Different aspects of electronic fetal monitoring during labor

Background: Cardiotocography (CTG) is a tool to assess fetal well-being during labor and to detect early signs of fetal distress and thereby enable timely interventions to reduce neonatal morbidity and mortality. CTG is associated with shortcomings; poor reliability in interpretation, low specificity with a high proportion of false positive tracings indicating fetal distress when not accurate, no proven effect on rare severe outcomes such as mortality and cerebral palsy, but rather contributing to an increased risk of operative delivery. The aims of this thesis was to determine I) if an extended CTG education could lead to better reliability in interpretation compared to a national standard education, II) if a computerized algorithm could be developed with precision in detecting and quantitating decelerations on CTG, III) if deceleration area was a better predictor of fetal acidemia during labor than deceleration depth and duration, IV) the proportion of fetuses with undetected small for gestational age (SGA) in a low-risk population, comparing women that present with normal CTG at admission to labor (admCTG) to those with abnormal admCTG and to compare neonatal outcomes in the two groups stratified on SGA or non-SGA. Material and methods: The CTG tracings used in paper I-III were extracted from a previous cohort of women in labor, from Karolinska University Hospital, Sweden. All women had undergone fetal blood sampling (FBS) during labor due to suspicious CTG patterns. Six obstetricians from two different hospitals were used as observers in paper I. Inter- and intra-observer reliability using Cohen’s and Fleiss kappa was determined for different parameters assessed on CTG. In paper II two obstetricians visually analyzed CTG tracings with variable decelerations and specified duration, depth and area for each deceleration. The computerized algorithm analyzed and quantified the same CTG traces and was compared to the observers using intra-class correlation and Bland-Altman analysis. In paper III the predictive value of deceleration area, duration, and depth for fetal acidemia, measured as lactate concentration at FBS, was explored using receiver operating characteristics, area under curve (ROC AUC). In paper IV, a register-based study, the risk of SGA in relation to the result of admCTG, normal vs abnormal was assessed in low-risk pregnancies. Neonatal outcomes were also determined by multiple logistic regression analysis. Results: I) The inter- and intra-observer reliability was moderate to excellent at both departments, kappa 0.41-0.93. The department with extended education reached significantly higher interobserver agreement for two of six CTG parameters assessed. II) Computerized assessment of decelerations on CTG compared to visual observers reached excellent intraclass correlation (0.89-0.95) and low bias in Bland-Altman analysis, comparable to that between the two observers. III) The deceleration measures with the best prediction of fetal acidemia was cumulative deceleration area and duration, ROC AUC 0.682 and 0.683 respectively compared to deceleration depth 0.631. IV) The proportion of SGA was two-fold higher among neonates presenting with abnormal admCTG (18.6%) compared to normal admCTG (9.7%). The risk of composite severe adverse neonatal complications was substantially higher in the group with abnormal admCTG/SGA compared to normal admCTG/non-SGA, adjusted odds ratio 23.7 (95% confidence interval 9.8-57.3) Conclusion: Inter- and intra-observer agreement was better than expected at both departments studied and extended education might have an impact on interpretation reliability. A novel computerized algorithm for CTG assessment has high precision in detecting and quantifying decelerations. Cumulative deceleration area and duration are better predictors of fetal acidemia than deceleration depth. In presumed low-risk pregnancies there is a group of undetected SGA fetuses that more often present with abnormal admCTG and are at higher risks of neonatal complications

Zastosowanie rozmytych reguł wnioskowania do automatycznej klasyfikacji zapisów częstości uderzeń serca płodu w odniesieniu do stanu urodzeniowego

Objectives: Fetal monitoring based on the analysis of the fetal heart rate (FHR) signal is the most common method of biophysical assessment of fetal condition during pregnancy and labor. Visual analysis of FHR signals presents a challenge due to a complex shape of the waveforms. Therefore, computer-aided fetal monitoring systems provide a number of parameters that are the result of the quantitative analysis of the registered signals. These parameters are the basis for a qualitative assessment of the fetal condition. The guidelines for the interpretation of FHR provided by FIGO are commonly used in clinical practice. On their basis a weighted fuzzy scoring system was constructed to assess the FHR tracings using the same criteria as those applied by expert clinicians. The effectiveness of the automated classification was evaluated in relation to the fetal outcome assessed by Apgar score. Material and methods: The proposed automated system for fuzzy classification is an extension of the scoring systems used for qualitative evaluation of the FHR tracings. A single fuzzy rule of the system corresponds to a single evaluation principle of a signal parameter derived from the FIGO guidelines. The inputs of the fuzzy system are the values of quantitative parameters of the FHR signal, whereas the system output, which is calculated in the process of fuzzy inference, defines the interpretation of the FHR tracing. The fuzzy evaluation process is a kind of diagnostic test, giving a negative or a positive result that can be compared with the fetal outcome assessment. The present retrospective study included a set of 2124 one-hour antenatal FHR tracings derived from 333 patients, recorded between 24 and 44 weeks of gestation (mean gestational age: 36 weeks). Various approaches for the research data analysis, depending on the method of interpretation of the individual patient-tracing relation, were used in the investigation. The quality of the fuzzy analysis was defined by the number of correct classifications (CC) and the additional index QI – the geometric mean of the sensitivity and specificity values. Results: The effectiveness of the fetal assessment varied, depending on the assumed relation between a patient and a set of her tracings. The approach, based on a common assessment of the whole set of tracings recorded for a single patient, provided the highest quality of automated classification. The best results (CC = 70.9% and QI = 84.0%) confirmed the possibility of predicting the neonatal outcome using the proposed fuzzy system based on the FIGO guidelines. Conclusions: It is possible to enhance the process of the fetal condition assessment with classification of the FHR records through the implementation of the heuristic rules of inference in the fuzzy signal processing algorithms.Cel pracy: Monitorowanie płodu na podstawie analizy sygnału czynności serca płodu (FHR) jest najczęściej stosowaną metodą biofizycznej oceny stanu płodu w czasie ciąży i porodu. Wzrokowa analiza krzywej FHR jest trudna z uwagi na jej złożony kształt. Z tego względu, komputerowo-wspomagane systemy monitorowania stanu płodu dostarczają szeregu parametrów będących rezultatem ilościowej analizy rejestrowanego sygnału. Parametry te są podstawą dla jakościowej oceny stanu płodu. Do najczęściej stosowanych wytycznych, określających sposób interpretacji sygnału FHR należą kryteria określone przez FIGO. Na ich podstawie skonstruowano ważony rozmyty system punktowy, którego zadaniem jest określenie stanu płodu na podstawie kryteriów oceny, jakimi posługuje się ekspert kliniczny. W pracy przedstawiono badania nad zgodnością rozmytej klasyfikacji z oceną stanu płodu wyznaczaną na podstawie punktacji Apgar. Materiał i metody: Proponowany system do automatycznej, rozmytej klasyfikacji stanowi rozwinięcie idei skal punktowych wykorzystywanych do jakościowej oceny zapisów czynności serca płodu. Za pomocą jednej reguły rozmytej modelowana jest zasada oceny pojedynczego parametru opisu ilościowego sygnału FHR zgodnie z wytycznymi FIGO. Wejściami systemu rozmytego są wartości parametrów ilościowych sygnału FHR, a stan wyjścia, wyznaczany w procesie wnioskowania rozmytego, definiuje interpretację zapisu. Proces rozmytej oceny sygnału jest rodzajem testu diagnostycznego, którego wynik, negatywny lub pozytywny, można porównać z oceną stanu urodzeniowego noworodka. Badaniem retrospektywnym objęto zbiór 2124 godzinnych zapisów ciążowych pochodzących od 333 pacjentek, zarejestrowanych pomiędzy 24 a 44 tygodniem ciąży (średni wiek ciążowy to 36 tygodni). W eksperymentach zastosowano różne konstrukcje zbiorów danych, w zależności od przyjętego sposobu interpretacji zbioru sygnałów zarejestrowanych dla pojedynczej pacjentki. Jakość rozmytej analizy automatycznej oceniano na podstawie liczby poprawnych klasyfikacji CC oraz wskaźnika QI będącego średnią geometryczną czułości oraz swoistości. Wyniki: W zależności od przyjętej metody analizy zbioru danych otrzymano różną skuteczność oceny stanu płodu. Podejście, w którym określano jedną wspólną ocenę dla całego zbioru zapisów zarejestrowanych dla pojedynczej pacjentki, pozwoliło na uzyskanie najwyższej jakości automatycznej klasyfikacji. Najlepsze z uzyskanych wyników (CC = 70.9% i QI = 84.0%) potwierdzają możliwość predykcji stanu urodzeniowego płodu na podstawie rozmytego wnioskowania opartego na wytycznych FIGO. Wnioski: Istnieje możliwość wspomagania procesu diagnostyki stanu płodu przez zastosowanie systemu rozmytej klasyfikacji sygnałów FHR, opartego o heurystyczne reguły wnioskowania właściwe doświadczonemu klinicyście

Machine learning algorithms combining slope deceleration and fetal heart rate features to predict acidemia

Electronic fetal monitoring (EFM) is widely used in intrapartum care as the standard method for monitoring fetal well-being. Our objective was to employ machine learning algorithms to predict acidemia by analyzing specific features extracted from the fetal heart signal within a 30 min window, with a focus on the last deceleration occurring closest to delivery. To achieve this, we conducted a case–control study involving 502 infants born at Miguel Servet University Hospital in Spain, maintaining a 1:1 ratio between cases and controls. Neonatal acidemia was defined as a pH level below 7.10 in the umbilical arterial blood. We constructed logistic regression, classification trees, random forest, and neural network models by combining EFM features to predict acidemia. Model validation included assessments of discrimination, calibration, and clinical utility. Our findings revealed that the random forest model achieved the highest area under the receiver characteristic curve (AUC) of 0.971, but logistic regression had the best specificity, 0.879, for a sensitivity of 0.95. In terms of clinical utility, implementing a cutoff point of 31% in the logistic regression model would prevent unnecessary cesarean sections in 51% of cases while missing only 5% of acidotic cases. By combining the extracted variables from EFM recordings, we provide a practical tool to assist in avoiding unnecessary cesarean sections

Fetal movements as a predictor of health

The key determinant to a fetus maintaining its health is through adequate perfusion and oxygen transfer mediated by the functioning placenta. When this equilibrium is distorted, a number of physiological changes including reduced fetal growth occur to favour survival. Technologies have been developed to monitor these changes with a view to prolong intrauterine maturity whilst reducing the risks of stillbirth. Many of these strategies involve complex interpretation, for example Doppler ultrasound for fetal blood flow and computerisedcomputerized analysis of fetal heart rate changes. However, even with these modalities of fetal assessment to determine the optimal timing of delivery, fetal movements remain integral to clinical decision making. In high risk cohorts with fetal growth restriction, the manifestation of a reduction in perceived movements may warrant an expedited delivery. Despite this, there remains has been little evolution in the development of technologies to objectively define evaluate normal fetal movement behavior for behavior, and where there has, there has been no linkage to clinical useapplication. In tThis review we is an attempt to understand synthesize currently available literature on the value of fetal movement analysis as a method of assessing fetal wellbeing, and show how interdisciplinary developments in this area may aid in improvements to clinical outcomes

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

AbstractObjectiveTo identify new parameters predicting fetal acidemia.MethodsA 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).ResultsA 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%).ConclusionsThe 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

Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK).

The second Signal Processing and Monitoring in Labor workshop gathered researchers who utilize promising new research strategies and initiatives to tackle the challenges of intrapartum fetal monitoring. The workshop included a series of lectures and discussions focusing on: new algorithms and techniques for cardiotocogoraphy (CTG) and electrocardiogram acquisition and analyses; the results of a CTG evaluation challenge comparing state-of-the-art computerized methods and visual interpretation for the detection of arterial cord pH <7.05 at birth; the lack of consensus about the role of intrapartum acidemia in the etiology of fetal brain injury; the differences between methods for CTG analysis "mimicking" expert clinicians and those derived from "data-driven" analyses; a critical review of the results from two randomized controlled trials testing the former in clinical practice; and relevant insights from modern physiology-based studies. We concluded that the automated algorithms performed comparably to each other and to clinical assessment of the CTG. However, the sensitivity and specificity urgently need to be improved (both computerized and visual assessment). Data-driven CTG evaluation requires further work with large multicenter datasets based on well-defined labor outcomes. And before first tests in the clinic, there are important lessons to be learnt from clinical trials that tested automated algorithms mimicking expert CTG interpretation. In addition, transabdominal fetal electrocardiogram monitoring provides reliable CTG traces and variability estimates; and fetal electrocardiogram waveform analysis is subject to promising new research. There is a clear need for close collaboration between computing and clinical experts. We believe that progress will be possible with multidisciplinary collaborative research

Fetal heart rate responses in chronic hypoxaemia with superimposed repeated hypoxaemia consistent with early labour: a controlled study in fetal sheep

Objective: Deceleration area (DA) and capacity (DC) of the fetal heart rate can help predict risk of intrapartum fetal compromise. However, their predictive value in higher risk pregnancies is unclear. We investigated whether they can predict the onset of hypotension during brief hypoxaemia repeated at a rate consistent with early labour in fetal sheep with pre-existing hypoxaemia. Design: Prospective, controlled study. Setting: Laboratory. Sample: Chronically instrumented, unanaesthetised near-term fetal sheep. Methods: One-minute complete umbilical cord occlusions (UCOs) were performed every 5 minutes in fetal sheep with baseline paO2 17 mmHg (normoxic, n = 11) for 4 hours or until arterial pressure fell <20 mmHg. Main outcome measures: DA, DC and arterial pressure. Results: Normoxic fetuses showed effective cardiovascular adaptation without hypotension and mild acidaemia (lowest arterial pressure 40.7 ± 2.8 mmHg, pH 7.35 ± 0.03). Hypoxaemic fetuses developed hypotension (lowest arterial pressure 20.8 ± 1.9 mmHg, P P = 0.04) and final (P = 0.012) 20 minutes of UCOs. DA was not different between groups. Conclusion: Chronically hypoxaemic fetuses had early onset of cardiovascular compromise during labour-like brief repeated UCOs. DA was unable to identify developing hypotension in this setting, while DC only showed modest differences between groups. These findings highlight that DA and DC thresholds need to be adjusted for antenatal risk factors, potentially limiting their clinical utility