Optimal duration of cardiotocography assessment using the iPREFACE score to predict fetal acidemia

Automatic classification of fetal heart rate based on a multi-scale LSTM network

Introduction

Fetal heart rate monitoring during labor can aid healthcare professionals in identifying alterations in the heart rate pattern. However, discrepancies in guidelines and obstetrician expertise present challenges in interpreting fetal heart rate, including failure to acknowledge findings or misinterpretation. Artificial intelligence has the potential to support obstetricians in diagnosing abnormal fetal heart rates.

Methods

Employ preprocessing techniques to mitigate the effects of missing signals and artifacts on the model, utilize data augmentation methods to address data imbalance. Introduce a multi-scale long short-term memory neural network trained with a variety of time-scale data for automatically classifying fetal heart rate. Carried out experimental on both single and multi-scale models.

Results

The results indicate that multi-scale LSTM models outperform regular LSTM models in various performance metrics. Specifically, in the single models tested, the model with a sampling rate of 10 exhibited the highest classification accuracy. The model achieves an accuracy of 85.73%, a specificity of 85.32%, and a precision of 85.53% on CTU-UHB dataset. Furthermore, the area under the receiver operating curve of 0.918 suggests that our model demonstrates a high level of credibility.

Discussion

Compared to previous research, our methodology exhibits superior performance across various evaluation metrics. By incorporating alternative sampling rates into the model, we observed improvements in all performance indicators, including ACC (85.73% vs. 83.28%), SP (85.32% vs. 82.47%), PR (85.53% vs. 82.84%), recall (86.13% vs. 84.09%), F1-score (85.79% vs. 83.42%), and AUC(0.9180 vs. 0.8667). The limitations of this research include the limited consideration of pregnant women's clinical characteristics and disregard the potential impact of varying gestational weeks.

The iPREFACE score is useful for predicting fetal acidemia: A retrospective cohort study of 113 patients who underwent emergency cesarean section for non-reassuring fetal status during labor

BACKGROUND

The iPREFACE score may aid in predicting fetal acidemia and neonatal asphyxia in emergency cesarean and vaginal deliveries, which may improve labor management precision in the future.

OBJECTIVE

This study aimed to assess the score use of the iPREFACE 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 delivery owing to a nonreassuring fetal status. The integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring-decision of emergency cesarean delivery score, calculated from a 30-minute cardiotocography waveform before the decision to perform emergency cesarean delivery, and the integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring-removal of cardiotocography transducer 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 integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring-removal of cardiotocography score.

RESULTS

The integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring-decision of emergency cesarean delivery and integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring-removal of cardiotocography transducer scores demonstrated the capability to predict an umbilical artery blood pH of <7.2. The integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring-decision of emergency cesarean delivery and -removal of cardiotocography transducer score, with cutoff values of 37 and 46 points, respectively, exhibited an area under the receiver operating characteristic curve of 0.82 and 0.87, respectively. The integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring-removal of cardiotocography transducer group with ≥46 points had higher incidence rates of an umbilical cord artery blood pH of <7.2, <7.1, and <7.0 and neonatal intensive care unit admissions for neonatal asphyxia.

CONCLUSION

The integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring, derived from cardiotocography during an emergency cesarean delivery, may enable clinicians to predict fetal acidemia in cases of nonreassuring fetal status. Improved prediction of fetal acidemia and facilitation of timely intervention hold promise for enhancing the outcomes of mothers and newborns during childbirth. Prospective studies are warranted to establish precise cutoff values and to validate the clinical application of these scores.

Contribution of proactive management of healthcare risks to the reduction of adverse events in a maternity hospital

BACKGROUND

The risks of the childbirth assistance process are still very high, both for mothers and babies. According to the WHO, birth-related asphyxia accounts for 23% of all 3.3 million annual neonatal deaths and an even larger number of survivors with disabilities. On the other hand, maternal mortality is still a global challenge, affecting 17 mothers per 100 000 births in the USA. This is associated with the use of outdated technologies and a lack of well-defined processes in monitoring labour and early recognition of maternal clinical deterioration.

METHOD

This study used Lean methodology to map the care flow for pregnant women in a Brazilian maternity hospital (Hospital Israelita Albert Einstein) in order to identify the risks within this process and a set of actions to minimise them. The work team consisted of 29 individuals, including local medical and nursing leaders, as well as healthcare professionals. The What-if tool was used to categorise the levels of risks, and the proportion of severe and catastrophic adverse events was evaluated before and after the implementation of changes.

RESULTS

After the implementation of the actions, 100% of the extreme risks (28 risks) and 8% of the high risks (4 risks) were eliminated. This led to a reduction in the interval between severe/catastrophic events from 126 to 284 days, even with an increase in the average monthly number of visits from 367 to 449. Consequently, the weighted value of events decreased from 7.91 to 3.29 per 1000 patients treated, resulting in an annual cost savings of R$693 646.80 (US$139 000.00).

DISCUSSION

The construction of a process based on Lean methodology was essential for mapping the involved risks and implementing a set of actions to minimise them. The participation of the healthcare team and leadership seemed to be important in choosing the measures to be adopted and their applicability. The results found can be attributed to both the established changes and the safety culture brought about by this constructive process.

A lightweight fetal distress-assisted diagnosis model based on a cross-channel interactive attention mechanism

Fetal distress is a symptom of fetal intrauterine hypoxia, which is seriously harmful to both the fetus and the pregnant woman. The current primary clinical tool for the assessment of fetal distress is Cardiotocography (CTG). Due to subjective variability, physicians often interpret CTG results inconsistently, hence the need to develop an auxiliary diagnostic system for fetal distress. Although the deep learning-based fetal distress-assisted diagnosis model has a high classification accuracy, the model not only has a large number of parameters but also requires a large number of computational resources, which is difficult to deploy to practical end-use scenarios. Therefore, this paper proposes a lightweight fetal distress-assisted diagnosis network, LW-FHRNet, based on a cross-channel interactive attention mechanism. The wavelet packet decomposition technique is used to convert the one-dimensional fetal heart rate (FHR) signal into a two-dimensional wavelet packet coefficient matrix map as the network input layer to fully obtain the feature information of the FHR signal. With ShuffleNet-v2 as the core, a local cross-channel interactive attention mechanism is introduced to enhance the model's ability to extract features and achieve effective fusion of multichannel features without dimensionality reduction. In this paper, the publicly available database CTU-UHB is used for the network performance evaluation. LW-FHRNet achieves 95.24% accuracy, which meets or exceeds the classification results of deep learning-based models. Additionally, the number of model parameters is reduced many times compared with the deep learning model, and the size of the model parameters is only 0.33 M. The results show that the lightweight model proposed in this paper can effectively aid in fetal distress diagnosis.