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. 2020 Apr 16;15(4):e0231461. doi: 10.1371/journal.pone.0231461

Near-infrared spectroscopy of the placenta for monitoring fetal oxygenation during labour

Katja Ražem 1,*, Juš Kocijan 2,3, Matej Podbregar 4,5, Miha Lučovnik 1,5
Editor: Anna Palatnik6
PMCID: PMC7162483  PMID: 32298307

Abstract

Although being the golden standard for intrapartum fetal surveillance, cardiotocography (CTG) has been shown to have poor specificity for detecting fetal acidosis. Non-invasive near-infrared-spectroscopy (NIRS) monitoring of placental oxygenation during labour has not been studied yet. The objective of the study was to determine whether changes in placental NIRS values during labour could identify intrapartum fetal hypoxia and resulting acidosis. We included 43 healthy women in active stage of labour at term. CTG and NIRS parameters in groups with vs. without neonatal umbilical artery pH ≤ 7.20 were compared using Mann-Whitney-U. Receiver-operating-characteristics (ROC) curves were used to estimate predictive value of CTG and NIRS parameters for neonatal pH ≤ 7.20. A computer-based statistical classification was also performed to further evaluate predictive values of CTG and NIRS for neonatal acidosis. Ten (23%) neonates were born with umbilical artery pH 7.20. Compared to group with pH > 7.20, fetal acidosis was associated with more episodes of placental NIRS deoxygenation (9 (range 2–37) vs. 2 (range 0–65); p<0.001), higher velocity of placental NIRS deoxygenation (2.31 (range 0–22) vs. 1 (range 0–49) %/s; p = 0.03), more decelerations on CTG (25 (range 3–91) vs. 10 (range 10–60); p = 0.02), and more prolonged decelerations on CTG (2 (range 0–4) vs. 1 (range 0–3); p = 0.04). Number of placental deoxygenations had the highest prognostic value for fetal/neonatal acidosis (area under the ROC curve 0.85 (95% confidence interval 0.70–0.99). Computer-based classification also identified number of placental deoxygenations as the most accurate classifier, with 25% false positive and 93% true positive rate in the training dataset, with 100% accuracy when applied to the testing dataset. Placental deoxygenations during labour measured by NIRS are associated with fetal/neonatal acidosis. Predictive value of placental NIRS for neonatal acidosis was superior to that of CTG.

Introduction

Intrapartum fetal surveillance is performed to prevent fetal/neonatal hypoxia which may lead to childbirth-related neonatal encephalopathy, cerebral palsy and perinatal death. Monitoring fetal heart rate (FHR) represents an important method for fetal surveillance—cardiotocography (CTG) being the golden standard in developed countries. The interpretation of FHR patterns in combination with uterine activity is key for CTG applicability. However, since the introduction of CTG into clinical practice, there has been no decrease in rates of cerebral palsy and perinatal death, while caesarean section and operative vaginal delivery rates have risen significantly [1]. While normal CTG reliably rules out fetal hypoxia with a negative predictive value of around 90%, abnormal CTG tracings only have a positive predictive value of 50–60% for neonatal hypoxia or acidosis [24]. Furthermore, large inter- and intra-observer variability in CTG interpretation has been documented even with experienced clinicians [57]. Despite these limitations of CTG, there is currently no other more effective, evidence-based adjunctive method for intrapartum fetal surveillance [8].

Near-infrared spectroscopy (NIRS) enables non-invasive, real-time assessment of tissue oxygenation. It was initially promoted as a brain monitor in cardiac surgery and neonatal intensive care, but its use has been extended to various non-cardiac surgeries and critical care settings [911]. NIRS has also been studied as a method for assessing fetal cerebral oxygenation during labour. Studies using special probes placed through the dilated cervix on the fetal head during labour showed that NIRS can identify fetuses at risk for intrapartum hypoxic damage [1215]. This method is, however, relatively invasive and associated with possible risks of probe placement into the uterus and onto the fetus. Additionally, probes used in these studies are not available for everyday clinical practice and are associated with significant costs. Non-invasive NIRS probes have also been investigated in pregnant women. Japanese researchers measured placental oxygenation during pregnancy by placing probes on maternal abdominal surface [1619]. They found higher placental tissue oxygenation in pregnancies complicated by hypertensive disorders and intrauterine fetal growth restriction (IUGR). Use of non-invasive NIRS monitoring of the placental oxygenation during labour has, however, not been studied yet.

The aim of our study was to determine whether changes in placental NIRS values during labour could identify intrapartum fetal hypoxia and resulting acidosis.

Materials and methods

Study participants

Women admitted to labour and delivery unit of the Department of Perinatology, University Medical Center Ljubljana, Slovenia in active first stage of labour were included in this prospective cohort study between September 2017 and March 2019.

Inclusion criteria were: singleton pregnancy, cephalic presentation, active phase of first stage of labour (regular contractions at least every 5 minutes with cervical change), gestational age between 37 and completed 41 weeks. Calculation of gestational age was based on the last menstrual period or determined by ultrasound (calculated by crown-rump length measured within the first trimester) when ultrasonographic estimation differed by ≥ 7 days from the one calculated by menstrual period. We only included women with anterior and fundal placental position.

Exclusion criteria were: suspected IUGR, oligohydramnios, polyhydramnios, diabetes mellitus (preexisting or gestational) requiring insulin treatment, and preeclampsia. Depth of subcutaneous tissue (from the skin surface to anterior uterine wall) measured by ultrasound > 5 cm was also an exclusion criterion.

Maternal and pregnancy information (age, parity, gestational age, body mass index at onset of pregnancy and at delivery, complications during pregnancy, smoking status, medications used) and information on course of labour (Bishop score at admission, medications applied, duration of labour, mode of delivery) were promptly entered into pre-established forms. At delivery, blood was sampled from the umbilical artery for acid-base analysis. Umbilical artery pH was chosen as pre-specified neonatal outcome.

All women provided written informed consent for study participation. The National Medical Ethics Committee approved the study on 14th March 2017 (reference number: 0120-65/2017-3; KME 60/03/17).

CTG and NIRS measurements and analysis

Measurements were performed by one operator (KR) throughout the 18-month research period, both during day and night shifts to avoid patient selection bias. Measurements were carried out continuously until the end of the second stage of labour with the exception of possible visits to bathroom facilities, administration of epidural analgesia or need for operative delivery by emergency Caesarean section.

Electronic CTG recordings were made using the Avalon FM30 (Philips, Netherlands) apparatus with paper speed of 1 cm/min. Data were sampled at a frequency of 0.25 seconds and archived as computer records. The outputs of Avalon FM 30 include FHR and uterus activity. NIRS probe was attached to the maternal abdominal surface at the position just above the placenta (uterine fundus or anterior uterine wall), which was determined by ultrasound. The distance from the optode to the anterior border and to the centre of the placenta was documented. Nonin Equanox 7600 regional oximetry system (Nonin Medical, Inc., Plymouth, MN, USA) and electrode model Sensmart sensor 8004CA (Nonin Medical, Inc., Plymouth, MN, USA) with interoptode distance of 20 and 40 milimeters were used for all NIRS measurements (Figs 1 and 2). Data were sampled at a frequency of 1 second. The output of Nonin Equanox 7600 is regional tissue oxygen saturation (SO2 –the percent of placental oxygenated blood). The light absorption information collected by the dual emitter/detector electrodes and transferred via sensor cables is automatically incorporated into Nonin’s Dynamic Compensation light processing algorithm, which provides real time oxygenation saturation values of tissue examined. Recordings were later transferred to a computer for further analysis.

Fig 1. Schematic representation of NIRS measurement.

Fig 1

Fig 2. NIRS electrode two light emitters and two detectors to provide measurements that are minimally affected by intervening tissues or surface effects.

Fig 2

CTG analysis was performed by two qualified obstetricians (KR and ML) who were unaware of patient’s NIRS results or birth outcomes at the time of analysis. The number of late decelerations (symmetrical gradual decrease and return of FHR, with nadir occurring after the peak of contraction)—considered reflex fetal responses to fetal hypoxia during contraction—were analyzed in each CTG recording. We also analyzed the number of variable decelerations (abrupt decrease in FHR to levels below the baseline, which may occur in isolation or vary in onset, depth and duration in relationship to uterine contractions)–considered a sign of transient interruption of oxygen delivery to the fetus due to umbilical cord compression. We seperately analysed prolonged decelerations (decrease in FHR to levels below the baseline that lasts at least 2 minutes), which are believed to indicate a fetal chemo-receptor response to hypoxemia. Although early decelerations (symmetrical, gradual decrease and return of FHR below the baseline; in most cases the onset, nadir, and recovery of the deceleration is coincident with the beginning, peak, and ending of the contraction, respectively) are not considered related to fetal oxygenation, we chose to analyze also this type of decelerations and report their numbers. The overall number of decelerations within a record was also reported.

Fig 3 shows different subtypes of FHR decelerations as seen on CTG recording.

Fig 3. Examples of different FHR deceleration subtypes on CTG.

Fig 3

a: Late FHR decelerations. b: Variable FHR decelerations. 3c: Prolonged FHR deceleration. c: Early FHR decelerations.

The variability of each CTG trace was assessed. Low variability was considered when duration of variability of < 5 bpm/min exceeded 50 minutes. In addition to evaluating these specific CTG parameters, the CTG tracing as a whole was assessed according to most commonly used CTG classification systems, i.e. those proposed by the International Federation of Gynecology and Obstetrics (FIGO), National Institute for Health and Care Excellence (NICE), American College of Obstetricians and Gynecologists (ACOG) and the classification proposed by Parer and Ikeda [2023].

NIRS recordings were analyzed by studying episodes of placental deoxygenation. We used the decrease of ≥ 5% from baseline placental oxygenation to determine events of placental deoxygenation. We limited the duration of events to ≥ 15–180 seconds. A shorter duration could represent an artefact, whilst a duration exceeding 180 seconds was considered a baseline shift in tissue oxygenation. Fig 4 shows examples of NIRS occurring events.

Fig 4. Schematic representation of NIRS occurring events.

Fig 4

a: Rises in oxygen tissue saturation. b: Tissue desaturations.

Since the velocity of tissue deoxygenation has been shown to be an important parameter in critically ill patients, we also chose to analyze this NIRS parameter [2427]. Tissue oxygenation velocity was calculated as the derivation of oxygenation, which is implemented as the change of oxygenation in one time sample of NIRS signal (% per second).

CTG and NIRS measurement systems have different recording formats as well as different sampling rates. Data processing was pursued in Matlab software, version R2016b (MathWorks, Natick, MA, USA) and converted to analysis-convenient data vectors without loss of information.

Statistical analysis

CTG and NIRS parameters in group with vs. without neonatal pH ≤ 7.20 were compared using Mann-Whitney-U test. Categorical variables were compared using Chi-square test.

Receiver-operating-characteristics (ROC) curves were used to estimate predictive value of CTG and NIRS parameters that were shown to be significantly associated with neonatal umbilical artery pH ≤ 7.20. The program IBM SPSS Statistics for Macintosh, version 23.0 (IBM Corp, Armonk, NY, USA) was used for statistical comparison of groups and ROC analysis. Statistical significance was determined at p < 0.05.

In addition to classical ROC analysis, a computer-based statistical classification was also performed in Matlab (version R2016b; MathWorks, Natick, MA, USA) to further evaluate predictive values of CTG and NIRS for neonatal acidosis. Data were divided into two datasets: the first used for training and regressor selection, while the second for testing the obtained statistical classifier. This allowed assessing consistency of the results. Regressors were selected with backward elimination method, systematically eliminating one by one until the cross-validation results confirmed the minimal set of regressors that provides the classification accuracy of richer set of regressors [28]. Different classification methods were also tested using the Classification Learner application of Matlab [29].

Results

CTG and NIRS were measured in 50 participants. Four measurements were excluded due to poor NIRS signal quality, umbilical cord blood sample was not available in three neonates, leaving 43 participants for analysis.

The age of participating women was between 18 and 48 years (median 31.0), gestational age between 38.4 and 41.4 weeks (median 40.1). Their median body mass index at onset of pregnancy was 22.1 (range 18.8 to 40.5) and 27.7 (range 23.2 to 41.7) at time of delivery. Eleven (26%) participants had anemia, treated with oral iron supplements. Nineteen women (44%) were nulliparous, the number of previous deliveries ranged from 1 to 6 in multiparous participants. Four women (9%) were regular smokers during pregnancy. There were no fetal or placental anomalies in the analyzed group. Median Bishop score at study inclusion was 6 (range 1–10). Placenta was located in the uterine fundus in 7 (16%) and anteriorly in 36 (84%) participants. The median distance from the probe to the anterior border and to the center of placenta was 15 mm (5–26 mm) and 35 mm (20–60 mm), respectively. Table 1 presents patient demographics in regard to umbilical artery pH value. Results show that pH ≤ 7.20 of the newborn was related to the mother being a nullipara, whilst smoking was a protective factor. No other patient demographic was shown to be statistically significantly related to the observed pH outcome.

Table 1. Patient clinical background variables in regard to umbilical artery pH value.

Variable pH ≤ 7.20 (N = 10) pH > 7.20 (N = 36) p
Maternal age (years) 29 (21–36) 31 (18–48) 0.42
Nulliparous 8 13 0.008 *
Parity 1 (1–2) 2 (1–6) 0.001 *
Gestational age 40 4/7 (39 0/7-41 4/7) 40 0/7 (38 4/7-41 4/7) 0.11
BMI before pregnancy (kg/m2) 21.1 (18.8–32.6) 23.1 (19.0–40.5) 0.21
BMI at delivery (kg/m2) 26.7 (24.1–36.8) 28.0 (23.2–41.7) 0.33
Bishop at admission 5 (3–9) 6 (1–10) 0.47
Smoker 0 4 0.04 *
Labour duration 5.3 (0.5–7.0) 3.0 (0.25–9.0) 0.12
d1 15 (5–25) 15 (5–26) 0.93
d2 33 (21–55) 36 (20–60) 0.70

Data are shown as median (range) or N. P value was calculated by Mann-Whitney U-test and Chi square test.

* represents statistical significance (p<0.05).

BMI: body mass index, d1: distance from electrode to placenta, d2: distance from electrode to centre of placenta

Median duration of labour was 3.5 hours (0.25–9 hours). Seventeen women (39%) opted for epidural analgesia during labour. Six (14%) women gave birth via operative delivery (five emergency Cesarean sections and one vacuum extraction were performed due to fetal distress, diagnosed according to CTG– 5 (83%) of these neonates had umbilical artery pH 7.20). Five (12%) neonates were born with umbilical artery pH 7.20 with no intrapartum signs of suspicious or pathological CTG.

Table 2 shows CTG and NIRS parametres, that were significantly higher in the groups with vs. without neonatal pH 7.20. According to CTG classification by FIGO, 22 (51%) tracings were considered suspicious—of these, seven (31.8%) had pH 7.20, whilst fifteen (68.2%) had a normal pH. Seven (16%) traces were considered pathological; one (14.3%) had pH 7.20 and six (85.7%) normal pH. Fourteen tracings were considered normal. According to NICE CTG guidelines three (7%) tracings were suspicious; one (33.3%) had pH 7.20 and two (66.6%) normal pH. Eighteen (42%) tracings showed pathological characteristics; five (27.8%) neonates had pH 7.20 and thirteen (72.2%) normal pH. Twenty-two tracings were considered normal. Using ACOG guidelines, no tracings were considered pathological (category III), while the majority (95%) were suspicious (category II). Of these, ten (24.4%) neonates had pH 7.20 and thirty-one (75.6%) normal pH. Following the Parer&Ikeda classification system, eight tracings (19%) were given the blue, one (2%) yellow and one (2%) the orange color score. Two neonates (25%) color coded blue had pH 7.20 and 6 (75%) normal pH, one yellow coded neonate had a normal pH and one orange-coded had pH 7.20. The remaining 33 tracings were given the green colour score. Differences in incidence of suspicious and pathological CTG traces, regardless of classifications used, were not shown to be statistically significantly different among the groups with vs. without neonatal pH 7.20.

Table 2. Cardiotocographic (CTG) and near infrared spectroscopy (NIRS) parameters in regard to umbilical arterial pH value.

CTG/NIRS parameter pH ≤ 7.20 (N = 10) pH > 7.20 (N = 33) p
Overall no. of decelerations 25 (3–91) 10 (0–60) 0.02*
No. of variable decelerations 9 (0–76) 7 (0–42) 0.29
No. of late decelerations 0 (0–50) 0 (0–0) 0.07
No. of early decelerations 3 (0–47) 1 (0–25) 0.14
No. of prolonged decelerations 2 (0–4) 1 (0–3) 0.04*
No. of CTG with low variability 1 (10%) 2 (6%) 0.46
Overall no. of placental deoxygenations 9 (2–37) 2 (0–65) 0.001*
Velocity of placental deoxygenation (%/s) 2.31 (0–22) 1 (0–49) 0.03*

Data are shown as median (range) or n (%). P value was calculated by Mann-Whitney test.

* represents statistical significance (p<0.05).

ROC curves were constructed for CTG and placental NIRS parameters which were statistically correlated with neonatal pH ≤ 7.20 (Fig 5). The figure shows a greater diagnostic reliability of NIRS in predicting fetal acidosis. Total number of placental deoxygenations has the highest area under the curve (AUC) of all parameters analysed.

Fig 5. Comparison of receiver-operating characteristics (ROC) curves for CTG and NIRS parameters to predict pH ≤ 7.20.

Fig 5

Overall no. of decelerations: AUC 0.75, 95% CI (0.57–0.94). Overall no. of prolonged decelerations: 0.71 (0.49–0.92). Overall no. of placental deoxygenation: 0.85 (0.70–0.99). Velocity of placental deoxygenation: 0.66 (0.49–0.83). AUC: area under the curve, CI: confidence interval.

ROC curves were also constructed for CTG categories, assessed by different classification systems. AUC with 95% confidence interval (95% CI) for classification system FIGO was 0.54 (0.35–0.73), NICE 0.57 (0.37–0.77), ACOG 0.53 (0.33–0.73), Parer & Ikeda 0.55 (0.34–0.77) respectively.

For the computer-based statistical classification, the first dataset (used for training and regressor selection) contained recordings and derived variables of 36 participants, including eight cases of neonatal acidosis. The second dataset (used for testing the obtained statistical classifier) contained recordings and derived variables of seven participants, including two cases of neonatal acidosis. The analysis carried out was 4-fold cross-validation on the first dataset. Number of folds, i.e. four, was determined according to the number of cases of neonatal acidosis in the first dataset. Each division of subgroups should have contained at least one case of neonatal acidosis. Three subgroups of the first dataset were used for training the classifier and for the selection of top ranking regressors. For the remaining subgroup, the previously created classifier was applied with the same regressors as the training subgroups (Fig 6). Among different classification methods, classification tree or decision tree method was chosen [30]. Following the backward elimination method, we were left with only one regressor: number of placental deoxygenations on NIRS.

Fig 6. Schematic illustration of computer-based statistical classification used for evaluation of predictive values of CTG and NIRS for neonatal acidosis.

Fig 6

The result of 4-fold cross-validation was the score, i.e., accuracy, of 75%. The best trained classifier had 25% false positive rate and 93% true positive rate. The final test of the obtained classifier was its test on the second dataset that was not used for selection and training. The result of the decision tree classifier using the number of deoxygenations on NIRS on the second dataset was the score of 100%.

The training procedure used a training dataset with prior knowledge of correctly classified results for the computer-based algorithm which trains classification function (upper scheme). In our case, number of deoxygenations on NIRS provided the highest accuracy of predicting neonatal umbilical artery pH ≤ 7.20. The trained classification function was then used for the classification of test data (bottom scheme). Number of placental deoxygenations correctly classified all cases into pH ≤ 7.20 and pH > 7.20 groups (100% accuracy).

Discussion

The aim of our study was to assess the applicability of non-invasive NIRS of the placenta for fetal surveillance during labour. We analysed specific CTG parametres and compared them to NIRS parameters. Since the interpretation of CTG is complex and is dependent also on other tracing characteristics (i.e. bradycardia, tachycardia, pathological prolonged decelerations), we included the assessment of the whole CTG tracing according to most commonly used CTG classification systems worldwide. Our results show that placental deoxygenations during labour measured by NIRS are associated with fetal/neonatal acidosis. Furthermore, predictive value of placental NIRS for neonatal acidosis was shown in both classic ROC analysis as well as in computer-based classification analysis to be superior to that of CTG, which is currently the gold standard for assessing fetal acid-base status during labour.

Despite the fact that NIRS is increasingly being adopted in different medical fields, studies on its potential uses in obstetrics and gynaecology (excluding anaesthesiologic care of the mother) are scarse. First studies date back almost 30 years, when Peebles et al. [12,31] reported that oxygen dynamics could be measured during labour by measuring cerebral NIRS of the fetus. Aldrich et al. reported that late decelerations in the CTG recording, as well as prolonged and variable decelerations, reflect inadequate fetal oxygenation and are associated with transient deoxygenation of the fetal brain [14,15]. The probes used in these studies were placed directly on the fetal head, making this a relatively invasive methodology. The drawbacks in these studies also included probe movement with labour progression, changes in maternal position and pressure on the probe during uterine contractions, which could alter the interoptode space, leading to a change in the NIRS optical path length and inaccurate readings [32]. Poor contact between the optodes and fetal scalp (rapid descent of fetal head, body fluids impeding suction) was also problematic, as were numerous artefacts in the recordings. As a result, intrapartum fetal cerebral NIRS was not adopted into everyday obstetric practice.

Almost a decade later, four studies assessing placental oxygenation dynamics using trans-abdominal NIRS during pregnancy were published [1619]. Kawamura et al. reported higher placental tissue oxygenation in pregnancies with IUGR, while found no association between placental oxygenation and gestational age [19]. Kakogawa et al. later described higher placental oxygenation also in pregnancies with gestational hypertension [16]. While all these studies were performed in pregnant women who were not in labour, our results could be viewed as being in contrast with these findings. Both IUGR and hypertensive disorders in pregnancy are well known factors of placental insufficiency which, according to these studies, could be associated with higher placental oxygenation. On the contrary, we found episodes of intrapartum deoxygenations of placental tissue to be associated with fetal acidosis. Hasegawa et al. suggested that placental oxygenation value depends on the aetiology of IUGR (decreased in umbilical abnormalities and increased in cases with placental abnormalities or preeclampsia) [18]. We did not include women with signs of chronic placental insufficiency in the present study, so further research will be needed to analyse baseline and intrapartum changes of placental NIRS in this population of parturitients.

Placental deoxygenation was defined as a decrease in NIRS value of ≥ 5% for a period of ≥ 15–180 seconds. Other fields of medicine generally use a preset cut-off for recognition of significant tissue desaturation. In our study, we could not use such a cut-off since this is a pilot study and there are no published data that could guide such a decision.

Small number of women included resulted in few severe cases of neonatal acidosis and no case of hypoxic ischaemic encephalophaty. Larger studies will be needed to determine if placental NIRS could also be a predictive measure for these severe adverse outcomes. Our study should, therefore, be viewed as “proof of concept” that measuring placental NIRS during labour is feasible and that these measurements could provide important information on fetal status. An important drawback of intrapartum placental NIRS is the possibility that women with posteriorly located placentas would not benefit from such additional monitoring methodology since scattering and absorption could affect the detection of changes in the near-infrared light. This is why we chose not to include women with posterior placental location, but this hypothesis will have to be confirmed in further research. Even if confirmed, however, placental NIRS measurements would still be feasible in a great proportion of the population since the prevalence of posterior placentas is around 25% [33]. Another limitation of this non-invasive monitoring method could be its potentially lower accuracy in obese women. Our results do not confirm this. Of all women screened for depth of subcutaneous tissue, we found no woman with more than 5 cm of subcutaneous tissue, which was chosen as an exclusion criterion due to limitations in the depth of NIRS analysis.

Supporting information

S1 Data

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S2 Data

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S3 Data

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S4 Data

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Data Availability

All relevant data are within the paper and its Supporting Information files. Actual recordings can be requested from the corresponding author.

Funding Statement

The author JK acknowledges research core funding No. P2-0001, which was financially supported by the Slovenian Research Agency. The Slovenian Research Agency had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Anna Palatnik

9 Jan 2020

PONE-D-19-25139

Near-infrared spectroscopy of the placenta for monitoring fetal oxygenation during labour

PLOS ONE

Dear Ražem,

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Reviewers' comments:

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Reviewer #1: Partly

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Reviewer #1: I Don't Know

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Reviewer #1: Yes

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Reviewer #1: This work describes an application of NIRS for monitoring the oxygenation state of fetuses during labour. Cardiotocography (CTG) is the standard method for predicting neonatal acidosis but it is characterized by a low positive predictive value and by large inter- intra operator variability for the interpretation of the CTG traces. In this study, 43 pregnant women were divided in two groups, 10 resulting on children having PH <7.2 and 33 having a PH > 7.2 at birth. Several parameters from CTG and NIRS were used to discriminate these two groups and for their predictive values for children acidosis, by standard ROC curves or by a machine learning based method. The method having the best predictive accuracy is based on the number of deoxygenation episodes measured by NIRS.

The paper shows indeed an original application of NIRS, which is used non-invasively (unlike previous works in the literature with the same application) for monitoring the fetuses’ oxygenation during labour. While the abstract is concise and well written, I think that some sections of the paper should be improved for the paper to be easily accessible to a broader audience. Especially I think that the paper would benefit of more figures (and/or schematics).

For example, the section on CTG and NIRS measurements and analysis should be expanded to provide more details. What are the outputs of Avalon FM 30 (other than FHR and uterus contraction) and Nonin Equanox 7600? More information (possibly literature references or a schematic) should be provided for the NIRS machine, like number of sources and detectors and principle of data analysis (saying that the instrument comprises two inter-optode distances does not describe fully the instrument). Does it measure the changes of oxy-, deoxy and total hemoglobin only, or also tissue oxygen saturation? How are the data at different source-detector distances combined? Also, in the same section the authors describe different CTG “deceleration” (probably heart rate deceleration), like “late”, “variable”, “prolonged”, “early” which are not easily grasped by non-medical audience. For NIRS measurements, the authors arbitrarily defined episodes of deoxygenation as those where there was a >5% decrease on placenta oxygenation (from baseline values) and lasting a time range of (15, 80) s. All this information could enormously benefit if it was complemented by plots of typical experimental results both for CTG and NIRS. The authors should include one or more examples of CTG plots where the different decelerations are marked; also, for NIRS the authors should add typical traces of the parameters being measured and the episodes of deoxygenation marked. It is unclear which parameter was measured for the change in oxygenation: a) decrease of oxy-hemoglobin concentration; b) increase in deoxy-hemoglobin concentration; c) both; d) decrease of tissue oxygen saturation. How does a typical NIRS recording looks like? I am concerned, given the type of application for the presence of motion artifacts that could affect the interpretation of the data. The authors describe changes of oxygenation that last more than 3 minutes as baseline shifts of tissue oxygenation, which are due I believe to motion artifacts. Also, motion artifacts could be present in the range (15,80) seconds. The authors should comment on these points and show typical traces.

How is the velocity of tissue deoxygenation measured from the traces?

About the section of statistical analysis, I wonder if the authors have tried to use different training datasets and therefore different testing datasets. In other words, why the test is done only on one data set?

Which classification methods were tested? Please add a reference for the classification learner approach.

About the results section, when the authors described the CTG classification according to FIGO, NICE, ACOG, etc., it seems that not all the 43 cases could be classified (for example 29 were classified by FIGO and 21 by NICE). This also should be explained.

In the discussion section the authors should try to address the critical issue of different choices for the definition of episodes of deoxygenation, with different thresholds for the changes and time duration. How would these choices affect the classification results? Is the choice of 5%, and especially the time range (15, 80) seconds a meaningful choice for the definition of the episodes of deoxygenation? Is this choice guided by the temporal evolution of other parameters, like the duration of uterus contractions?

Minor issues and typos:

In the abstract when describing the results of prolonged decelerations, the symbol of the p-value “p” is missing (p=0.04).

Line 44: the symbols 37+0 and 41+6 should be explained.

Line 77: change “electrode” into “optode”.

In the result section actual numbers and the words referring to the numbers are inconsistently used. For example, line 152: “17” instead of “Seventeen”. See also line 202: “eight” instead of “8” and line 204 “two” instead of “2”.

Line 176: “spectroscopy” is misspelled.

Have the authors done a comprehensive literature search about the use of NIRS during labour? For example, I found the following review that is not cited: D. M. Peebles et al., “Fetal cerebral oxygenation and hemodynamics during labour measured by NIRS,” Mental retardation and developmental disabilities, 59-68 (1997).

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PLoS One. 2020 Apr 16;15(4):e0231461. doi: 10.1371/journal.pone.0231461.r002

Author response to Decision Letter 0


31 Jan 2020

RESPONSES TO TO THE EDITOR AND REVIEWER:

RESPONSES TO THE EDITOR:

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

We have followed the journal’s requirements.

2. In your Methods section, please provide additional information about the participant recruitment method and the demographic details of your participants.

Please ensure you have provided sufficient details to replicate the analyses such as: a) a table of relevant demographic details and b) a description of how participants were recruited.

Table 1 presenting patients demographics has been added and the table with CTG and NIRS parameters renamed accordingly. We believe inclusion and exclusion criteria are presented in sufficient detail to allow replication of our work.

3. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

We are willing to share our data since there are no legal or ethical restriction for doing so. Tables with de-identified data have now been uploaded together with the revised manuscript, our responses and the revised cover letter.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

Data have now been uploaded.

RESPONSES TO THE REVIEWER:

INTRODUCTION:

The section on CTG and NIRS measurements and analysis should be expanded to provide more details:

- What are the outputs of Avalon FM 30 (other than FHR and uterus contraction) and Nonin Equanox 7600?

Thank you for your comment. We have incorporated the outputs analysed into the manuscript for better clarification:

The outputs of Avalon FM 30 are FHR and uterus activity, the output of Nonin Equanox 7600 is regional tissue oxygen saturation (SO2 – the percent of placental oxygenated blood).

- More information (possibly literature references or a schematic) should be provided for the NIRS machine, like number of sources and detectors and principle of data analysis (saying that the instrument comprises two inter-optode distances does not describe fully the instrument).

All authors agree with this comment and have therefore provided the following schematic representations:

Figure 1: Schematic representation of NIRS measurement

Figure 2: NIRS electrode used in our study. Two light emitters and two detectors provide measurements that are minimally affected by intervening tissues or surface effects

The light absorption information collected by the dual emitter/detector electrodes and transferred via sensor cables is automatically incorporated into Nonin’s Dynamic Compensation light processing algorithm, which provides real time oxygenation saturation values of tissue examined.

Recordings were later transferred to a computer for further analysis.

- Does it measure the changes of oxy-, deoxy and total hemoglobin only, or also tissue oxygen saturation? How are the data at different source-detector distances combined?

Thank you for your comment.

It measures tissue oxygen saturation, the detailed algorithm of Nonin is, however, proprietary.

Several NIRS devices are available for clinical use, which differ according to numerous aspects, including algorithms adopted, type of light source, wavelengths of light emitted, the number and distance between the light emitters and detectors (Bickler PE, Feiner JR, Rollins MD. Factors affecting the performance of 5 cerebral oximeters during hypoxia in healthy volunteers. Anesth Analg. 2013;117(4):813-23.,

Kovač P, Miš K, Pirkmajer S, Marš T, Klokočovnik T, Kotnik G, Podbregar E, Podbregar M. How to Measure Tissue Oxygenation Using Near-Infrared Spectroscopy in a Patient With Alkaptonuria. J Cardiothorac Vasc Anesth. 2018 Dec;32(6):2708-2711.).

The Nonin Equanox device uses a dual light emitting and detecting sensor architecture, which means that the measurements are less affected by intervening tissue or surface effects. Use of four wavelengths of NIR light (730 nm, 760 nm, 810 nm, 880 nm) increases the accuracy of reporting the actual percent of oxygenated hemoglobin in the targeted tissue and also allows the algorithm to reduce inter-subject variability, regardless of age weight or skin color.

- CTG decelerations “late”, “variable”, “prolonged”, “early” are not easily grasped by non-medical audience.

Thank you for pointing this out. The authors agree with your comment and have therefore added the definitions of terms into the manuscript, illustrated with examples.

Late decelerations (symmetrical gradual decrease and return of FHR, with nadir occurring after the peak of contraction) are considered reflex fetal responses to fetal hypoxia during contractions.

CTG 1: Late FHR decelerations

Variable decelerations (abrupt decrease in FHR to levels below the baseline, which may occur in isolation or vary in onset, depth and duration in relationship to uterine contractions) are considered a sign of transient interruption of oxygen delivery to the fetus due to umbilical cord compression.

CTG 2: Variable FHR decelerations

Prolonged decelerations (decrease in FHR to levels below the baseline that lasts at least 2 minutes), are believed to indicate a fetal chemo-receptor response to hypoxemia.

CTG 3: Prolonged FHR deceleration

Early decelerations (symmetrical, gradual decrease and return of FHR below the baseline. In most cases the onset, nadir, and recovery of the deceleration are coincident with the beginning, peak, and ending of the contraction, respectively) are not considered related to fetal oxygenation.

CTG 4: Early FHR decelerations

- For NIRS measurements, the authors arbitrarily defined episodes of deoxygenation as those where there was a >5% decrease on placenta oxygenation (from baseline values) and lasting a time range of (15, 180) s. All this information could enormously benefit if it was complemented by plots of typical experimental results both for CTG and NIRS: include one or more examples of CTG plots where the different decelerations are marked; also, for NIRS the authors should add typical traces of the parameters being measured and the episodes of deoxygenation marked.

Similarly to the comment above, we have included schematic examples of NIRS occurring events into the manuscript. We believe this does indeed equip the reader with a better understanding of described events.

NIRS 1: Rises in oxygen tissue saturation

NIRS 2: Desaturations

The examples of decelerations on CTG are shown above (CTG 1-4)

- It is unclear which parameter was measured for the change in oxygenation: a) decrease of oxy-hemoglobin concentration; b) increase in deoxy-hemoglobin concentration; c) both; d) decrease of tissue oxygen saturation.

Tissue oxygenation value was measured for observing change in oxygenation.

In the methods section, we describe how the light absorption information is collected by the dual emitter/detector electrodes and transferred via sensor cables, which provides real time oxygenation saturation values of tissue examined.

-How does a typical NIRS recording look like? I am concerned, given the type of application for the presence of motion artifacts that could affect the interpretation of the data. The authors describe changes of oxygenation that last more than 3 minutes as baseline shifts of tissue oxygenation, which are due I believe to motion artifacts. Also, motion artifacts could be present in the range (15,180) seconds. The authors should comment on these points and show typical traces.

Short term motion artefacts are present throughout the whole monitoring period, which is why a pre-specified 15 second threshold was chosen to limit their impact. No rise in in tissue oxygenation is documented in example 1, since the change is smaller than 5 % from the baseline. In example 2, no fall in desaturation is documented, since the events last less than 15 seconds each. In both cases, the threshold is not reached for defining occurring events - therefore limiting signal artefacts.

EXAMPLE 1

EXAMPLE 2

In our study, we believe that a > 3 min oxygenation change represents a new, stable, basal oxygenation level. In our opinion absolute changes in measurements are more indicative than arbitrary preset cut-off values. Example 3 shows three different average basal saturation values, each lasting more that 3 min each.

EXAMPLE 3

- How is the velocity of tissue deoxygenation measured from the traces?

Thank you for the question. For better understanding, we have added this explanation to the manuscript:

Tissue oxygenation velocity is calculated as the derivation of oxygenation, which is implemented as the change of oxygenation in one time sample of NIRS signal (% per second).

STATISTICAL ANALYSIS:

-Have authors tried to use different training datasets and therefore different testing datasets. In other words, why the test is done only on one data set?

Data were divided into two datasets: the first used for training and regressor selection, while the second for testing the obtained statistical classifier. For the computer-based statistical classification, the first dataset (used for training and regressor selection) contained recordings and derived variables of 36 participants, including eight cases of neonatal acidosis. The second dataset (used for testing the obtained statistical classifier) contained recordings and derived variables of seven participants, including two cases of neonatal acidosis. The analysis carried out was 4-fold cross-validation on the first dataset. Number of folds, i.e. four, was determined according to the number of cases of neonatal acidosis in the first dataset. Each division of subgroups should have contained at least one case of neonatal acidosis. Three subgroups of the first dataset were used for training the classifier and for the selection of top ranking regressors. For the remaining subgroup, the previously created classifier was applied with the same regressors as the training subgroups. More than 4-fold divison of the first set and more than one testing dataset were not possible due to the limited number of available measurements. Among different classification methods, classification tree or decision tree method was chosen (Grochtmann M, Grimm K. Classification trees for partition testing. Software Testing, Verification and Reliability. 1993;3: 63–82. doi:10.1002/stvr.4370030203)

- Which classification methods were tested? Please add a reference for the classification learner approach.

Different classification methods were also tested using the Classification Learner application of Matlab (reference: Statistics and Machine Learning Toolbox User’s Guide, Mathworks, Natick, MA, 2016).

This has been added to the manuscript.

RESULTS:

- About the results section, when the authors described the CTG classification according to FIGO, NICE, ACOG, etc., it seems that not all the 43 cases could be classified (for example 29 were classified by FIGO and 21 by NICE). This also should be explained.

Only the number of suspicious and pathological traces was mentioned in the text, the others were considered normal. To eliminate confusion, we have included this into the manuscript. Thank you for pointing this out.

DISCUSSION:

-Try to address the critical issue of different choices for the definition of episodes of deoxygenation, with different thresholds for the changes and time duration. How would these choices affect the classification results? Is the choice of 5%, and especially the time range (15, 180) seconds a meaningful choice for the definition of the episodes of deoxygenation? Is this choice guided by the temporal evolution of other parameters, like the duration of uterus contractions?

Our study was the first study analyzing placental NIRS values during labour. Therefore, we had no previously published data on which we could base our outcome definitions on.

We defined an occurring event on NIRS as a change of ≥ 5 % for a period of ≥ 15 -180 seconds. Understandably, a shorter duration and smaller change would result in more occurring events, however, we believe these would more frequently include artefacts (movement of patient, potential loss of signal...). Additionally, occurring CTG events have similar durations; definitions of accelerations and decelerations include at least 15 second changes of fetal heart rate, whilst the duration of prolonged decelerations according to FIGO and NICE classification systems exceeds 180 seconds. A change of ≥ 5 % was chosen based on other NIRS studies in non-pregnant populations and since we assumed such change could be of clinical importance.

As mentioned, this was a pre-specified outcome. We agree with the reviewer, that different NIRS parameters and different cut-offs could yield different (potentially even better) prognostic values as ones reported in our study. The study, therefore, highlights the need for further research in this field.

How would different episode definitions affect classification results?

As mentioned above, a change in signal > 5 % was a pre-specified outcome, determined before performing analysis of data. Further studies which would apply different specified episodes could show different (perhaps even better) results. Since our study was the first of this kind, a 5 % was considered an educated guess.

Minor issues and typos:

Thank you for your suggestions. These have all been corrected and inserted into the manuscript, along with the suggested reference.

- In the abstract when describing the results of prolonged decelerations, the symbol of the p-value “p” is missing (p=0.04).

- Line 44: the symbols 37+0 and 41+6 should be explained.

- Line 77: change “electrode” into “optode”.

- In the result section actual numbers and the words referring to the numbers are inconsistently used. For example, line 152: “17” instead of “Seventeen”. See also line 202: “eight” instead of “8” and line 204 “two” instead of “2”.

- Line 176: “spectroscopy” is misspelled.

- Have the authors done a comprehensive literature search about the use of NIRS during labour? For example, I found the following review that is not cited: D. M. Peebles et al., “Fetal cerebral oxygenation and hemodynamics during labour measured by NIRS,” Mental retardation and developmental disabilities, 59-68 (1997)

Attachment

Submitted filename: Response to reviewers.docx

Decision Letter 1

Anna Palatnik

25 Mar 2020

Near-infrared spectroscopy of the placenta for monitoring fetal oxygenation during labour

PONE-D-19-25139R1

Dear Dr. Ražem,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

With kind regards,

Anna Palatnik, M.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

All comments have been addressed

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

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Reviewer #1: All the comments have been addressed. The figures have added more clarity to the manuscript and the paper is definitely ready for publication.

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Reviewer #1: Yes: Angelo Sassaroli

Acceptance letter

Anna Palatnik

30 Mar 2020

PONE-D-19-25139R1

Near-infrared spectroscopy of the placenta for monitoring fetal oxygenation during labour

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on behalf of

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