A review of fetal cardiac monitoring, with a focus on low- and middle-income countries

Valderrama, Camilo E.; Ketabi, Nasim; Marzbanrad, Faezeh; Rohloff, Peter; Clifford, Gari D.

doi:10.1088/1361-6579/abc4c7

Cited by 16 publications

(5 citation statements)

References 181 publications

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“…Given the higher perinatal mortality rate, the need for remote fetal monitoring in developing countries may be more urgent. Furthermore, a recent review focused on LMICs concluded that mobile technology can overcome economic and geographic barriers by transmitting clinical information collected using low-cost devices, thereby increasing the perinatal care coverage of LMICs [5]. It can be argued that remote fetal monitoring supported by mobile technology appears to have greater potential in LMICs, where antenatal care services need to be improved.…”

Section: Comparison With Prior Studiesmentioning

confidence: 99%

“…Fetal monitoring is the primary means of monitoring to assess fetal safety and contributes to reducing the risk of stillbirth by detecting fetal hypoxia as early as possible [2,3]. Previous studies have repeatedly demonstrated the clinical value of fetal monitoring in reducing adverse perinatal outcomes (eg, neonatal cerebral palsy, hypoxic-ischemic encephalopathy, or stillbirth) [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Effectiveness of Remote Fetal Monitoring on Maternal-Fetal Outcomes: Systematic Review and Meta-Analysis

Li¹,

Niu²,

Liu³

2023

JMIR Mhealth Uhealth

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Background To solve the disadvantages of traditional fetal monitoring such as time-consuming, cumbersome steps and low coverage, it is paramount to develop remote fetal monitoring. Remote fetal monitoring expands time and space, which is expected to popularize fetal monitoring in remote areas with the low availability of health services. Pregnant women can transmit fetal monitoring data from remote monitoring terminals to the central monitoring station so that doctors can interpret it remotely and detect fetal hypoxia in time. Fetal monitoring involving remote technology has also been carried out, but with some conflicting results. Objective The review aimed to (1) examine the efficacy of remote fetal monitoring in improving maternal-fetal outcomes and (2) identify research gaps in the field to make recommendations for future research. Methods We did a systematic literature search with PubMed, Cochrane Library, Web of Science, Embase, MEDLINE, CINAHL, ProQuest Dissertations and Theses Global, ClinicalTrials.gov, and Open Grey in March 2022. Randomized controlled trials or quasi-experimental trials of remote fetal monitoring were identified. Two reviewers independently searched articles, extracted data, and assessed each study. Primary outcomes (maternal-fetal outcomes) and secondary outcomes (health care usage) were presented as relative risks or mean difference. The review was registered on PROSPERO as CRD42020165038. Results Of the 9337 retrieved literature, 9 studies were included in the systematic review and meta-analysis (n=1128). Compared with a control group, remote fetal monitoring reduced the risk of neonatal asphyxia (risk ratio 0.66, 95% CI 0.45-0.97; P=.04), with a low heterogeneity of 24%. Other maternal-fetal outcomes did not differ significantly between remote fetal monitoring and routine fetal monitoring, such as cesarean section (P=.21; I2=0%), induced labor (P=.50; I2=0%), instrumental vaginal birth (P=.45; I2=0%), spontaneous delivery (P=.85; I2=0%), gestational weeks at delivery (P=.35; I2=0%), premature delivery (P=.47; I2=0%), and low birth weight (P=.71; I2=0%). Only 2 studies performed a cost analysis, stating that remote fetal monitoring can contribute to reductions in health care costs when compared with conventional care. In addition, remote fetal monitoring might affect the number of visits and duration in the hospital, but it was not possible to draw definite conclusions about the effects due to the limited number of studies. Conclusions Remote fetal monitoring seems to reduce the incidence of neonatal asphyxia and health care costs compared with routine fetal monitoring. To strengthen the claims on the efficacy of remote fetal monitoring, further well-designed studies are necessary, especially in high-risk pregnant women, such as pregnant women with diabetes, pregnant women with hypertension, and so forth.

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Section: Comparison With Prior Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effectiveness of Remote Fetal Monitoring on Maternal-Fetal Outcomes: Systematic Review and Meta-Analysis

Li¹,

Niu²,

Liu³

2023

JMIR Mhealth Uhealth

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“…Limited access to complex medical devices for fetal cardiac monitoring in LMICs is well known. However, recent assessments of telemonitoring combined with portable ultrasound have been demonstrated to be more efficacious than the standard of care and yield cost-effective gestational outcomes [1,15].…”

Section: Portable Ultrasound and Hand-held Electrocardiogram Imagingmentioning

confidence: 99%

Emerging Opportunities for Sustainable Digital Health Enhanced Care Delivery Models for Improved Cardiovascular Surgery Outcomes

Koomson

Lang’at

2021

Sustainable Development Goals Series

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Rapid expansion of internet access and mobile technologies in low-and middle-income countries has accelerated interest in digital health strategies to improve cardiac surgery outcomes in resource-constrained areas. Several pilot studies of wearable and portable health monitoring devices, virtual reality, and telehealth platforms have shown great promise as governments strategically invest in information and communication technology expansion and costs continue to decline for internet-assisted devices. However, future research directions on digital health systems and interventions must focus on overcoming key challenges in achieving interoperability with existing health care infrastructures, ensuring coverage across diverse socio-economic regions, and user-centric design approaches that incorporate the local cultural context to increase adoption and usability.

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“…[21][22][23][24][25] Globally, It is estimated that 98% of perinatal deaths occur in low-and middleincome countries. [26] While technologies for the assessment of fetal wellbeing (e.g. fetal electrocardiography, ultrasound imaging and fetal magnetocardiography) are often prohibitively expensive, fetal heart rate monitoring is one of the most affordable solutions.…”

Section: Introductionmentioning

confidence: 99%

“…fetal electrocardiography, ultrasound imaging and fetal magnetocardiography) are often prohibitively expensive, fetal heart rate monitoring is one of the most affordable solutions. [26, 27] Recent efforts have also resulted in the development of significantly cheaper mobile-based technologies. [28] The technical training required for practitioners to use these devices is low.…”

Section: Introductionmentioning

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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A review of fetal cardiac monitoring, with a focus on low- and middle-income countries

Cited by 16 publications

References 181 publications

Effectiveness of Remote Fetal Monitoring on Maternal-Fetal Outcomes: Systematic Review and Meta-Analysis

Effectiveness of Remote Fetal Monitoring on Maternal-Fetal Outcomes: Systematic Review and Meta-Analysis

Emerging Opportunities for Sustainable Digital Health Enhanced Care Delivery Models for Improved Cardiovascular Surgery Outcomes

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

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