Evidence map›Paper›PMID 26316642›Full record

SynthesisBMJ (Clinical research ed.)2015

Risk prediction models for contrast induced nephropathy: systematic review.

Samuel A Silver, Prakesh M Shah, Glenn M Chertow, Shai Harel, Ron Wald, Ziv Harel

Erratum issuedAbstract readSystematic Review
In one paragraph

Synthesis in BMJ (Clinical research ed.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 104 papers, 12 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
104citing papers in PubMed, 12 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

104 citing papers in PubMed, 12 syntheses or guidelines pooled it.

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  15. Preprocedural Prediction Model for Contrast-Induced Nephropathy Patients.Journal of the American Heart Association · 2017
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44 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Samuel A SilverDivision of Nephrology, St Michael's Hospital, University of Toronto, Toronto, Canada.
Prakesh M ShahDepartment of Paediatrics, Mount Sinai Hospital, University of Toronto, Toronto, Canada.
Glenn M ChertowDivision of Nephrology, Stanford University School of Medicine, Palo Alto, CA, USA.
Shai HarelDivision of Nephrology, St Michael's Hospital, University of Toronto, Toronto, Canada harelz@smh.ca.
Ron WaldDivision of Nephrology, St Michael's Hospital, University of Toronto, Toronto, Canada Li Ka Shing Knowledge Institute of St Michael's Hospital, Toronto, ON, M5C 2T2, Canada.
Ziv HarelDivision of Nephrology, St Michael's Hospital, University of Toronto, Toronto, Canada Li Ka Shing Knowledge Institute of St Michael's Hospital, Toronto, ON, M5C 2T2, Canada harelz@smh.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo look at the available literature on validated prediction models for contrast induced nephropathy and describe their characteristics.

designSystematic review. DATA SOURCES: Medline, Embase, and CINAHL (cumulative index to nursing and allied health literature) databases. REVIEW

methodsDatabases searched from inception to 2015, and the retrieved reference lists hand searched. Dual reviews were conducted to identify studies published in the English language of prediction models tested with patients that included derivation and validation cohorts. Data were extracted on baseline patient characteristics, procedural characteristics, modelling methods, metrics of model performance, risk of bias, and clinical usefulness. Eligible studies evaluated characteristics of predictive models that identified patients at risk of contrast induced nephropathy among adults undergoing a diagnostic or interventional procedure using conventional radiocontrast media (media used for computed tomography or angiography, and not gadolinium based contrast).

results16 studies were identified, describing 12 prediction models. Substantial interstudy heterogeneity was identified, as a result of different clinical settings, cointerventions, and the timing of creatinine measurement to define contrast induced nephropathy. Ten models were validated internally and six were validated externally. Discrimination varied in studies that were validated internally (C statistic 0.61-0.95) and externally (0.57-0.86). Only one study presented reclassification indices. The majority of higher performing models included measures of pre-existing chronic kidney disease, age, diabetes, heart failure or impaired ejection fraction, and hypotension or shock. No prediction model evaluated its effect on clinical decision making or patient outcomes.

conclusionsMost predictive models for contrast induced nephropathy in clinical use have modest ability, and are only relevant to patients receiving contrast for coronary angiography. Further research is needed to develop models that can better inform patient centred decision making, as well as improve the use of prevention strategies for contrast induced nephropathy.

Indexed as

Acute Kidney InjuryAge FactorsAnemiaAngiographyContrast MediaDecision Support TechniquesDiabetes MellitusHeart FailureHumansHypertensionModels, StatisticalRenal Insufficiency, ChronicRisk AssessmentRisk FactorsSex FactorsTomography, X-Ray ComputedContrast Media

Identifiers

PMID26316642
PMCPMC4784870

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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 Socratic graph.