Self-monitoring of blood pressure in pregnancy: A mixed methods evaluation of a national roll-out in the context of a pandemic.

OBJECTIVE: To evaluate how English maternity units implemented self-monitoring of blood pressure (SMBP) in pregnancy in response to the COVID-19 pandemic. DESIGN: Mixed methods including surveys, anonymised patient data and in-depth interviews with women. SETTING: Maternity units across England. PARTICIPANTS: 45 maternity units completed a survey about the implementation of SMBP (supported by the provision of guidance and blood pressure monitors) during the pandemic, 166 women completed a survey about their experiences of SMBP, and 23 women took part in in-depth interviews. Clinical data from 627 women undertaking SMBP were available from 13 maternity units. RESULTS: SMBP was predominantly used to provide additional BP monitoring for hypertensive or high-risk pregnant women. Overall maternity units and women were positive about its use in terms of reducing the need for additional face-to-face contacts and giving women more control and insight into their own BP. However, there were challenges in setting up SMBP services rapidly and embedding them within existing care pathways, particularly around interpreting readings and managing the provision of monitors. CONCLUSIONS: A considerable proportion of maternity units in England commenced a SMBP service for hypertensive or high-risk women from March 2020. There is a need for further research into appropriate care pathways, including guidance around white coat or masked hypertension and the use of SMBP postnatally.

Vaccinations in pregnancy.

Vaccinations are a cost-effective means of preventing disease. They may be recommended primarily for maternal benefit or for prevention of intrauterine fetal or early neonatal infection. Data from the International Network of Obstetric Survey Systems relating to the COVID-19 pandemic showed that for all countries studied (the UK, the Netherlands, Norway, Denmark, Finland and Italy), at least 80% of pregnant women admitted to critical care were unvaccinated. In the UK this figure was 98%. The MBRRACE-UK 2014 report, covering 2009-2012 during the H1N1 epidemic, demonstrated that one in eleven maternal mortalities were directly from influenza virus: more than half could have been prevented by the flu vaccine in pregnancy. Research is ongoing to develop additional vaccines for infections that cause detrimental effects to pregnant women and their infants. Theoretical concerns regarding adverse effects to the fetus and lack of efficacy have, in general, not been confirmed by clinical evidence. Nevertheless, live attenuated vaccines remain contraindicated due to risk of fetal infection. As with any clinical decision, advice on antenatal vaccination should be based on the balance of risks and benefits to mother and fetus. This article aims to guide such decisions by discussing the issues surrounding commonly used vaccines and presenting current UK guidelines.

Postpartum blood pressure self-management following hypertensive pregnancy: protocol of the Physician Optimised Post-partum Hypertension Treatment (POP-HT) trial.

INTRODUCTION: New-onset hypertension affects approximately 10% of pregnancies and is associated with a significant increase in risk of cardiovascular disease in later life, with blood pressure measured 6 weeks postpartum predictive of blood pressure 5-10 years later. A pilot trial has demonstrated that improved blood pressure control, achevied via self-management during the puerperium, was associated with lower blood pressure 3-4 years postpartum. Physician Optimised Post-partum Hypertension Treatment (POP-HT) will formally evaluate whether improved blood pressure control in the puerperium results in lower blood pressure at 6 months post partum, and improvements in cardiovascular and cerebrovascular phenotypes. METHODS AND ANALYSIS: POP-HT is an open-label, parallel arm, randomised controlled trial involving 200 women aged 18 years or over, with a diagnosis of pre-eclampsia or gestational hypertension, and requiring antihypertensive medication at discharge. Women are recruited by open recruitment and direct invitation around time of delivery and randomised 1:1 to, either an intervention comprising physician-optimised self-management of postpartum blood pressure or, usual care. Women in the intervention group upload blood pressure readings to a 'smartphone' app that provides algorithm-driven individualised medication-titration. Medication changes are approved by physicians, who review blood pressure readings remotely. Women in the control arm follow assessment and medication adjustment by their usual healthcare team. The primary outcome is 24-hour average ambulatory diastolic blood pressure at 6-9 months post partum. Secondary outcomes include: additional blood pressure parameters at baseline, week 1 and week 6; multimodal cardiovascular assessments (CMR and echocardiography); parameters derived from multiorgan MRI including brain and kidneys; peripheral macrovascular and microvascular measures; angiogenic profile measures taken from blood samples and levels of endothelial circulating and cellular biomarkers; and objective physical activity monitoring and exercise assessment. An additional 20 women will be recruited after a normotensive pregnancy as a comparator group for endothelial cellular biomarkers. ETHICS AND DISSEMINATION: IRAS PROJECT ID 273353. This trial has received a favourable opinion from the London-Surrey Research Ethics Committee and HRA (REC Reference 19/LO/1901). The investigator will ensure that this trial is conducted in accordance with the principles of the Declaration of Helsinki and follow good clinical practice guidelines. The investigators will be involved in reviewing drafts of the manuscripts, abstracts, press releases and any other publications arising from the study. Authors will acknowledge that the study was funded by the British Heart Foundation Clinical Research Training Fellowship (BHF Grant number FS/19/7/34148). Authorship will be determined in accordance with the ICMJE guidelines and other contributors will be acknowledged. TRIAL REGISTRATION NUMBER: NCT04273854.

Early risk assessment for COVID-19 patients from emergency department data using machine learning.

Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic with more than 55 million reported cases and 1.3 million estimated deaths worldwide. While epidemiological and clinical characteristics of COVID-19 have been reported, risk factors underlying the transition from mild to severe disease among patients remain poorly understood. In this retrospective study, we analysed data of 879 confirmed SARS-CoV-2 positive patients admitted to a two-site NHS Trust hospital in London, England, between January 1st and May 26th, 2020, with a majority of cases occurring in March and April. We extracted anonymised demographic data, physiological clinical variables and laboratory results from electronic healthcare records (EHR) and applied multivariate logistic regression, random forest and extreme gradient boosted trees. To evaluate the potential for early risk assessment, we used data available during patients' initial presentation at the emergency department (ED) to predict deterioration to one of three clinical endpoints in the remainder of the hospital stay: admission to intensive care, need for invasive mechanical ventilation and in-hospital mortality. Based on the trained models, we extracted the most informative clinical features in determining these patient trajectories. Considering our inclusion criteria, we have identified 129 of 879 (15%) patients that required intensive care, 62 of 878 (7%) patients needing mechanical ventilation, and 193 of 619 (31%) cases of in-hospital mortality. Our models learned successfully from early clinical data and predicted clinical endpoints with high accuracy, the best model achieving area under the receiver operating characteristic (AUC-ROC) scores of 0.76 to 0.87 (F1 scores of 0.42-0.60). Younger patient age was associated with an increased risk of receiving intensive care and ventilation, but lower risk of mortality. Clinical indicators of a patient's oxygen supply and selected laboratory results, such as blood lactate and creatinine levels, were most predictive of COVID-19 patient trajectories. Among COVID-19 patients machine learning can aid in the early identification of those with a poor prognosis, using EHR data collected during a patient's first presentation at ED. Patient age and measures of oxygenation status during ED stay are primary indicators of poor patient outcomes.