Predicting Apnoeic Events in Preterm Infants

Kathleen Lim; Haimin Jiang; Andrew P Marshall; Brian Salmon; Timothy J Gale; Peter A Dargaville

doi:10.3389/fped.2020.00570

Predicting Apnoeic Events in Preterm Infants

Front Pediatr. 2020 Sep 16:8:570. doi: 10.3389/fped.2020.00570. eCollection 2020.

Authors

Kathleen Lim^{1

2}, Haimin Jiang³, Andrew P Marshall^{1

3}, Brian Salmon³, Timothy J Gale³, Peter A Dargaville^{1

2}

Affiliations

¹ Menzies Institute for Medical Research, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia.
² Neonatal and Pediatric Intensive Care Unit, Royal Hobart Hospital, Hobart, TAS, Australia.
³ School of Engineering, College of Science, Engineering and Technology, University of Tasmania, Hobart, TAS, Australia.

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.

Keywords: apnoea of prematurity; machine learning; neonatal intensive care; prediction; preterm infants.

Publication types

Review