Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis
<i>Background and Objectives</i>: Patent ductus arteriosus (PDA) is common in newborns, being associated with high morbidity and mortality. While maternal and neonatal conditions are known contributors, few studies use advanced machine learning (ML) as predictive factors. This study asse...
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MDPI AG
2025-03-01
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| author | Ana Maria Cristina Jura Daniela Eugenia Popescu Cosmin Cîtu Marius Biriș Corina Pienar Corina Paul Oana Maria Petrescu Andreea Teodora Constantin Alexandru Dinulescu Ioana Roșca |
| author_facet | Ana Maria Cristina Jura Daniela Eugenia Popescu Cosmin Cîtu Marius Biriș Corina Pienar Corina Paul Oana Maria Petrescu Andreea Teodora Constantin Alexandru Dinulescu Ioana Roșca |
| author_sort | Ana Maria Cristina Jura |
| collection | DOAJ |
| description | <i>Background and Objectives</i>: Patent ductus arteriosus (PDA) is common in newborns, being associated with high morbidity and mortality. While maternal and neonatal conditions are known contributors, few studies use advanced machine learning (ML) as predictive factors. This study assessed how maternal pathologies, medications, and neonatal factors affect the risk of PDA using traditional statistics and ML algorithms: Random Forest (RF) and XGBoost (XGB). <i>Materials and Methods</i>: A retrospective 3-year cohort study of 201 NICU neonates assessed maternal and neonatal factors. Logistic regression (LR) and chi-square analyses identified significant predictors, while ML models enhanced predictive accuracy and pinpointed key PDA factors. <i>Results</i>: LR identified prolonged rupture of membranes (>18 h) as the most significant predictor (OR: 13.03, <i>p</i> < 0.001). The ML models identified gestational age, maternal anemia, prenatal care level, birth weight, prolonged rupture of membranes, medication usage, diabetes, pregnancy-induced hypertension, SARS-CoV-2 infection, and cervical cerclage as key predictors. The RF model had 76.3% accuracy, moderate sensitivity (47.4%), and high specificity (90%). XGB performed better with 81.4% accuracy, an AUC of 0.872, sensitivity of 92.5%, and specificity of 57.9%. <i>Conclusions</i>: This study shows that maternal and neonatal factors significantly influence the risk of PDA. ML, particularly XGBoost, enhances predictive abilities, guiding targeted interventions and improving neonatal outcomes. |
| format | Article |
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| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Medicina |
| spelling | doaj-art-e0cb9d08dde44b49956f064eb0d3dd782025-08-20T02:28:37ZengMDPI AGMedicina1010-660X1648-91442025-03-0161460310.3390/medicina61040603Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning AnalysisAna Maria Cristina Jura0Daniela Eugenia Popescu1Cosmin Cîtu2Marius Biriș3Corina Pienar4Corina Paul5Oana Maria Petrescu6Andreea Teodora Constantin7Alexandru Dinulescu8Ioana Roșca9Department of Obstetrics and Gynecology, “Victor Babeş” University of Medicine and Pharmacy, Eftimie Murgu Sq. No. 2, 300041 Timişoara, RomaniaDepartment of Obstetrics and Gynecology, “Victor Babeş” University of Medicine and Pharmacy, Eftimie Murgu Sq. No. 2, 300041 Timişoara, RomaniaDepartment of Obstetrics and Gynecology, “Victor Babeş” University of Medicine and Pharmacy, Eftimie Murgu Sq. No. 2, 300041 Timişoara, RomaniaDepartment of Obstetrics and Gynecology, “Victor Babeş” University of Medicine and Pharmacy, Eftimie Murgu Sq. No. 2, 300041 Timişoara, Romania2nd Pediatrics Clinic, Department of Pediatrics, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania2nd Pediatrics Clinic, Department of Pediatrics, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, RomaniaPediatric Cardiology, Clinical Hospital of Obstetrics and Gynecology “Prof. Dr. P.Sirbu”, 060251 Bucharest, RomaniaDoctoral School, University of Medicine and Pharmacy “Carol Davila”, 020021 Bucharest, RomaniaDoctoral School, University of Medicine and Pharmacy “Carol Davila”, 020021 Bucharest, RomaniaDoctoral School, University of Medicine and Pharmacy “Carol Davila”, 020021 Bucharest, Romania<i>Background and Objectives</i>: Patent ductus arteriosus (PDA) is common in newborns, being associated with high morbidity and mortality. While maternal and neonatal conditions are known contributors, few studies use advanced machine learning (ML) as predictive factors. This study assessed how maternal pathologies, medications, and neonatal factors affect the risk of PDA using traditional statistics and ML algorithms: Random Forest (RF) and XGBoost (XGB). <i>Materials and Methods</i>: A retrospective 3-year cohort study of 201 NICU neonates assessed maternal and neonatal factors. Logistic regression (LR) and chi-square analyses identified significant predictors, while ML models enhanced predictive accuracy and pinpointed key PDA factors. <i>Results</i>: LR identified prolonged rupture of membranes (>18 h) as the most significant predictor (OR: 13.03, <i>p</i> < 0.001). The ML models identified gestational age, maternal anemia, prenatal care level, birth weight, prolonged rupture of membranes, medication usage, diabetes, pregnancy-induced hypertension, SARS-CoV-2 infection, and cervical cerclage as key predictors. The RF model had 76.3% accuracy, moderate sensitivity (47.4%), and high specificity (90%). XGB performed better with 81.4% accuracy, an AUC of 0.872, sensitivity of 92.5%, and specificity of 57.9%. <i>Conclusions</i>: This study shows that maternal and neonatal factors significantly influence the risk of PDA. ML, particularly XGBoost, enhances predictive abilities, guiding targeted interventions and improving neonatal outcomes.https://www.mdpi.com/1648-9144/61/4/603patent ductus arteriosusneonatal riskmachine learningmaternal pathologyneonatal outcomes |
| spellingShingle | Ana Maria Cristina Jura Daniela Eugenia Popescu Cosmin Cîtu Marius Biriș Corina Pienar Corina Paul Oana Maria Petrescu Andreea Teodora Constantin Alexandru Dinulescu Ioana Roșca Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis Medicina patent ductus arteriosus neonatal risk machine learning maternal pathology neonatal outcomes |
| title | Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis |
| title_full | Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis |
| title_fullStr | Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis |
| title_full_unstemmed | Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis |
| title_short | Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis |
| title_sort | predicting risk for patent ductus arteriosus in the neonate a machine learning analysis |
| topic | patent ductus arteriosus neonatal risk machine learning maternal pathology neonatal outcomes |
| url | https://www.mdpi.com/1648-9144/61/4/603 |
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