Effects of low maternal heart rate on fetal growth and birthweight

Abstract

Objective To evaluate the association between birthweight and maternal heart rate (MHR) or heart rate variability (HRV) under resting conditions at 20–24 gestational weeks and 34 weeks or later (34+ weeks). Methods Data were retrospectively reviewed from the Safe Passage Study, a prospective longitudinal cohort study of alcohol use in pregnancy and birth outcomes in Cape Town, South Africa, between August 2007 and January 2015. Using custom‐designed software, MHR and indicators of HRV were obtained from the recorded maternal electrocardiograms and compared with birthweight and z‐scores of birthweight adjusted for gestation and gender. Results Data from 5655 women were included. MHR increased from 84.6 bpm at 20–24 weeks to 88.3 bpm at 34+ weeks. Increasing MHR from 70–80 to 80–90 and 90–100 bpm at 20–24 weeks was associated with increasing birthweight from 2940 to 2998 and 3032 g, respectively (P<0.05). MHR and HRV contributed 29% to the variability associated with birthweight, whereas maternal body mass index at recruitment contributed 44%. Similar associations were observed for MHR at 34+ weeks. Conclusion The observed association of low maternal heart rate with birthweight might help to identify pregnancies at risk of poor fetal growth.

Maternal Low Volume Circulation Relates to Normotensive and Preeclamptic Fetal Growth Restriction

“…This observation has also been reported by others ( 29 ) and linked to a condition of low CO that is already present before conception ( 17 ). Low CO in FGR pregnancies mainly results from low SV ( 30 , 31 ) and to a lesser degree from low HR ( 32 , 33 ). Throughout an uncomplicated pregnancy, the fraction of CO deviated to the uterus doubles from 6 to 12% ( 34 ) and is achieved by an increase of (distal) internal iliac artery impedance in concert with a reduction of uterine artery impedance ( 35 ).…”

Mitigating Health-Related Uncertainties During Pregnancy: The Role of Smart Health Monitoring Technologies

“…By tracking weight gain, health care providers can assess the health of both the pregnant person and fetus, as inadequate weight gain may be harmful to the fetus, while rapid or excessive weight gain may be a sign of health concerns [ 19 , 20 ], such as gestational diabetes [ 21 , 22 ] or pre-eclampsia [ 23 ]. A pregnant person’s heart rate and blood pressure can also provide valuable information, with a low maternal heart rate potentially negatively affecting fetal growth and birth weight of the fetus [ 24 , 25 ], while high blood pressure or an abnormal heart rate is associated with increased risks of gestational diabetes [ 26 ] and maybe a sign of future cardiovascular disease [ 27 , 28 ]. Monitoring these vital signs is crucial for safeguarding the health of both the pregnant person and fetus, enabling timely interventions if necessary, and ultimately reducing health-related uncertainties during pregnancy.…”

A deep learning framework for noninvasive fetal ECG signal extraction

“…More specifically, the remarkable benefits of employing intelligent machines in the healthcare sector have attracted the attention of healthcare providers and decision-makers to invest in such applications ( Signorini et al, 2020 ; Liu et al, 2020 ). This resulted in numerous studies that focused on providing techniques to better understand the current health status of patients ( Odendaal et al, 2019 ; Diab et al, 2021 ). Such studies that focus on providing techniques for monitoring the health status of the heart include, but are not limited to, support vector machines to aid the auscultation procedure using computed tomography scan images () machine learning model based on activity tracker data to classify patient health status ( Meng et al, 2020 ), a bidirectional long short-term memory (bi-LSTM) regression network for noninvasive heart rate (HR) estimation from ballistocardiogram signals ( Jiao et al, 2021 ), a binary classification model for assessing the neonatal heart and lung sound quality for the heart, and breathing rate estimation for telehealth applications ( Grooby et al, 2021 ).…”