Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants

Objective: To test the potential utility of applying machine learning methods to regional cerebral (rcSO(2)) and peripheral oxygen saturation (SpO(2)) 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 (n = 46). All eligible infants were <28 weeks' gestational age and had continuous rcSO(2) measurements performed over the first 72 h and cranial ultrasounds performed during the first week after birth. SpO(2) data were available for 32 infants. The rcSO(2) and SpO(2) 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 rcSO(2) was 0.846 (95% CI: 0.720-0.948), outperforming the rcSO(2) threshold approach (AUC 0.593 95% CI 0.399-0.775). Neither the clinical model nor any of the SpO(2) models were significantly associated with brain injury. Conclusion: There was a significant association between the data-driven definition of PRDs in rcSO(2) 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.