Impact of Skull Thickness on Cerebral NIRS Oximetry in Neonates: An in silico Study.

Monitoring of cerebral tissue oxygen saturation (StO2) by near-infrared spectroscopy (NIRS oximetry) has great potential to reduce the incidence of hypoxic and hyperoxic events and thus prevent long-term disabilities in preterm neonates. Since the light has to penetrate superficial layers (bone, skin and cerebrospinal fluid) before it reaches the brain, the question arises whether these layers influence cerebral StO2 measurement. We assessed this influence on the accuracy of cerebral StO2 values. For that purpose, we simulated light propagation with 'N-layered medium' software. It was found that with a superficial layer thickness of ≤6 mm, typical for term and preterm neonates, StO2 accurately reflects cerebral tissue oxygenation.

Reducing False Alarm Rates in Neonatal Intensive Care: A New Machine Learning Approach.

In neonatal intensive care units (NICUs), 87.5% of alarms by the monitoring system are false alarms, often caused by the movements of the neonates. Such false alarms are not only stressful for the neonates as well as for their parents and caregivers, but may also lead to longer response times in real critical situations. The aim of this project was to reduce the rates of false alarms by employing machine learning algorithms (MLA), which intelligently analyze data stemming from standard physiological monitoring in combination with cerebral oximetry data (in-house built, OxyPrem). MATERIALS & METHODS: Four popular MLAs were selected to categorize the alarms as false or real: (i) decision tree (DT), (ii) 5-nearest neighbors (5-NN), (iii) naïve Bayes (NB) and (iv) support vector machine (SVM). We acquired and processed monitoring data (median duration (SD): 54.6 (± 6.9) min) of 14 preterm infants (gestational age: 26 6/7 (± 2 5/7) weeks). A hybrid method of filter and wrapper feature selection generated the candidate subset for training these four MLAs. RESULTS: A high specificity of >99% was achieved by all four approaches. DT showed the highest sensitivity (87%). The cerebral oximetry data improved the classification accuracy. DISCUSSION & CONCLUSION: Despite a (as yet) low amount of data for training, the four MLAs achieved an excellent specificity and a promising sensitivity. Presently, the current sensitivity is insufficient since, in the NICU, it is crucial that no real alarms are missed. This will most likely be improved by including more subjects and data in the training of the MLAs, which makes pursuing this approach worthwhile.

Comparison of tissue oximeters on a liquid phantom with adjustable optical properties: an extension.

Cerebral near-infrared spectroscopy (NIRS) oximetry may help clinicians to improve patient treatment. However, the application of NIRS oximeters is increasingly causing confusion to the users due to the inconsistency of tissue oxygen haemoglobin saturation (StO2) readings provided by different oximeters. To establish a comparability of oximeters, in our study we performed simultaneous measurements on the liquid phantom mimicking properties of neonatal heads and compared the tested device to a reference NIRS oximeter (OxiplexTS). We evaluated the NIRS oximeters FORE-SIGHT, NIRO and SenSmart, and reproduced previous results with the INVOS and OxyPrem v1.3 oximeters. In general, linear relationships of the StO2 values with respect to the reference were obtained. Device specific hypoxic and hyperoxic thresholds (as used in the SafeBoosC study, www.safeboosc.eu) and a table allowing for conversion of StO2 values are provided.

Comparison of tissue oximeters on a liquid phantom with adjustable optical properties.

The SafeBoosC trial showed that cerebral oximetry combined with a treatment guideline can reduce the the burden of hypoxia in neonates by 50% [Brit. Med. J.350, g7635 (2015)]. However, guidelines based on oximetry by one oximeter are not directly usable by other oximeters. We made a blood-lipid phantom simulating the neonatal head to determine the relation between oxygenation values obtained by different oximeters. We calculated coefficients for easy conversion from one oximeter to the other. We additionally determined the corresponding SafeBoosC intervention thresholds at which we measured an uncertainty of up to 9.2% when varying hemoglobin content from 25µM to 70µM. In conclusion, this paper makes the comparison of absolute values obtained by different oximeters possible.

Simulation of Preterm Neonatal Brain Metabolism During Functional Neuronal Activation Using a Computational Model.

We present a computational model of metabolism in the preterm neonatal brain. The model has the capacity to mimic haemodynamic and metabolic changes during functional activation and simulate functional near-infrared spectroscopy (fNIRS) data. As an initial test of the model's efficacy, we simulate data obtained from published studies investigating functional activity in preterm neonates. In addition we simulated recently collected data from preterm neonates during visual activation. The model is well able to predict the haemodynamic and metabolic changes from these observations. In particular, we found that changes in cerebral blood flow and blood pressure may account for the observed variability of the magnitude and sign of stimulus-evoked haemodynamic changes reported in preterm infants.

Comparison of Near-Infrared Oximeters in a Liquid Optical Phantom with Varying Intralipid and Blood Content.

The interpretation of cerebral tissue oxygen saturation values (StO2) in clinical settings is currently complicated by the use of different near-infrared spectrophotometry (NIRS) devices producing different StO2 values for the same oxygenation due to differences in the algorithms and technical aspects. The aim was to investigate the effect of changes in scattering and absorption on the StO2 of different NIRS devices in a liquid optical phantom. We compared three continuous-wave (CW) with a frequency domain (FD) NIRS device. Responsiveness to oxygenation changes was only slightly altered by different intralipid (IL) concentrations. However, alterations in haematocrit (htc) showed a strong effect: increased htc led to a 20-35% increased response of all CW devices compared to the FD device, probably due to differences in algorithms regarding the water concentration.