Cerebral oxygen saturation in neonates: a bedside comparison between neonatal and adult nirs sensors

ABSTRACT BACKGROUND The majority of neonatal NIRS literature recommends target ranges for cerebral saturation (rScO2) based on data using adult sensors. Neonatal sensors are now commonly

used in the neonatal intensive care unit (NICU). However, there is limited clinical data correlating these two measurements of cerebral oxygenation. METHODS A prospective observational study

was conducted in two NICUs between November 2019 and May 2021. An adult sensor was placed on infants undergoing routine cerebral NIRS monitoring with a neonatal sensor. Time-synchronized

rScO2 measurements from both sensors, heart rate, and systemic oxygen saturation values were collected over 6 h under varying clinical conditions and compared. RESULTS Time-series data from

44 infants demonstrated higher rScO2 measurements with neonatal sensors than with adult sensors; however, the magnitude of the difference varied depending on the absolute value of rScO2

(Adult = 0.63 × Neonatal + 18.2). While there was an approximately 10% difference when adult sensors read 85%, readings were similar when adult sensors read 55%. CONCLUSION rScO2 measured by

neonatal sensors is typically higher than measured by adult sensors, but the difference is not fixed and is less at the threshold indicative of cerebral hypoxia. Assuming fixed differences

between adult and neonatal sensors may lead to overdiagnosis of cerebral hypoxia. IMPACT * In comparison to adult sensors, neonatal sensors rScO2 readings are consistently higher, but the

magnitude of the difference varies depending on the absolute value of rScO2. * Marked variability during high and low rScO2 readings was noted, with approximately 10% difference when adult

sensors read 85%, but nearly similar (58.8%) readings when adult sensors read 55%. * Estimating fixed differences of approximately 10% between adult and neonatal probes may lead to an

inaccurate diagnosis of cerebral hypoxia and result in subsequent unnecessary interventions. You have full access to this article via your institution. Download PDF SIMILAR CONTENT BEING

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SPECTROSCOPY CEREBRAL OXIMETRY: A RETROSPECTIVE OBSERVATIONAL STUDY Article Open access 07 December 2023 INTRODUCTION Near-infrared spectroscopy (NIRS) is a non-invasive monitoring technique

that has been clinically used in the neonatal intensive care unit (NICU) for over a decade to continuously measure regional tissue oxygenation at the bedside. It may be a useful tool in

critically ill neonates as a long-term trend monitor, assessing the balance of tissue oxygen delivery and consumption, providing cerebral and somatic oximetry values, and allowing earlier

detection of hemodynamic and brain perfusion abnormalities.1,2 Near-infrared light is emitted from a light source, passes through the infant’s skin and subcutaneous tissue, and is partially

absorbed by oxygenated and deoxygenated hemoglobin before being reflected to detectors on the same sensor. The tissue saturation level (rSO2) is calculated based on the ratio of arterial and

venous blood (25:75 ratio) and the balance of oxygen delivery and consumption in the underlying tissue.3 Cerebral oxygen saturation (rScO2) is measured with a sensor placed on either side

of the forehead. Values have been validated in neonates using jugular venous saturation.4,5,6 Interpretation of rScO2 measurements must take into account additional variables that may alter

cerebral blood flow and oxygenation including systemic oxygenation (SpO2), cardiac output, anemia, carbon dioxide (CO2) tension, and metabolic demand. Different types of sensors are

available for clinical use, ranging from neonatal to adult sizes, with the majority of early neonatal NIRS literature describing the use of adult sensors.7,8 Recommended target ranges for

rScO2 (55-85%) were established from population normative data using the small adult sensors and formed the basis for published and ongoing clinical interventional NIRS trials.7,8,9 Neonatal

sensors were designed to better fit a newborn’s head and are now more commonly used in the NICU. Due to the shorter distances between the emitting light source and the detectors, there are

known differences in measurements between the sensors. Generally, in vitro experimental models suggest the neonatal sensor has a linear correlation and reads approximately 10% higher than

the adult sensor.10,11,12 This implies clinically significant differences, particularly in terms of cerebral hypoxia and hyperoxia cut-off values for regional tissue oxygenation, which may

require intervention.8 However, there is a lack of clinical data to support this assumption, especially considering that studies directly comparing neonatal and adult sensors enrolled only a

small number of neonates (_n_ = 16).13 Therefore, it is essential that sensor differences be studied in a rigorous fashion in a prospective study of a neonatal population undergoing

cerebral NIRS monitoring. We performed a two-center prospective observational study with the primary objective of determining the absolute difference in measurements of cerebral oxygenation

in infants when using neonatal sensors versus adult sensors. This study was conducted in two level III/IV NICUs in Brazil and the US, where NIRS is routinely utilized in specific populations

of neonates at risk for brain injury. We also investigated differences in sensor measurements under varying conditions of systemic hypoxia, bradycardia, and anemia as would be encountered

in the clinical setting. METHODS STUDY POPULATION Infants were eligible for this study if they were admitted to the NICU and undergoing routine rScO2 monitoring at Lucile Packard Children’s

Hospital Stanford, Palo Alto, CA, USA or at Irmandade da Santa Casa de Misericórdia de São Paulo, São Paulo, Brazil between November 2019 and May 2021. Per clinical guidelines at both

centers, NIRS monitoring is performed in preterm infants <32 weeks’ gestation during the first 7 days of life or when a hemodynamically significant patent ductus arteriosus (PDA) is

suspected. Monitoring is also done in term or preterm infants with congenital heart disease, hypoxic–ischemic encephalopathy (HIE), respiratory failure, hemodynamic instability or metabolic

disorders.14 Exclusion criteria were skin integrity insufficient to allow placement of both neonatal and adult sensors. Approval of the institutional review board at each site was obtained,

and written informed consent was required for participation. INTERVENTION Standard placement of a neonatal cerebral sensor (INVOS™ OxyAlert™ Infant/Neonatal NIRSensor, IS, Medtronic) on the

left or right forehead was matched with a small adult sensor (INVOS™ Small Adult SomaSensors, SAFB-SM, Medtronic) on the opposing side of the forehead for simultaneous measurement of rScO2.

Two separate NIRS devices (INVOS™ 5100C, Medtronic, Minneapolis, MN) collected data in real-time: one calibrated for the neonatal sensor and the other calibrated for the small adult sensor.

Mepitel® (Molnlycke, Gothenburg, Sweden) skin dressing was used under the sensors for skin protection as per unit policy. Measurements were continuously obtained for a 3-h period followed by

exchanging the location of the neonatal and adult sensors (left to right side and right to left side) for a subsequent 3 h of monitoring. At the end of the monitoring period, the adult

sensor was removed, and the neonatal sensor was left in place as per clinical team’s discretion. All data were downloaded for offline data processing. Demographic and perinatal variables

including birth weight, gestational age, sex, antenatal steroid exposure, maternal race, Apgar scores at 1 and 5 min, small for gestational age status, and mode of delivery were recorded.

During the study, healthcare providers at both sites used clinical guidelines published in the SafeBoosC phase II clinical trial to treat cerebral hypoxia/hyperoxia.15 DATA PROCESSING The

raw time series data for each patient included rScO2 measurements every 3 to 7 s from the two NIRS sensors, as well as heart rate (HR) and SpO2 measures. Datapoints were flagged and

discarded for non-physiologic measures (NIRS value of <15 or >95; HR ≥ 250 or =0; SpO2 = 0) or by an anomaly detection algorithm, which involved fitting a Loess regression to smooth

data on a time scale of 2 minutes and removing data points of ≥3 standard deviations (SDs) (Supplementary Fig. 1). Data was synchronized by matching to the closest timepoint within 3 s and

rolled up to the 1-min time scale by averaging. STATISTICS Descriptive statistics of the patient cohort were performed with means with SDs or medians with interquartile ranges (IQRs) for

continuous variables and counts and percentages for categorical variables. For statistical modeling of time series data, a linear regression model with a generalized estimating equation

(GEE) approach was used to account for the temporal autocorrelation within patients. The primary focus was to examine calibration of the sensors against each other: specifically, given a

neonatal sensor measurement, the expected value from the adult sensor would be determined. To address this question, a linear GEE model was trained and also tested for possible

non-linearities by adding logarithmic and quadratic terms. To study the impact of laterality of sensor position, as well as patient demographics, interaction terms were added to this linear

model. Measurement error in the adult sensor may lead to regression dilution bias and underestimation of the slope of the relationship between the two sensors, which was corrected using the

method of Rosner et al.16 We estimated the correction factor _λ_ by comparing within-patient variation to between-patient variation in the adult sensor measures. This comparison suggested

for the linear model a correction factor of _λ_ = 1.22 ± 0.06, corresponding to a measurement error of about 5 percentage points in either direction associated with the adult sensor. A

simulation study suggested that this method is effective for this parameter regime.17 Confidence intervals combining the error from the regression estimate with the error from the estimate

of _λ_ were constructed using published techniques.18 We conducted two sensitivity analyses to confirm the robustness of this correction procedure, shown in Supplementary Figs. 2 and 3.

First, we used a Demming regression,19 which is an error-in-variables model, and we assumed the two sensors had commensurate levels of measurement error (_δ_ = 1). To handle autocorrelation,

we randomly sampled datapoints from between 30 to 90 min apart, and to determine confidence intervals we bootstrapped this entire procedure. Second, we conducted a standard Bland-Altman

approach to test for systematic miscalibration and level-dependence between the two sensors. Our results were robust to both of these sensitivity analyses. Finally, Supplementary Fig. 4

shows the calibration of the two sensors compared by left/right placement of the sensors. The analytic plan for our primary analysis was registered publicly at https://osf.io/5rfws prior to

conducting the analysis, although correcting for regression dilution bias was not considered in the original analysis plan. Data cleaning was conducted in R version 4.1.2. All regression

models were trained in Python version 3.8.5, using the statsmodel package. RESULTS POPULATION Forty-four infants were enrolled over an 18-month period. Demographic and perinatal

characteristics, as well as indications for NIRS monitoring are shown in Table 1. The majority of infants were not receiving inotropic support, sedation, or anti-epileptic medications at the

time of monitoring, and only 4 infants (9%) had a concomitant diagnosis of severe intraventricular hemorrhage. RELATIONSHIP BETWEEN NEONATAL AND ADULT SENSOR The neonatal sensor on average

demonstrated higher values than the corresponding adult sensor as shown in the case example in Fig. 1. The relationship between the adult and neonatal sensor values was modeled as a linear

equation with mean and 95% confidence intervals shown in Fig. 2, with similarity of models considering data at the patient-level or data from all time points. Note that there was

considerably more noise at the time point-level: the standard deviation of the model residuals was 7.2 percentage points for the time point-level model compared to 4.5 for the patient-level

model. While the two sensors can be discordant at any given time point, they were strongly correlated when averaged over a longer time period. Table 2 provides conversion values between

neonatal and adult sensor readings for a range of values. At the traditional thresholds of intervention for cerebral hypoxia (55%) or hyperoxia (85%), there were notable discrepancies

between the neonatal and adult sensor differences (Fig. 3). For an adult sensor value of 55%, the neonatal sensor values demonstrated a somewhat normal distribution around a slightly higher

mean value of 58.8 ± 4.0%. However, for an adult sensor value of 85%, the neonatal sensor values were primarily clustered at a median value of 95% (the highest measurable value displayed by

the NIRS device) with an IQR of 92.4-95.0%. At a clinically acceptable, mid-range adult sensor value of 70%, the corresponding neonatal sensor values demonstrated a higher median of 79.7%

(IQR 73.7–83.7%). No significant differences in rScO2 values were found with sensor placement on the left versus right forehead with either the neonatal or the adult sensor, when infants

with IVH were excluded from this analysis. Similarly, the calibration between the neonatal and adult sensors did not change based on side of sensor placement (Neonatal sensor: Left side,

mean (SD) = 72.9% ± 4.0%, Right side, mean (SD) = 71.6% ± 4.8%, _p_ = 0.25; Adult sensor: Left side, mean (SD) = 63.0% ± 3.7% Right side, mean (SD) = 64.0 ± 3.1%, _p_ = 0.33). SENSOR

DIFFERENCES UNDER VARYING CLINICAL CONDITIONS Under conditions of systemic hypoxia with SpO2 < 80%, rScO2 was lower than under conditions of normoxia for both the neonatal sensor (66.5 ± 

4.3% vs. 70.9 ± 4.1%, _p_ = 0.004) and for the adult sensor (60.4 ± 3.4% vs. 62.7 ± 3.1%, _p_ = 0.03). However, differences between the neonatal and adult sensor readings under hypoxic

conditions were smaller compared to differences under normoxic conditions (Fig. 4a): the mean difference in rScO2 between neonatal and adult sensors was 6.1 ± 2.0% during hypoxic conditions

and 8.3 ± 1.9% under normoxic conditions with SpO2 ≥ 80% (_p_ = 0.05). Minimal differences in rScO2 were seen among periods of bradycardia (HR < 80 bpm), periods of normal heart rate (HR

80-180 bpm), and periods of tachycardia (HR > 180 bpm). While the neonatal sensor on average displayed higher values than the adult sensor, HR range did not significantly impact this

relationship (Fig. 4b). More anemic infants with hematocrit <35% demonstrated lower rScO2 compared to those with hematocrit >45%, independent of sensor type (neonatal versus adult).

However, the differences between neonatal and adult sensor readings were not significantly different at various hematocrit ranges (Fig. 4c). NIRS measures did not differ as a function of

demographic variables including gestational age at birth, birth weight, and sex. The impact of other perinatal conditions including indication for NIRS monitoring or the presence of IVH were

similarly not significant and were not adjusted for in the calibration model. DISCUSSION Our study found a difference in rScO2 between the adult and neonatal NIRS sensors using INVOS™ 5100C

(Medtronic, Minneapolis, MN) device. Neonatal sensors consistently displayed higher values than adult sensors, but the difference varied depending on the absolute value of rScO2. For an

adult sensor reading of 85% (cerebral hyperoxia threshold), the neonatal sensor values were predominantly grouped at 95%. For an adult sensor reading of 55% (cerebral hypoxia threshold), the

neonatal sensor values showed a normal distribution around a higher, mean value of 58.8% ± 4.0%. We found no difference when comparing sensor placement on the left versus right forehead.

Systemic hypoxia (SpO2 < 80%) reduced the disparity between neonatal and adult sensor measurements, but heart rate and hematocrit level did not appear to have a significant impact on the

relationship between the neonatal and adult sensor values. Our findings differ from previous studies that described a fixed difference between neonatal and adult sensor readings, with

neonatal sensors consistently reading approximately 10% higher. Dix et al. compared readings from 16 neonates with the INVOS™ 5100 C adult and neonatal sensor, measuring periods of 1 h

each.13 The authors reported a close relationship between the two sensors (_r_ = 0.88, _p_ < 0.001) and an average difference of 10 ± 5%. Sorensen et al. used an in vitro model with a

blood-lipid phantom that consisted of a mixture of isotonic saline, erythrocyte suspension, and Intralipid® 200 mg/ml. The INVOS adult and pediatric sensors were linearly correlated with the

pediatric sensor reading systematically higher than the adult sensor (_y_ = 0.96_x_ + 17.91; _r_2 = 0.99).10 In a similar study, Kleiser et al. also utilized a blood-lipid phantom model

mimicking the neonatal brain to establish a relationship between oxygenation values acquired using various oximeters and sensors. Specific intervention thresholds corresponding to rSO2 = 55%

and 85% (measured by the INVOS adult oximeter) for a typical neonate with total hemoglobin concentration (ctHb) of 45 μM were estimated. Calculated neonatal rScO2 hypoxic and hyperoxic

thresholds were 63% and 96%, respectively.12 Technical aspects such as differences in processing algorithms or scattering subtraction are a potential explanation for higher neonatal rScO2

values compared with adult sensors. The level dependence of the discrepancy may be explained due to a limitation of processing algorithms and different absorption properties of de-oxygenated

hemoglobin. The INVOS™ 5100C (Medtronic) sensors use light-emitting diodes to emit near-infrared light of two wavelengths (730 and 810 nm). Two detectors are located next to the

light-emitting diodes. By subtracting the shallow (shorter) signal from the deeper (further) signal, surface interference is minimized.20,21 Both adult and neonatal sensors from the INVOS™

5100C device used in this study have two wavelengths and similar source-detector separation of 3 cm and 4 cm. However, the neonatal sensor was designed to have higher sensitivity, and the

processing algorithm was adjusted to boost signal intensities transmitted through the infant’s thinner skull.22 Although the blood-lipid phantom experiment represents a robust in vitro model

to analyze several components of rScO2 variability, clinical data is important to define the normal range of rScO2 values in neonates. The cerebral hypoxia threshold of 63% for the neonatal

INVOS sensor is being utilized in the treatment guideline for SafeBoosC III, the ongoing largest randomized, pragmatic phase III clinical trial investigating interventions for cerebral

hypoxia in preterm infants below 28 weeks gestational age to decrease the composite outcome of severe brain injury or death at 36 weeks postmenstrual age.9 This cerebral hypoxia threshold

was established from preterm population-based rScO2 data of 55% using a small adult INVOS sensor10 and subsequently extrapolated to 63% based on the in vitro blood-lipid phantom model.12

Analyses of SafeBoosC III data may provide additional guidance on the effectiveness of this cerebral hypoxia threshold. The use of higher hypoxic thresholds, based on a small number of human

comparative studies and in vitro models, may lead to unnecessary and potentially harmful interventions (e.g., increase in oxygen administration, red blood cell transfusion, volume boluses,

or inotropes) and ultimately affect patient outcomes. In a separate multicenter study using neonatal sensors, Chock et al. studied the association between rScO2 values and the adverse

outcome of death or severe neuroradiographic abnormalities. They found rScO2 < 50% was associated with this adverse outcome (area under the curve, 0.76). The use of risk based normative

values rather than population norms may be an alternative approach to reduce unnecessary interventions.23 The differences in the hyperoxia threshold values also have several implications. In

infants with HIE, high rScO2 value can be explained by low energy metabolism after severe brain injury. Previous studies using adult sensors described association between supranormal rScO2

at 24 h of life with death and adverse neurodevelopmental outcomes in this population.24,25,26 Since the difference between adult and neonatal sensors rScO2 measurements can be over 10% when

absolute rScO2 is high, the sensitivity and specificity of these findings may significantly change when using neonatal sensors. Our findings related to sensor placement on the left versus

right forehead agree with previous literature.22,27 Lemmers et al. conducted a prospective study simultaneously monitoring rScO2 in 36 very preterm neonates during the first 3 days of life.

Authors found a close correlation between left and right rScO2 (_r_ = 0.89, _p_ = 0.01). During stable and normal SpO2, differences between left and right NIRS-monitored rScO2 rarely

exceeded 7%. This pattern was affected by an unstable arterial oxygenation pattern with substantial drops of SpO2, during which differences between left and right SpO2 values up to more than

10% were observed.27 Our study was unable to replicate this finding as we did not compare left-to-right differences with the same type of sensor during periods of unstable arterial

oxygenation. The reduced disparity between neonatal and adult sensor measurements during hypoxia may be explained due to the noted closer correlation between these sensors’ measurements when

rScO2 values were lower. Previous studies have shown that low blood pressure and bradycardia, independent of hypoxemia, commonly affect regional tissue oxygenation.28 Decreased heart rate

may lead to lower cardiac output and regional tissue oxygenation. Similarly, anemia may result in lower rScO2, although it remains unclear if there is a hematocrit threshold below which

rScO2 is notably impacted.29 In our study, conditions of bradycardia and hematocrit <35% did not significantly decrease rScO2 to the same extent as systemic hypoxia. To the best of our

knowledge, this clinical study describes the largest cohort of infants being monitored simultaneously with adult and neonatal NIRS sensors, with approximately 4500 time points analyzed per

infant. Additionally, our study evaluated the sensor discrepancies in a wide range of clinical scenarios and populations (prematurity, HIE, congenital heart disease, PDA), including periods

of hemodynamic and ventilatory instability, which is important for generalization of results to a broader NICU population. Our study has several limitations. First of all, we evaluated

sensor readings from a single NIRS device. Several previous studies report significant differences between absolute values across different types of NIRS monitors.12,13,30,31 The discrepancy

may even be higher when oxygenation is low. Andresen et al. monitored rScO2 in 10 preterm infants during apneic episodes with neonatal sensors from the INVOS™ 5100C and Nonin SenSmart™

X-100. The individual regressions displayed large and statistically significant variations in both infants and adults, suggesting that different NIRS devices give very different estimates

when the oxygenation is low.32 Additionally, our study did not control for ctHb levels during monitoring periods. NIRS measures the average concentrations of oxyhemoglobin and

deoxyhemoglobin, and previous studies showed pronounced dependence of sensitivity of oximeters based on the different ctHb.33,34 Kleisser et al. using a blood-lipid phantom model, revealed a

ctHb dependence at the SafeBoosC intervention thresholds.12 At the hypoxic threshold rSO2, INVOS adult and neonatal sensors showed dependence on ctHb, with an uncertainty range of 9.2%.

While low ctHb level may influence oxygen delivery and extraction, it is unlikely that the effect was significant in our study as most recent hematocrit, measured within a maximum of 82 h

(mean 19.3 h) from NIRS data collection, did not affect the difference in readings between sensors. In conclusion, this study adds relevant information regarding differences in rScO2 using

neonatal and adult sensors in a large cohort of neonates monitored with a single device. Marked variability in differences during high and low rScO2 readings was noted, with approximately

10% difference when adult sensors read 85%, but nearly similar (58.8%) readings when adult sensors read 55%. These findings raise a concern that estimating fixed differences of approximately

10% between adult and neonatal sensors may lead to an inaccurate diagnosis of cerebral hypoxia and result in subsequent unnecessary interventions. Further technical investigations into why

the adult and neonatal sensors read differently are needed and future studies with neonatal NIRS monitoring should evaluate the association between optimal cerebral hypoxia thresholds and

outcomes in larger clinical trials. DATA AVAILABILITY Datasets generated and analyzed during the current study are available from corresponding author on reasonable request. REFERENCES * Van

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study did not receive financial assistance. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Division of Neonatology, Department of Pediatrics, Irmandade da Santa Casa de Misericórdia de São

Paulo, São Paulo, Brazil Gabriel F. T. Variane, Julianna S. Kaneko & Marcella S. Gouveia * Protecting Brains and Saving Futures Organization, São Paulo, Brazil Gabriel F. T. Variane 

& Julianna S. Kaneko * Quantitative Sciences Unit, Stanford University School of Medicine, Palo Alto, CA, USA Alex Dahlen & Jingying Zeng * Division of Neonatal and Developmental

Medicine, Stanford University School of Medicine and Lucile Packard Children’s Hospital Stanford, Palo Alto, CA, USA Caroline Y. Noh, Elisabeth S. Yan, Krisa P. Van Meurs & Valerie Y.

Chock Authors * Gabriel F. T. Variane View author publications You can also search for this author inPubMed Google Scholar * Alex Dahlen View author publications You can also search for this

author inPubMed Google Scholar * Caroline Y. Noh View author publications You can also search for this author inPubMed Google Scholar * Jingying Zeng View author publications You can also

search for this author inPubMed Google Scholar * Elisabeth S. Yan View author publications You can also search for this author inPubMed Google Scholar * Julianna S. Kaneko View author

publications You can also search for this author inPubMed Google Scholar * Marcella S. Gouveia View author publications You can also search for this author inPubMed Google Scholar * Krisa P.

Van Meurs View author publications You can also search for this author inPubMed Google Scholar * Valerie Y. Chock View author publications You can also search for this author inPubMed 

Google Scholar CONTRIBUTIONS G.F.T.V. made substantial contributions to the study conception and design, interpretation of data, drafting the article and revising it critically for

intellectual content, and final approval of version to be approved. A.D. and J.Z. made substantial contributions to the study conception and design, interpretation of data, drafting the

article, and final approval of version to be approved. C.Y.N. made substantial contributions to the study conception and design, acquisition of data, interpretation of data, drafting the

article and revising it critically for intellectual content, and final approval of version to be approved. E.S.Y., J.K., and M.G. made substantial contributions to the acquisition of data.

K.P.V.M. and V.Y.C. made substantial contributions to the study conception and design, analysis and interpretation of data, drafting the article and revising it critically for intellectual

content, and final approval of version to be approved. CORRESPONDING AUTHOR Correspondence to Gabriel F. T. Variane. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing

interests. ETHICS APPROVAL AND CONSENT TO PARTICIPATE The free and informed consent form was required and signed by the legal guardians of the patients. ADDITIONAL INFORMATION PUBLISHER’S

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agreement and applicable law. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Variane, G.F.T., Dahlen, A., Noh, C.Y. _et al._ Cerebral oxygen saturation in neonates: a bedside

comparison between neonatal and adult NIRS sensors. _Pediatr Res_ 94, 1810–1816 (2023). https://doi.org/10.1038/s41390-023-02705-z Download citation * Received: 03 August 2022 * Revised: 13

May 2023 * Accepted: 30 May 2023 * Published: 30 June 2023 * Issue Date: November 2023 * DOI: https://doi.org/10.1038/s41390-023-02705-z SHARE THIS ARTICLE Anyone you share the following

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