Evidence map›Paper›PMID 33428996›Full record

ArticleAmerican journal of kidney diseases : the official journal of the National Kidney Foundation2021

Validation of Risk Prediction Models to Inform Clinical Decisions After Acute Kidney Injury.

Simon Sawhney, Zhi Tan, Corri Black, Angharad Marks, David J Mclernon, Paul Ronksley, Matthew T James

Open access · hybridAbstract readComparative StudyValidation Study
In one paragraph

Article in American journal of kidney diseases : the official journal of the National Kidney Foundation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
3.4field-weighted citation impact, top 7% 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

18 citing papers in PubMed, 30 citations in OpenAlex.

  1. Article
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  4. Review
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  6. Biomarker Panels for Predicting Progression of Kidney Disease in Acute Kidney Injury Survivors.Clinical journal of the American Society of Nephrology : CJASN · 2025
    Article
  7. Review
  8. Article
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  10. 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
    Article
  11. Research Progress of Pyroptosis in Renal Diseases.Current medicinal chemistry · 2024
    Review
  12. Article
  13. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 2 countries.

Simon SawhneyAberdeen Centre for Health Data Sciences, University of Aberdeen, Aberdeen, Scotland; National Health Service Grampian, Aberdeen, Scotland. Electronic address: simon.sawhney@abdn.ac.uk.
Zhi TanCumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Corri BlackAberdeen Centre for Health Data Sciences, University of Aberdeen, Aberdeen, Scotland; National Health Service Grampian, Aberdeen, Scotland.
Angharad MarksRenal Unit, Abertawe Bro Morgannwg University Health Board, Swansea, Wales.
David J MclernonAberdeen Centre for Health Data Sciences, University of Aberdeen, Aberdeen, Scotland.
Paul RonksleyCumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Matthew T JamesCumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
University of Calgary · CANational Health Service · GBNHS Grampian · GBSwansea Bay University Health Board · GBUniversity of Aberdeen · GB

Funding

Wellcome Trust
6 · The paper itself

Abstract

RATIONALE &

objectiveThere is limited evidence to guide follow-up after acute kidney injury (AKI). Knowledge gaps include which patients to prioritize, at what time point, and for mitigation of which outcomes. In this study, we sought to compare the net benefit of risk model-based clinical decisions following AKI. STUDY

designExternal validation of 2 risk models of AKI outcomes: the Grampian -Aberdeen (United Kingdom) AKI readmissions model and the Alberta (Canada) kidney disease risk model of chronic kidney disease (CKD) glomerular (G) filtration rate categories 4 and 5 (CKD G4 and G5). Process mining to delineate existing care pathways. SETTING &

participantsValidation was based on data from adult hospital survivors of AKI from Grampian, 2011-2013. PREDICTORS: KDIGO-based measures of AKI severity and comorbidities specified in the original models. OUTCOMES: Death or readmission within 90 days for all hospital survivors. Progression to new CKD G4-G5 for patients surviving at least 90 days after AKI. ANALYTICAL APPROACH: Decision curve analysis to assess the "net benefit" of use of risk models to guide clinical care compared to alternative approaches (eg, prioritizing all AKI, severe AKI, or only those without kidney recovery).

results26,575 of 105,461 hospital survivors in Grampian (mean age, 60.9 ± 19.8 [SD] years) were included for validation of the death or readmission model, and 9,382 patients (mean age, 60.9 ± 19.8 years) for the CKD G4-G5 model. Both models discriminated well (area under the curve [AUC], 0.77 and 0.86, respectively). Decision curve analysis showed greater net benefit for follow up of all AKI than only severe AKI in most cases. Both original and refitted models provided net benefit superior to any other decision strategy. In process mining of all hospital discharges, 41% of readmissions and deaths occurred among people recovering after AKI. 1,464 of 3,776 people (39%) readmitted after AKI had received no intervening monitoring. LIMITATIONS: Both original models overstated risks, indicating a need for regular updating.

conclusionsFollow up after AKI has potential net benefit for preempting readmissions, death, and subsequent CKD progression. Decisions could be improved by using risk models and by focusing on AKI across a full spectrum of severity. The current lack of monitoring among many with poor outcomes indicates possible opportunities for implementation of decision support.

Indexed as

AftercareModels, StatisticalAcute Kidney InjuryAgedAged, 80 and overClinical Decision-MakingCohort StudiesFemaleHumansMaleMiddle AgedRisk Assessmentacute kidney injury (AKI)chronic kidney disease (CKD)CKD progressionCKD surveillancedeathfollow-up carehospital readmissionmodel-guided decisionsmortalitynet benefitpost-AKI carepost-discharge monitoringrisk prediction

Identifiers

PMID33428996
PMCPMC8234511
OpenAlexW3119394847

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.