Analysis of CTG patterns in cases with metabolic acidosis at birth with and without neonatal neurological alterations.

OBJECTIVE: To determine cardiotocographic patterns in newborns with metabolic acidosis, based on clinical signs of neurological alteration (NA) and the need for hypothermic treatment. METHODS: All term newborns with metabolic acidosis in a single center from 2016 to 2020 were included in the study. Three segments of intrapartum CTG (cardiotocography) were considered (first 30 min of active labor, 90 to 30 min before birth, and last 30 min before delivery) and a longitudinal analysis of CTG pattern was performed according to the 2015 FIGO classification. RESULTS: Three hundred and twenty-four neonates with metabolic acidosis diagnosed at birth were divided into three groups: the first group included all neonates with any clinical sign of neurological alteration, requiring hypothermia according to the recommendation of the Italian Society of Neonatology (group TNA-Treated neurological Alteration, n = 17), the second encompassed neonates with any clinical sign of neurological alteration not requiring hypothermia (group NTNA-Not Treated neurological Alteration, n = 83), and the third enclosed all neonates without any sign of clinical neurological involvement (group NoNA-No neurological Alteration, n = 224). The most frequent alterations of CTG in TNA group were late decelerations, reduced variability, bradycardia, and tachysystole. Unexpectedly, from the longitudinal analysis of the CTG, 49% of all cases with metabolic acidosis never showed a pathological CTG with normal trace at the beginning of labor followed by normal or suspicious trace in the final part of labor, the same as in TNA and NTNA groups (10 and 39%, respectively). CONCLUSIONS: CTG has limited specificity in identifying cases of acidosis at birth, even in babies who will develop NA.

Rapid detection of fetal compromise using input length invariant deep learning on fetal heart rate signals.

Standard clinical practice to assess fetal well-being during labour utilises monitoring of the fetal heart rate (FHR) using cardiotocography. However, visual evaluation of FHR signals can result in subjective interpretations leading to inter and intra-observer disagreement. Therefore, recent studies have proposed deep-learning-based methods to interpret FHR signals and detect fetal compromise. These methods have typically focused on evaluating fixed-length FHR segments at the conclusion of labour, leaving little time for clinicians to intervene. In this study, we propose a novel FHR evaluation method using an input length invariant deep learning model (FHR-LINet) to progressively evaluate FHR as labour progresses and achieve rapid detection of fetal compromise. Using our FHR-LINet model, we obtained approximately 25% reduction in the time taken to detect fetal compromise compared to the state-of-the-art multimodal convolutional neural network while achieving 27.5%, 45.0%, 56.5% and 65.0% mean true positive rate at 5%, 10%, 15% and 20% false positive rate respectively. A diagnostic system based on our approach could potentially enable earlier intervention for fetal compromise and improve clinical outcomes.

Antenatal cardiotocography in primary midwife-led care: a budget impact analysis.

OBJECTIVES: In many countries, the healthcare sector is dealing with important challenges such as increased demand for healthcare services, capacity problems in hospitals and rising healthcare costs. Therefore, one of the aims of the Dutch government is to move care from in-hospital to out-of-hospital care settings. An example of an innovation where care is moved from a more specialised setting to a less specialised setting is the performance of an antenatal cardiotocography (aCTG) in primary midwife-led care. The aim of this study was to assess the budget impact of implementing aCTG for healthy pregnant women in midwife-led care compared with usual obstetrician-led care in the Netherlands. METHODS: A budget impact analysis was conducted to estimate the actual costs and reimbursement of aCTG performed in midwife-led care and obstetrician-led care (ie, base-case analysis) from the Dutch healthcare perspective. Epidemiological and healthcare utilisation data describing both care pathways were obtained from a prospective cohort, survey and national databases. Different implementation rates of aCTG in midwife-led care were explored. A probabilistic sensitivity analysis was conducted to estimate the uncertainty surrounding the budget impact estimates. RESULTS: Shifting aCTG from obstetrician-led care to midwife-led-care would increase actual costs with €311 763 (97.5% CI €188 574 to €426 072) and €1 247 052 (97.5% CI €754 296 to €1 704 290) for implementation rates of 25% and 100%, respectively, while it would decrease reimbursement with -€7 538 335 (97.5% CI -€10 302 306 to -€4 559 661) and -€30 153 342 (97.5% CI -€41 209 225 to -€18 238 645) for implementation rates of 25% and 100%, respectively. The sensitivity analysis results were consistent with those of the main analysis. CONCLUSIONS: From the Dutch healthcare perspective, we estimated that implementing aCTG in midwife-led care may increase the associated actual costs. At the same time, it might lower the healthcare reimbursement.

Transcutaneous Discrimination of Fetal Heart Rate from Maternal Heart Rate: A Fetal Oximetry Proof-of-Concept.

Intrapartum care uses electronic fetal heart rate monitoring (EFHRM) for over 50 years to indirectly assess fetal oxygenation. However, this approach has been associated with an increase in cesarean delivery rates and limited improvements in neonatal hypoxic outcome. To address these shortcomings, a novel transabdominal fetal pulse oximeter (TFO) is being developed to provide an objective measurement of fetal oxygenation. Previous studies have evaluated the performance of TFO on pregnant ewe. Building on the animal model, this study aims to determine whether TFO can successfully capture human fetal heart rate (FHR) signals during non-stress testing (NST) as a proof-of-concept. Eight ongoing pregnancies meeting specific inclusion criteria (18-40 years old, singleton, and at least 36 weeks' gestation) were enrolled with consent. Each study session was 15 to 20 min long. Reference maternal heart rate (MHR) and FHR were obtained using finger pulse oximetry and cardiotocography for subsequent comparison. The overall root-mean-square error was 9.7BPM for FHR and 4.4 for MHR, while the overall mean-absolute error was 7.6BPM for FHR and 1.8 for MHR. Bland-Altman analysis displayed a mean bias ± standard deviation between TFO and reference of -3.9 ± 8.9BPM, with limits of agreement ranging from -21.4 to 13.6 BPM. Both maternal and fetal heart rate measurements obtained from TFO exhibited a p-value < 0.001, showing significant correlation with the reference. This proof-of-concept study successfully demonstrates that TFO can accurately differentiate maternal and fetal heart signals in human subjects. This achievement marks the initial step towards enabling fetal oxygen saturation measurement in humans using TFO.

How does the use of continuous electronic fetal monitoring influence women's experiences of labour? A systematic integrative review of the literature from high income countries.

BACKGROUND: A variety of technologies are used to monitor fetal wellbeing in labour. Different types of fetal monitoring devices impact women's experiences of labour and birth. AIM: This review aims to understand how continuous electronic fetal monitoring (CEFM) influences women's experiences, with a focus on sense of control, active decision-making and mobility. METHODS: A systematic search of the literature was conducted. Findings from qualitative, quantitative and mixed methods studies were analysed to provide a review of current evidence. FINDINGS: Eighteen publications were included. The findings were synthesised into three themes: 'Feeling reassured versus anxious about the welfare of their baby', 'Feeling comfortable and free to be mobile versus feeling uncomfortable and restricted', and 'Feeling respected and empowered to make decisions versus feeling depersonalised with minimal control '. Women experienced discomfort and a lack of mobility as a result of some CEFM technologies. They often felt anxious and had mixed feelings about their baby's welfare whilst these were in use. Some women valued the data produced by CEFM technologies about the welfare of their baby. Many women experienced a sense of depersonalisation and lack of control whilst CEFM technologies were used. DISCUSSION: Fetal monitoring technologies influence women's experiences of labour both positively and negatively. Wireless devices were associated with the most positive response as they enabled greater freedom of movement. CONCLUSION: The design of emerging fetal monitoring technologies should incorporate elements which foster freedom of movement, are comfortable and provide women with a sense of choice and control. The implementation of fetal monitoring that enables these elements should be prioritised by health professionals.

Multicentric intelligent cardiotocography signal interpretation using deep semi-supervised domain adaptation via minimax entropy and domain invariance.

BACKGROUND AND OBJECTIVE: Obstetricians use Cardiotocography (CTG), which is the continuous recording of fetal heart rate and uterine contraction, to assess fetal health status. Deep learning models for intelligent fetal monitoring trained on extensively labeled and identically distributed CTG records have achieved excellent performance. However, creation of these training sets requires excessive time and specialist labor for the collection and annotation of CTG signals. Previous research has demonstrated that multicenter studies can improve model performance. However, models trained on cross-domain data may not generalize well to target domains due to variance in distribution among datasets. Hence, this paper conducted a multicenter study with Deep Semi-Supervised Domain Adaptation (DSSDA) for intelligent interpretation of antenatal CTG signals. This approach helps to align cross-domain distribution and transfer knowledge from a label-rich source domain to a label-scarce target domain. METHODS: We proposed a DSSDA framework that integrated Minimax Entropy and Domain Invariance (DSSDA-MMEDI) to reduce inter-domain gaps and thus achieve domain invariance. The networks were developed using GoogLeNet to extract features from CTG signals, with fully connected, softmax layers for classification. We designed a Dynamic Gradient-driven strategy based on Mutual Information (DGMI) to unify the losses from Minimax Entropy (MME), Domain Invariance (DI), and supervised cross-entropy during iterative learning. RESULTS: We validated our DSSDA model on two datasets collected from collaborating healthcare institutions and mobile terminals as the source and target domains, which contained 16,355 and 3,351 CTG signals, respectively. Compared to the results achieved with deep learning networks without DSSDA, DSSDA-MMEDI significantly improved sensitivity and F1-score by over 6%. DSSDA-MMEDI also outperformed other state-of-the-art DSSDA approaches for CTG signal interpretation. Ablation studies were performed to determine the unique contribution of each component in our DSSDA mechanism. CONCLUSIONS: The proposed DSSDA-MMEDI is feasible and effective for alignment of cross-domain data and automated interpretation of multicentric antenatal CTG signals with minimal annotation cost.

Intrapartum pyrexia, cardiotocography traces and histologic chorioamnionitis: a case-control study.

OBJECTIVES: To compare characteristics of labor, cardiotocography traces, and maternal and neonatal outcomes, in a cohort of pregnancies at term complicated by maternal intrapartum pyrexia, with or without a histologic diagnosis of chorioamnionitis. METHODS: This is a retrospective case-control study including pregnancies at term with detection of maternal intrapartum pyrexia, delivered between January 2020 and June 2021. Cardiotocography traces were entirely evaluated, since admission till delivery, and classified according to the International Federation of Obstetrics and Gynecology (FIGO) guideline. Maternal and neonatal outcomes were also recorded as secondary outcomes. Placentas have been studied according to the Amniotic Fluid Infection Nosology Committee. RESULTS: Forty four patients met the inclusion criteria and were included in the study cohort. There was a significant association between the use of oxytocin augmentation in labor and the histologic diagnosis of chorioamnionitis. A significative recurrence of loss and/or absence of accelerations at the point of pyrexia was also documented in women with histological chorioamnionitis compared to the others. CONCLUSIONS: Chorioamnionitis appears to be associated with myometrial disfunction, as suggested by the increased use of oxytocin augmentation during active labor of women at term with intrapartum pyrexia and histologic diagnosis of chorioamnionitis.

Computer Vision for Identification of Increased Fetal Heart Variability in Cardiotocogram.

INTRODUCTION: Increased fetal heart rate variability (IFHRV), defined as fetal heart rate (FHR) baseline amplitude changes of >25 beats per minute with a duration of ≥1 min, is an early sign of intrapartum fetal hypoxia. This study evaluated the level of agreement of machine learning (ML) algorithms-based recognition of IFHRV patterns with expert analysis. METHODS: Cardiotocographic recordings and cardiotocograms from 4,988 singleton term childbirths were evaluated independently by two expert obstetricians blinded to the outcomes. Continuous FHR monitoring with computer vision analysis was compared with visual analysis by the expert obstetricians. FHR signals were graphically processed and measured by the computer vision model labeled SALKA. RESULTS: In visual analysis, IFHRV pattern occurred in 582 cardiotocograms (11.7%). Compared with visual analysis, SALKA recognized IFHRV patterns with an average Cohen's kappa coefficient of 0.981 (95% CI: 0.972-0.993). The sensitivity of SALKA was 0.981, the positive predictive rate was 0.822 (95% CI: 0.774-0.903), and the false-negative rate was 0.01 (95% CI: 0.00-0.02). The agreement between visual analysis and SALKA in identification of IFHRV was almost perfect (0.993) in cases (N = 146) with neonatal acidemia (i.e., umbilical artery pH <7.10). CONCLUSIONS: Computer vision analysis by SALKA is a novel ML technique that, with high sensitivity and specificity, identifies IFHRV features in intrapartum cardiotocograms. SALKA recognizes potential early signs of fetal distress close to those of expert obstetricians, particularly in cases of neonatal acidemia.

Midwives' attitudes toward the use of cardiotocograph (CTG) machines in labor units: A study in Jordan.

BACKGROUND: The purpose of cardiotocograph (CTG) usage is to detect any alterations in fetal heart rate (FHR) early before they are prolonged and profound. However, the use of CTG machines on a routine basis is not an evidence-supported practice. There is no Jordanian study that assesses the midwives' attitudes toward this machine. This study aimed to identify Jordanian midwives' attitudes towards the use of cardiotocograph (CTG) machines in labor units, alongside examining the relationships between midwives' personal sociodemographic characteristics and such attitudes. METHODS: A descriptive research design was used to identify Jordanian midwives' attitudes towards the use of CTG machines in both public and private labor units in Jordan. Data were collected using the valid and reliable tool designed by Sinclair (2001), and these were used to identify midwives' attitudes towards CTG usage. A total of 329 midwives working in the labor units of governmental and private hospitals in the center and north of Jordan participated in the study from May to July 2022. RESULTS: The total mean score for the attitude scale was M = 3.14 (SD = 0.83). More than half of the sample (N = 187, 58.4 %) demonstrated a mean score greater than 3.14, however, which indicates generally positive attitudes toward CTG usage in labor units. Midwives working in private hospitals and those holding Bachelor's degrees had more positive attitudes toward the use of CTG machines. CONCLUSION: This study provides new insights into the attitudes of Jordanian midwives towards CTG use in labor units. These suggest that it is critical to conduct training courses for registered midwives to help them develop and/or regain confidence and competence with respect to various key aspects of intrapartum care, including intermittent auscultation and the appropriate use of CTG.

Machine learning on cardiotocography data to classify fetal outcomes: A scoping review.

INTRODUCTION: Uterine contractions during labour constrict maternal blood flow and oxygen delivery to the developing baby, causing transient hypoxia. While most babies are physiologically adapted to withstand such intrapartum hypoxia, those exposed to severe hypoxia or with poor physiological reserves may experience neurological injury or death during labour. Cardiotocography (CTG) monitoring was developed to identify babies at risk of hypoxia by detecting changes in fetal heart rate (FHR) patterns. CTG monitoring is in widespread use in intrapartum care for the detection of fetal hypoxia, but the clinical utility is limited by a relatively poor positive predictive value (PPV) of an abnormal CTG and significant inter and intra observer variability in CTG interpretation. Clinical risk and human factors may impact the quality of CTG interpretation. Misclassification of CTG traces may lead to both under-treatment (with the risk of fetal injury or death) or over-treatment (which may include unnecessary operative interventions that put both mother and baby at risk of complications). Machine learning (ML) has been applied to this problem since early 2000 and has shown potential to predict fetal hypoxia more accurately than visual interpretation of CTG alone. To consider how these tools might be translated for clinical practice, we conducted a review of ML techniques already applied to CTG classification and identified research gaps requiring investigation in order to progress towards clinical implementation. MATERIALS AND METHOD: We used identified keywords to search databases for relevant publications on PubMed, EMBASE and IEEE Xplore. We used Preferred Reporting Items for Systematic Review and Meta-Analysis for Scoping Reviews (PRISMA-ScR). Title, abstract and full text were screened according to the inclusion criteria. RESULTS: We included 36 studies that used signal processing and ML techniques to classify CTG. Most studies used an open-access CTG database and predominantly used fetal metabolic acidosis as the benchmark for hypoxia with varying pH levels. Various methods were used to process and extract CTG signals and several ML algorithms were used to classify CTG. We identified significant concerns over the practicality of using varying pH levels as the CTG classification benchmark. Furthermore, studies needed to be more generalised as most used the same database with a low number of subjects for an ML study. CONCLUSION: ML studies demonstrate potential in predicting fetal hypoxia from CTG. However, more diverse datasets, standardisation of hypoxia benchmarks and enhancement of algorithms and features are needed for future clinical implementation.

The utility of fetal heart rate deceleration's descending slope in searching for a non-National Institute of Child Health and Human Development parameter for the detection of fetal acidosis.

OBJECTIVE: To identify new parameters predicting fetal acidemia. METHODS: A retrospective case-control study in a cohort of deliveries from a tertiary referral hospital-based cohort deliveries in Zaragoza, Spain between 2018 and 2021 was performed. To predict fetal acidemia, the NICHD categorizations and non-NICHD parameters were analyzed in the electronic fetal monitoring (EFM). Those included total reperfusion time, total deceleration area and the slope of the descending limb of the fetal heart rate of the last deceleration curve. The accuracy of the parameters was evaluated using the specificity for (80%, 85%, 90%, 95%) sensitivity and the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 10 362 deliveries were reviewed, with 224 cases and 278 controls included in the study. The NICHD categorizations showed reasonable discriminatory ability (AUC = 0.727). The non-NICHD parameters measured during the 30-min fetal monitoring, total deceleration area (AUC = 0.807, 95% CI: 0.770, 0.845) and total reperfusion time (AUC = 0.750, 95% CI: 0.707, 0.792), exhibited higher discriminatory ability. The slope of the descending limb of the fetal heart rate of the last deceleration curve had the best AUC value (0.853, 95% CI: 0.816, 0.889). The combination of total deceleration area or total reperfusion time with the slope demonstrated high discriminatory ability (AUC = 0.908, 95% CI: 0.882, 0.933; specificities of 71.6% and 72.7% for a sensitivity of 90%). CONCLUSIONS: The slope of the descending limb of the fetal heart rate of the last deceleration curve is the strongest predictor of fetal acidosis, but its combination with the total reperfusion time shows better clinical utility.

Inter- and intraobserver agreement of antenatal cardiotocography assessments by maternity care professionals: A prospective study.

OBJECTIVE: In the Netherlands, antenatal cardiotocography (aCTG) to assess fetal well-being is performed in obstetrician-led care. An innovative initiative was started to evaluate whether aCTG for specific indications-reduced fetal movements, external cephalic version, or postdate pregnancy-is feasible in non-obstetrician-led care settings by independent primary care midwives. Quality assessment is essential when reorganizing and shifting tasks and responsibilities. Therefore, we aimed to assess the inter- and intraobserver agreement for aCTG assessments between and within four professional groups involved in Dutch maternity care regarding the overall classification and assessment of the various components of aCTG. METHOD: This was a prospective study among 47 Dutch primary care midwives, hospital-based midwives, residents, and obstetricians. Ten aCTG traces were assessed twice at a 1 month interval. To ensure a representative sample, we used two different sets of 10 aCTG traces each. We calculated the degree of agreement using the proportions of agreement. RESULTS: The proportions of agreement for interobserver agreement on the classification of aCTG between and within the four professional groups varied from 0.82 to 0.94. The proportions of agreement for each professional group were slightly higher for intraobserver (0.86-0.94) than for interobserver agreement. For the various aCTG components, the proportions of agreement for interobserver agreement varied from 0.64 (presence of contractions) to 0.98 (baseline heart frequency). CONCLUSION: The proportion of agreement levels between and within the maternity care professionals in the classification of aCTG traces among healthy women were comparable. This means that these professional groups are equally well able to classify aCTGs in healthy pregnant women.

Fetal Heart Rate Auscultation, 4th Edition.

Intermittent auscultation (IA) is an evidence-based method of fetal surveillance during labor for birthing people with low-risk pregnancies. It is a central component of efforts to reduce the primary cesarean rate and promote vaginal birth (American College of Obstetricians and Gynecologists, 2019; Association of Women's Health, Obstetric and Neonatal Nurses, 2022a). The use of intermittent IA decreased with the introduction of electronic fetal monitoring, while the increased use of electronic fetal monitoring has been associated with an increase of cesarean births. This practice monograph includes information on IA techniques; interpretation and documentation; clinical decision-making and interventions; communication; education, staffing, legal issues; and strategies to implement IA.

Artificial intelligence based cardiotocogram assessment during labor.

OBJECTIVE: To assess whether artificial intelligence, inspired by clinical decision-making procedures in delivery rooms, can correctly interpret cardiotocographic tracings and distinguish between normal and pathological events. STUDY DESIGN: A method based on artificial intelligence was developed to determine whether a cardiotocogram shows a normal response of the fetal heart rate to uterine activity (UA). For a given fetus and given the UA and previous FHR, the method predicts a fetal heart rate response, under the assumption that the fetus is still in good condition and based on how that specific fetus has responded so far. We hypothesize that this method, when having only learned from fetuses born in good condition, is incapable of predicting the response of a compromised fetus or an episode of transient fetal distress. The (in)capability of the method to predict the fetal heart rate response would then yield a method that can help to assess fetal condition when the obstetrician is in doubt. Cardiotocographic data of 678 deliveries during labor were selected based on a healthy outcome just after birth. The method was trained on the cardiotocographic data of 548 fetuses of this group to learn their heart rate response. Subsequently it was evaluated on 87 fetuses, by assessing whether the method was able to predict their heart rate responses. The remaining 43 cardiotocograms were segment-by-segment annotated by three experienced gynecologists, indicating normal, suspicious, and pathological segments, while having access to the full recording and neonatal outcome. This future knowledge makes the expert annotations of a quality that is unachievable during live interpretation. RESULTS: The comparison between abnormalities detected by the method (only using past and present input) and the annotated CTG segments by gynecologists (also looking at future input) yields an area under the curve of 0.96 for the distinction between normal and pathological events in majority-voted annotations. CONCLUSION: The developed method can distinguish between normal and pathological events in near real-time, with a performance close to the agreement between three gynecologists with access to the entire CTG tracing and fetal outcome. The method has a strong potential to support clinicians in assessing fetal condition in clinical practice.

Large-scale analysis of interobserver agreement and reliability in cardiotocography interpretation during labor using an online tool.

BACKGROUND: While the effectiveness of cardiotocography in reducing neonatal morbidity is still debated, it remains the primary method for assessing fetal well-being during labor. Evaluating how accurately professionals interpret cardiotocography signals is essential for its effective use. The objective was to evaluate the accuracy of fetal hypoxia prediction by practitioners through the interpretation of cardiotocography signals and clinical variables during labor. MATERIAL AND METHODS: We conducted a cross-sectional online survey, involving 120 obstetric healthcare providers from several countries. One hundred cases, including fifty cases of fetal hypoxia, were randomly assigned to participants who were invited to predict the fetal outcome (binary criterion of pH with a threshold of 7.15) based on the cardiotocography signals and clinical variables. After describing the participants, we calculated (with a 95% confidence interval) the success rate, sensitivity and specificity to predict the fetal outcome for the whole population and according to pH ranges, professional groups and number of years of experience. Interobserver agreement and reliability were evaluated using the proportion of agreement and Cohen's kappa respectively. RESULTS: The overall ability to predict a pH level below 7.15 yielded a success rate of 0.58 (95% CI 0.56-0.60), a sensitivity of 0.58 (95% CI 0.56-0.60) and a specificity of 0.63 (95% CI 0.61-0.65). No significant difference in the success rates was observed with respect to profession and number of years of experience. The success rate was higher for the cases with a pH level below 7.05 (0.69) and above 7.20 (0.66) compared to those falling between 7.05 and 7.20 (0.48). The proportion of agreement between participants was good (0.82), with an overall kappa coefficient indicating substantial reliability (0.63). CONCLUSIONS: The use of an online tool enabled us to collect a large amount of data to analyze how practitioners interpret cardiotocography data during labor. Despite a good level of agreement and reliability among practitioners, the overall accuracy is poor, particularly for cases with a neonatal pH between 7.05 and 7.20. Factors such as profession and experience level do not present notable impact on the accuracy of the annotations. The implementation and use of a computerized cardiotocography analysis software has the potential to enhance the accuracy to detect fetal hypoxia, especially for ambiguous cardiotocography tracings.

Fetal Heart Rate Auscultation, 4th Edition.

Intermittent auscultation (IA) is an evidence-based method of fetal surveillance during labor for birthing people with low-risk pregnancies. It is a central component of efforts to reduce the primary cesarean rate and promote vaginal birth (American College of Obstetricians and Gynecologists, 2019; Association of Women's Health, Obstetric and Neonatal Nurses, 2022a). The use of intermittent IA decreased with the introduction of electronic fetal monitoring, while the increased use of electronic fetal monitoring has been associated with an increase of cesarean births. This practice monograph includes information on IA techniques; interpretation and documentation; clinical decision-making and interventions; communication; education, staffing, legal issues; and strategies to implement IA.

Psychosocial outcomes from one cohort participating in the STan Australian Randomised controlled Trial (START).

BACKGROUND: In an Australian randomized controlled trial (RCT), two techniques for intrapartum fetal surveillance were compared: ST analysis (STan) as an adjunct to cardiotocography (CTG), compared with CTG alone. The aim was to determine whether CTG + STan could reduce emergency cesarean birth rates while maintaining or improving neonatal outcomes. Secondary aims were to compare clinical, economic, and psychosocial outcomes. The purpose of this paper was to present psychosocial outcomes from one cohort enrolled in the trial. METHODS: The study was conducted at one tertiary referral hospital. Participants who had taken part in the trial from the outset were invited to complete a questionnaire between March 2018 and January 2020, approximately 8 weeks after giving birth. Outcomes included depression, psychological distress, health-related quality of life, and infant feeding practices. Analysis was by intention to treat. RESULTS: N = 207/527 participants completed the questionnaire (n = 113, STan; n = 94, CTG alone). Overall, no statistically significant or clinically meaningful differences were found in the two groups for symptoms of depression, psychological distress, quality of life, or infant feeding. A statistically significant difference was observed for the subscale of pain-discomfort, where scores were higher on average in the CTG alone arm relative to that in the CTG + STan arm. CONCLUSIONS: Although STan as an adjunct to CTG constitutes a different clinical technology from CTG alone, both monitoring types appeared to produce similar results in terms of postnatal psychosocial outcomes for women. Findings from this study provide service users and staff with a comprehensive assessment of STan that can be used to make evidence-informed decisions about monitoring options should STan become more widely available.