Jun Li Jun Li Yuanyuan Xie Yuanyuan Xie Yuanyuan Xie Lei Yan Lei Yan Lei Yan Siqi Hu Siqi Hu Siqi Hu Zhichun Feng Zhichun Feng Zhichun Feng Zhichun Feng |
| author_facet | Yanhua Wang Yanhua Wang Yanhua Wang Yanhua Wang Yi Wang Yi Wang Yi Wang Linhong Song Jun Li Jun Li Jun Li Yuanyuan Xie Yuanyuan Xie Yuanyuan Xie Lei Yan Lei Yan Lei Yan Siqi Hu Siqi Hu Siqi Hu Zhichun Feng Zhichun Feng Zhichun Feng Zhichun Feng |
| author_sort | Yanhua Wang |
| collection | DOAJ |
| description | Bronchopulmonary dysplasia (BPD), also known as chronic lung disease, is the most common cause of respiratory morbidity in preterm infants. Sepsis plays a significant role in the pathogenesis of BPD, and the systemic inflammatory response caused by sepsis is associated with lung development, leading to simplified alveoli and abnormal vascular development, which are the histological hallmarks of BPD. In this study, we conducted a retrospective analysis of the clinical characteristics of 306 preterm infants with BPD treated at our hospital from December 2019 to December 2022. We subsequently utilized ten machine learning (ML) algorithms and used clinical features to acquire models to predict BPD with sepsis. The performance of the model was evaluated according to the mean area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. The mean area under the curve (AUC) of the best predictive model was 0.93. A nomogram for sepsis onset was developed in the primary cohort with four factors: invasive respiratory support, CRIB II score, NEC, and chorioamnionitis. By including clinical features, ML algorithms can predict BPD with sepsis, and the random forest (RF) model (sorted by the mean AUC) performs the best. Our prediction model and nomogram can help clinicians make early diagnoses and formulate better treatment plans for preterm infants with BPD. |
| format | Article |
| id | doaj-art-eb56755cb8e64077a86c66f9d986d2bd |
| institution | Kabale University |
| issn | 2296-2360 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pediatrics |
| spelling | doaj-art-eb56755cb8e64077a86c66f9d986d2bd2025-08-20T03:30:19ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602025-06-011310.3389/fped.2025.15667471566747Clinical characteristics of bronchopulmonary dysplasia and the risk of sepsis onset prediction via machine learning modelsYanhua Wang0Yanhua Wang1Yanhua Wang2Yanhua Wang3Yi Wang4Yi Wang5Yi Wang6Linhong Song7Jun Li8Jun Li9Jun Li10Yuanyuan Xie11Yuanyuan Xie12Yuanyuan Xie13Lei Yan14Lei Yan15Lei Yan16Siqi Hu17Siqi Hu18Siqi Hu19Zhichun Feng20Zhichun Feng21Zhichun Feng22Zhichun Feng23The Second School of Clinical Medicine, Southern Medical University, Guangzhou, ChinaInstitute of Pediatrics, Faculty of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, ChinaNational Engineering Laboratory for Birth Defect Prevention and Control of Key Technology, Beijing, ChinaBeijing Key Laboratory of Pediatric Organ Failure, Beijing, ChinaInstitute of Pediatrics, Faculty of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, ChinaNational Engineering Laboratory for Birth Defect Prevention and Control of Key Technology, Beijing, ChinaBeijing Key Laboratory of Pediatric Organ Failure, Beijing, ChinaDepartment of Pediatric Cardiology, Faculty of Pediatrics, the Seventh Medical Center of PLA General Hospital, Beijing, ChinaInstitute of Pediatrics, Faculty of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, ChinaNational Engineering Laboratory for Birth Defect Prevention and Control of Key Technology, Beijing, ChinaBeijing Key Laboratory of Pediatric Organ Failure, Beijing, ChinaInstitute of Pediatrics, Faculty of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, ChinaNational Engineering Laboratory for Birth Defect Prevention and Control of Key Technology, Beijing, ChinaBeijing Key Laboratory of Pediatric Organ Failure, Beijing, ChinaInstitute of Pediatrics, Faculty of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, ChinaNational Engineering Laboratory for Birth Defect Prevention and Control of Key Technology, Beijing, ChinaBeijing Key Laboratory of Pediatric Organ Failure, Beijing, ChinaInstitute of Pediatrics, Faculty of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, ChinaNational Engineering Laboratory for Birth Defect Prevention and Control of Key Technology, Beijing, ChinaBeijing Key Laboratory of Pediatric Organ Failure, Beijing, ChinaThe Second School of Clinical Medicine, Southern Medical University, Guangzhou, ChinaInstitute of Pediatrics, Faculty of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, ChinaNational Engineering Laboratory for Birth Defect Prevention and Control of Key Technology, Beijing, ChinaBeijing Key Laboratory of Pediatric Organ Failure, Beijing, ChinaBronchopulmonary dysplasia (BPD), also known as chronic lung disease, is the most common cause of respiratory morbidity in preterm infants. Sepsis plays a significant role in the pathogenesis of BPD, and the systemic inflammatory response caused by sepsis is associated with lung development, leading to simplified alveoli and abnormal vascular development, which are the histological hallmarks of BPD. In this study, we conducted a retrospective analysis of the clinical characteristics of 306 preterm infants with BPD treated at our hospital from December 2019 to December 2022. We subsequently utilized ten machine learning (ML) algorithms and used clinical features to acquire models to predict BPD with sepsis. The performance of the model was evaluated according to the mean area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. The mean area under the curve (AUC) of the best predictive model was 0.93. A nomogram for sepsis onset was developed in the primary cohort with four factors: invasive respiratory support, CRIB II score, NEC, and chorioamnionitis. By including clinical features, ML algorithms can predict BPD with sepsis, and the random forest (RF) model (sorted by the mean AUC) performs the best. Our prediction model and nomogram can help clinicians make early diagnoses and formulate better treatment plans for preterm infants with BPD.https://www.frontiersin.org/articles/10.3389/fped.2025.1566747/fullbronchopulmonary dysplasiasepsismachine learning algorithmsnomogramprediction model |
| spellingShingle | Yanhua Wang Yanhua Wang Yanhua Wang Yanhua Wang Yi Wang Yi Wang Yi Wang Linhong Song Jun Li Jun Li Jun Li Yuanyuan Xie Yuanyuan Xie Yuanyuan Xie Lei Yan Lei Yan Lei Yan Siqi Hu Siqi Hu Siqi Hu Zhichun Feng Zhichun Feng Zhichun Feng Zhichun Feng Clinical characteristics of bronchopulmonary dysplasia and the risk of sepsis onset prediction via machine learning models Frontiers in Pediatrics bronchopulmonary dysplasia sepsis machine learning algorithms nomogram prediction model |
| title | Clinical characteristics of bronchopulmonary dysplasia and the risk of sepsis onset prediction via machine learning models |
| title_full | Clinical characteristics of bronchopulmonary dysplasia and the risk of sepsis onset prediction via machine learning models |
| title_fullStr | Clinical characteristics of bronchopulmonary dysplasia and the risk of sepsis onset prediction via machine learning models |
| title_full_unstemmed | Clinical characteristics of bronchopulmonary dysplasia and the risk of sepsis onset prediction via machine learning models |
| title_short | Clinical characteristics of bronchopulmonary dysplasia and the risk of sepsis onset prediction via machine learning models |
| title_sort | clinical characteristics of bronchopulmonary dysplasia and the risk of sepsis onset prediction via machine learning models |
| topic | bronchopulmonary dysplasia sepsis machine learning algorithms nomogram prediction model |
| url | https://www.frontiersin.org/articles/10.3389/fped.2025.1566747/full |
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