Wearable technology for health monitoring during pregnancy: an observational cross-sectional survey study.

PURPOSE: To evaluate how women of child-bearing age perceive the use of remote fetal ECG monitoring technologies. Telemedicine has advanced to the forefront of healthcare delivery, including maternal-fetal medicine. Smart wearable electrocardiogram (ECG) devices can enable pregnant women to monitor their health and that of their fetuses. Such technology would be a logical extension of the telemedicine ecosystem. METHODS: We conducted an observational cross-sectional study via online surveying in the United States. Study participants were recruited using the SurveyMonkey Audience Polling system and responded virtually. In all, the sample consisted of 507 women, aged 18-45 from 45 states, who are expecting to become pregnant in the next five years. Women were asked to identify their willingness to use a wearable ECG device the size of a patch-sized large band-aid on their abdomen. Ten binary or multiple-choice questions were used to gauge population interest and related demographics toward the usage of a wearable ECG device. RESULTS: Of the 507 participants, 461 (91%) women expressed an acceptance of wearable ECG technology throughout the pregnancy as a mechanism for increased frequency of monitoring of maternal and fetal health outside the hospital. 395 (78%) women demonstrated a willingness to wear devices day and night or at least during sleep and 213 (42%) of the women would spend up to $200 on such a device. CONCLUSION: Even though conducted prior to the COVID-19 pandemic, this study clearly indicates a high degree of readiness of prospective pregnant women for telemedicine with continuous health monitoring of the mother-fetus dyad.

Predicting Patients Requiring Treatment for Depression in the Postpartum Period Using Common Electronic Medical Record Data Available Antepartum.

Introduction: Depression requiring treatment in the postpartum period significantly impacts maternal and neonatal health. Although preventive management of depression in pregnancy has been shown to decrease the negative impacts, current methods for identifying at-risk patients are insufficient. Given the complexity of the diagnosis and interplay of clinical/demographic factors, we tested whether machine learning techniques can accurately identify at-risk patients in the postpartum period. Methods: This is a retrospective cohort study of the NIH Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be, which enrolled 10,038 nulliparous people. The primary outcome was depression in the postpartum period. We constructed and optimized 4 machine learning models using distributed random forest modeling and 1 logistic regression model on the basis of the NIH Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be dataset. Model 1 utilized only readily obtainable sociodemographic data. Model 2 added maternal prepregnancy mental health data. Model 3 utilized recursive feature elimination to construct a parsimonious model. Model 4 further titrated the input data to simplify prepregnancy mental health variables. The logistic regression model used the same input data as Model 3 as a proof of concept. Results: Of 8,454 births, 338 (4%) were complicated by depression in the postpartum period. Model 3 was the highest performing, showing the area under the receiver operating characteristics curve of 0.91 (±0.02). Models 1-3 identified the 9 variables most predictive of depression hierarchically, ranging from depression history (highest), history of mental health condition, recent psychiatric medication use, BMI, income, age, anxiety history, education, and preparedness for pregnancy (lowest). In Model 4, the area under the receiver operating characteristics curve remained at 0.79 (±0.05). Conclusions: Postpartum depression can be predicted with high accuracy for individual patients using antepartum information commonly found in electronic medical records. In addition, baseline mental health status and sociodemographic factors have a larger role in the postpartum period than previously understood.

Intrathecal Nicardipine for Severe Intractable Reversible Cerebral Vasoconstriction Syndrome: A Novel Case Report.

Reversible cerebral vasoconstriction syndrome (RCVS) is a poorly understood but increasingly recognized entity, likely multifactorial in nature and characterized by diffuse cerebral vasospasm that presents as sudden, intense, and fluctuating headaches. Due to insufficient evidence, there is currently no consensus on RCVS treatment guidelines. However, nicardipine, an L-type calcium channel blocker, may prove effective in RCVS treatment because of its ability to penetrate the blood-brain barrier. We report the concomitant use of intrathecal (IT) nicardipine and continuous intraarterial (IA) nicardipine infusion via microcatheter placed in the intracranial circulation for the treatment of a 58-year-old female with severe refractory RCVS. On presentation, this patient was noted to have a non-traumatic non-aneurysmal subarachnoid hemorrhage secondary to RCVS. Initially managed with oral verapamil, she later developed refractory symptomatic vasoconstriction requiring multiple angiograms for spasmolysis via balloon angioplasty and IA nicardipine. Due to the refractory nature of her spasm despite the IA therapy, we decided to attempt intrathecal nicardipine, starting at 4 mg q12 h via an external ventricular drain. This dose was escalated to 4 mg q6 h. The patient stabilized for 24 h but again decompensated, requiring continuous IA spasmolysis via a microcatheter placed in the left middle cerebral artery and left for continuous IA nicardipine infusion (5 mg/h). The patient showed slow incremental improvement clinically and a decrease in vasospasms on imaging, ultimately suffering minimal stroke burden. This patient's hospital course demonstrates that nicardipine, administered intrathecally or intraarterially, could be beneficial in select patients with refractory RCVS as a means of minimizing repeat angiography/angioplasty. Further studies are needed to better define a treatment paradigm for these patients.

Factors Determining the Success of the Chronically Instrumented Non-anesthetized Fetal Sheep Model of Human Development: A Retrospective Cohort Study.

Background The chronically instrumented non-anesthetized fetal sheep (CINAFS) model has been a mainstay of human fetal development research for the past 60 years. As a large "two for one" animal model, involving the instrumentation of the ewe and her fetus, the model poses challenges to implement de novo and maintain overtime at the highest standards of operating procedures to ensure ongoing performance. A common yet conventionally underreported issue researchers face is a high rate of animal loss. Here, we investigate what determines the success of the CINAFS model of human development. Methods We conducted a retrospective cohort analysis consisting of 82 experiments spanning the course of six years. Our team identified 10 variables that we anticipated were likely to influence the experimental outcome, such as the time of year, animal size, and surgical complexity. To evaluate the role of each variable in contributing to the success of the model, a binary logit regression analysis with a Fisher scoring optimization was fit to the data (SAS, V9 engine, release 3.8, SAS Institute, Cary, NC, USA). A higher predictive probability indicates a larger impact by the given variable on the outcome of the experiment. A Wald chi-squared analysis was run on the data to control for confounders and determine significance. Results The single variable identified in this study as determining the success of experiment outcomes using the CINAFS model is the experience level of the team. Conclusion The CINAFS model offers enormous potential to further our understanding of human fetal development and create interventional technologies related to fetal health. However, to improve experimental outcomes using the CINAFS model, stronger communication and training are needed. We discuss the implications of our findings for the successful implementation of this challenging yet scientifically advantageous animal model of human physiology.