Discharge after acute kidney injury : recognising and managing risk

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Chronic Kidney Disease – Where Next? : Predicting Outcomes And Planning Care Pathways

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Diagnostic sensitivity and false positive AKI alerts through unlinking of an integrated Grampian biochemistry service

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Long Term Prognosis after Acute Kidney Injury (AKI) - What is the Role of Baseline Kidney Function and Recovery? A Systematic Review

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Urban–rural and socioeconomic status : Impact on multimorbidity prevalence in hospitalized patients

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by NHS Grampian, Public Health Directorate. This work was also supported by Health Data Research UK which receives its funding from HDR UK Ltd (HDR-5012) funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (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 (BHF) and the Wellcome Trust. Data accessibility statement De-identified data used for this study are held by Grampian Data Safe Haven. These data are available provided the necessary permissions have been obtained. Further information is available at http://www.abdn.ac.uk/iahs/facilities/grampian-data-safe-haven. php and requests for data may be made to Professor Corri Black on behalf of Grampian Data Safe Haven, [email protected] reviewedPublisher PD

Measuring multimorbidity in hospitalised patients using linked hospital episode data : comparison of two measures

Acknowledgements: This work was funded by NHS Grampian. We thank NHS Grampian who provided data and also the Grampian Data Safe Haven, who hosted the data and provided data management support and the linkage service. We acknowledge the support from The Farr Institute of Health Informatics Research, Scotland. 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), the Wellcome Trust, (MRC Grant Nos: Scotland MR/K007017/1). We also acknowledge the support of our Study Steering Committee, which included clinical, epidemiological and health intelligence representation. Funding: The study was funded by NHS Grampian, Public Health Directorate.Peer reviewedPublisher PD

Models of care for chronic kidney disease : a systematic review

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Cost-effectiveness and value of information analysis of multiple frequency bioimpedance devices for fluid management in people with chronic kidney disease having dialysis

Acknowledgements The authors would like to thank the Thomas Walker and Rebecca Albrow (National Institute for Health and Care Excellence) for comments on earlier versions of the model. We also wish to thank the NICE expert committee members who provided information and data to support the development of the model: Dr Andrew Davenport (Royal Free Hospital, London), Dr Simon Roe (Nottingham University Hospitals NHS Trust), Dr Elizabeth Lindley (St James’s University Hospital), Dr Wesley Hayes (Great Ormond Street Hospital), Ms Joanne Prince (Central Manchester University Hospitals NHS Foundation Trust), Mr Nick McAleer (Royal Devon & Exeter NHS Foundation Trust), Dr Kay Tyerman (Leeds General Infirmary), Dr Graham Woodrow (St James’s University Hospital) and Mr Paul Taylor (lay specialist committee member). Funding This paper presents independent research funded by the National Institute for Health Research (NIHR), commissioned through the NICE Diagnostic Assessment Programme (Project no 15/17/07). The views expressed are those of the authors and not necessarily those of NICE, the NHS, the NIHR or the Department of Health. The Health Economics Research Unit and the Health Services Research Unit, University of Aberdeen, are funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorates.Peer reviewedPublisher PD

Comparing two measures of multimorbidity in hospitalised patients

Background Multimorbidity is recognised as a complex and growing health challenge. Currently there is no “gold standard” multimorbidity measure and few studies have compared measures in hospitalised patients. Objectives We aimed to evaluate two published multimorbidity measures in routine hospital episode data in NHS Grampian, Scotland. Methods We used the Scottish Morbidity Record (SMR) data for the years 2009-2016. We included all adults admitted to hospital in the Grampian region of Scotland (population 588,100) during 2014. Morbidities were identified from inpatient admissions during the five years prior to admission date in 2014 (ICD-10 codes). Two multimorbidity measures were used: Charlson (Quan 2005), and Tonelli et al (2015); and multimorbidity was defined as ≥2 morbidities. Kappa statistics assessed agreement between the two measures in classifying patients as multimorbid. The association between multimorbidity and mortality, readmissions, and length of stay was examined using regression methods with odds ratios (OR) or incidence rate ratios (IRR) calculated as appropriate. Findings In 41,545 adults (median age 62 years, 52.6% female), multimorbidity prevalence was 15.1% (95% CI 14.8%-15.5%) using Charlson and 27.4% (27.0%-27.8%) using Tonelli - agreement 85.1% (Kappa 0.57). After adjusting for covariates, multimorbidity was associated with an increased risk of longer length of stay, (Charlson IRR 1.10 (1.03, 1.18; p=0.005); Tonelli IRR 1.11 (1.04, 1.18; p<0.001)) and readmission (Charlson OR 2.06 (1.94, 2.19; p<0.001); Tonelli OR 2.12 (2.01, 2.22; p<0.001)). Multimorbidity had a higher risk of mortality when measured using Charlson (Charlson OR 2.71 (2.52, 2.92; p=<0.001); Tonelli OR (1.84 (1.72, 1.98; p<0.001)). Conclusions Multimorbidity measures operationalised in hospital episode data identified those at risk of poor outcomes and will be useful for future multimorbidity research and use in secondary care data systems

Characterizing infection in anti-neutrophil cytoplasmic antibody-associated vasculitis:results from a longitudinal, matched-cohort data linkage study

We wish to thank Information Division Services Scotland for assisting with data linkage and data access in the National Safe Haven. Information presented in this article was previously presented as a poster at the American College of Rheumatology Annual Conference 2018, Chicago, IL, USA. The study was conceived by S.H.S., A.M., C.B. and N.B. All authors contributed to the study design and data collection. Data analysis and interpretation and drafting of the manuscript were conducted by all authors. C.B. and N.B. were joint senior authors. All authors critically reviewed the manuscript and approved the final version. Funding: S.H.S. and the study were funded by the Aberdeen Development Trust and the Farr Institute of Health Informatics Research. 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 Trust (Scotland MR/K007017/1). Disclosure statement: L.E. is a GlaxoSmithKline employee. The other authors have declared no conflicts of interest.Peer reviewedPublisher PD