Newborn Cardiovascular Digital Twins: Personalised Identifiable Computational Models of the Neonatal Circulation

<p>Introduction: Preterm babies have an increased lifetime cardiovascular risk. We aimed to collect ultrasound data on the developing cardiovascular system in healthy late preterm and term newborn and to develop newborn cardiovascular digital twins: personalisable identifiable computational models of the neonatal cardiovascular system. Methods: Single-centre, prospective, observational cohort study recruited healthy term (≥37+0 weeks’ gestation) and late preterm (34+0–36+6 weeks’ gestation) babies. Ultrasound data were collected within 48 h of birth and 3 to 6 weeks later. These data inform personalised 0D closed-loop bond graph models that simulate blood pressure and flows in the newborn cardiovascular system, and model identifiability was demonstrated. Results: Fifteen term and 10 preterm babies were assessed at 2 days old, and 12 (80%) term and 7 (70%) preterm babies underwent a second assessment at median post-menstrual age 295 days. We found no evidence that late preterm birth is associated with structural cardiovascular changes in early life. Although our term and preterm models had similar lower body resistances at birth, by 3 to 6 weeks of age, the preterm group showed evidence of greater vascular resistance. Conclusion: These newborn cardiovascular digital twins suggest a possible mechanism of cardiovascular remodelling in late preterm neonates. </p>

Institutions

Publication Details

Journal
Maternal and Children s Health
Published
2025-06-02
DOI
https://doi.org/10.1159/000546724
Citations
1
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
2.48
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Newborn Cardiovascular Digital Twins: Personalised Identifiable Computational Models of the Neonatal Circulation

1 citations
Maternal and Children s Health
Geochemistry and Geologic Mapping
2.48
article

Newborn Cardiovascular Digital Twins: Personalised Identifiable Computational Models of the Neonatal Circulation

article en
1 citations

Abstract

<p>Introduction: Preterm babies have an increased lifetime cardiovascular risk. We aimed to collect ultrasound data on the developing cardiovascular system in healthy late preterm and term newborn and to develop newborn cardiovascular digital twins: personalisable identifiable computational models of the neonatal cardiovascular system. Methods: Single-centre, prospective, observational cohort study recruited healthy term (≥37+0 weeks’ gestation) and late preterm (34+0–36+6 weeks’ gestation) babies. Ultrasound data were collected within 48 h of birth and 3 to 6 weeks later. These data inform personalised 0D closed-loop bond graph models that simulate blood pressure and flows in the newborn cardiovascular system, and model identifiability was demonstrated. Results: Fifteen term and 10 preterm babies were assessed at 2 days old, and 12 (80%) term and 7 (70%) preterm babies underwent a second assessment at median post-menstrual age 295 days. We found no evidence that late preterm birth is associated with structural cardiovascular changes in early life. Although our term and preterm models had similar lower body resistances at birth, by 3 to 6 weeks of age, the preterm group showed evidence of greater vascular resistance. Conclusion: These newborn cardiovascular digital twins suggest a possible mechanism of cardiovascular remodelling in late preterm neonates. </p>

Maternal and Children s HealthVol. 1(1)
University of Auckland (NZ), Auckland City Hospital (NZ), Starship Children's Health (NZ)
Openalex Percentile: Top 7%
Geochemistry and Geologic Mapping
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.