A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population

Sample entropy correlates with intraventricular hemorrhage and mortality in premature infants early in life

BACKGROUND

Mortality and intraventricular hemorrhage (IVH) are common adverse outcomes in preterm infants and are challenging to predict clinically. Sample entropy (SE), a measure of heart rate variability (HRV), has shown predictive power for sepsis and other morbidities in neonates. We evaluated associations between SE and mortality and IVH in the first week of life.

METHODS

Participants were 389 infants born before 32 weeks of gestation for whom bedside monitor data were available. A total of 29 infants had IVH grade 3 or 4 and 31 infants died within 2 weeks of life. SE was calculated with the PhysioNet open-source benchmark. Logistic regressions assessed associations between SE and IVH and/or mortality with and without common clinical covariates over various hour of life (HOL) censor points.

RESULTS

Lower SE was associated with mortality by 4 HOL, but higher SE was very strongly associated with IVH and mortality at 24-96 HOL. Bootstrap testing confirmed SE significantly improved prediction using clinical variables at 96 HOL.

CONCLUSION

SE is a significant predictor of IVH and mortality in premature infants. Given IVH typically occurs in the first 24-72 HOL, affected infants may initially have low SE followed by a sustained period of high SE.

IMPACT

SE correlates with IVH and mortality in preterm infants early in life. SE combined with clinical factors yielded ROC AUCs well above 0.8 and significantly outperformed the clinical model at 96 h of life. Previous studies had not shown predictive power over clinical models. First study using the PhysioNet Cardiovascular Toolbox benchmark in young infants. Relative to the generally accepted timing of IVH in premature infants, we saw lower SE before or around the time of hemorrhage and a sustained period of higher SE after. Higher SE after acute events has not been reported previously.

Hierarchical Attentive Network for Gestational Age Estimation in Low-Resource Settings

Assessing fetal development is essential to the provision of healthcare for both mothers and fetuses. In low- and middle-income countries, conditions that increase the risk of fetal growth restriction (FGR) are often more prevalent. In these regions, barriers to accessing healthcare and social services exacerbate fetal maternal health problems. One of these barriers is the lack of affordable diagnostic technologies. To address this issue, this work introduces an end-to-end algorithm applied to a low-cost, hand-held Doppler ultrasound device for estimating gestational age (GA), and by inference, FGR. The Doppler ultrasound signals used in this study were collected from 226 pregnancies (45 low birth weight at delivery) between 5 and 9 months GA by lay midwives in highland Guatemala. We designed a hierarchical deep sequence learning model with an attention mechanism to learn the normative dynamics of fetal cardiac activity in different stages of development. This resulted in a state-of-the-art GA estimation performance, with an average error of 0.79 months. This is close to the theoretical minimum for the given quantization level of one month. The model was then tested on Doppler recordings of the fetuses with low birth weight and the estimated GA was shown to be lower than the GA calculated from last menstruation. Thus, this could be interpreted as a potential sign of developmental retardation (or FGR) associated with low birth weight, and referral and intervention may be necessary.

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

There is limited evidence regarding the utility of fetal monitoring during pregnancy, particularly during labor and delivery. Developed countries rely on consensus ‘best practices’ of obstetrics and gynecology professional societies to guide their protocols and policies. Protocols are often driven by the desire to be as safe as possible and avoid litigation, regardless of the cost of downstream treatment. In high-resource settings, there may be a justification for this approach. In low-resource settings, in particular, interventions can be costly and lead to adverse outcomes in subsequent pregnancies. Therefore, it is essential to consider the evidence and cost of different fetal monitoring approaches, particularly in the context of treatment and care in low-to-middle income countries.

This article reviews the standard methods used for fetal monitoring, with particular emphasis on fetal cardiac assessment, which is a reliable indicator of fetal well-being. An overview of fetal monitoring practices in low-to-middle income counties, including perinatal care access challenges, is also presented. Finally, an overview of how mobile technology may help reduce barriers to perinatal care access in low-resource settings is provided.