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Using electronic health records to quantify and stratify the severity of type 2 diabetes in primary care in England: rationale and cohort study design
  1. Salwa S Zghebi1,2,
  2. Martin K Rutter3,4,
  3. Darren M Ashcroft5,
  4. Chris Salisbury6,
  5. Christian Mallen7,
  6. Carolyn A Chew-Graham7,
  7. David Reeves1,
  8. Harm van Marwijk8,
  9. Nadeem Qureshi9,
  10. Stephen Weng9,
  11. Niels Peek10,
  12. Claire Planner1,
  13. Magdalena Nowakowska1,2,
  14. Mamas Mamas11,
  15. Evangelos Kontopantelis1,2
  1. 1 Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre (MAHSC), University of Manchester, Manchester, UK
  2. 2 NIHR School for Primary Care Research, Centre for Primary Care, Manchester Academic Health Science Centre (MAHSC), University of Manchester, Manchester, UK
  3. 3 Division of Diabetes, Endocrinology and Gastroenterology, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre (MAHSC), University of Manchester, Manchester, UK
  4. 4 Manchester Diabetes Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre (MAHSC), Manchester, UK
  5. 5 Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre (MAHSC), University of Manchester, Manchester, UK
  6. 6 Centre for Academic Primary Care, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
  7. 7 Research Institute for Primary Care and Health Sciences, Keele University, Staffordshire, UK
  8. 8 Division of Primary Care and Public Health, Brighton and Sussex Medical School, University of Brighton, Brighton, UK
  9. 9 Division of Primary Care, School of Medicine, University of Nottingham, Nottingham, UK
  10. 10 Division of Informatics, Imaging & Data Sciences (L5), School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre (MAHSC), University of Manchester, Manchester, UK
  11. 11 Keele Cardiovascular Research group, Centre for Prognosis Research, Institute for Primary Care and Health Sciences, Keele University, Stoke-on-Trent, UK
  1. Correspondence to Dr Salwa S Zghebi; salwa.zghebi{at}manchester.ac.uk

Abstract

Introduction The increasing prevalence of type 2 diabetes mellitus (T2DM) presents a significant burden on affected individuals and healthcare systems internationally. There is, however, no agreed validated measure to infer diabetes severity from electronic health records (EHRs). We aim to quantify T2DM severity and validate it using clinical adverse outcomes.

Methods and analysis Primary care data from the Clinical Practice Research Datalink, linked hospitalisation and mortality records between April 2007 and March 2017 for patients with T2DM in England will be used to develop a clinical algorithm to grade T2DM severity. The EHR-based algorithm will incorporate main risk factors (severity domains) for adverse outcomes to stratify T2DM cohorts by baseline and longitudinal severity scores. Provisionally, T2DM severity domains, identified through a systematic review and expert opinion, are: diabetes duration, glycated haemoglobin, microvascular complications, comorbidities and coprescribed treatments. Severity scores will be developed by two approaches: (1) calculating a count score of severity domains; (2) through hierarchical stratification of complications. Regression models estimates will be used to calculate domains weights. Survival analyses for the association between weighted severity scores and future outcomes—cardiovascular events, hospitalisation (diabetes-related, cardiovascular) and mortality (diabetes-related, cardiovascular, all-cause mortality)—will be performed as statistical validation. The proposed EHR-based approach will quantify the T2DM severity for primary care performance management and inform the methodology for measuring severity of other primary care-managed chronic conditions. We anticipate that the developed algorithm will be a practical tool for practitioners, aid clinical management decision-making, inform stratified medicine, support future clinical trials and contribute to more effective service planning and policy-making.

Ethics and dissemination The study protocol was approved by the Independent Scientific Advisory Committee. Some data were presented at the National Institute for Health Research School for Primary Care Research Showcase, September 2017, Oxford, UK and the Diabetes UK Professional Conference March 2018, London, UK. The study findings will be disseminated in relevant academic conferences and peer-reviewed journals.

  • type 2 diabetes
  • electronic health records
  • primary care
  • hospitalisation records
  • cardiovascular disease
  • diabetes severity algorithm

This is an open access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/

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Footnotes

  • Contributors EK, SSZ, MM, MKR and HvM developed the study design and data analysis plan. SSZ, MM, MKR and HvM agreed on provisional clinical code lists. SSZ prepared the first draft of the manuscript, and EK, MM, MKR and HvM critically reviewed initial versions. CP contributed to the planned PPIE work. DR, CACG, CM, NP, DMA, CS, NQ, MN and SW reviewed and critically edited the manuscript. All authors approved the final version of the protocol before submission. SSZ is the guarantor.

  • Funding This study is funded by the National Institute for Health Research School for Primary Care Research (NIHR SPCR), grant number 331. This report is an independent research by the National Institute for Health Research.

  • Disclaimer The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health.

  • Competing interests EK, HvM, MM, DR, CACG, CP, CM, NP and MN declare no competing interests. SSZ reports support by the NIHR SPCR during this study. DMA has received grant funding from Abbvie and has served on advisory boards for Pfizer and GSK. MKR has received educational grant support from MSD and Novo Nordisk; has modest stock ownership in GSK; and has consulted for Roche. NQ reports grants from the NIHR SPCR during the conduct of the study. CS reports grants from NIHR SPCR during the conduct of the study; grants from NHS CLAHRC West, grants from Avon Primary Care Research Collaborative, outside the submitted work. SW serves as a member of the Clinical Practice Research Datalink Independent Scientific Committee (ISAC) at the UK Medicines and Health Regulatory Agency.

  • Patient consent Not required.

  • Ethics approval The CPRD’s Independent Scientific Advisory Committee (ISAC) approved this study protocol.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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