ArticleBMC pediatrics2023
Establishment and evaluation of nomogram for predicting intraventricular hemorrhage in neonatal acute respiratory distress syndrome.
Article in BMC pediatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Construction and evaluation of neonatal respiratory failure risk prediction model for neonatal respiratory distress syndrome.BMC pulmonary medicine · 2024Trial
- Quality improvement project to reduce intraventricular haemorrhage in very preterm infants failed due to increased life-sustaining intensive care at low gestational age.BMJ open quality · 2025Article
- Development of a machine learning model to identify intraventricular hemorrhage using time-series analysis in preterm infants.Scientific reports · 2024Article
- Do Different Amounts of Exogenous Surfactant Differently Influence Cerebrovascular Instability in a Consecutive Group of Preterm Babies? Preliminary Results from a Single-Center Experience.Children (Basel, Switzerland) · 2024Article
- A nomogram for predicting neonatal acute respiratory distress syndrome in patients with neonatal pneumonia after 34 weeks of gestation.Frontiers in pediatrics · 2024Article
- Risk factors for brain injury in premature infants with twin-to-twin transfusion syndrome: a retrospective cohort study.Translational pediatrics · 2023Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIntraventricular hemorrhage (IVH) is the most common type of brain injury in newborns, especially in newborns with Neonatal acute respiratory distress syndrome (ARDS). IVH can cause brain parenchyma damage and long-term neurological sequelae in children. Early identification and prevention of sequelae are essential. This study aims to establish a predictive nomogram for the early prediction of IVH in newborns with ARDS.
methodsFrom 2019 to 2021, we collected data from 222 infants diagnosed with ARDS in the Department of Neonatology, First Affiliated Hospital of Xinjiang Medical University. Infants have been randomly assigned to the training set (n = 161) or the validation set (n = 61) at a ratio of 7:3. Variables were screened using the Least Absolute Contract and Selection Operator (LASSO) regression to create a risk model for IVH in infants with ARDS. The variables chosen in the LASSO regression model were used to establish the prediction model using multivariate logistic regression analysis.
resultsWe recognized 4 variables as independent risk factors for IVH in newborns with ARDS via LASSO analysis, consisting of premature rupture of membranes (PROM), pulmonary surfactant (PS) dosage, PH
conclusionIn conclusion, the nomogram based on 4 factors shows good identification, calibration and clinical practicability. Our nomographs can help clinicians make clinical decisions, screen high-risk ARDS newborns, and facilitate early identification and management of IVH patients.
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