Evidence map›Paper›PMID 28270930›Full record

ArticleCanadian journal of kidney health and disease2017

Derivation of a Predictive Model for Graft Loss Following Acute Kidney Injury in Kidney Transplant Recipients.

Amber O Molnar, Carl van Walraven, Dean Fergusson, Amit X Garg, Greg Knoll

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Canadian journal of kidney health and disease, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 31% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. 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

5 authors at 4 institutions in 1 country.

Amber O MolnarDivision of Nephrology, Department of Medicine, McMaster University, Hamilton, Ontario, Canada; Institute for Clinical Evaluative Sciences, London, Ontario, Canada.
Carl van WalravenInstitute for Clinical Evaluative Sciences, London, Ontario, Canada; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ontario, Canada; Department of Medicine, University of Ottawa, Ontario, Canada.
Dean FergussonClinical Epidemiology Program, Ottawa Hospital Research Institute, Ontario, Canada.
Amit X GargInstitute for Clinical Evaluative Sciences, London, Ontario, Canada; Department of Epidemiology and Biostatistics, Western University, London, Ontario, Canada; Division of Nephrology, Western University, London, Ontario, Canada.
Greg KnollClinical Epidemiology Program, Ottawa Hospital Research Institute, Ontario, Canada; Division of Nephrology, Department of Medicine, University of Ottawa, Ontario, Canada.
Institute for Clinical Evaluative Sciences · CAOttawa Hospital · CAUniversity of Ottawa · CAWestern University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAcute kidney injury (AKI) is common in the kidney transplant population.

objectiveTo derive a multivariable survival model that predicts time to graft loss following AKI.

designRetrospective cohort study using health care administrative and laboratory databases.

settingSouthwestern Ontario (1999-2013) and Ottawa, Ontario, Canada (1996-2013). PATIENTS: We included first-time kidney only transplant recipients who had a hospitalization with AKI 6 months or greater following transplant. MEASUREMENTS: AKI was defined using the Acute Kidney Injury Network criteria (stage 1 or greater). The first episode of AKI was included in the analysis. Graft loss was defined by return to dialysis or repeat kidney transplant.

methodsWe performed a competing risk survival regression analysis using the Fine and Gray method and modified the model into a simple point system. Graft loss with death as a competing event was the primary outcome of interest.

resultsA total of 315 kidney transplant recipients who had a hospitalization with AKI 6 months or greater following transplant were included. The median (interquartile range) follow-up time was 6.7 (3.3-10.3) years. Graft loss occurred in 27.6% of the cohort. The final model included 6 variables associated with an increased risk of graft loss: younger age, increased severity of AKI, failure to recover from AKI, lower baseline estimated glomerular filtration rate, increased time from kidney transplant to AKI admission, and receipt of a kidney from a deceased donor. The risk score had a concordance probability of 0.75 (95% confidence interval [CI], 0.69-0.82). The predicted 5-year risk of graft loss fell within the 95% CI of the observed risk more than 95% of the time. LIMITATIONS: The CIs of the estimates were wide, and model overfitting is possible due to the limited sample size; the risk score requires validation to determine its clinical utility.

conclusionsOur prognostic risk score uses commonly available information to predict the risk of graft loss in kidney transplant patients hospitalized with AKI. If validated, this predictive model will allow clinicians to identify high-risk patients who may benefit from closer follow-up or targeted enrollment in future intervention trials designed to improve outcomes.

Indexed as

acute kidney injurygraft lossrisk scoretransplant

Identifiers

PMID28270930
PMCPMC5308519
OpenAlexW2583525397

What OpenQuestion holds

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LicenceCC BY-NC
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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.