Development of a machine learning-based multicenter study for predicting severe intraventricular hemorrhage in preterm infants using perinatal variables
- 1Department of Neonatology, Children’s Hospital of Nanjing Medical University , , ,
- 2Department of Urology, Children’s Hospital of Nanjing Medical University , , ,
- 3Department of Hematology and Oncology, Children’s Hospital of Nanjing Medical University , , ,
Abstract
Aim: To develop and validate machine learning models for predicting severe intraventricular hemorrhage (SIVH) in preterm infants using only prenatal and intrapartum variables, and to identify the optimal model.
Method: This multicenter retrospective cohort study included preterm infants (gestational age ≤34 weeks) who required noninvasive or invasive respiratory support, using data from the Jiangsu Neonatal Respiratory Failure Collaborative Network (2019–2021). Data were split 7:3 into training and validation sets. Eight machine learning algorithms were compared. Performance was assessed by area under the curve (AUC), calibration, and decision curve analysis. An independent cohort served for temporal validation.
Results: Among 3,320 infants, the incidence of SIVH was 6.66%. Six predictors were identified: gestational age, birth weight, endotracheal intubation in the delivery room, chest compressions in the delivery room, surfactant administration in the delivery room, and chorioamnionitis. In the training set, logistic regression achieved the highest mean AUC based on 10-fold cross-validation. Bootstrap internal validation of the LR model yielded a corrected AUC of 0.809. In the independent hold-out validation set, the LR model again demonstrated the best discriminative ability among the eight algorithms (AUC: 0.804), with satisfactory calibration and favorable net benefit. Sensitivity analyses showed that the LR model maintained robust performance in temporal validation using an independent cohort (AUC: 0.822) and across geographic subgroups (AUC range: 0.729–0.885). An online interactive tool was also developed to facilitate clinical application.
Interpretation: A LR model using six readily available perinatal variables provides good discrimination and calibration for predicting SIVH in preterm infants, enabling immediate risk stratification at birth for SIVH occurring during the neonatal hospitalization period.
Related
- Respiratory Distress Syndrome: An Important Risk Factor for Intraventricular Hemorrhage in Preterm Neonates
- Human umbilical cord mesenchymal stem cells alleviate brain injury and promote neuroregeneration via BDNF/TrkB/CREB/PI3K-Akt pathway in preterm rats with intraventricular hemorrhage
- Head-to-head comparison of glucose-to-albumin ratio against five albumin-derived indices for predicting 28-day ICU mortality in critically ill patients with spontaneous intracerebral hemorrhage: a multicenter retrospective study.
- Hemorrhage distribution, imaging-defined post-hemorrhagic ventricular dilatation, and clinical outcomes in neonates with intraventricular and cerebral parenchymal hemorrhage: a single-center retrospective cohort study
- Development and Validation of an Interpretable Machine‐Learning Model for Predicting Suboptimal Clinical Response After Sleeve Gastrectomy: A Multicenter Study
BibTeX
@article{shen2026development,
title = {{Development of a machine learning-based multicenter study for predicting severe intraventricular hemorrhage in preterm infants using perinatal variables}},
author = {Fei Shen and Cheng Jin and Feixiang Wang and Rui Cheng and Hui Rong and Yang Yang and Caihong Fu},
journal = {Frontiers in neurology},
year = {2026},
month = oct,
doi = {10.3389/fneur.2026.1953566},
url = {https://europepmc.org/article/PMC/PMC13668575},
note = {CC BY},
}
RIS
TY - JOUR
TI - Development of a machine learning-based multicenter study for predicting severe intraventricular hemorrhage in preterm infants using perinatal variables
AU - Fei Shen
AU - Cheng Jin
AU - Feixiang Wang
AU - Rui Cheng
AU - Hui Rong
AU - Yang Yang
AU - Caihong Fu
PY - 2026
DA - 2026/10/01
JO - Frontiers in neurology
DO - 10.3389/fneur.2026.1953566
UR - https://europepmc.org/article/PMC/PMC13668575
AB - Aim: To develop and validate machine learning models for predicting severe intraventricular hemorrhage (SIVH) in preterm infants using only prenatal and intrapartum variables, and to identify the optimal model. Method: This multicenter retrospective cohort study included preterm infants (gestational age ≤34 weeks) who required noninvasive or invasive respiratory support, using data from the Jiangsu Neonatal Respiratory Failure Collaborative Network (2019–2021). Data were split 7:3 into training and validation sets. Eight machine learning algorithms were compared. Performance was assessed by area under the curve (AUC), calibration, and decision curve analysis. An independent cohort served for temporal validation. Results: Among 3,320 infants, the incidence of SIVH was 6.66%. Six predictors were identified: gestational age, birth weight, endotracheal intubation in the delivery room, chest compressions in the delivery room, surfactant administration in the delivery room, and chorioamnionitis. In the training set, logistic regression achieved the highest mean AUC based on 10-fold cross-validation. Bootstrap internal validation of the LR model yielded a corrected AUC of 0.809. In the independent hold-out validation set, the LR model again demonstrated the best discriminative ability among the eight algorithms (AUC: 0.804), with satisfactory calibration and favorable net benefit. Sensitivity analyses showed that the LR model maintained robust performance in temporal validation using an independent cohort (AUC: 0.822) and across geographic subgroups (AUC range: 0.729–0.885). An online interactive tool was also developed to facilitate clinical application. Interpretation: A LR model using six readily available perinatal variables provides good discrimination and calibration for predicting SIVH in preterm infants, enabling immediate risk stratification at birth for SIVH occurring during the neonatal hospitalization period.
SN - 1664-2295
ER -