Pregnancy Outcomes in Women with Sickle Cell Disease in Ireland: A Retrospective Review

Background: Sickle cell disease (SCD) is one of the most common single gene disorders worldwide and is characterised by significant morbidity and early mortality.[1] Pregnancy in SCD is associated with an increased risk of maternal and foetal complications.[2,3] The 2011 RCOG and the 2021 BSH guidelines[5,6] on the management of pregnancy in SCD have provided the basis for best practice care in the UK over the past decade and is the guidance which we follow in Ireland. To date, there is no published data on outcomes for pregnant women with SCD in Ireland. The number of Irish patients with SCD has risen over the past 20 years. Without a national database, the exact prevalence is not known but currently there are at least 600 adults and children with SCD in Ireland, whose population is just over 5 million.[4] Aims: Our study assesses outcomes of pregnant patients with SCD from 2015 to 2022. Our aims were to: * Assess adherence to current guidelines * Assess pregnancy outcomes and maternal complications * Assess transfusion rates amongst our patient cohort. Method(s): This is a retrospective cohort study. We do not have a directly matched cohort, but have compared our findings to published data on Irish pregnancy outcomes from the Irish Maternity Indicator System National Report and have correlated our findings with studies of women with SCD who were managed in UK centres.[8,9,10] Results: We reviewed outcomes of 29 pregnancies in 19 women over a 7-year period. The median age was 29 (range 20-41) and the predominant maternal sickle genotype was HbSS (65.5%). Before conception, 55.2% of cases had pre-existing complications of SCD, including acute chest syndrome (ACS), pulmonary hypertension (PHTN) and prior stroke. In accordance with current guidelines, 100% of women (n=29) were prescribed folic acid, penicillin, and aspirin prophylaxis. 51.7% (n=15) of women had documented maternal complications during pregnancy, including ACS (34%), vaso-occlusive crisis (34%), gestational diabetes (10%), VTE (3%) and UTI (3%). Two women (7%) developed Covid-19 pneumonitis despite vaccination. There was one case of maternal bacteraemia (3%). 65.5% of cases (n=19) required blood transfusion during pregnancy. One woman was already on a blood transfusion programme for disease modification prior to pregnancy. In 6 cases (20.6%), a transfusion programme was commenced during pregnancy due to prior pregnancy complications or intrauterine growth restriction. During pregnancy, 27.6% (n=8) of women required emergency red cell exchange for ACS. Prior studies have suggested that between 30% and 70% of pregnant women with SCD require at least one blood transfusion during pregnancy.[8,9,10] By comparison, only 2.6% of the Irish general obstetric population required transfusion during pregnancy.[7] 20.6% (n=6) of births were preterm at <37 weeks' gestation. There was one live preterm birth (3%) at <34 weeks and one intrauterine death (3%) at 23 weeks' gestation. Similar to UK data[9], 31% of women required critical care stay (n=9) during pregnancy, in comparison with 1.44% nationwide in 2020.[7] Conclusion(s): It is well established that pregnancy in SCD is high risk, and despite adherence to current guidelines, we have shown very high rates of critical care admission, significant transfusion requirement and hospital admissions. Our findings are comparable to published UK outcomes and they further support the need for a comprehensive specialist care setting for this patient cohort.

Predictive Student Modelling in an Online Reading Platform

Use of technology-enhanced education and online learning systems has become more popular, especially since the onset of the COVID-19 pandemic. These systems capture a rich array of data as students interact with them. Predicting student performance is an essential part of technology-enhanced education systems to enable the generation of hints and provide recommendations to students. Typically, this is done through use of data on student interactions with questions without utilizing important data on the temporal ordering of students' other interaction behavior, (e.g., reading, video watching). In this paper, we hypothesize that to predict students' question performance, it is necessary to (i) consider other learning activities beyond question-answering and (ii) understand how these activities are related to question-solving behavior. We collected middle school physical science students' data within a K12 reading platform, Actively Learn. This platform provides reading-support to students and collects trace data on their use of the system. We propose a transformer-based model to predict students' question scores utilizing question interaction and reading-related behaviors. Our findings show that integrating question attempts and reading-related behaviors results in better predictive power compared to using only question attempt features. The interpretable visualization of transformer's attention can be helpful for teachers to make tailored interventions in students' learning.

It's Challenging but Doable: Lessons Learned from a Remote Collaborative Coding Camp for Elementary Students

The COVID-19 pandemic shifted many U.S. schools from in-person to remote instruction. While collaborative CS activities had become increasingly common in classrooms prior to the pandemic, the sudden shift to remote learning presented challenges for both teachers and students in implementing and supporting collaborative learning. Though some research on remote collaborative CS learning has been conducted with adult learners, less has been done with younger learners such as elementary school students. This experience report describes lessons learned from a remote after-school camp with 24 elementary school students who participated in a series of individual and paired learning activities over three weeks. We describe the design of the learning activities, participant recruitment, group formation, and data collection process. We also provide practical implications for implementation such as how to guide facilitators, pair students, and calibrate task difficulty to foster collaboration. This experience report contributes to the understanding of remote CS learning practices, particularly for elementary school students, and we hope it will provoke methodological advancement in this important area. © 2022 ACM.

Neurological biomarkers in COVID-19: Comparing patient biomarkers across hospital discharge status

Objective: Evaluate the use of neurological biomarkers to predict discharge outcomes in COVID-19 patients. Background: Altered levels of brain-derived molecular biomarkers in patients with nonneurological critical illness associates with worse outcomes following these systemic insults. We hypothesized that COVID-19 critical illness would increase expression of brain-derived biomarkers and portend worse outcomes. Design/Methods: 38 adults admitted for COVID-19 at a single tertiary care medical center were prospectively enrolled (M = 63.63 ±19.51, 53% female, 71% requiring ICU admission) and clinical information collected including discharge disposition to home/rehabilitation (n=18) or expired/skilled nursing facility (SNF;n=20). Plasma GFAP, Tau, NfL, and UCHL1 were measured by digital ELISA. Results: COVID-19 patients admitted to the ICU exhibited significantly higher levels of NfL (p=0.003, d=1.25) and GFAP (p=0.03, d=0.88). We used binary logistic regression to determine if biomarkers predicted discharge outcome. Models were examined for best fit using biomarker level, age, ICU status, and history of prior neurological disease. A model including NfL level (Wald's χ =6.614, p=0.010, OR=1.043, 95%CI (1.010, 1.076)) predicting disposition was significant (χ = 22.247, p<0.001, Nagelkerke R = .591). The model's prediction success was 84.2% (90.0% for home/rehab and 77.8% for SNF/expired) A model including GFAP level (Wald's χ =3.055, p=0.080, OR=1.003, 95%CI (1.000, 1.007)) and ICU status (Wald's χ =4.073, p=0.044, OR=0.096, 95%CI (0.010, 0.935)) on disposition was also significant (χ = 17.377, p<0.01, Nagelkerke R = .490). The model successfully predicted disposition status at 78.9% (85% for home/rehab and 72.2% for SNF/expired). Adding age, ICU status, or prior neurological history did not improve outcome prediction. Conclusions: COVID-19 patients requiring ICU admission exhibit increases in circulating brainderived proteins. Higher levels of GFAP and NfL is associated with worse discharge outcomes, even after controlling for age, ICU status and prior neurological disease. Future work examining COVID-19 recovery will help determine if these biomarkers are predictive of long-term neurological consequences.

State heterogeneity in the associations of human mobility with COVID-19 epidemics in the European Union

Background: Human mobility was associated with epidemic changes of coronavirus disease 2019 (COVID-19) in the countries, where strict public health interventions reduced human mobility and COVID-19 epidemics. But its association with COVID-19 epidemics in the European Union (EU) is unclear. Methods: In this quasi-experimental interrupted time-series study, we modelled trends in human mobility and epidemics of COVID-19 in 27 EU states between January 15 and May 9, 2020. The associations of lockdown-date, and turning points of these trends were assessed. Results: There were 982,332 laboratory-confirmed COVID-19 cases in the EU states (median 7,896, interquartile 1,689 to 25,702 for individual states) during the study-period. COVID-19 and human mobility had 3 trend-segments, including an upward trend in COVID-19 daily incidence and a downward trend in most human mobilities in the middle segment. Compared with the states farther from Italy, the state-wide lockdown dates were more likely linked to turning points of human mobilities in the states closer to Italy, which were also more likely linked to second turning points of COVID-19 epidemics. Among the examined human mobilities, the second turning points in driving mobility and the first turning points in parks mobility were the best factors that connected lockdown dates and COVID-19 epidemics in the EU states closer to Italy. Conclusions: We show state- and mobility-heterogeneity in the associations of public health interventions and human mobility with the changes of COVID-19 epidemics in the EU. These findings may help inform policymakers on the best timing and monitoring-parameters of state-level interventions in the EU.

COVID-19: how prepared are front-line healthcare workers in England?

National efforts are underway to prepare the UK National Health Service (NHS) for the COVID-19 pandemic; however, the efficacy of these interventions is unknown. In view of this, a cross-sectional survey of front-line healthcare workers (HCWs) at two large acute NHS hospital trusts in England was undertaken to assess their confidence and perceived level of preparedness for the virus. The survey found that there has been moderate success in readying HCWs to manage COVID-19, but that more still needs to be done, particularly in relation to educating HCWs about laboratory diagnostics.