Construction of a comprehensive fetal monitoring database for the study of perinatal hypoxic ischemic encephalopathy.

This article describes the methods used to build a large-scale database of more than 250,000 electronic fetal monitoring (EFM) records linked to a comprehensive set of clinical information about the infant, the mother, the pregnancy, labor, and outcome. The database can be used to investigate how birth outcome is related to clinical and EFM features. The main steps involved in building the database were: (1) Acquiring the raw EFM recording and clinical records for each birth. (2) Assigning each birth to an objectively defined outcome class that included normal, acidosis, and hypoxic-ischemic encephalopathy. (3) Removing all personal health information from the EFM recordings and clinical records. (4) Preprocessing the deidentified EFM records to eliminate duplicates, reformat the signals, combine signals from different sensors, and bridge gaps to generate signals in a format that can be readily analyzed. (5) Post-processing the repaired EFM recordings to extract key features of the fetal heart rate, uterine activity, and their relations. (6) Populating a database that links the clinical information, EFM records, and EFM features to support easy querying and retrieval. •A multi-step process is required to build a comprehensive database linking electronic temporal fetal monitoring signals to a comprehensive set of clinical information about the infant, the mother, the pregnancy, labor, and outcome.•The current database documents more than 250,000 births including almost 4,000 acidosis and 400 HIE cases. This represents more than 80% of the births that occurred in 15 Northern California Kaiser Permanente Hospitals between 2011-2019. This is a valuable resource for studying the factors predictive of outcome.•The signal processing code and schemas for the database are freely available. The database will not be permitted to leave Kaiser firewalls, but a process is in place to allow interested investigators to access it.

New labor curves of dilation and station to improve the accuracy of predicting labor progress.

BACKGROUND: The diagnosis of failure to progress, the most common indication for intrapartum cesarean delivery, is based on the assessment of cervical dilation and station over time. Labor curves serve as references for expected changes in dilation and fetal descent. The labor curves of Friedman, Zhang et al, and others are based on time alone and derived from mothers with spontaneous labor onset. However, labor induction is now common, and clinicians also consider other factors when assessing labor progress. Labor curves that consider the use of labor induction and other factors that influence labor progress have the potential to be more accurate and closer to clinical decision-making. OBJECTIVE: This study aimed to compare the prediction errors of labor curves based on a single factor (time) or multiple clinically relevant factors using two modeling methods: mixed-effects regression, a standard statistical method, and Gaussian processes, a machine learning method. STUDY DESIGN: This was a longitudinal cohort study of changes in dilation and station based on data from 8022 births in nulliparous women with a live, singleton, vertex-presenting fetus ≥35 weeks of gestation with a vaginal delivery. New labor curves of dilation and station were generated with 10-fold cross-validation. External validation was performed using a geographically independent group. Model variables included time from the first examination in the 20 hours before delivery; dilation, effacement, and station recorded at the previous examination; cumulative contraction counts; and use of epidural anesthesia and labor induction. To assess model accuracy, differences between each model's predicted value and its corresponding observed value were calculated. These prediction errors were summarized using mean absolute error and root mean squared error statistics. RESULTS: Dilation curves based on multiple parameters were more accurate than those derived from time alone. The mean absolute error of the multifactor methods was better (lower) than those of the single-factor methods (0.826 cm [95% confidence interval, 0.820-0.832] for the multifactor machine learning and 0.893 cm [95% confidence interval, 0.885-0.901] for the multifactor mixed-effects method and 2.122 cm [95% confidence interval, 2.108-2.136] for the single-factor methods; P<.0001 for both comparisons). The root mean squared errors of the multifactor methods were also better (lower) than those of the single-factor methods (1.126 cm [95% confidence interval, 1.118-1.133] for the machine learning [P<.0001] and 1.172 cm [95% confidence interval, 1.164-1.181] for the mixed-effects methods and 2.504 cm [95% confidence interval, 2.487-2.521] for the single-factor [P<.0001 for both comparisons]). The multifactor machine learning dilation models showed small but statistically significant improvements in accuracy compared to the mixed-effects regression models (P<.0001). The multifactor machine learning method produced a curve of descent with a mean absolute error of 0.512 cm (95% confidence interval, 0.509-0.515) and a root mean squared error of 0.660 cm (95% confidence interval, 0.655-0.666). External validation using independent data produced similar findings. CONCLUSION: Cervical dilation models based on multiple clinically relevant parameters showed improved (lower) prediction errors compared to models based on time alone. The mean prediction errors were reduced by more than 50%. A more accurate assessment of departure from expected dilation and station may help clinicians optimize intrapartum management.

Data-Driven Insights into Labor Progression with Gaussian Processes.

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.

Timely Detection of Infants at Risk of Intrapartum Acidosis and Hypoxic-Ischemic Encephalopathy Using Cardiotocography.

This work aims to improve the intrapartum detection of fetuses with an increased risk of developing fetal acidosis or hypoxic-ischemic encephalopathy (HIE) using fetal heart rate (FHR) and uterine pressure (UP) signals. Our study population comprised 40,831 term births divided into 3 classes based on umbilical cord or early neonatal blood gas assessments: 374 with verified HIE, 3,047 with acidosis but no encephalopathy and 37,410 healthy babies with normal gases. We developed an intervention recommendation system based on a random forest classifier. The classifier was trained using classical and novel features extracted electronically from 20-minute epochs of FHR and UP. Then, using the predictions of the classifier on each epoch, we designed a decision rule to determine when to recommended intervention. Compared to the Caesarean rates in each study group, our system identified an additional 5.68% of babies who developed HIE (54.55% vs 60.23%, p < 0.01) with a specific alert threshold. Importantly, about 75% of these recommendations were made more than 200 minutes before birth. In the acidosis group, the system identified an additional 17.44% (37.15% vs 54.59%, p < 0.01) and about 2/3 of these recommendations were made more than 200 minutes before birth. Compared to the Caesarean rate in the healthy group, the associated false positive rate was increased by 1.07% (38.80% vs 39.87%, p<0.01).Clinical Relevance- This method recommended intervention in more babies affected by acidosis or HIE, than the intervention rate observed in practice and most often did so 200 minutes before delivery. This was early enough to expect that interventions would have clinical benefit and reduce the rate of HIE. Given the high burden associated with HIE, this would justify the marginal increase in the normal Cesarean rate.

Accounting for Nulliparity in the Prediction of Hypoxic-Ischemic Encephalopathy Using Cardiotocography.

Nulliparous pregnancies, those where the mother has not previously given birth, are associated with longer labors and hence expose the fetus to more contractions and other adverse intrapartum conditions such as chorioamnionitis. The objective of the present study was to test if accounting for nulliparity could improve the detection of fetuses at increased risk of developing hypoxic-ischemic encephalopathy (HIE). During labor, clinicians assess the fetal heart rate and uterine pressure signals to identify fetuses at risk of developing HIE. In this study, we performed random forest classification using fetal heart rate and uterine pressure features from 40,831 births, including 374 that developed HIE. We analyzed a two-path classification approach that analyzed separately the fetuses from nulliparous and multiparous mothers, and a one-path classification approach that included the clinical variable for nulliparity as a classification feature. We compared these two approaches to a one-path classifier that had no information about the parity of the mothers. We also compared our results to the rate of Caesarean deliveries in each group, which is used clinically to interrupt the progression towards HIE. All the classifiers detected more fetuses that developed HIE than the observed Caesarean rate, but accounting for nulliparity did not improve performance.

The evolution of the labor curve and its implications for clinical practice: the relationship between cervical dilation, station, and time during labor.

The assessment of labor progress is germane to every woman in labor. Two labor disorders-arrest of dilation and arrest of descent-are the primary indications for surgery in close to 50% of all intrapartum cesarean deliveries and are often contributing indications for cesarean deliveries for fetal heart rate abnormalities. Beginning in 1954, the assessment of labor progress was transformed by Friedman. He published a series of seminal works describing the relationship between cervical dilation, station of the presenting part, and time. He proposed nomenclature for the classification of labor disorders. Generations of obstetricians used this terminology and normal labor curves to determine expected rates of dilation and fetal descent and to decide when intervention was required. The analysis of labor progress presents many mathematical challenges. Clinical measurements of dilation and station are imprecise and prone to variation, especially for inexperienced observers. Many interrelated factors influence how the cervix dilates and how the fetus descends. There is substantial variability in when data collection begins and in the frequency of examinations. Statistical methods to account for these issues have advanced considerably in recent decades. In parallel, there is growing recognition among clinicians of the limitations of using time alone to assess progress in cervical dilation in labor. There is wide variation in the patterns of dilation over time and most labors do not follow an average dilation curve. Reliable assessment of labor progression is important because uncertainty leads to both over-use and under-use of cesarean delivery and neither of these extremes are desirable. This review traces the evolution of labor curves, describes how limitations are being addressed to reduce uncertainty and to improve the assessment of labor progression using modern statistical techniques and multi-dimensional data, and discusses the implications for obstetrical practice.

Multi-Chain Semi-Markov Analysis of Intrapartum Cardiotocography.

Visual assessment of the evolution of fetal heart rate (FHR) and uterine pressure (UP) patterns is the standard of care in the intrapartum period. Unfortunately, this assessment has high levels of intra- and inter-observer variability. This study processed and analyzed FHR and UP patterns using computerized pattern recognition tools. The goal was to evaluate differences in FHR and UP patterns between fetuses with normal outcomes and those who developed hypoxic-ischemic encephalopathy (HIE). For this purpose, we modeled the sequence of FHR patterns and uterine contractions using Multi-Chain Semi-Markov models (MCSMMs). These models estimate the probability of transitioning between FHR or UP patterns and the dwell time of each pattern. Our results showed that in comparison to the control group, the HIE group had: (1) more frequent uterine contractions during the last 12 hours before birth; (2) more frequent FHR decelerations during the last 12 hours before birth; (3) longer decelerations during the last eight hours before birth; and (4) shorter baseline durations during the last five hours before birth. These results demonstrate that the fetuses in the HIE group were subject to a more stressful environment than those in the normal group. Clinical Relevance- Our results revealed statistically significant differences in FHR/UP patterns between the normal and HIE groups in the hours before birth. This indicates that features derived using MCSMMs may be useful in a machine learning framework to detect infants at increased risk of developing HIE allowing preventive interventions.

Arrhythmia classification of 12-lead and reduced-lead electrocardiograms via recurrent networks, scattering, and phase harmonic correlation.

We describe an automatic classifier of arrhythmias based on 12-lead and reduced-lead electrocardiograms. Our classifier comprises four modules: scattering transform (ST), phase harmonic correlation (PHC), depthwise separable convolutions (DSC), and a long short-term memory (LSTM) network. It is trained on PhysioNet/Computing in Cardiology Challenge 2021 data. The ST captures short-term temporal ECG modulations while the PHC characterizes the phase dependence of coherent ECG components. Both reduce the sampling rate to a few samples per typical heart beat. We pass the output of the ST and PHC to a depthwise-separable convolution layer (DSC) which combines lead responses separately for each ST or PHC coefficient and then combines resulting values across all coefficients. At a deeper level, two LSTM layers integrate local variations of the input over long time scales. We train in an end-to-end fashion as a multilabel classification problem with a normal and 25 arrhythmia classes. Lastly, we use canonical correlation analysis (CCA) for transfer learning from 12-lead ST and PHC representations to reduced-lead ones. After local cross-validation on the public data from the challenge, our team 'BitScattered' achieved the following results: 0.682 ± 0.0095 for 12-lead; 0.666 ± 0.0257 for 6-lead; 0.674 ± 0.0185 for 4-lead; 0.661 ± 0.0098 for 3-lead; and 0.662 ± 0.0151 for 2-lead.

The Nonlinear Dynamic Response of Intrapartum Fetal Heart Rate to Uterine Pressure.

The research objective of our group is to improve the intrapartum detection of cardiotocography tracings associated with an increased risk of developing fetal acidosis and subsequent hypoxic-ischemic encephalopathy (HIE). The detection methods that we aim to develop must be sensitive to abnormal tracings without causing excessive unnecessary interventions. Past studies showed that the dynamic response of fetal heart rate (FHR) to uterine pressure (UP) during the intrapartum could be modelled using linear systems. In this study, we examined the assumption of linearity by comparing the performance of linear dynamic and nonlinear dynamic models of the UP-FHR system. The linear systems were defined by second-order state-space models. The nonlinear systems were defined by Hammerstein models: a cascade of a static nonlinearity and a linear second-order state-space model. Our results showed that nonlinear dynamic models were better than linear systems in 81.8% of UP-FHR segments.

Estimation and Discriminability of Doppler Ultrasound Fetal Heart Rate Variability Measures.

Continuous electronic fetal monitoring and the access to databases of fetal heart rate (FHR) data have sparked the application of machine learning classifiers to identify fetal pathologies. However, most fetal heart rate data are acquired using Doppler ultrasound (DUS). DUS signals use autocorrelation (AC) to estimate the average heartbeat period within a window. In consequence, DUS FHR signals loses high frequency information to an extent that depends on the length of the AC window. We examined the effect of this on the estimation bias and discriminability of frequency domain features: low frequency power (LF: 0.03-0.15 Hz), movement frequency power (MF: 0.15-0.5 Hz), high frequency power (HF: 0.5-1 Hz), the LF/(MF + HF) ratio, and the nonlinear approximate entropy (ApEn) as a function of AC window length and signal to noise ratio. We found that the average discriminability loss across all evaluated AC window lengths and SNRs was 10.99% for LF 14.23% for MF, 13.33% for the HF, 10.39% for the LF/(MF + HF) ratio, and 24.17% for ApEn. This indicates that the frequency domain features are more robust to the AC method and additive noise than the ApEn. This is likely because additive noise increases the irregularity of the signals, which results in an overestimation of ApEn. In conclusion, our study found that the LF features are the most robust to the effects of the AC method and noise. Future studies should investigate the effect of other variables such as signal drop, gestational age, and the length of the analysis window on the estimation of fHRV features and their discriminability.

Temporal Evolution of Intrapartum Fetal Heart Rate Features.

Our research goal is to improve the intrapartum identification of tracings associated with severe acidosis at birth and subsequent hypoxic-ischemic encephalopathy so that timely interventions could avoid such complications without causing excessive unnecessary interventions in births with normal outcomes. The present study examines the evolution of fetal heart rate (FHR) features over the course of labor. We analyzed FHR signals collected in the last 6 hours before delivery in 21,853 births with normal neonatal outcomes and in 163 that developed hypoxic-ischemic encephalopathy (HIE) from 15 hospitals of Kaiser Permanente Northern California. We divided these six hours into 18 nonoverlapping 20-minute epochs. The power spectral density of each epoch was divided into three bands: low frequency (LF, 30-150 mHz), movement frequency (MF, 150-500 mHz), and high frequency (HF, 500-1000 mHz). We also estimated the LF/(MF+HF) ratio, the mean and standard deviation of the FHR signal, the approximate entropy (ApEn), and the deceleration capacity (DC). In our results, ApEn, the standard deviation, and DC showed a promising ability to detect risk of HIE as early as 120 minutes before birth, which gives enough leading time for timely interventions.

Remote health diagnosis and monitoring in the time of COVID-19.

Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper, we present a review of remote health monitoring initiatives taken in 20 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy.

Information theoretic measures of perinatal cardiotocography synchronization.

We examined the use of bivariate mutual information (MI) and its conditional variant transfer entropy (TE) to address synchronization of perinatal uterine pressure (UP) and fetal heart rate (FHR). We used a nearest-neighbour based Kraskov entropy estimator, suitable to the non-Gaussian distributions of the UP and FHR signals. Moreover, the estimates were robust to noise by use of surrogate data testing. Estimating degree of synchronicity and UP-FHR delay length is useful since they are physiological correlates to fetal hypoxia. Mutual information of the UP-FHR discriminated normal and pathological fetuses early (160 min before delivery) and discriminated normal and metabolic acidotic fetuses slightly later (110 min before delivery), with higher mutual information for progressively pathological classes. The delay in mutual information transfer was also discriminating in the last 50 min of labour. Transfer entropy discriminated normal and pathological cases 110 min before delivery with lower TE values and longer information transfer delays in pathological cases, to our knowledge, the first report of this phenomena in the literature.

Estimating risk of severe neonatal morbidity in preterm births under 32 weeks of gestation.

Background: A large recent study analyzed the relationship between multiple factors and neonatal outcome and in preterm births. Study variables included the reason for admission, indication for delivery, optimal steroid use, gestational age, and other potential prognostic factors. Using stepwise multivariable analysis, the only two variables independently associated with serious neonatal morbidity were gestational age and the presence of suspected intrauterine growth restriction as a reason for admission. This finding was surprising given the beneficial effects of antenatal steroids and hazards associated with some causes of preterm birth. Multivariable logistic regression techniques have limitations. Without testing for multiple interactions, linear regression will identify only individual factors with the strongest independent relationship to the outcome for the entire study group. There may not be a single "best set" of risk factors or one set that applies equally well to all subgroups. In contrast, machine learning techniques find the most predictive groupings of factors based on their frequency and strength of association, with no attempt to identify independence and no assumptions about linear relationships.Objective: To determine if machine learning techniques would identify specific clusters of conditions with different probability estimates for severe neonatal morbidity and to compare these findings to those based on the original multivariable analysis.Materials and methods: This was a secondary analysis of data collected in a multicenter, prospective study on all admissions to the neonatal intensive care unit between 2013 and 2015 in 10 hospitals. We included all patients with a singleton, stillborn, or live newborns, with a gestational age between 23 0/7 and 31 6/7 week. The composite endpoint, severe neonatal morbidity, defined by the presence of any of five outcomes: death, grade 3 or 4 intraventricular hemorrhage (IVH), and ≥28 days on ventilator, periventricular leukomalacia (PVL), or stage III necrotizing enterocolitis (NEC), was present in 238 of the 1039 study patients. We studied five explanatory variables: maternal age, parity, gestational age, admission reason, and status with respect to antenatal steroid administration. We concentrated on Classification and Regression Trees because the resulting structure defines clusters of risk factors that often bear resemblance to clinical reasoning. Model performance was measured using area under the receiver-operator characteristic curves (AUC) based on 10 repetitions of 10-fold cross-validation.Results: A hybrid technique using a combination of logistic regression and Classification and Regression Trees had a mean cross-validated AUC of 0.853. A selected point on its receiver-operator characteristic (ROC) curve corresponding to a sensitivity of 81% was associated with a specificity of 76%. Rather than a single curve representing the general relationship between gestational age and severe morbidity, this technique found seven clusters with distinct curves. Abnormal fetal testing as a reason for admission with or without growth restriction and incomplete steroid administration would place a 20-year-old patient on the highest risk curve.Conclusions: Using a relatively small database and a few simple factors known before birth it is possible to produce a more tailored estimate of the risk for severe neonatal morbidity on which clinicians can superimpose their medical judgment, experience, and intuition.

Hybrid scattering-LSTM networks for automated detection of sleep arousals.

OBJECTIVE: Early detection of sleep arousal in polysomnographic (PSG) signals is crucial for monitoring or diagnosing sleep disorders and reducing the risk of further complications, including heart disease and blood pressure fluctuations. APPROACH: In this paper, we present a new automatic detector of non-apnea arousal regions in multichannel PSG recordings. This detector cascades four different modules: a second-order scattering transform (ST) with Morlet wavelets; depthwise-separable convolutional layers; bidirectional long short-term memory (BiLSTM) layers; and dense layers. While the first two are shared across all channels, the latter two operate in a multichannel formulation. Following a deep learning paradigm, the whole architecture is trained in an end-to-end fashion in order to optimize two objectives: the detection of arousal onset and offset, and the classification of the type of arousal. Main results and Significance: The novelty of the approach is three-fold: it is the first use of a hybrid ST-BiLSTM network with biomedical signals; it captures frequency information lower (0.1 Hz) than the detection sampling rate (0.5 Hz); and it requires no explicit mechanism to overcome class imbalance in the data. In the follow-up phase of the 2018 PhysioNet/CinC Challenge the proposed architecture achieved a state-of-the-art area under the precision-recall curve (AUPRC) of 0.50 on the hidden test data, tied for the second-highest official result overall.

Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection.

OBJECTIVE: Atrial fibrillation is a common type of heart rhythm abnormality caused by a problem with the heart's electrical system. Early detection of this disease has important implications for stroke prevention and management. Our objective is to construct an intelligent tool that assists cardiologists in identifying automatically cardiac arrhythmias and noise in electrocardiogram (ECG) recordings. APPROACH: Our base deep classifier combined a convolutional neural network (CNNs) and a sequence of long short-term memory units, with pooling, dropout and normalization techniques to improve their accuracy. The network predicted a classification at every 18th input sample and the final prediction was selected for classification. Ten standalone models that used our base classifier architecture were first cross-validated separately on 90% of the PhysioNet/CinC Challenge 2017 dataset and then tested on 10%. An ensemble classifier selected the label of the best average probability from the ten sub-models to improve prediction quality. MAIN RESULTS: Our original result submitted to the challenge gave a mean F1-measure of 80%. The new proposed method improved the test score to 82%, which was tied for the third-highest score in the follow-up phase of the challenge. SIGNIFICANCE: Without employing a time-consuming feature engineering step, the ensemble classifier trained with this architecture provided a robust solution to the problem of detecting cardiac arrhythmia from noisy ECG signals. In addition, interpretation of the classifier by inspection of its network parameters and predictions revealed what aspects of the ECG signal the classifier considered most discriminating.

Ensemble methods with outliers for phonocardiogram classification.

OBJECTIVE: Heart sound classification and analysis play an important role in the early diagnosis and prevention of cardiovascular disease. To this end, this paper introduces a novel method for automatic classification of normal and abnormal heart sound recordings. APPROACH: Signals are first preprocessed to extract a total of 131 features in the time, frequency, wavelet and statistical domains from the entire signal and from the timings of the states. Outlier signals are then detected and separated from those with a standard range using an interquartile range algorithm. After that, feature extreme values are given special consideration, and finally features are reduced to the most significant ones using a feature reduction technique. In the classification stage, the selected features either for standard or outlier signals are fed separately into an ensemble of 20 two-step classifiers for the classification task. The first step of the classifier is represented by a nested set of ensemble algorithms which was cross-validated on the training dataset provided by PhysioNet Challenge 2016, while the second one uses a voting rule of the class label. MAIN RESULTS: The results show that this method is able to recognize heart sound recordings efficiently, achieving an overall score of 96.30% for standard signals and 90.18% for outlier signals on a cross-validated experiment using the available training data. SIGNIFICANCE: The approach of our proposed method helped reduce overfitting and improved classification performance, achieving an overall score on the hidden test set of 80.1% (79.6% sensitivity and 80.6% specificity).

The limits of electronic fetal heart rate monitoring in the prevention of neonatal metabolic acidemia.

BACKGROUND: Despite intensive efforts directed at initial training in fetal heart rate interpretation, continuing medical education, board certification/recertification, team training, and the development of specific protocols for the management of abnormal fetal heart rate patterns, the goals of consistently preventing hypoxia-induced fetal metabolic acidemia and neurologic injury remain elusive. OBJECTIVE: The purpose of this study was to validate a recently published algorithm for the management of category II fetal heart rate tracings, to examine reasons for the birth of infants with significant metabolic acidemia despite the use of electronic fetal heart rate monitoring, and to examine critically the limits of electronic fetal heart rate monitoring in the prevention of neonatal metabolic acidemia. STUDY DESIGN: The potential performance of electronic fetal heart rate monitoring under ideal circumstances was evaluated in an outcomes-blinded examination fetal heart rate tracing of infants with metabolic acidemia at birth (base deficit, >12) and matched control infants (base deficit, <8) under the following conditions: (1) expert primary interpretation, (2) use of a published algorithm that was developed and endorsed by a large group of national experts, (3) assumption of a 30-minute period of evaluation for noncritical category II fetal heart rate tracings, followed by delivery within 30 minutes, (4) evaluation without the need to provide patient care simultaneously, and (5) comparison of results under these circumstances with those achieved in actual clinical practice. RESULTS: During the study period, 120 infants were identified with an arterial cord blood base deficit of >12 mM/L. Matched control infants were not demographically different from subjects. In actual practice, operative intervention on the basis of an abnormal fetal heart rate tracings occurred in 36 of 120 fetuses (30.0%) with metabolic acidemia. Based on expert, algorithm-assisted reviews, 55 of 120 patients with acidemia (45.8%) were judged to need operative intervention for abnormal fetal heart rate tracings. This difference was significant (P=.016). In infants who were born with a base deficit of >12 mM/L in which blinded, algorithm-assisted expert review indicated the need for operative delivery, the decision for delivery would have been made an average of 131 minutes before the actual delivery. The rate of expert intervention for fetal heart rate concerns in the nonacidemic control group (22/120; 18.3%) was similar to the actual intervention rate (23/120; 19.2%; P=1.0) Expert review did not mandate earlier delivery in 65 of 120 patients with metabolic acidemia. The primary features of these 65 cases included the occurrence of sentinel events with prolonged deceleration just before delivery, the rapid deterioration of nonemergent category II fetal heart rate tracings before realistic time frames for recognition and intervention, and the failure of recognized fetal heart rate patterns such as variability to identify metabolic acidemia. CONCLUSIONS: Expert, algorithm-assisted fetal heart rate interpretation has the potential to improve standard clinical performance by facilitating significantly earlier recognition of some tracings that are associated with metabolic acidemia without increasing the rate of operative intervention. However, this improvement is modest. Of infants who are born with metabolic acidemia, only approximately one-half potentially could be identified and have delivery expedited even under ideal circumstances, which are probably not realistic in current US practice. This represents the limits of electronic fetal heart rate monitoring performance. Additional technologies will be necessary if the goal of the prevention of neonatal metabolic acidemia is to be realized.

Assessing first-stage labor progression and its relationship to complications.

BACKGROUND: New labor curves have challenged the traditional understanding of the general pattern of dilation and descent in labor. They also revealed wide variation in the time to advance in dilation. An interval of arrest such as 4 hours did not fall beyond normal limits until dilation had reached 6 cm. Thus, the American College of Obstetricians and Gynecologists/Society for Maternal-Fetal Medicine first-stage arrest criteria, based in part on these findings, are applicable only in late labor. The wide range of time to dilate is unavoidable because cervical dilation has neither a precise nor direct relationship to time. Newer statistical techniques (multifactorial models) can improve precision by incorporating several factors that are related directly to labor progress. At each examination, the calculations adapt to the mother's current labor conditions. They produce a quantitative assessment that is expressed in percentiles. Low percentiles indicate potentially problematic labor progression. OBJECTIVE: The purpose of this study was to assess the relationship between first-stage labor progress- and labor-related complications with the use of 2 different assessment methods. The first method was based on arrest of dilation definitions. The other method used percentile rankings of dilation or station based on adaptive multifactorial models. STUDY DESIGN: We included all 4703 cephalic-presenting, term, singleton births with electronic fetal monitoring and cord gases at 2 academic community referral hospitals in 2012 and 2013. We assessed electronic data for route of delivery, all dilation and station examinations, newborn infant status, electronic fetal monitoring tracings, and cord blood gases. The labor-related complication groups included 272 women with cesarean delivery for first-stage arrest, 558 with cesarean delivery for fetal heart rate concerns, 178 with obstetric hemorrhage, and 237 with neonatal depression, which left 3004 women in the spontaneous vaginal birth group. Receiver operating characteristic curves were constructed for each assessment method by measurement of the sensitivity for each complication vs the false-positive rate in the normal reference group. RESULTS: The duration of arrest at ≥6 cm dilation showed poor levels of discrimination for the cesarean delivery interventions (area under the curve, 0.55-0.65; P < .01) and no significant relationship to hemorrhage or neonatal depression. The dilation and station percentiles showed high discrimination for the cesarean delivery-related outcomes (area under the curve, 0.78-0.93; P < .01) and low discrimination for the clinical outcomes of hemorrhage and neonatal depression (area under the curve, 0.58-0.61; P < .01). CONCLUSIONS: Duration of arrest of dilation at ≥6 cm showed little or no discrimination for any of the complications. In comparison, percentile rankings that were based on the adaptive multifactorial models showed much higher discrimination for cesarean delivery interventions and better, but low discrimination for hemorrhage. Adaptive multifactorial models present a different method to assess labor progress. Rather than "pass/fail" criteria that are applicable only to dilation in late labor, they produce percentile rankings, assess 2 essential processes for vaginal birth (dilation and descent), and can be applied from 3 cm onward. Given the limitations of labor-progress assessment based solely on the passage of time and because of the extreme variation in decision-making for cesarean delivery for labor disorders, the types of mathematic analyses that are described in this article are logical and promising steps to help standardize labor assessment.

Descent of the fetal head (station) during the first stage of labor.

BACKGROUND: High station at specific points in the first stage of labor, such as a floating head on admission, or at 4-cm dilation or when arrest of dilation occurs, is associated with higher rates of failure to deliver vaginally. Therefore it could be useful to know if station is within an expected range at a given dilation during first stage. Arrest of descent disorders have been defined thus far on criteria applicable in the second stage. Statistical modeling is an attractive methodology to characterize the relationship between station and dilation because the resulting mathematical expressions could be used as a reference for comparison in the future. In addition, they can be used to produce a finely graded assessment of descent using numerical terms such as percentile rankings. A 2-step approach to potentially improving the assessment of station could be to develop a statistical model that describes the general relationship between station and dilation in the first stage of uncomplicated births and then determine if such a model would have identified births with complications related to poor labor progress. Given the complex nature of labor data, especially the imprecision of dilation and station measurement, it is not immediately evident that such a model is identifiable or what its precision would be. OBJECTIVE: We sought to characterize in mathematical terms the relationship of station to dilation during the first stage of labor for nulliparous and multiparous women with spontaneous vaginal births. STUDY DESIGN: This retrospective cohort study included 28,121 exams from 5555 women with singleton cephalic presentations at ≥37 weeks' gestation with electronic fetal monitoring tracings, who delivered vaginally without instrumentation and had 5-minute Apgar scores >6 at 2 academic community referral hospitals in 2012 through 2013. Women with a previous cesarean birth were excluded. We used longitudinal statistical techniques suitable to biological data that were irregularly sampled with repeated measures over time. RESULTS: A linear relationship was observed between station and dilation. For both nulliparous and multiparous women the final model was a linear regression with random effects for intercept and slope and a first-order autoregressive correlation structure. The 5th-95th range of station at any given dilation spanned about 3-4 cm. CONCLUSION: Our results demonstrate a general trend of increasing descent of the presenting part as dilation advances during the first stage of labor in women who delivered vaginally without instrumentation. We propose that the mathematical expressions describing this relationship may be valuable in the assessment of first-stage labor progression.