Evidence map›Paper›PMID 33391753›Full record

ArticleClinical kidney journal2020

Assessing the accuracy of ICD-10 coding for measuring rates of and mortality from acute kidney injury and the impact of electronic alerts: an observational cohort study.

Rachael Logan, Peter Davey, Nicosha De Souza, David Baird, Bruce Guthrie, Samira Bell

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Article in Clinical kidney journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
–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

30 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  16. Cigarette Smoking and Risk of Hospitalization With Acute Kidney Injury: The Atherosclerosis Risk in Communities (ARIC) Study.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2024
    Observational
  17. Recovery of kidney function after acute kidney disease-a multi-cohort analysis.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2024
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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.

Rachael LoganDivision of Population Health and Genomics, Medical Research Institute, University of Dundee, Dundee, UK.
Peter DaveyDivision of Population Health and Genomics, Medical Research Institute, University of Dundee, Dundee, UK.
Nicosha De SouzaDivision of Population Health and Genomics, Medical Research Institute, University of Dundee, Dundee, UK.
David BairdRenal Unit, Ninewells Hospital and Medical School, NHS Tayside, Dundee, UK.
Bruce GuthrieCentre for Population Health Sciences, University of Edinburgh, Edinburgh, UK.
Samira BellDivision of Population Health and Genomics, Medical Research Institute, University of Dundee, Dundee, UK.ORCID 0000-0001-9100-1575

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe application of a uniform definition for acute kidney injury (AKI) is vital to advance understanding and management of AKI. International Classification of Diseases (Tenth Revision) (ICD-10) coding is frequently used to define AKI, but its accuracy is unclear. The aim of this study was to determine whether ICD-10 coding is a reliable method of monitoring rates and outcomes of AKI in inpatients compared with biochemically defined AKI, and whether electronic alerts (e-alerts) for AKI affect ICD-10 AKI coding.

methodsAn observational cohort study of all 505 662 adult admissions to acute hospitals in two Scottish Health Boards [National Health Service (NHS) Tayside and NHS Fife] from January 2013 to April 2017 was performed. AKI e-alerts were implemented in NHS Tayside in April 2015. Sensitivity, specificity, positive and negative predictive values of ICD-10 coding for AKI compared with biochemically defined AKI using the Kidney Disease: Improving Global Outcomes definition and relative risk of 30-day mortality in people with ICD-10 and biochemically defined AKI before and after AKI e-alert implementation were performed.

resultsSensitivity of ICD-10 coding for identifying biochemically defined AKI was very poor in both health boards for all AKI (Tayside 25.7% and Fife 35.8%) and for Stages 2 and 3 AKI (Tayside 43.8% and Fife 53.8%). Positive predictive value was poor both for all AKI (Tayside 76.1% and Fife 45.5%) and for Stages 2 and 3 AKI (Tayside 45.5% and Fife 36.8%). Measured mortality fell following implementation of AKI e-alerts in the ICD-10-coded population but not in the biochemically defined AKI population, reflecting an increase in the proportion of Stage 1 AKI in ICD-10-coded AKI. There was no evidence that the introduction of AKI e-alerts in Tayside improved ICD-10 coding of AKI.

conclusionICD-10 coding should not be used for monitoring of rates and outcomes of AKI for either research or improvement programmes.

Indexed as

acute kidney injuryelectronic alertsepidemiologyICD-10 coding

Identifiers

PMID33391753
PMCPMC7769533

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