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Predicting apnoeic events in preterm infants
Citation
Lim, KL and Jiang, H and Marshall, AP and Salmon, B and Gale, TJ and Dargaville, PA, Predicting apnoeic events in preterm infants, Frontiers in Pediatrics, 8 Article 570. ISSN 2296-2360 (2020) [Refereed Article]
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Copyright Statement
Copyright 2020 Lim, Jiang, Marshall, Salmon, Gale and Dargaville. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
DOI: doi:10.3389/fped.2020.00570
Abstract
Apnoea, a pause in respiration, is almost ubiquitous in preterm infants born before completing 30 weeks gestation. Apnoea often begets hypoxemia and/or bradycardia, and has the potential to result in adverse neurodevelopmental consequences. Our current inability to predict apnoeic events in preterm infants requires apnoea to first be detected by monitoring device/s in order to trigger an intervention by bedside (medical or nursing) staff. Such a reactive management approach is laborious, and makes the consequences of apnoeic events inevitable. Recent technological advances and improved signal processing have allowed the possibility of developing prediction models for apnoeic events in preterm infants. However, the development of such models has numerous challenges and is only starting to show potential. This paper identifies requisite components and current gaps in developing prediction models for apnoeic events, and reviews previous studies on predicting apnoeic events in preterm infants.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | apnoea of prematurity, machine learning, neonatal intensive care, prediction, preterm infants |
| Research Division: | Biomedical and Clinical Sciences |
| Research Group: | Paediatrics |
| Research Field: | Paediatrics not elsewhere classified |
| Objective Division: | Health |
| Objective Group: | Specific population health (excl. Indigenous health) |
| Objective Field: | Neonatal and child health |
| UTAS Author: | Lim, KL (Miss Kai Lim) |
| UTAS Author: | Jiang, H (Mr Haimin Jiang) |
| UTAS Author: | Marshall, AP (Mr Andrew Marshall) |
| UTAS Author: | Salmon, B (Dr Brian Salmon) |
| UTAS Author: | Gale, TJ (Dr Timothy Gale) |
| UTAS Author: | Dargaville, PA (Professor Peter Dargaville) |
| ID Code: | 141298 |
| Year Published: | 2020 |
| Web of Science® Times Cited: | 5 |
| Deposited By: | Menzies Institute for Medical Research |
| Deposited On: | 2020-10-13 |
| Last Modified: | 2022-08-25 |
| Downloads: | 22 View Download Statistics |
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