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Relative uteroplacental insufficiency of labor (RUPI‐L) is a clinical condition that refers to alterations in the fetal oxygen “demand–supply” equation caused by the onset of regular uterine activity. The term RUPI‐L indicates a condition of “relative” uteroplacental insufficiency which is relative to a specific stressful circumstance, such as the onset of regular uterine activity. RUPI‐L may be more prevalent in fetuses in which the ratio between the fetal oxygen supply and demand is already slightly reduced, such as in cases of subclinical placental insufficiency, post‐term pregnancies, gestational diabetes, and other similar conditions. Prior to the onset of regular uterine activity, fetuses with a RUPI‐L may present with normal features on the cardiotocography. However, with the onset of uterine contractions, these fetuses start to manifest abnormal fetal heart rate patterns which reflect the attempt to maintain adequate perfusion to essential central organs during episodes of transient reduction in oxygenation. If labor is allowed to continue without an appropriate intervention, progressively more frequent, and stronger uterine contractions may result in a rapid deterioration of the fetal oxygenation leading to hypoxia and acidosis. In this Commentary, we introduce the term relative uteroplacental insufficiency of labor and highlight the pathophysiology, as well as the common features observed in the fetal heart rate tracing and clinical implications. [ABSTRACT FROM AUTHOR]

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. Synopsis: Slope combined with total reperfusion time exhibit higher discriminatory ability to detect fetal acidosis in comparison to previous categorizations and better clinical utility to predict fetal acidosis. [ABSTRACT FROM AUTHOR]

Background: The use of cardiotocography (CTG) to improve neonatal outcomes is controversial. The medical settings, subjects, utilizations, and interpretation guidelines of CTG are unclear for low‐ and middle‐income countries (LMICs). Objectives: To assess and review CTG use for studies identified in LMICs and provide insights on the potential for effective use of CTG to improve maternal and neonatal outcomes. Search Strategy: The databases Medline, CINAHL, EMBASE, and Cochrane Central Register of Controlled Trials (CENTRAL) were searched for published and unpublished literature through September 2023. Selection Criteria: Publications were identified which were conducted in LMICs, based on the World Bank list of economies for 2019; targeting pregnant women in childbirth; and focusing on the utilization of CTG and neonatal outcomes. Data Collection and Analysis: Publications were screened, and duplicates were removed. A scoping review was conducted using PRISMA‐ScR guidelines. Results: The searches generated 1157 hits, of which 67 studies were included in the review. In the studies there was considerable variation and ambiguity regarding the study settings, target populations, utilizations, timing, frequency, and duration of CTG. While cesarean section rates were extensively investigated as an outcome of studies of CTG itself and the effect of additional techniques on CTG, other clinically significant outcomes, including neonatal mortality, were not well reported. Conclusions: Variations and ambiguities were found in the use of CTG in LMICs. Due to the limited amount of evidence, studies are needed to examine CTG availability in the context of LMICs. Synopsis: Cardiotocography (CTG) use in low‐ and middle‐income countries (LMICs) lacks consistency and clarity, with limited evidence on outcomes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: High risk pregnancy is one which is complicated by risk factor that adversely affects the maternal outcome or perintal outcome or both. The antepartum fetal surveillance of high risk pregnancies with Cardiotocographycan effectively help for reducing perintal mortality and morbidity. One of the biophysical methods which is being used extensively in the management of high risk pregnancy is (CTG) Cardiotocography. Aims & Objectives: 1. To assess the benefit of cardiotocography to improve the fetal outcome in high risk pregnancy. 2. To find out the suitable time and mode of delivery of the fetus at risk. Materials and Methods: This is a prospective observational study conducted at Government General Hospital attached to Government Medical College, Kadapa from the month of July 2023 to the month of june 2024. Study includes 100 high risk pregnancies with singleton fetus of 32 weeks or more than 32 weeks of gestation CTG performed within 3 days prior to delivery will be considered for fetal outcome. In all cases detailed history, clinical and obstetric examination USG and alntepartum, intrapartum CTG will be performed and decision for mode of delivary is planned and different Perinatal outcome are assessed as 1 min and 5 min apgar score and NICU admissions. Results: Among 100 high risk pregnancies included in the study the most common risk factor is preeclampsia (25%) followed by oligos (16%). The incidence of non-reactive intrapartum CTG are 33 in number (33%). Out of 100, 43 underwent cesarean section, 54 delivered vaginally and 3 delivered instrumentally. Out of 100 cases one minute apgar was 5-7 in 41 subjects, 8-10 in 58 subjects and <4 in 1 subject. Among the study group 30 (30%) had NICU admissions. Conclusion: CTG is simple, cheap, non invasive cost effective with less training & best screening test to identity patients at a greater risk of intrapartum fetal hypoxia. CTG is effective tool to evaluate fetal condition, to detect fetal distress and there by early intervention to improve the Perinatal outcome. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

Cardiotocography (CTG) is used in pregnancy to monitor fetal heart rate and contractility, especially in the third trimester, to ensure fetal well-being and to detect early signs of distress CTG a inconsistency may indicate the need for further research and possible interventions. The objective is to increase the accuracy and reliability of cervical health classification by integrating machine learning algorithms with traditional CTG data. This approach seeks to improve the early detection of fetal distress to identify timely medical interventions. The system combines CTG data collection with machine learning algorithms to identify fetal health risks. It uses transducers to monitor fetal heart rate and contractions. Machine learning models are used to analyze CTG data, such as random forest, logistic regression, decision tree and KNN, the results showed that the random forest model outperformed the others, achieving an accuracy of 97.58 %. [ABSTRACT FROM AUTHOR]

Cardiotocography (CTG) is considered the gold standard for monitoring fetal heart rate (FHR) during pregnancy and labor to estimate the danger of oxygen deprivation. Visual interpretation of CTG traces is complex and frequently results in high rates of false positives and false negatives, leading to unfavorable and unwanted outcomes such as fetal mortality or needless cesarean surgery. If the data are well-balanced, which is uncommon in medical datasets, machine learning techniques can be helpful in interpretation. This study is designed to determine classification performance under various data balance approaches. We propose a robust methodology for the automated extraction of features that use a deep learning model based on the one-dimensional convolutional neural network (1D-CNN). We used a public database containing 552 intrapartum CTG recordings. Due to the imbalance in the dataset, the experiments were conducted under a variety of conditions such as (i) an unbalanced dataset, (ii) undersampling, (iii) a weighted binary cross-entropy approach, and (iv) oversampling utilizing the synthetic minority oversampling technique (SMOTE). We found an excellent sensitivity (99.80% for the unbalanced dataset, 96.25% for the weighted binary cross-entropy approach, and 99.81% with SMOTE) except for the under sampling situation, in which the sensitivity was 85.71%. Moreover, the 1D-CNN model incorporating SMOTE yielded promising results in 88% specificity, 93.72% quality index (QI), and 95.10% area under the curve. The model exhibited excellent performance in terms of sensitivity in every scenario except for undersampling. The oversampling of training data with SMOTE yielded a decent level of specificity, demonstrating the model’s strong predictive capacity. In addition, the SMOTE scenario resulted in fewer training epochs, which is another accomplishment.

Fetal hypoxia is a condition characterized by a lack of oxygen supply in a developing fetus in the womb. It can cause potential risks, leading to abnormalities, birth defects, and even mortality. Cardiotocograph (CTG) monitoring is among the techniques that can detect any signs of fetal distress, including hypoxia. Due to the critical importance of interpreting the results of this test, it is essential to accompany these tests with the evolving available technology to classify cases of hypoxia into three cases: normal, suspicious, or pathological. Furthermore, Machine Learning (ML) is a blossoming technique constantly developing and aiding in medical studies, particularly fetal health prediction. Notwithstanding the past endeavors of health providers to detect hypoxia in fetuses, implementing ML and Deep Learning (DL) techniques ensures more timely and precise detection of fetal hypoxia by efficiently and accurately processing complex patterns in large datasets. Correspondingly, this review paper aims to explore the application of artificial intelligence models using cardiotocographic test data. The anticipated outcome of this review is to introduce guidance for future studies to enhance accuracy in detecting cases categorized within the suspicious class, an aspect that has encountered challenges in previous studies that holds significant implications for obstetricians in effectively monitoring fetal health and making informed decisions.

Background: Electronic foetal heart rate monitoring (EFM) using cardiotocography (CTG) is crucial for assessing foetal well-being during labour and aiding in the early detection of intrapartum hypoxia. Admission CTG, short-term monitoring upon labour ward entry, identifies high-risk factors and guides subsequent interventions to prevent perinatal morbidity and mortality. Methods: A cross-sectional study conducted at Al Ameen Medical College and Hospital from August 2022 to July 2023 included booked and unbooked antenatal patients in latent labour. The study included 950 women in the first stage of labour in the low-(n=550) and high-risk (n=400) groups based on identified risk factors. Perinatal outcomes were assessed, including mode of delivery, liquor colour, APGAR scores, and NICU admissions. Results: High-risk factors such as hypertensive disorders (20%), anaemia (23.75%), gestational diabetes (6.25%), previous LSCS (37.50%), and post-date pregnancies (32.25%) were prevalent in the high-risk group. The majority of the participants were aged 20–30 years (52.7%), booked (39.2%), and presented at gestational ages ≥37 to

Fetal hypoxia is a condition characterized by a lack of oxygen supply in a developing fetus in the womb. It can cause potential risks, leading to abnormalities, birth defects, and even mortality. Cardiotocograph (CTG) monitoring is among the techniques that can detect any signs of fetal distress, including hypoxia. Due to the critical importance of interpreting the results of this test, it is essential to accompany these tests with the evolving available technology to classify cases of hypoxia into three cases: normal, suspicious, or pathological. Furthermore, Machine Learning (ML) is a blossoming technique constantly developing and aiding in medical studies, particularly fetal health prediction. Notwithstanding the past endeavors of health providers to detect hypoxia in fetuses, implementing ML and Deep Learning (DL) techniques ensures more timely and precise detection of fetal hypoxia by efficiently and accurately processing complex patterns in large datasets. Correspondingly, this review paper aims to explore the application of artificial intelligence models using cardiotocographic test data. The anticipated outcome of this review is to introduce guidance for future studies to enhance accuracy in detecting cases categorized within the suspicious class, an aspect that has encountered challenges in previous studies that holds significant implications for obstetricians in effectively monitoring fetal health and making informed decisions. [ABSTRACT FROM AUTHOR]

Objective: To propose a computerized system utilizing multiscene analysis based on a support vector machine (SVM) and convolutional neural network (CNN) to assess cardiotocography (CTG) intelligently. Methods: We retrospectively collected 2542 CTG records of singleton pregnancies delivered at the maternity ward of the First Affiliated Hospital of Xi'an Jiaotong University from October 10, 2020, to August 7, 2021. CTG records were divided into five categories (baseline, variability, acceleration, deceleration, and normality). Apart from the category of normality, the other four different categories of abnormal data correspond to four scenes. Each scene was divided into training and testing sets at 9:1 or 7:3. We used three computer algorithms (dynamic threshold, SVM, and CNN) to learn and optimize the system. Accuracy, sensitivity, and specificity were performed to evaluate performance. Results: The global accuracy, sensitivity, and specificity of the system were 93.88%, 93.06%, and 94.33%, respectively. In acceleration and deceleration scenes, when the convolution kernel was 3, the test data set reached the highest performance. Conclusion: The multiscene research model using SVM and CNN is a potential effective tool to assist obstetricians in classifying CTG intelligently. Synopsis: The computer‐aided diagnosis system based on support vector machine and convolutional neural network is valuable for classification of cardiotocography. [ABSTRACT FROM AUTHOR]

Monitoring fetal heart rate (FHR) through cardiotocography is crucial for the early diagnosis of fetal distress situations, necessitating prompt obstetrical intervention. However, FHR signals are often marred by various contaminants, making preprocessing techniques essential for accurate analysis. This scoping review, following PRISMA-ScR guidelines, describes the preprocessing methods in original research articles on human FHR (or beat-to-beat intervals) signal preprocessing from PubMed and Web of Science, published from their inception up to May 2021. From the 322 unique articles identified, 54 were included, from which prevalent preprocessing approaches were identified, primarily focusing on the detection and correction of poor signal quality events. Detection usually entailed analyzing deviations from neighboring samples, whereas correction often relied on interpolation techniques. It was also noted that there is a lack of consensus regarding the definition of missing samples, outliers, and artifacts. Trends indicate a surge in research interest in the decade 2011–2021. This review underscores the need for standardizing FHR signal preprocessing techniques to enhance diagnostic accuracy. Future work should focus on applying and evaluating these methods across FHR databases aiming to assess their effectiveness and propose improvements. [ABSTRACT FROM AUTHOR]

Abstract 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.

Abstract Background In clinical medicine, fetal heart rate (FHR) monitoring using cardiotocography (CTG) is one of the most commonly used methods for assessing fetal acidosis. However, as the visual interpretation of CTG depends on the subjective judgment of the clinician, this has led to high inter-observer and intra-observer variability, making it necessary to introduce automated diagnostic techniques. Methods In this study, we propose a computer-aided diagnostic algorithm (Hybrid-FHR) for fetal acidosis to assist physicians in making objective decisions and taking timely interventions. Hybrid-FHR uses multi-modal features, including one-dimensional FHR signals and three types of expert features designed based on prior knowledge (morphological time domain, frequency domain, and nonlinear). To extract the spatiotemporal feature representation of one-dimensional FHR signals, we designed a multi-scale squeeze and excitation temporal convolutional network (SE-TCN) backbone model based on dilated causal convolution, which can effectively capture the long-term dependence of FHR signals by expanding the receptive field of each layer’s convolution kernel while maintaining a relatively small parameter size. In addition, we proposed a cross-modal feature fusion (CMFF) method that uses multi-head attention mechanisms to explore the relationships between different modalities, obtaining more informative feature representations and improving diagnostic accuracy. Results Our ablation experiments show that the Hybrid-FHR outperforms traditional previous methods, with average accuracy, specificity, sensitivity, precision, and F1 score of 96.8, 97.5, 96, 97.5, and 96.7%, respectively. Conclusions Our algorithm enables automated CTG analysis, assisting healthcare professionals in the early identification of fetal acidosis and the prompt implementation of interventions.

Pregnancy complications significantly impact women and pose potential threats to the developing child’s health. Early identification of these complications is imperative for life-saving interventions. The manual analysis of cardiotocography (CTG) tests, a conventional practice among obstetricians, is both labor-intensive and unreliable. Consequently, the development of efficient fetal health classification models becomes crucial for optimizing medical resources and saving time.This study addresses the imperative for advanced fetal health classification through the application of machine learning (ML) techniques. The objective is to explore, develop, and analyze ML models capable of accurately classifying fetal health based on CTG data. The overarching goal is to enhance diagnostic precision and facilitate timely interventions.Utilizing a freely available cardiotocography data set, despite its relatively small size, the research acknowledges its rich characteristics. Various ML models, including Random Forests, Logistic Regression, Decision Trees, Support Vector Classifiers, Voting Classes, and K-Nearest Neighbors, are deployed on the data set. The analysis involves rigorous training and testing of these models to assess their efficacy in classifying fetal health.The study yields promising outcomes, with the implemented ML models achieving a notable accuracy level of 93%, surpassing previous methods. This underscores the effectiveness of the proposed models in elevating the precision of fetal health classification based on CTG data.The findings advocate for the integration of ML models into routine clinical practices, streamlining fetal health assessments. The study not only underscores the significance of early complication detection but also demonstrates the potential of ML in optimizing medical resource allocation and time efficiency. Further research is warranted to refine and expand ML applications in the context of fetal health assessment, promising advancements in prenatal care.

Background: Cesarean section rate is rising globally, now accounting for more than 1 in 5 (21%) of all childbirths. This number is set to continue increasing over the coming decade unless the factors which increase the rate of cesarean section are kept in check. Intrapartum cardiotocography (CTG) has shown a false-positive rate of cesarean section. This has been correlated with neonatal umbilical cord blood pH analysis. Aims and Objectives: (1) Correlation of non-reassuring CTG, resulting in cesarean section with umbilical cord arterial blood pH. (2) To find the false-positive indications of cesarean section due to CTG. Materials and Methods: This observational study was conducted in the Department of Obstetrics and Gynecology of SKIMS, Soura, from September 2020 to July 2022 over a period of 22 months. Women with a gestational age of more than 34 weeks with singleton pregnancy were included in the study. Those with CTG-documented fetal distress were subjected to umbilical cord arterial blood pH monitoring. Results: A total of 85 patients underwent cesarean sections in view of fetal distress documented by CTG, but only 45 neonates had actual distress as documented by umbilical cord blood sampling. Conclusion: Social and non-medical factors no doubt have caused an alarming rise in cesarean sections, but at the same time, there are some iatrogenic causes, namely CTG, which lead to the rise in the cesarean section rate due to false-positive indications. This has been further supported by the fact that the pH of the umbilical cord blood of neonates with intrapartum non-reassuring CTG does not correspond to fetal acidemia in all cases. Hence, a significant number of cesarean sections is being done for false-positive indications.

Introduction: ST waveform analysis (STAN) was introduced as an adjunct to cardiotocography (CTG) to improve neonatal and maternal outcomes. The aim of the present study was to quantify the efficacy of STAN vs CTG and assess the quality of the evidence using GRADE. Material and methods: We performed systematic literature searches to identify randomized controlled trials and assessed included studies for risk of bias. We performed meta‐analyses, calculating pooled risk ratio (RR) or Peto odds ratio (OR). We also performed post hoc trial sequential analyses for selected outcomes to assess the risk of false‐positive results and the need for additional studies. Results: Nine randomized controlled trials including 28 729 women were included in the meta‐analysis. There were no differences between the groups in operative deliveries for fetal distress (10.9 vs 11.1%; RR 0.96; 95% confidence interval [CI] 0.82–1.11). STAN was associated with a significantly lower rate of metabolic acidosis (0.45% vs 0.68%; Peto OR 0.66; 95% CI 0.48–0.90). Accordingly, 441 women need to be monitored with STAN instead of CTG alone to prevent one case of metabolic acidosis. Women allocated to STAN had a reduced risk of fetal blood sampling compared with women allocated to conventional CTG monitoring (12.5% vs 19.6%; RR 0.62; 95% CI 0.49–0.80). The quality of the evidence was high to moderate. Conclusions: Absolute effects of STAN were minor and the clinical significance of the observed reduction in metabolic acidosis is questioned. There is insufficient evidence to state that STAN as an adjunct to CTG leads to important clinical benefits compared with CTG alone. [ABSTRACT FROM AUTHOR]

• Intrapartum infection/inflammation is the leading cause of "non-hypoxic" fetal injury. • Increased levels of IL-6 in the umbilical arterial blood at birth are a hallmark of intrapartum infection/inflammation. • Intrapartum CTG findings "suggestive of fetal inflammation" ("SOFI") are associated with higher levels of IL-6 in the umbilical arterial blood. to investigate the correlation between the intrapartum CardioTocoGraphic (CTG) findings "suggestive of fetal inflammation" ("SOFI") and the interleukin (IL)-6 level in the umbilical arterial blood. prospective cohort study conducted at a tertiary maternity unit and including 447 neonates born at term. IL-6 levels were systematically measured at birth from a sample of blood taken from the umbilical artery. The intrapartum CTG traces were retrospectively reviewed by two experts who were blinded to the postnatal umbilical arterial IL-6 values as well as to the neonatal outcomes. The CTG traces were classified into "suggestive of fetal inflammation (SOFI)" and "no evidence of fetal inflammation (NEFI) according to the principles of physiologic interpretation the CTG traces. The CTG was classified as "SOFI" if there was a persistent fetal heart rate (FHR) increase > 10 % compared with the observed baseline FHR observed at the admission or at the onset of labor without any preceding repetitive decelerations. The occurrence of Composite Adverse Outcome (CAO) was defined as Neonatal Intensive Care Unit (NICU) or Special Care Baby Unit (SCBU) admission due to one or more of the following: metabolic acidaemia, Apgar score at 5 min ≤ 7, need of neonatal resuscitation, respiratory distress, tachypnoea/polypnea, jaundice requiring phototherapy, hypotension, body temperature instability, poor perinatal adaptation, suspected or confirmed early neonatal sepsis. To compare the umbilical IL-6 values between the cases with intrapartum CTG traces classified as "SOFI" and those classified as "NEFI"; to assess the correlation of umbilical IL-6 values with the neonatal outcome. 43 (9.6 %) CTG traces were categorized as "SOFI"; IL-6 levels were significantly higher in this group compared with the "NEFI" group (82.0[43.4–325.0] pg/ml vs. 14.5[6.8–32.6] pg/mL; p <.001). The mean FHR baseline assessed 1 h before delivery and the total labor length showed an independent and direct association with the IL-6 levels in the umbilical arterial blood (p <.001 and p = 0.005, respectively). CAO occurred in 33(7.4 %) cases; IL-6 yielded a good prediction of the occurrence of the CAO with an AUC of 0.72 (95 % CI 0.61–0.81). Intrapartum CTG findings classified as "SOFI" are associated with higher levels of IL-6 in the umbilical arterial blood. [ABSTRACT FROM AUTHOR]

Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic. Various cardiotocography measures infer wrongly and give wrong predictions because of human error. The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well. Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being. In the current period Machine learning (ML) is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional results. ML techniques play a pivotal role in detecting fetal disease in its early stages. This research article uses Federated machine learning (FML) and ML techniques to classify the condition of the fetus. This study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the data. So, the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06% and 0.94% of prediction accuracy and misprediction rate, respectively, and parallel the proposed model applying K-nearest neighbor (KNN) and achieves 82.93% and 17.07% of prediction accuracy and misprediction accuracy, respectively. So, by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Cardiotocography (CTG) is the most common technique for electronic fetal monitoring and consists of the simultaneous recording of fetal heart rate (FHR) and uterine contractions. In analogy with the adult case, spectral analysis of the FHR signal can be used to assess the functionality of the autonomic nervous system. To do so, several methods can be employed, each of which has its strengths and limitations. This paper aims at performing a methodological investigation on FHR spectral analysis adopting 4 different spectrum estimators and a novel PRSA-based spectral method. The performances have been evaluated in terms of the ability of the various methods to detect changes in the FHR in two common pregnancy complications: intrauterine growth restriction (IUGR) and gestational diabetes. A balanced dataset containing 2178 recordings distributed between the 32nd and 38th week of gestation was used. The results show that the spectral method derived from the PRSA better differentiates high-risk pregnancies vs. controls compared to the others. Specifically, it more robustly detects an increase in power percentage within the movement frequency band and a decrease in high frequency between pregnancies at high risk in comparison to those at low risk. [ABSTRACT FROM AUTHOR]

Background: In clinical medicine, fetal heart rate (FHR) monitoring using cardiotocography (CTG) is one of the most commonly used methods for assessing fetal acidosis. However, as the visual interpretation of CTG depends on the subjective judgment of the clinician, this has led to high inter-observer and intra-observer variability, making it necessary to introduce automated diagnostic techniques. Methods: In this study, we propose a computer-aided diagnostic algorithm (Hybrid-FHR) for fetal acidosis to assist physicians in making objective decisions and taking timely interventions. Hybrid-FHR uses multi-modal features, including one-dimensional FHR signals and three types of expert features designed based on prior knowledge (morphological time domain, frequency domain, and nonlinear). To extract the spatiotemporal feature representation of one-dimensional FHR signals, we designed a multi-scale squeeze and excitation temporal convolutional network (SE-TCN) backbone model based on dilated causal convolution, which can effectively capture the long-term dependence of FHR signals by expanding the receptive field of each layer's convolution kernel while maintaining a relatively small parameter size. In addition, we proposed a cross-modal feature fusion (CMFF) method that uses multi-head attention mechanisms to explore the relationships between different modalities, obtaining more informative feature representations and improving diagnostic accuracy. Results: Our ablation experiments show that the Hybrid-FHR outperforms traditional previous methods, with average accuracy, specificity, sensitivity, precision, and F1 score of 96.8, 97.5, 96, 97.5, and 96.7%, respectively. Conclusions: Our algorithm enables automated CTG analysis, assisting healthcare professionals in the early identification of fetal acidosis and the prompt implementation of interventions. [ABSTRACT FROM AUTHOR]

Background: Perinatal deaths due to hypoxia are preventable with continuous monitoring. Cardiotocography (CTG) is standard for intrapartum foetal assessment, aiming to detect signs of distress and intervene promptly to mitigate risk. Objective: To determine the accuracy of advanced intrapartum monitoring system in comparison with electronic CTG in tracking of non-reassuring foetal symptoms by pattern analysis of the FHR. Materials and methods: A single-centre, observation study was conducted at a tertiary care hospital from January 2020 to December 2021. CTG and an advanced portable intrapartum monitoring system (KEYAR) were applied simultaneously to all participants. Twenty-minute-long recording traces were obtained from both devices. Mode of delivery was noted for all patients along with Apgar score at 1 minute and 5 minutes. Results: The final study cohort was constituted by 94 participants. Most participants (57.4%) were primigravida. The mean (95% CI) sensitivity, specificity, positive predictive value and negative predictive value of KEYAR tracing were 33% (9.6, 70), 97.7% (92.09, 99.37), 50% (15, 85) and 95.5% (89.12, 98.26), respectively. The overall, diagnostic accuracy of KEYAR was 93.6% (86.77, 97.04). Conclusions: Advanced portable intrapartum monitoring device (KEYAR) is a new application with the benefits of easy user operability and portability. [ABSTRACT FROM AUTHOR]

Clinicians routinely perform pelvic examinations to assess the progress of labor. Clinical guidelines to interpret these examinations, using time-based models of cervical dilation, are not always followed and have not contributed to reducing cesarean-section rates. We present a novel Gaussian process model of labor progress, suitable for real-time use, that predicts cervical dilation and fetal station based on clinically relevant predictors available from the pelvic exam and cardiotocography. We show that the model is more accurate than a statistical approach using a mixed-effects model. In addition, it provides confidence estimates on the prediction, calibrated to the specific delivery. Finally, we show that predicting both dilation and station with a single Gaussian process model is more accurate than two separate models with single predictions. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To compare the neonatal outcomes in normal vs abnormal cardiotocograhy in term pregnancy. MATERIALS AND METHODS: This was a prospective analytical study carried out in A unit of obstetrics and Gynaecology department of Khyber Teaching Hospital Peshawar from 15th April 2022 to 15th October 2022. A total 224 patients meeting inclusion criteria were included in study. In Group A (normal CTG) there were 111 patients and 113 were in Group B (abnormal CTG). Patients with known fetal congenital abnormalities, intrauterine growth restriction, and fetal mal presentations were excluded from the study. RESULTS: The mean age of sample population was 26.02+4.497. Multigravidae were more than primigravidae in both groups (86.4% vs 13.6% in Group A and 72.5% vs 27.5% in Group B). In Group B, caesarean section rate was higher (82.3%) than Group A (9%). From Group B, 26 (23%) newborns went to NICU for admission, whereas only 8 (7.2%) newborns from Group A needed NICU admission. Hypoxic ischemic encephalopathy was also observed more in newborns in Group B compared to Group A (10 vs 1). In Group A 12 babies had APGAR score <7 at 1 minute while in Group B 18 babies had APGAR score < 7 at 1 minute. In Group A 2 babies had APGAR score <7 at 5 minutes whereas in Group B 7 babies had APGAR score < 7. CONCLUSION: The caesarean section rate, NICU admission and hypoxic ischemic encephalopathy were more in Group B than in Group A and this difference was statistically significant (p-value 0.000, 0.000 and 0.006 respectively). Whereas there was no statistically significant difference in APGAR Score at 1 and 5 minutes in both groups (p-value 0.26 and 0.094 respectively). [ABSTRACT FROM AUTHOR]

Introduction: It is a shortcoming of traditional cardiotocography (CTG) classification table formats that CTG traces are frequently classified differently by different users, resulting in poor interobserver agreements. A fast‐and‐frugal tree (FFTree) flow chart may help provide better concordance because it is straightforward and has clearly structured binary questions with understandable "yes" or "no" responses. The initial triage to determine whether a fetus is suitable for labor when utilizing fetal ECG ST analysis (STAN) is very important, since a fetus with restricted capacity to respond to hypoxic stress may not generate STAN events and therefore may become falsely negative. This study aimed to compare physiology‐focused FFTree CTG interpretation with FIGO classification for assessing the suitability for STAN monitoring. Material and methods: A retrospective study of 36 CTG traces with a high proportion of adverse outcomes (17/36) selected from a European multicenter study database. Eight experienced European obstetricians evaluated the initial 40 minutes of the CTG recordings and judged whether STAN was a suitable fetal surveillance method and whether intervention was indicated. The experts rated the CTGs using the FFTree and FIGO classifications at least 6 weeks apart. Interobserver agreements were calculated using proportions of agreement and Fleiss' kappa (κ). Results: The proportions of agreement for "not suitable for STAN" were for FIGO 47% (95% confidence interval [CI] 42%–52%) and for FFTree 60% (95% CI 56–64), ie a significant difference; the corresponding figures for "yes, suitable" were 74% (95% CI 71–77) and 70% (95% CI 67–74). For "intervention needed" the figures were 52% (95% CI 47–56) vs 58% (95% CI 54–62) and for "expectant management" 74% (95% CI 71–77) vs 72% (95% CI 69–75). Fleiss' κ agreement on "suitability for STAN" was 0.50 (95% CI 0.44–0.56) for the FIGO classification and 0.57 (95% CI 0.51–0.63) for the FFTree classification; the corresponding figures for "intervention or expectancy" were 0.53 (95% CI 0.47–0.59) and 0.57 (95% CI 0.51–0.63). Conclusions: The proportion of agreement among expert obstetricians using the FFTree physiological approach was significantly higher compared with the traditional FIGO classification system in rejecting cases not suitable for STAN monitoring. That might be of importance to avoid false negative STAN recordings. Other agreement figures were similar. It remains to be shown whether the FFTree simplicity will benefit less experienced users and how it will work in real‐world clinical scenarios. [ABSTRACT FROM AUTHOR]

Background: Cesarean section rate is rising globally, now accounting for more than 1 in 5 (21%) of all childbirths. This number is set to continue increasing over the coming decade unless the factors which increase the rate of cesarean section are kept in check. Intrapartum cardiotocography (CTG) has shown a false-positive rate of cesarean section. This has been correlated with neonatal umbilical cord blood pH analysis. Aims and Objectives: (1) Correlation of non-reassuring CTG, resulting in cesarean section with umbilical cord arterial blood pH. (2) To find the false-positive indications of cesarean section due to CTG. Materials and Methods: This observational study was conducted in the Department of Obstetrics and Gynecology of SKIMS, Soura, from September 2020 to July 2022 over a period of 22 months. Women with a gestational age of more than 34 weeks with singleton pregnancy were included in the study. Those with CTG-documented fetal distress were subjected to umbilical cord arterial blood pH monitoring. Results: A total of 85 patients underwent cesarean sections in view of fetal distress documented by CTG, but only 45 neonates had actual distress as documented by umbilical cord blood sampling. Conclusion: Social and nonmedical factors no doubt have caused an alarming rise in cesarean sections, but at the same time, there are some iatrogenic causes, namely CTG, which lead to the rise in the cesarean section rate due to false-positive indications. This has been further supported by the fact that the pH of the umbilical cord blood of neonates with intrapartum non-reassuring CTG does not correspond to fetal acidemia in all cases. Hence, a significant number of cesarean sections is being done for false-positive indications. [ABSTRACT FROM AUTHOR]

Background: Given that AI-driven decision support systems (AI-DSS) are intended to assist in medical decision making, it is essential that clinicians are willing to incorporate AI-DSS into their practice. This study takes as a case study the use of AI-driven cardiotography (CTG), a type of AI-DSS, in the context of intrapartum care. Focusing on the perspectives of obstetricians and midwives regarding the ethical and trust-related issues of incorporating AI-driven tools in their practice, this paper explores the conditions that AI-driven CTG must fulfill for clinicians to feel justified in incorporating this assistive technology into their decision-making processes regarding interventions in labor. Methods: This study is based on semi-structured interviews conducted online with eight obstetricians and five midwives based in England. Participants were asked about their current decision-making processes about when to intervene in labor, how AI-driven CTG might enhance or disrupt this process, and what it would take for them to trust this kind of technology. Interviews were transcribed verbatim and analyzed with thematic analysis. NVivo software was used to organize thematic codes that recurred in interviews to identify the issues that mattered most to participants. Topics and themes that were repeated across interviews were identified to form the basis of the analysis and conclusions of this paper. Results: There were four major themes that emerged from our interviews with obstetricians and midwives regarding the conditions that AI-driven CTG must fulfill: (1) the importance of accurate and efficient risk assessments; (2) the capacity for personalization and individualized medicine; (3) the lack of significance regarding the type of institution that develops technology; and (4) the need for transparency in the development process. Conclusions: Accuracy, efficiency, personalization abilities, transparency, and clear evidence that it can improve outcomes are conditions that clinicians deem necessary for AI-DSS to meet in order to be considered reliable and therefore worthy of being incorporated into the decision-making process. Importantly, healthcare professionals considered themselves as the epistemic authorities in the clinical context and the bearers of responsibility for delivering appropriate care. Therefore, what mattered to them was being able to evaluate the reliability of AI-DSS on their own terms, and have confidence in implementing them in their practice. [ABSTRACT FROM AUTHOR]

BACKGROUND: The iPREFACE score may aid in predicting fetal acidemia and neonatal asphyxia in emergency cesarean sections and vaginal deliveries, which may improve labor management precision in the future. OBJECTIVE: This study aimed to assess the use of the iPREFACE score as an objective indicator of the need for rapid delivery in cases of repeated abnormal waveforms without concurrent indications for immediate medical intervention during labor. STUDY DESIGN: This retrospective cohort study was conducted among term (37+ 0 days to 41+6 days) singleton pregnant women who underwent emergency cesarean section owing to a nonreassuring fetal status. The iPREFACE-DCS score, calculated from a 30-minute cardiotocography waveform before the decision to perform emergency cesarean section, and the iPREFACE-RCT score, calculated from a 30-minute cardiotocography waveform before cardiotocography transducer removal, were employed. The primary outcome was the assessment of the predictive ability of these scores for fetal acidemia, whereas the secondary outcomes were differences in umbilical artery blood gas findings and postnatal outcomes between the 2 groups, divided by the cutoff values of the iPREFACE-RCT score. RESULTS: The iPREFACE-DCS and iPREFACE-RCT scores demonstrated the capability to predict an umbilical artery blood pH of

Abstract Background Intelligent cardiotocography (CTG) classification can assist obstetricians in evaluating fetal health. However, high classification performance is often achieved by complex machine learning (ML)-based models, which causes interpretability concerns. The trade-off between accuracy and interpretability makes it challenging for most existing ML-based CTG classification models to popularize in prenatal clinical applications. Methods Aiming to improve CTG classification performance and prediction interpretability, a hybrid model was proposed using a stacked ensemble strategy with mixed features and Kernel SHapley Additive exPlanations (SHAP) framework. Firstly, the stacked ensemble classifier was established by employing support vector machines (SVM), extreme gradient boosting (XGB), and random forests (RF) as base learners, and backpropagation (BP) as a meta learner whose input was mixed with the CTG features and the probability value of each category output by base learners. Then, the public and private CTG datasets were used to verify the discriminative performance. Furthermore, Kernel SHAP was applied to estimate the contribution values of features and their relationships to the fetal states. Results For intelligent CTG classification using 10-fold cross-validation, the accuracy and average F1 score were 0.9539 and 0.9249 in the public dataset, respectively; and those were 0.9201 and 0.8926 in the private dataset, respectively. For interpretability, the explanation results indicated that accelerations (AC) and the percentage of time with abnormal short-term variability (ASTV) were the key determinants. Specifically, the probability of abnormality increased and that of the normal state decreased as the value of ASTV grew. In addition, the likelihood of the normal status rose with the increase of AC. Conclusions The proposed model has high classification performance and reasonable interpretability for intelligent fetal monitoring.

Background: Given the demanding nature of labour, alterations in the cardiotocography (CTG) trace may be an indication of how the foetus is responding to continuous hypoxic or mechanical stressors, such as compression of the umbilical cord or a decrease in placental blood flow. Any foetus deemed to be at a "high risk" of experiencing intrapartum hypoxic damage requires constant foetal monitoring. Objective: The aim of our study was to evaluate CTG in high-risk pregnancy and its correlation with maternal and fetal outcomes. Methodology: Two hundred antenatal patients with ≥ 37 weeks period of gestation with high-risk pregnancies were included in the study. As per the RCOG guidelines, the CTG analysis of recruited patients was performed. Maternal and fetal outcomes were then correlated with the CTG. Results: The mean age of the patients was 27.84 ± 4.58 years. Patients with non-reactive CTG found to have significantly high risk of premature rupture of membranes. Prolonged labour was also encountered significantly high in patients with non-reactive CTG. No significant difference in the mode of delivery was observed between patients with reactive or non-reactive CTG. APGAR score was significantly low in patients with non-reactive CTG. NICU admissions was also significantly high in patients non-reactive CTG. Conclusion: A non-reactive CTG indicate a lack of fetal well-being, which may be due to various causes such as fetal distress, umbilical cord compression, or low fetal oxygenation. In present study, we found that patients with non-reactive CTG tends to have poor maternal and foetal outcomes compared to the patients with reactive CTG which indicate potential role of CTG in predicting outcomes in high-risk pregnancy. [ABSTRACT FROM AUTHOR]

Introduction: With category II fetal heart rate tracings, the preferred timing of interventions to prevent fetal hypoxic brain damage while limiting operative interventions remains unclear. We aimed to estimate fetal extracellular base deficit (BDecf) during labor with category II tracings to quantify the timing of potential interventions to prevent severe fetal metabolic acidemia. Material and methods: A longitudinal study was conducted using the database of the Recurrence Prevention Committee, Japan Obstetric Compensation System for Cerebral Palsy, including infants with severe cerebral palsy born at ≥34 weeks' gestation between 2009 and 2014. Cases included those presumed to have an intrapartum onset of hypoxic–ischemic insult based on the fetal heart rate pattern evolution from reassuring to an abnormal pattern during delivery, in association with category II tracings marked by recurrent decelerations and an umbilical arterial BDecf ≥ 12 mEq/L. BDecf changes during labor were estimated based on stages of labor and the frequency/severity of fetal heart rate decelerations using the algorithm of Ross and Gala. The times from the onset of recurrent decelerations to BDecf 8 and 12 mEq/L (Decels‐to‐BD8, Decels‐to‐BD12) and to delivery were determined. Cases were divided into two groups (rapid and slow progression) based upon the rate of progression of acidosis from onset of decelerations to BDecf 12 mEq/L, determined by a finite‐mixture model. Results: The median Decels‐to‐BD8 (28 vs. 144 min, p < 0.01) and Decels‐to‐BD12 (46 vs. 177 min, p < 0.01) times were significantly shorter in the rapid vs slow progression. In rapid progression cases, physicians' decisions to deliver the fetus occurred at ~BDecf 8 mEq/L, whereas the "decisions" did not occur until BDecf reached 12 mEq/L in slow progression cases. Conclusions: Fetal BDecf reached 12 mEq/L within 1 h of recurrent fetal heart rate decelerations in the rapid progression group and within 3 h in the slow progression group. These findings suggest that cases with category II tracings marked by recurrent decelerations (i.e., slow progression) may benefit from operative intervention if persisting for longer than 2 h. In contrast, cases with sudden bradycardia (i.e., rapid progression) represent a challenge to prevent severe acidosis and hypoxic brain injury due to the limited time opportunity for emergent delivery. [ABSTRACT FROM AUTHOR]

Background: Intelligent cardiotocography (CTG) classification can assist obstetricians in evaluating fetal health. However, high classification performance is often achieved by complex machine learning (ML)-based models, which causes interpretability concerns. The trade-off between accuracy and interpretability makes it challenging for most existing ML-based CTG classification models to popularize in prenatal clinical applications. Methods: Aiming to improve CTG classification performance and prediction interpretability, a hybrid model was proposed using a stacked ensemble strategy with mixed features and Kernel SHapley Additive exPlanations (SHAP) framework. Firstly, the stacked ensemble classifier was established by employing support vector machines (SVM), extreme gradient boosting (XGB), and random forests (RF) as base learners, and backpropagation (BP) as a meta learner whose input was mixed with the CTG features and the probability value of each category output by base learners. Then, the public and private CTG datasets were used to verify the discriminative performance. Furthermore, Kernel SHAP was applied to estimate the contribution values of features and their relationships to the fetal states. Results: For intelligent CTG classification using 10-fold cross-validation, the accuracy and average F1 score were 0.9539 and 0.9249 in the public dataset, respectively; and those were 0.9201 and 0.8926 in the private dataset, respectively. For interpretability, the explanation results indicated that accelerations (AC) and the percentage of time with abnormal short-term variability (ASTV) were the key determinants. Specifically, the probability of abnormality increased and that of the normal state decreased as the value of ASTV grew. In addition, the likelihood of the normal status rose with the increase of AC. Conclusions: The proposed model has high classification performance and reasonable interpretability for intelligent fetal monitoring. [ABSTRACT FROM AUTHOR]

Objective: To assess the association between undetected small‐for‐gestational age (SGA) fetuses and abnormal admission cardiotocography (admCTG) in a low‐risk population. Design: An observational study. Setting: Four hospitals in Stockholm‐Gotland, Sweden. Sample: A cohort of 127 461 deliveries between 1 February 2012 and 15 June 2020. Methods: This cohort was linked to the Swedish Neonatal Quality Register. Pregnancies were designated as high or low risk at the time of admission to the labour ward according to pre‐defined risk measures. SGA was defined as a birthweight at or below the tenth centile and at or below the third centile for gestational age. Main outcome measures: The main outcome was the proportion of undetected SGA by admCTG (normal or abnormal). The secondary outcome was a composite severe adverse neonatal outcome for fetuses born less than 6 hours after admission (Apgar score <4 at 5 minutes, hypoxic–ischaemic encephalopathy grade of 2–3, neonatal seizures and neonatal death). Results: The rate of abnormal admCTG was 4.9%. The proportion of SGA at or below the tenth centile was higher in the abnormal admCTG group than in the normal admCTG group, 18.6% versus 9.7% (odds ratio 2.1, 95% CI 1.9–2.3). Abnormal admCTG and SGA (≤10th) was associated with a more than 20‐fold increased risk of an adverse outcome compared with normal admCTG and non‐SGA (adjusted odds ratio 23.7, 95% CI 9.8–57.3). The latter had a risk of 1/2000 of an adverse outcome. Conclusions: In this low‐risk population, undetected SGA fetuses were more prone to having abnormal admCTG and had a substantially higher risk of severe adverse neonatal outcomes. [ABSTRACT FROM AUTHOR]

Objectives: ST waveform analysis (STAN) was introduced to improve the interpretation of cardiotocography (CTG) resulting in reduction of unnecessary interventions and metabolic acidosis. A systematic review was conducted with the aim to evaluate the effect of STAN method compared with isolated CTG on perinatal and neonatal outcomes. Materials and methods: A search of electronic databases (PubMed, Cochrane, Scopus) was conducted to identify randomized controlled trials (RCTs) in English language. Outcomes considered operative deliveries, fetal blood sampling (FBS), metabolic acidosis, perinatal and neonatal death, neonatal seizures, neonatal encephalopathy, transfer to the neonatal intensive care unit (NICU) and Apgar score. Results: Seven RCTs were included in the present review. The first two RCTs showed that the combination of STAN and CTG was a better option than using CTG alone, because there was a documented reduction in the rate of operative deliveries due to fetal distress and metabolic acidosis. The following studies showed no statistically significant changes with the combination of methods, except from a reduction in FBS. Conclusions: The findings from the RCTs were inconclusive. Most studies did not demonstrate a superiority of the combination regarding operative deliveries and neonatal outcomes but there were many methodological differences between the trials. [ABSTRACT FROM AUTHOR]

Objective: To investigate whether use of ST analysis of the fetal electrocardiogram (STan) as an adjunct to continuous cardiotocography (CTG) reduces the rate of emergency Cesarean section (EmCS) compared with CTG alone. Methods: This was a randomized controlled trial of patients with a singleton fetus in cephalic presentation at ≥ 36 weeks' gestation, requiring continuous electronic fetal monitoring during labor at a tertiary maternity hospital in Adelaide, Australia, between January 2018 and July 2021. Participants were randomized to undergo CTG + STan or CTG alone. The calculated sample size was 1818 participants. The primary outcome was EmCS. Secondary outcomes included metabolic acidosis, a composite adverse perinatal outcome, and other maternal and neonatal morbidity and safety outcomes. Results: The present study enrolled 970 women, of whom 967 were included in the primary analysis. EmCS occurred in 107/482 (22.2%) deliveries in the CTG + STan arm and in 107/485 (22.1%) in the CTG arm (adjusted relative risk, 1.02 (95% CI, 0.81–1.27); P = 0.89). There was no difference in the rate of adverse maternal or neonatal outcomes between arms. Conclusions: The addition of STan as an adjunct to continuous CTG did not reduce the EmCS rate. The smaller‐than‐anticipated sample size meant that this study was underpowered to detect absolute differences of ≤ 5% and, therefore, this negative finding could be due to a Type‐2 error. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the rate of adverse neonatal or maternal outcomes in parturients with fetal heart rate tracings categorized as I, II or, III within the last 30 to 120 minutes of delivery. The MEDLINE Ovid, Scopus, Embase, CINAHL, and Clinicaltrials.gov databases were searched electronically up to May 2022, using combinations of the relevant medical subject heading terms, keywords, and word variants that were considered suitable for the topic. Only observational studies of term infants reporting outcomes of interest with category I, II, or III fetal heart rate tracings were included. The coprimary outcome was the rate of either Apgar score <7 at 5 minutes or umbilical artery pH <7.00. Secondary outcomes were divided into neonatal and maternal adverse outcomes. Quality assessment of the included studies was performed using the Newcastle-Ottawa Scale. Random-effect meta-analyses of proportions were used to estimate the pooled rates of each categorical outcome in fetal heart rate tracing category I, II, and III patterns, and random-effect head-to-head meta-analyses were used to directly compare fetal heart rate tracings category I vs II and fetal heart rate tracing category II vs III, expressing the results as summary odds ratio or as mean differences with relative 95% confidence intervals. Of the 671 articles reviewed, 3 publications met the inclusion criteria. Among them were 47,648 singletons at ≥37 weeks' gestation. Fetal heart rate tracings in the last 30 to 120 minutes before delivery were characterized in the following manner: 27.0% of deliveries had category I tracings, 72.9% had category II tracings, and 0.1% had category III tracings. A single study, which was rated to be of poor quality, contributed 82.1% of the data and it did not provide any data for category III fetal heart rate tracings. When compared with category I fetal heart rate tracings (0.74%), the incidence of an Apgar score <7 at 5 minutes were significantly higher among deliveries with category II fetal heart rate tracings (1.51%) (odds ratio, 1.56; 95% confidence interval, 1.23–1.99) and among those with category III tracings (14.63%) (odds ratio, 14.46; 95% confidence interval, 2.77–75.39). When compared with category II tracings, category III tracings also had a significantly higher likelihood of a low Apgar score at 5 minutes (odds ratio, 14.46; 95% confidence interval, 2.77–75.39). The incidence of an umbilical artery pH <7.00 were similar among those with category I and those with category II tracings (0.08% vs 0.24%; odds ratio, 2.85; 95% confidence interval, 0.41–19.55). When compared with category I tracings, the incidence of an umbilical artery pH <7.00 was significantly more common among those with category III tracings (31.04%; odds ratio, 161.56; 95% confidence interval, 25.18–1036.42); likewise, when compared with those with category II tracings, those with category III tracings had a significantly higher likelihood of having an umbilical artery pH <7.00 (odds ratio, 42.29; 95% confidence interval, 14.29–125.10). Hypoxic-ischemic encephalopathy occurred with similar frequency among those with categories I and those with category II tracings (0 vs 0.81%; odds ratio, 5.86; 95% confidence interval, 0.75–45.89) but was significantly more common among those with category III tracings (0 vs 18.97%; odds ratio, 61.43; 95% confidence interval, 7.49–503.50). Cesarean delivery occurred with similar frequency among those with category I (13.41%) and those with category II tracings (11.92%) (odds ratio, 0.87; 95% confidence interval, 0.72–1.05) but was significantly more common among those with with category III tracings (14.28%) (odds ratio, 3.97; 95% confidence interval, 1.62–9.75). When compared with those with category II tracings, cesarean delivery was more common among those with category III tracings (odds ratio, 4.55; 95% confidence interval, 1.88–11.01). Although the incidence of an Apgar score <7 at 5 minutes and umbilical artery pH <7.00 increased significantly with increasing fetal heart rate tracing category, about 98% of newborns with category II tracings do not have these adverse outcomes. The 3-tiered fetal heart rate tracing interpretation system provides an approximate but imprecise measurement of neonatal prognosis. [ABSTRACT FROM AUTHOR]

Objectives: Fully effective intrapartum cardiotocographic (CTG) fetal heart monitoring is still missing. Visual analysis is far from credibility. Additional, computerized analysis techniques were proposed however they did not substantially decrease possible risks of fetal asphyxia. In twin pregnancies the problem is even more complicated. Our goal is to find the most valuable parameters in intrapartum CTG surveillance in twins, based on actual FIGO criteria. Material and methods: Study included 58 women in labor who had been admitted to Delivery Department of tertiary care hospital with twin pregnancy in a period of one year. The features of the CTG (e.g., baseline, oscillation, decelerations, brady- or tachycardia) were grouped to create three variables that were closest to the FIGO CTG scale. All three groups were compared according to neonatal status (Apgar score at 5 min ≥ 7 or < 7; pH value in umbilical artery ≥ 7.20, < 7.20 or < 7.10 and BE (base excess) > or ≤ –12). Fetal status and its acid — base equilibrium was compared either with long term variability (LTV), short term variability (STV), or percentage of the signal loss. Results: Out of 58 twin pregnancies, a total of 116 babies were born. One baby was born dead. From this group, 11 deliveries were natural births and 47 deliveries were C-sections. None of the analyzed features (pH, BE, Apgar, CTG features except tracing length, CTG FIGO categories) were statistically different between groups of singleton and twin pregnancies, except percentage of C-sections. No differences were found either for STV or LTV and fetal status.org CTG categories. Conclusions: Prior to cardiotocographic tracing of twins during labor, ultrasound examination should be mandatory. Considerable loss of signal in CTG tracing in twins should provoke ultrasonographic confirmation of the fetal status. [ABSTRACT FROM AUTHOR]