Evidence map›Paper›PMID 29961021›Full record

ArticleBMJ open2018

Using electronic health records to quantify and stratify the severity of type 2 diabetes in primary care in England: rationale and cohort study design.

Salwa S Zghebi, Martin K Rutter, Darren M Ashcroft, Chris Salisbury, Christian Mallen, Carolyn A Chew-Graham, David Reeves, Harm van Marwijk, Nadeem Qureshi, Stephen Weng and 5 more

Abstract read
In one paragraph

Article in BMJ open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Salwa S ZghebiDivision 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.ORCID 0000-0002-7978-1094
Martin K RutterDivision 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.
Darren M AshcroftDivision 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.
Chris SalisburyCentre for Academic Primary Care, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.ORCID 0000-0002-4378-3960
Christian MallenResearch Institute for Primary Care and Health Sciences, Keele University, Staffordshire, UK.
Carolyn A Chew-GrahamResearch Institute for Primary Care and Health Sciences, Keele University, Staffordshire, UK.
David ReevesDivision 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.
Harm van MarwijkDivision of Primary Care and Public Health, Brighton and Sussex Medical School, University of Brighton, Brighton, UK.
Nadeem QureshiDivision of Primary Care, School of Medicine, University of Nottingham, Nottingham, UK.
Stephen WengDivision of Primary Care, School of Medicine, University of Nottingham, Nottingham, UK.
Niels PeekDivision 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.
Claire PlannerDivision 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.
Magdalena NowakowskaDivision 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.
Mamas MamasKeele Cardiovascular Research group, Centre for Prognosis Research, Institute for Primary Care and Health Sciences, Keele University, Stoke-on-Trent, UK.
Evangelos KontopantelisDivision 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.ORCID 0000-0001-6450-5815

Funding

Medical Research Council MC_PC_13042
6 · The paper itself

Abstract

introductionThe 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.

Indexed as

AdultAgedAged, 80 and overAlgorithmsCardiovascular DiseasesCohort StudiesComorbidityDiabetes Mellitus, Type 2Electronic Health RecordsEnglandFemaleGlycated HemoglobinHospitalizationHumansMaleMiddle AgedGlycated Hemoglobincardiovascular diseasediabetes severity algorithmelectronic health recordshospitalisation recordsprimary caretype 2 diabetes

Identifiers

PMID29961021
PMCPMC6042592

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.