Purpose: Intraventricular hemorrhage (IVH) is a common and severe complication in premature neonates, leading to long-term neurological impairments. Early prediction and identification of risk factors for IVH in premature neonates are crucial for improving clinical outcomes. This study aimed to predict IVH in premature neonates and determine risk factors using machine learning (ML) algorithms.
Methods: This study investigated the medical records of premature neonates admitted to the neonatal intensive care unit. The patients were labeled as case (IVH) and control (No IVH). The independent variables included demographic, clinical, laboratory, and imaging data. Machine learning algorithms, including random Forest, support vector machine, logistic regression, and k-nearest neighbor, were used to train the models after data preprocessing and feature selection. The performance of the trained models was evaluated using various performance metrics.
Results: Data from 160 premature neonates were collected including 70 patients with IVH. The identified risk factors for IVH were the gestational age, birth weight, low Apgar scores at 1 min and 5 min, delivery method, head circumference, and various laboratory findings. The random forest algorithm demonstrated the highest sensitivity, specificity, accuracy, and F1 score in predicting IVH in premature neonates, with a great area under the receiver operating characteristic curve of 0.99.
Conclusion: This study revealed that the random forest model effectively predicted IVH in premature neonates. The early identification of premature neonates at higher risk of IVH allows for preventive measures and interventions to reduce the incidence and morbidity of IVH in these patients.
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http://dx.doi.org/10.1007/s00381-024-06714-z | DOI Listing |
PLoS One
January 2025
Department of Pediatrics II (Neonatology), Medical University of Innsbruck, Innsbruck, Austria.
Introduction: After the release of the Bayley Scales of Infant and Toddler Development, third edition (Bayley-III), US norms, an overestimation of outcome was observed. But, the conformity between the Bayley Scales of Infant Development, second edition (BSID-II), and the Bayley-III German norms is unknown. This retrospective analysis aimed to compare outcomes of very preterm infants tested with BSID-II and Bayley-III German norms.
View Article and Find Full Text PDFCochrane Database Syst Rev
January 2025
Cochrane Sweden, Department of Research, Development, Education and Innovation, Lund University, Skåne University Hospital, Lund, Sweden.
This is a protocol for a Cochrane Review (intervention). The objectives are as follows: To assess the benefits and harms of individualized developmental care interventions for promoting development and preventing morbidity in preterm infants.
View Article and Find Full Text PDFPak J Med Sci
January 2025
Lin Lin Department of Obstetrics and Gynecology, Fujian Maternity and Child Health Hospital College of Clinical, Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, Fujian Province 350001, China.
Objective: This study examined the potential link between maternal pre-pregnancy body mass index (PPBMI) with neonatal outcomes in twin pregnancies.
Methods: This retrospective analysis records of 1,270 women with twin pregnancies, delivered at the Fujian Maternity and Child Health Hospital between 2019 and 2021, were retrospectively analysed. Women were diagnosed as underweight, normal BMI, and overweight/obese according to their PPBMI.
Pak J Med Sci
January 2025
Lianghui Zheng Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics, Gynecology and Pediatrics, Fujian Medical University. P.R. China.
Objective: This retrospective cohort study aimed to investigate the effects of parity on gestational weight gain (GWG) and its association with maternal and neonatal outcomes in women with gestational diabetes mellitus (GDM).
Methods: This retrospective cohort study data from 2,909 pregnant women with GDM who delivered between 2021 and 2023 at Fujian Maternity and Child Health hospital, were analyzed. Participants were categorized into nulliparous (no previous births), primiparous (one previous birth), and multiparous (two or more previous births) groups.
Int J Reprod Biomed
November 2024
Department of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Background: Osteopenia of prematurity (OP) is characterized by reduced bone mineral content, and vitamin D deficiency may worsen OP by affecting bone metabolism.
Objective: This study aimed to investigate the correlation between maternal vitamin D levels and biochemical markers related to OP.
Materials And Methods: This analytical cross-sectional study, conducted at Shahid Sadoughi hospital, Yazd, Iran, from June 2022 to September 2023, included 49 pregnant women and their preterm infants.
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