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Record W2883965892 · doi:10.1016/j.jtcvs.2018.06.091

Early intraoperative iron-binding proteins are associated with acute kidney injury after cardiac surgery

2018· article· en· W2883965892 on OpenAlexafffund
Nora Choi, Reid Whitlock, Jessica Klassen, Michael Zappitelli, Rakesh C. Arora, Claudio Rigatto, Julie Ho

Bibliographic record

VenueJournal of Thoracic and Cardiovascular Surgery · 2018
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsSeven Oaks General HospitalUniversity of TorontoHospital for Sick ChildrenSickKids FoundationUniversity of Manitoba
FundersCanadian Institutes of Health ResearchKidney Foundation of CanadaResearch Manitoba
KeywordsMedicineAcute kidney injuryCardiopulmonary bypassTransferrin saturationFerritinOdds ratioHepcidinInternal medicineKidney diseaseCardiac surgeryArea under the curveConfidence intervalGastroenterologySurgeryUrologyCardiologyAnemia

Abstract

fetched live from OpenAlex

OBJECTIVES: Iron regulation is an important modifier of renal ischemia-reperfusion injury, but the role of iron-binding proteins during cardiopulmonary bypass remains unclear. The goal was to characterize iron-binding proteins throughout ischemia-reperfusion injury to determine their association with acute kidney injury development. METHODS: A prospective observational cohort of adult patients who underwent cardiac surgery (n = 301) was obtained, and acute kidney injury was defined by Kidney Disease Improving Global Outcomes. Serum ferritin, transferrin saturation, and urine hepcidin-25 were measured. RESULTS: Intraoperative serum ferritin was lower at the start of cardiopulmonary bypass (P = .005) and 1-hour cardiopulmonary bypass (P = .001) in patients with acute kidney injury versus patients without acute kidney injury. Lower serum ferritin and higher transferrin saturation at 1-hour cardiopulmonary bypass were independent predictors of acute kidney injury (serum ferritin odds ratio, 0.66; 95% confidence interval [CI], 0.48-0.91; transferrin saturation odds ratio, 1.26; 95% CI, 1.02-1.55) and improved model discrimination (area under the curve [AUC], 0.76; 95% CI, 0.67-0.85) compared with clinical prediction alone (AUC, 0.72; 95% CI, 0.62-0.81; ΔAUC and net reclassification index, P = .01). Lower ferritin, higher transferrin saturation at 1-hour cardiopulmonary bypass, and lower urine hepcidin-25 at postoperative day 1 were also independent predictors for acute kidney injury development, and this model demonstrated an AUC of 0.80 (0.72-0.87), which was superior to clinical prediction (ΔAUC P = .002, integrated discrimination improvement and net reclassification index P = .003). CONCLUSIONS: Our findings suggest that lower levels of intraoperative iron-binding proteins may reflect an impaired capacity to rapidly handle catalytic iron released during cardiopulmonary bypass, leading to kidney injury. These data highlight the importance of iron homeostasis in human ischemia-reperfusion injury and suggest it is a potentially modifiable risk during cardiac surgery. Intraoperative detection of incipient acute kidney injury may be feasible and could be used as an enrichment strategy for clinical trials.

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)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.250
Teacher spread0.240 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations45
Published2018
Admission routes2
Has abstractno

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