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Association of Definition of Acute Kidney Injury by Cystatin C Rise With Biomarkers and Clinical Outcomes in Children Undergoing Cardiac Surgery

2015· article· en· W2017207880 on OpenAlexaffabout
Michael Zappitelli, Jason H. Greenberg, Steven G. Coca, Catherine D. Krawczeski, Simon Li, Heather Thiessen‐Philbrook, Michael Bennett, Prasad Devarajan, Chirag R. Parikh

Bibliographic record

VenueJAMA Pediatrics · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsWestern UniversityMcGill University Health CentreMontreal Children's Hospital
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood Institute
KeywordsMedicineAcute kidney injuryCystatin CRenal functionCreatinineBiomarkerInternal medicineKidney diseaseReceiver operating characteristicOdds ratioSuPARProspective cohort studyIntensive care medicine

Abstract

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IMPORTANCE: Research has identified improved biomarkers of acute kidney injury (AKI). Cystatin C (CysC) is a better glomerular filtration rate marker than serum creatinine (SCr) and may improve AKI definition. OBJECTIVE: To determine if defining clinical AKI by increases in CysC vs SCr alters associations with biomarkers and clinical outcomes. DESIGN, SETTING, AND PARTICIPANTS: Three-center prospective cohort study of intensive care units in New Haven, Connecticut, Cincinnati, Ohio, and Montreal, Quebec, Canada. Participants were 287 patients 18 years or younger without preoperative AKI or end-stage renal disease who were undergoing cardiac surgery. The study dates were July 1, 2007, through December 31, 2009. EXPOSURES: For biomarker vs clinical AKI associations, the exposures were first postoperative (0-6 hours after surgery) urine interleukin 18, neutrophil gelatinase-associated lipocalin, kidney injury molecule 1, and liver fatty acid-binding protein. For clinical AKI outcome associations, the exposure was Kidney Disease: Improving Global Outcomes AKI definition (based on SCr or CysC). MAIN OUTCOMES AND MEASURES: Clinical AKI, length of stay, and length of mechanical ventilation. We determined areas under the receiver operating characteristic curve and odds ratios for first postoperative biomarkers to predict AKI. RESULTS: The SCr-defined vs CysC-defined AKI incidence differed substantially (43.6% vs 20.6%). Percentage agreement was 71% (κ = 0.38); stage 2 or worse AKI percentage agreement was 95%. Interleukin 18 and kidney injury molecule 1 discriminated for CysC-defined AKI better than for SCr-defined AKI. For interleukin 18 and kidney injury molecule 1, the areas under the receiver operating characteristic curve were 0.74 and 0.65, respectively, for CysC-defined AKI, and 0.66 and 0.58, respectively, for SCr-defined AKI. Fifth (vs first) quintile concentrations of both biomarkers were more strongly associated with CysC-defined AKI. For interleukin 18 and kidney injury molecule 1, the odds ratios were 16.19 (95% CI, 3.55-73.93) and 6.93 (95% CI, 1.88-25.59), respectively, for CysC-defined AKI vs 6.60 (95% CI, 2.76-15.76) and 2.04 (95% CI, 0.94-4.38), respectively, for SCr-defined AKI. Neutrophil gelatinase-associated lipocalin and liver fatty acid-binding protein associations with both definitions were similar. The CysC definitions and SCr definitions were similarly associated with clinical outcomes of resource use. CONCLUSIONS AND RELEVANCE: Compared with the SCr-based definition, the CysC-based definition is more strongly associated with urine interleukin 18 and kidney injury molecule 1 in children undergoing cardiac surgery. Consideration should be made for defining AKI based on CysC in clinical care and future studies.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.322
Teacher spread0.297 · 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".

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Citations81
Published2015
Admission routes2
Has abstractyes

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