Evidence map›Paper›PMID 38645891›Full record

ArticleBMJ medicine2024

Identification of patients undergoing chronic kidney replacement therapy in primary and secondary care data: validation study based on OpenSAFELY and UK Renal Registry.

Shalini Santhakumaran, Louis Fisher, Bang Zheng, Viyaasan Mahalingasivam, Lucy Plumb, Edward Pk Parker, Retha Steenkamp, Caroline Morton, Amir Mehrkar, Sebastian Bacon and 6 more

Open access · goldAbstract read
In one paragraph

Article in BMJ medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.1field-weighted citation impact, top 11% 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

6 citing papers in PubMed, 3 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

16 authors at 5 institutions in 1 country.

Shalini SanthakumaranUK Renal Registry, UK Kidney Association, Bristol, UK.ORCID 0000-0003-0988-9339
Louis FisherBennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Bang ZhengLondon School of Hygiene and Tropical Medicine, London, UK.ORCID 0000-0003-0988-9339
Viyaasan MahalingasivamLondon School of Hygiene and Tropical Medicine, London, UK.
Lucy PlumbUK Renal Registry, UK Kidney Association, Bristol, UK.
Edward Pk ParkerLondon School of Hygiene and Tropical Medicine, London, UK.ORCID 0000-0001-5847-109X
Retha SteenkampUK Renal Registry, UK Kidney Association, Bristol, UK.
Caroline MortonBennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Amir MehrkarBennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Sebastian BaconBennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Sue LyonUKKA Patient Council, UK Kidney Association, Bristol, UK.
Rob Konstant-HamblingSpecialised Commissioning, NHS England, Cheshire, UK.
Ben GoldacreBennett Institute for Applied Data Science, University of Oxford, Oxford, UK.ORCID 0000-0002-5127-4728
Brian MacKennaBennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Laurie A TomlinsonLondon School of Hygiene and Tropical Medicine, London, UK.
Dorothea NitschUK Renal Registry, UK Kidney Association, Bristol, UK.
University of Oxford · GBLondon School of Hygiene & Tropical Medicine · GBKidney Care UK · GBNHS EnglandUniversity of Bristol · GB

Funding

Medical Research Council MC_PC_20051Wellcome Trust
6 · The paper itself

Abstract

Objective: To validate primary and secondary care codes in electronic health records to identify people receiving chronic kidney replacement therapy based on gold standard registry data. Design: Validation study using data from OpenSAFELY and the UK Renal Registry, with the approval of NHS England. Setting: Primary and secondary care electronic health records from people registered at 45% of general practices in England on 1 January 2020, linked to data from the UK Renal Registry (UKRR) within the OpenSAFELY-TPP platform, part of the NHS England OpenSAFELY covid-19 service. Participants: 38 745 prevalent patients (recorded as receiving kidney replacement therapy on 1 January 2020 in UKRR data, or primary or secondary care data) and 10 730 incident patients (starting kidney replacement therapy during 2020), from a population of 19 million people alive and registered with a general practice in England on 1 January 2020. Main outcome measures: Sensitivity and positive predictive values of primary and secondary care code lists for identifying prevalent and incident kidney replacement therapy cohorts compared with the gold standard UKRR data on chronic kidney replacement therapy. Agreement across the data sources overall, and by treatment modality (transplantation or dialysis) and personal characteristics. Results: Primary and secondary care code lists were sensitive for identifying the UKRR prevalent cohort (91.2% (95% confidence interval (CI) 90.8% to 91.6%) and 92.0% (91.6% to 92.4%), respectively), but not the incident cohort (52.3% (50.3% to 54.3%) and 67.9% (66.1% to 69.7%)). Positive predictive values were low (77.7% (77.2% to 78.2%) for primary care data and 64.7% (64.1% to 65.3%) for secondary care data), particularly for chronic dialysis (53.7% (52.9% to 54.5%) for primary care data and 49.1% (48.0% to 50.2%) for secondary care data). Sensitivity decreased with age and index of multiple deprivation in primary care data, but the opposite was true in secondary care data. Agreement was lower in children, with 30% (295/980) featuring in all three datasets. Half (1165/2315) of the incident patients receiving dialysis in UKRR data had a kidney replacement therapy code in the primary care data within three months of the start date of the kidney replacement therapy. No codes existed whose exclusion would substantially improve the positive predictive value without a decrease in sensitivity. Conclusions: Codes used in primary and secondary care data failed to identify a small proportion of prevalent patients receiving kidney replacement therapy. Codes also identified many patients who were not recipients of chronic kidney replacement therapy in UKRR data, particularly dialysis codes. Linkage with UKRR kidney replacement therapy data facilitated more accurate identification of incident and prevalent kidney replacement therapy cohorts for research into this vulnerable population. Poor coding has implications for any patient care (including eligibility for vaccination, resourcing, and health policy responses in future pandemics) that relies on accurate reporting of kidney replacement therapy in primary and secondary care data.

Indexed as

COVID-19dialysisepidemiologykidney failure, chronickidney transplantation

Identifiers

PMID38645891
PMCPMC11029353
OpenAlexW4394912147

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.