Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants

• Main Authors: Minoo Ashoori (Author), John M. O'Toole (Author), Ken D. O'Halloran (Author), Gunnar Naulaers (Author), Liesbeth Thewissen (Author), Jan Miletin (Author), Po-Yin Cheung (Author), Afif EL-Khuffash (Author), David Van Laere (Author), Zbyněk Straňák (Author), Eugene M. Dempsey (Author), Fiona B. McDonald (Author)
• Format: Book
• Published: MDPI AG, 2023-05-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

Objective: To test the potential utility of applying machine learning methods to regional cerebral (rcSO₂) and peripheral oxygen saturation (SpO₂) signals to detect brain injury in extremely preterm infants. Study design: A subset of infants enrolled in the Management of Hypotension in Preterm infants (HIP) trial were analysed (<i>n</i> = 46). All eligible infants were <28 weeks' gestational age and had continuous rcSO₂ measurements performed over the first 72 h and cranial ultrasounds performed during the first week after birth. SpO₂ data were available for 32 infants. The rcSO₂ and SpO₂ signals were preprocessed, and prolonged relative desaturations (PRDs; data-driven desaturation in the 2-to-15-min range) were extracted. Numerous quantitative features were extracted from the biosignals before and after the exclusion of the PRDs within the signals. PRDs were also evaluated as a stand-alone feature. A machine learning model was used to detect brain injury (intraventricular haemorrhage-IVH grade II-IV) using a leave-one-out cross-validation approach. Results: The area under the receiver operating characteristic curve (AUC) for the PRD rcSO₂ was 0.846 (95% CI: 0.720-0.948), outperforming the rcSO₂ threshold approach (AUC 0.593 95% CI 0.399-0.775). Neither the clinical model nor any of the SpO₂ models were significantly associated with brain injury. Conclusion: There was a significant association between the data-driven definition of PRDs in rcSO₂ and brain injury. Automated analysis of PRDs of the cerebral NIRS signal in extremely preterm infants may aid in better prediction of IVH compared with a threshold-based approach. Further investigation of the definition of the extracted PRDs and an understanding of the physiology underlying these events are required.

Item Description

10.3390/children10060917
2227-9067