Umbilical Cord Thrombosis: A Rare but Life-threatening Occurrence

Background: Thrombosis of umbilical vessels is a rare but life-threatening pregnancy complication. The correct diagnosis and clinical management of umbilical cord thrombosis remain a challenge. This study aimed to evaluate the meaningful clinical manifestations and features of umbilical cord thrombosis and its optimal management options.Methods: This retrospective study analyzed umbilical cord thrombosis cases enrolled from January 1, 2011, to May 31, 2021. Data were collected from medical archives where the diagnoses of all patients were established by histopathology.Results: A total of 66 patients with umbilical cord thrombosis were enrolled, including 20 patients with intrauterine fetal death and 6 with fatal labor induction in the second trimester. The overall incidence of umbilical cord thrombosis was 0.05% (66/123,746), and the incidence increased significantly in the last 2 years, reaching 0.19% in 2021. The chief complaint was decreased fetal movement (25/60) or nonreactive nonstress test (NST) (19/40). In the 20 patients with intrauterine stillbirth, 8 patients had ignored fetal movement and were referred to the hospital because of ultrasound findings of intrauterine stillbirth. Five patients were misdiagnosed with a single umbilical artery on prenatal ultrasound. Twenty patients underwent emergency cesarean section due to decreased fetal movement and unresponsive fetal monitoring; all neonates were alive. The gross examination of the placenta and cord revealed umbilical cord abnormalities in 26 patients (39.4%, 26/66). The pathological examination revealed venous, venous and arterial, and arterial thrombosis in 74.2%, 12.1%, and 13.6% of patients, respectively.Conclusions: The main manifestation of umbilical cord thrombosis was decreased or disappeared fetal movement. The importance of self-counting fetal movement should be emphasized to patients. The abnormalities in the umbilical cord are the main cause of this phenomenon. Focus on counting fetal movements, electronic fetal monitoring, and specific signs during a prenatal ultrasound can help early identification of umbilical cord thrombi. Emergency cesarean section is recommended to reduce the risk of interrupting the umbilical cord blood flow.

Passive Fetal Movement Recognition Approaches Using Hyperparameter Tuned LightGBM Model and Bayesian Optimization

Fetal movement is an important clinical indicator to assess fetus growth and development status in the uterus. In recent years, a noninvasive intelligent sensing fetal movement detection system that can monitor high-risk pregnancies at home has received a lot of attention in the field of wearable health monitoring. However, recovering fetal movement signals from a continuous low-amplitude background that is heavily contaminated with noise and recognizing real fetal movements is a challenging task. In this paper, fetal movement can be efficiently recognized by combining the strength of Kalman filtering, time and frequency domain and wavelet domain feature extraction, and hyperparameter tuned Light Gradient Boosting Machine (LightGBM) model. Firstly, the Kalman filtering (KF) algorithm is used to recover the fetal movement signal in a continuous low-amplitude background contaminated by noise. Secondly, the time domain, frequency domain, and wavelet domain (TFWD) features of the preprocessed fetal movement signal are extracted. Finally, the Bayesian Optimization algorithm (BOA) is used to optimize the LightGBM model to obtain the optimal hyperparameters. Through this, the accurate prediction and recognition of fetal movement are successfully achieved. In the performance analysis of the Zenodo fetal movement dataset, the proposed KF + TFWD + BOA-LGBM approach’s recognition accuracy and F1-Score reached 94.06% and 96.85%, respectively. Compared with 8 existing advanced methods for fetal movement signal recognition, the proposed method has better accuracy and robustness, indicating its potential medical application in wearable smart sensing systems for fetal prenatal health monitoring.

Development of a core outcome set (COS) for studies relating to awareness and clinical management of reduced fetal movement: study protocol

Background Concerns regarding reduced fetal movements (RFM) are reported in 5–15% of pregnancies, and RFM are associated with adverse pregnancy outcomes including fetal growth restriction and stillbirth. Studies have aimed to improve pregnancy outcomes by evaluating interventions to raise awareness of RFM in pregnancy, such as kick counting, evaluating interventions for the clinical management of RFM, or both. However, there is not currently a core outcome set (COS) for studies of RFM. This study aims to create a COS for use in research studies that aim to raise awareness of RFM and/or evaluate interventions for the clinical management of RFM. Methods A systematic review will be conducted, to identify outcomes used in randomised and non-randomised studies with control groups that aimed to raise awareness of RFM (for example by using mindfulness techniques, fetal movement counting, or other tools such as leaflets or mobile phone applications) and/or that evaluated the clinical management of RFM. An international Delphi consensus will then be used whereby stakeholders will rate the importance of the outcomes identified in the systematic review in (i) awareness and (ii) clinical management studies. The preliminary lists of outcomes will be discussed at a consensus meeting where one final COS for awareness and management, or two discrete COS (one for awareness and one for management), will be agreed upon. Discussion A well-developed COS will provide researchers with the minimum set of outcomes that should be measured and reported in studies that aim to quantify the effects of interventions.

Pregnant Black Women and Emergency Department Utilization: Assessing Self-Reported Receipt of Prenatal Counseling

There is limited literature on emergency department (ED) use among pregnant women. In this article, we examined the associations between prenatal counseling with the use of the ED during pregnancy. In our cohort of Black women in the Metro Detroit area, we found that approximately 70.5% of the women had an ED visit at some point during pregnancy. In unadjusted models of prevalence ratios, we found women reporting receipt of prenatal counseling regarding fetal movement, what to do about baby’s movement slowing down, and smoking (but not what to do about smoking) were at statistically significantly greater risk of ED utilization during pregnancy. Adjustment for confounders slightly weakened the associations for counseling about baby’s movement or smoking, so that the associations were no longer statistically significant. These findings call for further research on ED utilization among this population, especially differentiating urgent versus non-urgent use of the ED during pregnancy.

Passive Fetal Movement Signal Detection System Based on Intelligent Sensing Technology

Fetal movement (FM) is an essential physiological parameter to determine the health status of the fetus. To address the problems of harrowing FM signal extraction and the low recognition rate of traditional machine learning classifiers in FM signal detection, this paper develops a passive FM signal detection system based on intelligent sensing technology. FM signals are obtained from the abdomen of the pregnant woman by using accelerometers. The FM signals are extracted and identified according to the clinical nature of the features hidden in the amplitude and waveform of the FM signals that fluctuate in duration. The system consists of four main stages: (i) FM signal preprocessing, (ii) maternal artifact signal preidentification, (iii) FM signal identification, and (iv) FM classification. Firstly, Kalman filtering is used to reconstruct the FM signal in a continuous low-amplitude noise background. Secondly, the maternal artifact signal is identified using an amplitude threshold algorithm. Then, an innovative dictionary learning algorithm is used to construct a dictionary of FM features, and orthogonal matching pursuit and adaptive filtering algorithms are used to identify the FM signals, respectively. Finally, mask fusion classification is performed based on the multiaxis recognition results. Experiments are conducted to evaluate the performance of the proposed FM detection system using publicly available and self-built accelerated FM datasets. The classification results showed that the orthogonal matching pursuit algorithm was more effective than the adaptive filtering algorithm in identifying FM signals, with a positive prediction value of 89.74%. The proposed FM detection system has great potential and promise for wearable FM health monitoring.