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Record W4285741498 · doi:10.1177/20543581221097455

Canadian Association of Radiologists Guidance on Contrast-Associated Acute Kidney Injury

2022· article· en· W4285741498 on OpenAlexaffabout
D. Blair Macdonald, Casey Hurrell, Andreu F. Costa, Matthew D. F. McInnes, Martin O’Malley, Brendan J. Barrett, Pierre Antoine Brown, Edward G. Clark, Anastasia Hadjivassiliou, Iain D. C. Kirkpatrick, Jeremy Rempel, Paul Jeon, Swapnil Hiremath

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of AlbertaUniversity of British ColumbiaDalhousie UniversityUniversity of TorontoUniversity of OttawaMemorial University of NewfoundlandUniversity of ManitobaOttawa Hospital
Fundersnot available
KeywordsMedicineIodinated contrastIodinated contrast mediaAcute kidney injuryIntensive care medicineSurgeryRadiologyInternal medicineComputed tomography

Abstract

fetched live from OpenAlex

Purpose: Iodinated contrast media is one of the most frequently administered pharmaceuticals. In Canada, over 5.4 million computed tomography (CT) examinations were performed in 2019, of which 50% were contrast enhanced. Acute kidney injury (AKI) occurring after iodinated contrast administration was historically considered a common iatrogenic complication which was managed by screening patients, prophylactic strategies, and follow-up evaluation of renal function. The Canadian Association of Radiologists (CAR) initially published guidelines on the prevention of contrast induced nephropathy in 2007, with an update in 2012. However, new developments in the field have led to the availability of safer contrast agents and changes in clinical practice, prompting a complete revision of the earlier recommendations. Information sources: Published literature, including clinical trials, retrospective cohort series, review articles, and case reports, along with expert opinions from radiologists and nephrologists across Canada. Methods: The leadership of the CAR formed a working group of radiologists and nephrologists with expertise in contrast administration and patient management related to contrast-associated AKI. We conducted a comprehensive review of the published literature to evaluate the evidence about contrast as a cause of AKI, and to inform evidence-based recommendations. Based on the available literature, the working group developed consensus recommendations. Key Findings: The working group developed 21 recommendations, on screening, choice of iodinated contrast media, prophylaxis, medication considerations, and post contrast administration management. The key changes from the 2012 guidelines were (1) Simplification of screening to a simple questionnaire, and not delaying emergent examinations due to a need for creatinine measurements (2) Prophylaxis considerations only for patients with estimated glomerular filtration rate (eGFR) less than 30 mL/min/1.73 m2 (3) Not recommending the routine discontinuation of any drugs to decrease risk of AKI, except metformin when eGFR is less than 30 mL/min/1.73 m2 and (4) Not requiring routine follow up serum creatinine measurements post iodinated contrast administration. Limitations: We did not conduct a formal systematic review or meta-analysis. We did not evaluate our specific suggestions in the clinical environment. Implications: Given the importance of iodinated contrast media use in diagnosis and management, and the low risk of AKI after contrast use, these guidelines aim to streamline the processes around iodinated contrast use in most clinical settings. As newer evidence arises that may change or add to the recommendations provided, the working group will revise these guidelines.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.489
Threshold uncertainty score0.984

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0090.007
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0040.003
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0330.014

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.305
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations24
Published2022
Admission routes2
Has abstractyes

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