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Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants

M. Ashoori, JM. O'Toole, KD. O'Halloran, G. Naulaers, L. Thewissen, J. Miletin, PY. Cheung, A. El-Khuffash, D. Van Laere, Z. Straňák, EM. Dempsey, FB. McDonald

. 2023 ; 10 (6) : . [pub] 20230523

Status neindexováno Jazyk angličtina Země Švýcarsko

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/bmc23009712

Grantová podpora
SFI 18/SIRG/5483 Science Foundation Ireland - Ireland
SFI 15/SIRG/3580 Science Foundation Ireland - Ireland
12/RC/2272 Science Foundation Ireland - Ireland

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 (n = 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.

Citace poskytuje Crossref.org

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$a OBJECTIVE: To test the potential utility of applying machine learning methods to regional cerebral (rcSO2) and peripheral oxygen saturation (SpO2) signals to detect brain injury in extremely preterm infants. STUDY DESIGN: A subset of infants enrolled in the Management of Hypotension in Preterm infants (HIP) trial were analysed (n = 46). All eligible infants were <28 weeks' gestational age and had continuous rcSO2 measurements performed over the first 72 h and cranial ultrasounds performed during the first week after birth. SpO2 data were available for 32 infants. The rcSO2 and SpO2 signals were preprocessed, and prolonged relative desaturations (PRDs; data-driven desaturation in the 2-to-15-min range) were extracted. Numerous quantitative features were extracted from the biosignals before and after the exclusion of the PRDs within the signals. PRDs were also evaluated as a stand-alone feature. A machine learning model was used to detect brain injury (intraventricular haemorrhage-IVH grade II-IV) using a leave-one-out cross-validation approach. RESULTS: The area under the receiver operating characteristic curve (AUC) for the PRD rcSO2 was 0.846 (95% CI: 0.720-0.948), outperforming the rcSO2 threshold approach (AUC 0.593 95% CI 0.399-0.775). Neither the clinical model nor any of the SpO2 models were significantly associated with brain injury. CONCLUSION: There was a significant association between the data-driven definition of PRDs in rcSO2 and brain injury. Automated analysis of PRDs of the cerebral NIRS signal in extremely preterm infants may aid in better prediction of IVH compared with a threshold-based approach. Further investigation of the definition of the extracted PRDs and an understanding of the physiology underlying these events are required.
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$a Naulaers, Gunnar $u Department of Development and Regeneration, Katholieke Universiteit Leuven, Herestraat 49, 3000 Leuven, Belgium $u Neonatal Intensive Care, Katholieke Universiteit Hospital Leuven, Herestraat 49, 3000 Leuven, Belgium
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$a Van Laere, David $u Neonatale Intensive Care Unit, Universitair Ziekenhuis, (UZ) Antwerp, Drie Eikenstraat 655, 2650 Antwerp, Belgium
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