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

Ito, Ayumu; Hayata, Eijiro; Nagasaki, Sumito; Kotaki, Hikari; Shimabukuro, Makiko; Sakuma, J.; Takano, Mayumi; Oji, Ayako; Maemura, Toshimitsu; Nakata, Masahiko

doi:10.1038/s41598-022-17364-z

Cited by 5 publications

(7 citation statements)

References 29 publications

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“…Data with a pH value greater than 7.05 are considered normal, and data with a pH value less than or equal to 7.05 are considered abnormal. Based on this discriminant, 44 abnormal samples and 508 normal samples are obtained ( Ito et al, 2022 ). predicted fetal acidemia by calculating iPREFACE (10), iPREFACE (30) and iPREFACE (60) at 10, 30, and 60 min before delivery.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Resultsmentioning

confidence: 99%

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

et al. 2023

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“…[63] Most studies developing machine learning models analysing the FHR have focussed on the intrapartum period. [37,64] Some studies attempted to develop predictive models using fetal heart rate patterns identified by human visual inspection of the FHR trace, limiting the reproducibility of these models due to the inherent problems with human evaluation. [46] Other studies have either utilised significantly smaller datasets, did not use established clinical outcomes or were developed using a restricted subset of pregnancies.…”

Section: Discussionmentioning

confidence: 99%

Identifying high-risk pre-term pregnancies using the fetal heart rate and machine learning

Davis Jones,

Cooke,

Vatish

2024

Preprint

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Introduction: Fetal heart rate (FHR) monitoring is one of the commonest and most affordable tests performed during pregnancy worldwide. It is critical for evaluating the health status of the baby, providing real-time insights into the physiology of the fetus. While the relationship between patterns in these signals and adverse pregnancy outcomes is well-established, human identification of these complex patterns remains sub-optimal, with experts often failing to recognise babies at high-risk of outcomes such as asphyxia, growth restriction and stillbirth. These outcomes are especially relevant in low- and middle-income countries where an estimated 98% of perinatal deaths occur. Pre-term birth complications are also the leading cause of death in children <5 years of age, 75% of which can be prevented. While advances have been made in developing low-cost digital solutions for antenatal fetal monitoring, there is still substantial progress to be made in developing tools for the identification of high-risk, adverse outcome pre-term pregnancies using these FHR systems. In this study, we have developed the first machine learning algorithm for the identification of high-risk preterm pregnancies with associated adverse outcomes using fetal heart rate monitoring. Methods: We sourced antepartum fetal heart rate traces from high-risk, pre-term pregnancies that were assigned at least one of ten adverse conditions. These were matched with normal pregnancies delivered at term. Using an automated, clinically-validated algorithm, seven distinct fetal heart rate patterns were extracted from each trace, subsequently filtered for outliers and normalized. The data were split into 80% for model development and 20% for validation. Six machine learning algorithms were trained using k-fold cross-validation to identify each trace as either normal or high-risk preterm. The best-performing algorithm was further evaluated using the validation dataset based on metrics including the AUC, sensitivity, and specificity at three distinct classification thresholds. Additional assessments included decision curve analysis and gestational age-specific and outcome-specific performance evaluations. Results: We analysed antepartum fetal heart rate recordings from 4,867 high-risk, pre-term pregnancies with adverse outcomes and 4,014 normal pregnancies. Feature extraction and preprocessing revealed significant differences between the groups (p<0.001). The random forest classifier was the most effective model, achieving an AUC of 0.88 (95% CI 0.87-0.88). When evaluating specific adverse outcomes, the median AUC was 0.85 (IQR 0.81-0.89) and the model consistently exceeded an AUC of 0.80 across all gestational ages. The model's robustness was confirmed on the validation dataset with an AUC of 0.88 (95% CI 0.86-0.90) and a Brier score of 0.14. Decision curve analysis showed the model surpassed both the treat-none and treat-all strategies over most probability thresholds (0.11-1.0). Performance metrics when using the Youden index were as follows: sensitivity 76.2% (95% CI 72.6-80.5%), specificity 87.5% (95% CI 83.3-91.0), F1 score 81.7 (95% CI 79.6-83.9), and Cohen's kappa 62.8 (95% CI 59.6-66.4), indicating high discriminative ability between pregnancy outcomes. Conclusions: Our study successfully demonstrated machine learning algorithms are capable of identifying high-risk preterm pregnancies with associated adverse outcomes through fetal heart rate monitoring. These findings demonstrate the potential of machine learning in enhancing the accuracy and effectiveness of antenatal fetal monitoring, particularly for high-risk cases where timely intervention is crucial. This algorithm could substantially improve pregnancy outcome prediction and consequently, maternal and neonatal care, especially in low- to middle-income countries where the burden of adverse outcomes is high.

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“…The creation of these two different protocols established guidelines for standardising care practices, such as performing cardiotocography every 3 hours, a maximum resolution time of 20 min for category 3 cardiotocography cases and 60 min for category 2, with continuous monitoring under these conditions. Particularly in category 3 cases, the aim of the approach was to provide an additional 10 min of safety to the proposed 30 min for preintervention examination analysis 30. On detecting changes in fetal vitality, a childbirth code is activated, which summons, through mobile extensions, the on-call obstetrician, neonatologist and anaesthesiologist to the caesarean section room.…”

Section: Methodsmentioning

confidence: 99%

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

Negrini,

Appel,

Beck

et al. 2024

BMJ Open Qual

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BackgroundThe 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.MethodThis 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.ResultsAfter 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).DiscussionThe 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.

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

Cited by 5 publications

References 29 publications

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

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

Identifying high-risk pre-term pregnancies using the fetal heart rate and machine learning

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

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