ArticleJAMA network open2023
Predicting the Neurodevelopmental Outcome in Extremely Preterm Newborns Using a Multimodal Prognostic Model Including Brain Function Information.
Article in JAMA network open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- The Establishment of a Simplified Clinical Scoring System for Neurodevelopmental Follow-Up and Its Application in Very Preterm Infants.Child: care, health and development · 2026Article
- [Research progress in artificial intelligence for the diagnosis and management of diseases in preterm infants].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Review
- Association between neonatal brain volume and school-age executive function in children born moderate-to-late preterm.Pediatric research · 2026Article
- Reimagining maternal and infant wellbeing through AI-embedded integrative arts-based care: a public health perspective.Frontiers in public health · 2026Article
- Machine learning classification of EEG responses to pain-related vs non-pain-related stimulus in preterm infants.Pain reports · 2025Article
- Multimodal ensemble machine learning predicts neurological outcome within three hours after out of hospital cardiac arrest.Scientific reports · 2025Article
- The shifting landscape of the preterm brain.Neuron · 2025Review
- External Validation of a Model for Predicting Outcomes in Preterm Newborns.JAMA network open · 2025Article
- Using heart rate variability to predict neurological outcomes in preterm infants: a scoping review.Pediatric research · 2025Article
- Association of a Count of Inpatient Morbidities with 2-Year Outcomes among Infants Born Extremely Preterm.The Journal of pediatrics · 2025Observational
- Prediction of Neurodevelopmental Outcomes in Very Preterm Infants: Comparing Machine Learning Methods to Logistic Regression.Children (Basel, Switzerland) · 2024Article
- Deep Learning Model for Predicting Neurodevelopmental Outcome in Very Preterm Infants Using Cerebral Ultrasound.Mayo Clinic proceedings. Digital health · 2024Article
- Article
- The Persisting Value of Autopsies on Extremely Preterm Infants in the 21Maternal-fetal medicine (Wolters Kluwer Health, Inc.) · 2023Article
- Semi-Automatic GUI Platform to Characterize Brain Development in Preterm Children Using Ultrasound Images.Journal of imaging · 2023Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: Early assessment of the prognosis of preterm newborns is crucial for accurately informing parents and making treatment decisions. The currently available prognostic models rarely incorporate functional brain information from conventional electroencephalography (cEEG). Objective: To examine the performance of a multimodal model combining (1) brain function information with (2) brain structure information (cranial ultrasonography), and (3) perinatal and (4) postnatal risk factors for the prediction of death or neurodevelopmental impairment (NDI) in extremely preterm infants. Design, Setting, and Participants: Preterm newborns (23-28 weeks' gestational age) admitted to the neonatal intensive care unit at Amiens-Picardie University Hospital were retrospectively included (January 1, 2013, to January 1, 2018). Risk factors from the 4 categories were collected during the first 2 weeks post delivery. Neurodevelopmental impairment was assessed at age 2 years with the Denver Developmental Screening Test II. No or moderate NDI was considered a favorable outcome. Death or severe NDI was considered an adverse outcome. Data analysis was performed from August 26, 2021, to March 31, 2022. Main Outcomes and Measures: After the selection of variables significantly associated with outcome, 4 unimodal prognostic models (considering each category of variable independently) and 1 multimodal model (considering all variables simultaneously) were developed. After a multivariate analysis for models built with several variables, decision-tree algorithms were run on each model. The areas under the curve for decision-tree classifications of adverse vs favorable outcomes were determined for each model, compared using bootstrap tests, and corrected for type I errors. Results: A total of 109 newborns (58 [53.2% male]) born at a mean (SD) gestational age of 26.3 (1.1) weeks were included. Among them, 52 (47.7%) had a favorable outcome at age 2 years. The multimodal model area under the curve (91.7%; 95% CI, 86.4%-97.0%) was significantly higher than those of the unimodal models (P < .003): perinatal model (80.6%; 95% CI, 72.5%-88.7%), postnatal model (81.0%; 95% CI, 72.6%-89.4%), brain structure model (cranial ultrasonography) (76.6%; 95% CI, 67.8%-85.3%), and brain function model (cEEG) (78.8%; 95% CI, 69.9%-87.7%). Conclusions and Relevance: In this prognostic study of preterm newborns, the inclusion of brain information in a multimodal model was associated with significant improvement in the outcome prediction, which may have resulted from the complementarity of the risk factors and reflected the complexity of the mechanisms that interfered with brain maturation and led to death or NDI.
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