Evidence map›Paper›PMID 39097856›Full record

ArticleJournal of nephrology2024

Consistency of alerts generated by, and implementation of, the NHS England acute kidney injury detection algorithm in English laboratories.

Ryan Aylward, Anna Casula, Nicki Tiffin, Yoav Ben-Shlomo, Brian Rayner, Kate Birnie, Fergus John Caskey

Abstract read
In one paragraph

Article in Journal of nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Observational
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

7 authors.

Ryan AylwardBristol Medical School, Population Health Sciences, University of Bristol, First Floor, 5 Tyndall Avenue, Bristol, BS8 1UD, UK.ORCID 0000-0002-3830-1795
Anna CasulaUK Renal Registry, UK Kidney Association, Bristol, UK.
Nicki TiffinSouth African National Bioinformatics Institute, University of Western Cape, Cape Town, South Africa.ORCID 0000-0001-5083-2735
Yoav Ben-ShlomoBristol Medical School, Population Health Sciences, University of Bristol, First Floor, 5 Tyndall Avenue, Bristol, BS8 1UD, UK.ORCID 0000-0001-6648-3007
Brian RaynerDivision of Nephrology and Hypertension, Department of Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.ORCID 0000-0002-0167-9591
Kate Birnie *Bristol Medical School, Population Health Sciences, University of Bristol, First Floor, 5 Tyndall Avenue, Bristol, BS8 1UD, UK. Kate.birnie@bristol.ac.uk.ORCID 0000-0002-9083-4097
Fergus John Caskey *Bristol Medical School, Population Health Sciences, University of Bristol, First Floor, 5 Tyndall Avenue, Bristol, BS8 1UD, UK.ORCID 0000-0002-5199-3925

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNational Health Services (NHS) England mandates that an acute kidney injury (AKI) detection algorithm be embedded in laboratories. We evaluated the implementation of the algorithm and the consistency of alerts submitted to the United Kingdom Renal Registry (UKRR).

methodsCode was developed to simulate the syntax of the AKI detection algorithm, executed on data from local laboratories submitted to the UKRR, including alerts and serum creatinine (SCr) results spanning 15 months before and after the alert submission. Acute kidney injury alerts were categorized into stages 0/1/2/3. Inter-rater agreement (Gwet's AC1) was used to compare local and centrally derived alerts at individual laboratory and commercial laboratory information management system (LIMS) levels, penalizing extreme disagreements.

resultsThe analysis included 9,096,667 SCr results from 29 labs (475,634 patients; median age 72 years, 47% female) between algorithm activation and data extraction (September 30, 2020). Laboratories and the central simulation generated 1,579,633 and 1,646,850 non-zero AKI alerts, respectively. Agreement was high within known laboratory information management system providers (0.97-0.98) but varied across individual laboratories (overall range 0.17-0.98, 0.17-0.23 in three). Agreement tended to be lower (Gwet's AC1 0.88) with the highest baseline SCr quartile (median 164 μmol/L).

conclusionsOverall, alerts submitted to the UKRR are a valid source of AKI surveillance but there are concerns about inconsistent laboratory practices, incomplete adoption of the NHSE algorithm code, alert suppression, and variable interpretation of guidelines. Future efforts should audit and support laboratories with low agreement rates, and explore reasons for lower agreement in individuals with pre-existing CKD.

Indexed as

Acute Kidney InjuryAlgorithmsCreatinineAgedClinical Laboratory Information SystemsEnglandFemaleHumansLaboratoriesMaleRegistriesState MedicineCreatinineAcute kidney injuryAlgorithmDetectionRenal registry

Identifiers

PMID39097856
PMCPMC11649820

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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.