Features of cerebral oxygenation detects brain injury in premature infants

Machine learning models of cerebral oxygenation (rcSO2) for brain injury detection in neonates with hypoxic-ischaemic encephalopathy

The present study was designed to test the potential utility of regional cerebral oxygen saturation (rcSO2) in detecting term infants with brain injury. The study also examined whether quantitative rcSO2 features are associated with grade of hypoxic ischaemic encephalopathy (HIE). We analysed 58 term infants with HIE (>36 weeks of gestational age) enrolled in a prospective observational study. All newborn infants had a period of continuous rcSO2 monitoring and magnetic resonance imaging (MRI) assessment during the first week of life. rcSO2 Signals were pre-processed and quantitative features were extracted. Machine-learning and deep-learning models were developed to detect adverse outcome (brain injury on MRI or death in the first week) using the leave-one-out cross-validation approach and to assess the association between rcSO2 and HIE grade (modified Sarnat - at 1 h). The machine-learning model (rcSO2 excluding prolonged relative desaturations) significantly detected infant MRI outcome or death in the first week of life [area under the curve (AUC) = 0.73, confidence interval (CI) = 0.59-0.86, Matthew's correlation coefficient = 0.35]. In agreement, deep learning models detected adverse outcome with an AUC = 0.64, CI = 0.50-0.79. We also report a significant association between rcSO2 features and HIE grade using a machine learning approach (AUC = 0.81, CI = 0.73-0.90). We conclude that automated analysis of rcSO2 using machine learning methods in term infants with HIE was able to determine, with modest accuracy, infants with adverse outcome. De novo approaches to signal analysis of NIRS holds promise to aid clinical decision making in the future. KEY POINTS: Hypoxic-induced neonatal brain injury contributes to both short- and long-term functional deficits. Non-invasive continuous monitoring of brain oxygenation using near-infrared- spectroscopy offers a potential new insight to the development of serious injury. In this study, characteristics of the NIRS signal were summarised using either predefined features or data-driven feature extraction, both were combined with a machine learning approach to predict short-term brain injury. Using data from a cohort of term infants with hypoxic ischaemic encephalopathy, the present study illustrates that automated analysis of regional cerebral oxygen saturation rcSO2, using either machine learning or deep learning methods, was able to determine infants with adverse outcome.

Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants

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.

Optical Monitoring in Neonatal Seizures

BACKGROUND

Neonatal seizures remain a significant cause of morbidity and mortality worldwide. The past decade has resulted in substantial progress in seizure detection and understanding the impact seizures have on the developing brain. Optical monitoring such as cerebral near-infrared spectroscopy (NIRS) and broadband NIRS can provide non-invasive continuous real-time monitoring of the changes in brain metabolism and haemodynamics.

AIM

To perform a systematic review of optical biomarkers to identify changes in cerebral haemodynamics and metabolism during the pre-ictal, ictal, and post-ictal phases of neonatal seizures.

METHOD

A systematic search was performed in eight databases. The search combined the three broad categories: (neonates) AND (NIRS) AND (seizures) using the stepwise approach following PRISMA guidance.

RESULTS

Fifteen papers described the haemodynamic and/or metabolic changes observed with NIRS during neonatal seizures. No randomised controlled trials were identified during the search. Studies reported various changes occurring in the pre-ictal, ictal, and post-ictal phases of seizures.

CONCLUSION

Clear changes in cerebral haemodynamics and metabolism were noted during the pre-ictal, ictal, and post-ictal phases of seizures in neonates. Further studies are necessary to determine whether NIRS-based methods can be used at the cot-side to provide clear pathophysiological data in real-time during neonatal seizures.

Does Early Cerebral Near-Infrared Spectroscopy Monitoring Predict Outcome in Neonates with Hypoxic Ischaemic Encephalopathy? A Systematic Review of Diagnostic Test Accuracy

INTRODUCTION

Hypoxic ischaemic encephalopathy (HIE) remains one of the top 10 contributors to the global burden of disease. Early objective biomarkers are required. Near-infrared spectroscopy (NIRS) may provide a valuable insight into cerebral perfusion and metabolism. We aimed to determine whether early NIRS monitoring (<6 h of age) can predict outcome as defined by grade of encephalopathy, brain MRI findings, and/or neurodevelopmental outcome at 1-2 years in infants with HIE.

METHODS

We searched PubMed, Scopus, Web of Science, Embase, and The Cochrane Library databases (July 2019). Studies of infants born ≥36+0 weeks gestation with HIE who had NIRS recording commenced before 6 h of life were included. We planned to provide a narrative of all the studies included, and if similar clinically and methodologically, the results would be pooled in a meta-analysis to determine test accuracy.

RESULTS

Seven studies were included with a combined total of 161 infants. Only 1 study included infants with mild HIE. A range of different oximeters and probes were utilized with varying outcome measures making comparison difficult. Although some studies showed a trend towards higher cSO2 values before 6 h in infants with adverse neurodevelopmental outcomes, in the majority, this was not significant until beyond 24 h of life.

CONCLUSION

Very little data currently exists to assess the use of early NIRS to predict outcome in infants with HIE. Further studies using a standardized approach are required before NIRS can be evaluated as a potential objective assessment tool for early identification of at-risk infants.

Extracting transients from cerebral oxygenation signals of preterm infants: a new singular-spectrum analysis method

Many infants born prematurely develop brain injury within the first few days after birth. Near infrared spectroscopy (NIRS) is a safe technology that can continuously monitor the varying levels of oxygenation in the brain. Analysis of this signal has the potential to detect the onset of brain injury. We develop a method that extracts transient waveforms from the oxygenation signal. This method uses the cosine transform and singular-spectrum analysis to decompose the signal. We test different procedures to select a threshold for estimating the transient component. As part of the development of the method, we build a model of the cerebral oxygenation signals combining clusters of transient waveforms and nonstationary coloured noise. After development, we test on cerebral oxygenation recordings from 10 extremely preterm infants. We find that using the decomposition method to remove the transient components improves detection performance of brain injury, from an area-under the receiver operator characteristic of 0.91 to 1.00. These findings highlight the importance of specific signal processing methods for the cerebral oxygenation signal and the potential for NIRS as a neuromonitoring technology in neonatal intensive care.

Interpretation of Cerebral Oxygenation Changes in the Preterm Infant

Near-infrared spectroscopy (NIRS) allows for continuous, non-invasive monitoring of end-organ tissue oxygenation. The use of NIRS, cerebral NIRS (cNIRS) in particular, in neonatal care has increased significantly over the last few years. This dynamic monitoring technique provides real-time information on the cerebral and haemodynamic status of the neonate and has the potential to serve as an important adjunct to patient care with some centres routinely utilising cNIRS to aid decision-making at the bedside. cNIRS values may be influenced by many variables, including cardiac, respiratory and metabolic parameters, and therefore it is essential to understand the pathophysiology behind alterations in cNIRS values. Correct interpretation is required to direct appropriate patient-specific interventions. This article aims to assist clinicians in deciphering cNIRS values by providing an overview of potential causes of fluctuations in cNIRS values, illustrated by common clinical scenarios, with particular emphasis on the preterm infant.

Monitoring Circulation During Transition in Extreme Low Gestational Age Newborns: What's on the Horizon?

Echocardiography and near-infrared spectroscopy have significantly changed our view on hemodynamic transition of the extreme preterm infant. Instead of focusing on maintaining an arbitrary target value of blood pressure, we aim for circulatory well-being by a comprehensive holistic assessment of markers of cardiovascular instability. Most of these clinical and biochemical indices are influenced by transition itself and remain poor discriminators to identify patients with a potential need for therapeutic intervention. At the same time, the evolution in data capturing and storage has led to a change in our approach to monitor vital parameters. Continuous trend monitoring has become more and more relevant. By using signal extraction methods, changes in trends over time can be quantified. In this review, we will discuss the impact of these innovations on the current monitoring practices and explore some of the potential benefits these techniques may have in improving real-time detection of extreme low birth weight infants at risk for morbidity related to impaired hemodynamic transition.