Evidence map›Paper›PMID 32232167›Full record

ArticleEuropean stroke journal2020

Temporal trends in the accuracy of hospital diagnostic coding for identifying acute stroke: A population-based study.

Linxin Li, Lucy E Binney, Ramon Luengo-Fernandez, Louise E Silver, Peter M Rothwell, Oxford Vascular Study

Open access · bronzeAbstract read
In one paragraph

Article in European stroke journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 2 pooled it
4.3field-weighted citation impact, top 5% of its field
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

26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 23 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Linxin LiCentre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-3636-8355
Lucy E BinneyCentre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK.
Ramon Luengo-FernandezCentre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-2118-2662
Louise E SilverCentre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK.
Peter M RothwellCentre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0001-9739-9211
Oxford Vascular Study
University of Oxford · GB

Funding

Wellcome Trust
6 · The paper itself

Abstract

introductionAdministrative hospital diagnostic coding data are increasingly being used in identifying incident and prevalent stroke cases, for outcome audit and for 'big data' research. Validity of administrative coding has varied in previous studies, but little is known about the temporal trends of coding accuracy, which could bias analyses. PATIENTS AND

methodsUsing all incident and recurrent strokes in a population-based cohort (Oxford Vascular Study/OXVASC) with multiple sources of ascertainment as the reference, we determined the temporal trends in sensitivity and positive predictive value of hospital diagnostic codes for identifying acute stroke from 2002 to 2017.

resultsOf 1883 hospitalised strokes, 1341 (71.2%) were correctly identified by coding. Sensitivity of coding improved over time for all strokes (p

conclusionsAlthough accuracy of hospital diagnostic coding for identifying acute strokes improved over the last 15 years, residual insensitivity supports linkage to other sources in large epidemiological studies. Moreover, differences in the time trends of coding sensitivity in relation to stroke severity might bias studies of trends in stroke outcome if only administrative coding is used.

Indexed as

Diagnostic codingepidemiologypopulation-based cohortpositive predictive valuesensitivitystroketrend

Identifiers

PMID32232167
PMCPMC7092737
OpenAlexW2979725818

What OpenQuestion holds

Textmetadata
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Registered trials

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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.