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ABSTRACT
ObjectivesPatients with chronic kidney disease (CKD ≥3) and diabetes mellitus comprise approximately 25% patients with diabetes. These patients are at a higher risk of cardiovascular morbidity and mortality and furthermore therapies targeting glucose control are limited. The management of glycaemic control in type 2 diabetes and chronic renal disease is difficult with limited therapeutic choices. This issue has been a matter of longstanding debate. Following a number of joint Diabetes-Renal meetings between the Diabetes and Renal teams based in Hywel Dda and ABM University Health Boards, a proposal was put forward to the SAIL team to examine the relationship between diabetes therapies in relation to eGFR, as this may influence further practice and guidance for patients with type 2 diabetes and renal impairment.
ApproachLinkage and re-use of routinely collected anonymised clinical data held in the SAIL databank was employed, to identify a cohort of adult patients in Abertawe Bro Morgannwg Health Board (ABMU) having type 2 diabetes (excluding type 1 diabetes). Diagnosis of diabetes was achieved by use of National Health Service 'Read' codes. Creatinine, eGFR, age, gender, weight, height, cholesterol, LDL, HDL, TG, systolic blood pressure, diastolic blood pressure, diabetes medication prescriptions, the use of statins, ACEis, aspirin, CHD status, CVD status, duration of diabetes were identified in primary care GP and pathology datasets.
Results42170 (6.0%) of adults in ABMU were identified as having type 2 (excluding type 1) diabetes , 13369 of which had good GP registration coverage. The gender split was male 56%, female 44%. Duration of diabetes (years) was (mean/median/SD/IQR) 9.96/8.97/6.78/8.10; weight (Kg) was 86.94/85.00/21.23/28.19; age (years) 65.49/66.74/13.75/19.03; BMI 31.57/30.70/6.62/7.99. Incidence of CKD as defined by GP coded data was 24%, renal replacement therapy 0.4%, Ischaemic Heart Disease 22%. Prevalence of prescriptions during 2014 was: Anti-diabetic medication 72%, statins 75%, aspirin 34%, ACEi/ARB 61%. The import of pathology laboratory data into SAIL is currently pending, and is anticipated before April 2016. This will allow the accurate stratification of CKD status and detailed description of use of anti-diabetic agents.
ConclusionThe project methods and coding structure are well place to provide anticipated results as soon as pathology data arrives. The percentage of ABMU patients having type 2 diabetes is in line with other literature for adults in the UK.

IntroductionAcute Kidney Injury (AKI) is a common, serious condition effecting up to 20% of all hospital admissions in the UK. AKI has an agreed definition for its recognition, however there is no consensus for the duration of an AKI episode.
Objectives and ApproachWe aimed to describe four different potential definitions of an AKI episode. AKI was identified using an SQL (Structured Query Language) based algorithm (an implementation of the NHS England eAlert algorithm) applied to serum creatinine (SCr) results from a South Wales population of ~518,000 people, held in the Secure Anonymised Information Linkage (SAIL) Databank. Using a person's index AKI case, we applied four different rules to define an episode of AKI. These definitions are: ALERTS - until they no longer trigger an AKI eAlert, 90 DAYS - until 90 days post first AKI test and <1.2/<1.5 until the SCr recovers to <1.2 or 1.5 times their baseline creatinine.
ResultsThere were 1,832,122 SCr tests in 340,908 people between 2011-2013, of which 93,843 were alerts (5.12%). This fell to 81,948 alerts in 21,979 patients when dialysis and transplant patients were excluded. Of these patients with AKI 7,792 (35.5%) were dead at 1 year after their first episode. There were 31,482, 33,758, 26,657, 34,904 episodes in patients by <1.2, <1.5, 90 Days and ALERTS definitions respectively.
8333 patients had first episodes occurring in 2011. The number of these having 2/3/4/5+ later episodes within 1yr were 2332/869/344/326, 2602/1095/485/609, 1534/331/71/17, 2728/1190/543/730 by <1.2, <1.5, 90 Days and ALERTS definitions respectively.
Conclusion / ImplicationsAKI episodes can be created in SAIL using SQL, and by adjusting the definition we see a variation in the number of episodes that a patient experiences. Once described, this cohort can be used to define a gold standard for AKI in future analysis.

BackgroundAcute Kidney Injury (AKI) is a common, serious condition effecting up to 20% of all hospital admissions in the UK. AKI has an agreed definition for its recognition, however there is no consensus for the duration of an AKI episode.
Main AimTo describe four different potential definitions of an AKI episode.
MethodWe identified AKI using an SQL (Structured Query Language) based algorithm (an implementation of the NHS England eAlert algorithm) applied to serum creatinine (SCr) results from a South Wales population of ~518,000 people, held in the Secure Anonymised Information Linkage (SAIL) Databank. Using a person's index AKI case, we applied four different rules to define an episode of AKI. These definitions are: ALERTS - until they no longer trigger an AKI eAlert, 90 DAYS - until 90 days post first AKI test and <1.2/<1.5 until the SCr recovers to <1.2 or 1.5 times their baseline creatinine.
ResultsThere were 1,832,122 SCr tests in 340,908 people between 2011-2013, of which 93,843 were alerts (5.12%). This fell to 81,948 alerts in 21,979 patients when dialysis and transplant patients were excluded. Of these patients with AKI 7,792 (35.5%) were dead at 1 year after their first episode. There were 31,505, 33,759, 26,657, 34,904 episodes in patients by <1.2, <1.5, 90 Days and ALERTS definitions respectively.
ConclusionAKI episodes can be created in SAIL using SQL, and by adjusting the definition we see a variation in the number of episodes that a patient experiences. Once described, this cohort can be used to define a gold standard for AKI in future analysis.

IntroductionAcute Kidney Injury (AKI) is a serious condition associated with increased mortality/morbidity and occurs in approximately 20% of hospital admissions. A Welsh AKI alert (e-alert) system has been in place since 2014, aiming to improve patient outcomes and care standards through early recognition of AKI using a retrospective dataset.
Objectives and ApproachThe e-alert algorithm was re-produced in SQL (structured query language) and applied to serum creatinine (SCr) values for patients from pathology departments in Abertawe Bro Morgannwg health board (ABMUHB) 2011-2014, held in the Secure Anonymised Information Linkage (SAIL) databank. The algorithm utilises ratios between current SCr value (C1) to lowest SCr value within the previous 7 days (RV1), SCr median values from the previous 8-365 days (RV2), and lowest SCr within 48hrs (D). >50% increase in RV1 or RV2 or >26µmol/L above D triggers an alert. Using a renal dataset to create a timeline we created a temporal AKI cohort.
Results2,407,590 SCr tests were performed on adult patients with 2,077,493 of these coming from people in the local area who were not on renal replacement during the time-period. The average ABMUHB population for 2011-2014 was 520,293 (2011: 517,981; 2012: 519,481; 2013: 520,710; 2014: 523,001). 85,272 (4.1%) of these tests triggered alerts for AKI. The incidence per 100,000 population of AKI for 2011-2014 were 1767, 1723, 1717, 1660 (average 1,717). The first AKI episodes per year for 2011-2014 respectively were stage 1 (least severe): 78.9%, 79.3%, 79.3%, 79.4% (average 79.2%); stage 2: 13.3%, 13.7%, 13.1%, 13.7% (average 13.5%); stage 3 (most severe): 7.8%, 7.0%, 7.6%, 6.9% (average 7.3%).
Conclusion/ImplicationsThe AKI e-alert algorithm can be effectively reproduced using standard query language. The AKI findings in this population are comparable to others published. The use of a renal dataset using both records of renal replacement timeline and individual dialysis session may identify and rectify where alerts have not been generated.

BackgroundAcute Kidney Injury (AKI) is a serious condition occurring in approximately 20% of emergency hospital admissions. A Welsh AKI e-Alert (electronic alerts) system was introduced from 2013 with the aim of improving patient outcomes through early recognition of AKI.
ObjectivesOur objective is to replicate the AKI e-Alert system which has been implemented throughout Wales and create a retrospective AKI cohort to facilitate data linkage.
MethodsThe e-alert algorithm was re-produced in SQL (Structured Query Language) and applied to serum creatinine (SCr) values 2011-2014 held in the Secure Anonymised Information Linkage (SAIL) Databank. The algorithm utilises ratios between current SCr value (C1) to lowest SCr value within the previous 7 days (RV1), SCr median values from the previous 8-365 days (RV2), and lowest SCr within 48hrs (D). >50% increase in RV1 or RV2 or >26µmol/L above D triggers an alert. We created a temporal AKI cohort by using a renal dataset to exclude patients undergoing chronic dialysis.
Findings2,407,590 SCr tests were performed on adult patients with 2,077,493 of these coming from people in Abertawe Bro Morgannwg University Health Board who were not on dialysis at the time of the test. The average population for 2011-2014 was 520,293. 85,272 (4.1%) of these tests triggered alerts for AKI. The average incidence of AKI per 100,000 population per year for 2011-2014 was 1,717. The average first AKI episodes per year for 2011-2014 respectively were stage 1 (least severe): 79.2%; stage 2: 13.5%; stage 3 (most severe): 7.3%.
ConclusionsThe e-alert can be effectively reproduced using SQL to identify AKI in this large dataset, enabling subsequent validation of these alerts and evaluation of their impact through multisource data linkage.

IntroductionAcute 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 ApproachOur 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.
ResultsUsing 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 / ImplicationsWe 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.

This work was funded by a grant from the UK's Farr Institute for Health Informatics Research (UKHIRN/XFarrRP001). The Farr Institute is supported by a 10-funder consortium: Arthritis Research UK, the British Heart Foundation, Cancer Research UK, the Economic and Social Research Council, the Engineering and Physical Sciences Research Council, the Medical Research Council, the National Institute of Health Research, the National Institute for Social Care and Health Research (Welsh Assembly Government), the Chief Scientist Office (Scottish Government Health Directorates) and the Wellcome (MRC grant nos. CIPHER MR/K006525/1, HeRC MR/K006665/1, London MR/ K006584/1, Scotland MR/K007017/1). We also acknowledge the data management support of Grampian Data Safe Haven (DaSH) and the associated financial support of NHS Research Scotland, through NHS Grampian investment in the Grampian DaSH. Work on this project was also part funded by Health Care and Research Wales, and by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care Wessex at Southampton NHS Hospitals Foundation Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. SS was supported by a research training fellowship from the Wellcome Trust to study the outcomes of acute kidney injury (WT102729/Z/13/Z). ; Peer reviewed ; Publisher PDF

This work was supported by Health Data Research UK (HDR-9006; CFC0110) and the Medical Research Council (MR/S027750/1). Health Data Research UK is funded by: UK Medical Research Council; Engineering and Physical Sciences Research Council; Economic and Social Research Council; National Institute for Health Research (England); Chief Scientist Office of the Scottish Government Health and Social Care Directorates; Health and Social Care Research and Development Division (Welsh Government); Public Health Agency (Northern Ireland); British Heart Foundation and Wellcome Trust. ; Introduction Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence. We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity. Methods and analysis The WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity.Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation. Ethics and dissemination The SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals. ; Publisher PDF ; Peer reviewed

This work was supported by Health Data Research UK (HDR-9006; CFC0110) and the Medical Research Council (MR/S027750/1). Health Data Research UK is funded by: UK Medical Research Council; Engineering and Physical Sciences Research Council; Economic and Social Research Council; National Institute for Health Research (England); Chief Scientist Office of the Scottish Government Health and Social Care Directorates; Health and Social Care Research and Development Division (Welsh Government); Public Health Agency (Northern Ireland); British Heart Foundation and Wellcome Trust. ; Introduction Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence. We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity. Methods and analysis The WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity. Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation. Ethics and dissemination The SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals. ; Publisher PDF ; Peer reviewed