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Introduction Acute Kidney Injury (AKI) effects up to 20% of emergency hospital admissions. It can be identified from serum creatinine (SCr) levels and as such an e-alert system has been introduced to hospitals in England and Wales to identify AKI. This system relies on manual review by pathology laboratory staff to prevent false alerts in dialysis patients. Objectives and Approach Our aim is to use routinely collected dialysis timeline and dialysis treatment data in order to create dialysis treatment spells using Structured Query Language (SQL), which can then be compared with dialysis prevalence results in the UK renal registry. Furthermore, we will link this data with biochemical results in Secure Anonymised Information Linkage (SAIL) Databank to create an AKI cohort. Through identifying when patients are on renal replacement therapy (RRT) at a specific time we are able to exclude false positive AKI Alerts. This is done in two ways – one from a timeline within the data which is used in the UK renal registry. The other using individual dialysis sessions to get accurate coverage, as short periods of acute dialysis are often not been recorded in UK renal registry data. Results Using the renal dataset in SAIL we were able identify more renal dialysis patients than the UK renal registry between (2009-17). By applying this to Swansea and Bridgend pathology data in SAIL we were able to identify 60,072 SCr tests in dialysis patients between 2011 and 2013 out of 1,776,101 SCr tests. In this cohort, without ruling out dialysis patients there would be 91,140 AKI alerts, however 19.6% (17,855) of the alerts are during dialysis treatments spells. Conclusion / Implications We were able to recreate the ‘e-Alert’ algorithm using SQL with SAIL and get an accurate picture of RRT in Wales, enabling more accurate AKI analysis.

Introduction The emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and control the spread of infection in the general and vulnerable populations. Rapid analyses are needed to inform policy development and target interventions to at-risk groups to prevent serious health outcomes. We aim to provide an accessible research platform to determine demographic, socioeconomic and clinical risk factors for infection, morbidity and mortality of COVID-19, to measure the impact of COVID-19 on healthcare utilisation and long-term health, and to enable the evaluation of natural experiments of policy interventions.Methods and analysis Two privacy-protecting population-level cohorts have been created and derived from multisourced demographic and healthcare data. The C20 cohort consists of 3.2 million people in Wales on the 1 January 2020 with follow-up until 31 May 2020. The complete cohort dataset will be updated monthly with some individual datasets available daily. The C16 cohort consists of 3 million people in Wales on the 1 January 2016 with follow-up to 31 December 2019. C16 is designed as a counterfactual cohort to provide contextual comparative population data on disease, health service utilisation and mortality. Study outcomes will: (a) characterise the epidemiology of COVID-19, (b) assess socioeconomic and demographic influences on infection and outcomes, (c) measure the impact of COVID-19 on short -term and longer-term population outcomes and (d) undertake studies on the transmission and spatial spread of infection.Ethics and dissemination The Secure Anonymised Information Linkage-independent Information Governance Review Panel has approved this study. The study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.

ObjectivesTo explore associations between exposure to antidepressants, their discontinuation, depression [medicated or unmediated] and preterm birth [MethodsDesign: A population-based cohort study. Setting: The Secure Anonymised Information Linkage [SAIL] databank in Wales, linking maternal primary care data with infant outcomes. Participants: 107,573, 105,331, and 38,725 infants born 2000-2010 with information on prematurity, SGA and breastfeeding respectively, after exclusions. Exposures: Maternal antidepressant prescriptions in trimesters 2 or 3, discontinuation after trimester 1, recorded diagnosis of depression [medicated or unmediated] in pregnancy. Methods: Odds ratios for adverse pregnancy outcomes were calculated, adjusted for smoking, parity, socio-economic status, and depression.ResultsExclusive formula feeding at 6-8 weeks was associated with prescriptions in trimesters 2 or 3 for any antidepressants (adjusted odds ratio [aOR] 0.81, 95% confidence intervals 0.67-0.98), SSRIs [aOR 0.77, 0.62-0.95], particularly higher doses [aOR 0.45, 0.23-0.86], discontinuation of antidepressants or SSRIs after trimester 1 (aOR 0.70, 0.57-0.83 and 0.66, 0.51-0.87), diagnosis of depression aOR 0.76 [0.70-0.82), particularly if medicated (aOR 0.70, 0.58-0.85), rather than unmedicated (aOR 0.87, 0.82-0.92). Preterm birth at ImplicationsExposure to antidepressants or depression increased risks of exclusive formula feeding at 6-8 weeks, and prescription of antidepressants was associated with SGA

Hypothesised associations between in utero exposure to selective serotonin reuptake inhibitors (SSRIs) and congenital anomalies, particularly congenital heart defects (CHD), remain controversial. We investigated the putative teratogenicity of SSRI prescription in the 91 days either side of first day of last menstrual period (LMP).Three population-based EUROCAT congenital anomaly registries- Norway (2004-2010), Wales (2000-2010) and Funen, Denmark (2000-2010)-were linked to the electronic healthcare databases holding prospectively collected prescription information for all pregnancies in the timeframes available. We included 519,117 deliveries, including foetuses terminated for congenital anomalies, with data covering pregnancy and the preceding quarter, including 462,641 with data covering pregnancy and one year either side. For SSRI exposures 91 days either side of LMP, separately and together, odds ratios with 95% confidence intervals (ORs, 95%CI) for all major anomalies were estimated. We also explored: pausing or discontinuing SSRIs preconception, confounding, high dose regimens, and, in Wales, diagnosis of depression. Results were combined in meta-analyses. SSRI prescription 91 days either side of LMP was associated with increased prevalence of severe congenital heart defects (CHD) (as defined by EUROCAT guide 1.3, 2005) (34/12,962 [0.26%] vs. 865/506,155 [0.17%] OR 1.50, 1.06-2.11), and the composite adverse outcome of 'anomaly or stillbirth' (473/12962, 3.65% vs. 15829/506,155, 3.13%, OR 1.13, 1.03-1.24). The increased prevalence of all major anomalies combined did not reach statistical significance (3.09% [400/12,962] vs. 2.67% [13,536/506,155] OR 1.09, 0.99-1.21). Adjusting for socio-economic status left ORs largely unchanged. The prevalence of anomalies and severe CHD was reduced when SSRI prescriptions were stopped or paused preconception, and increased when >1 prescription was recorded, but differences were not statistically significant. The dose-response relationship between severe CHD and SSRI dose (meta-regression OR 1.49, 1.12-1.97) was consistent with SSRI-exposure related risk. Analyses in Wales suggested no associations between anomalies and diagnosed depression.The additional absolute risk of teratogenesis associated with SSRIs, if causal, is small. However, the high prevalence of SSRI use augments its public health importance, justifying modifications to preconception care.