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Cardiac Biomarkers for Risk Stratification of Acute Kidney Injury After Pediatric Cardiac Surgery

2020· article· en· W3015934252 on OpenAlexaff
Jason H. Greenberg, Michael J. Parsons, Michael Zappitelli, Yaqi Jia, Heather Thiessen‐Philbrook, Prasad Devarajan, Allen D. Everett, Chirag R. Parikh

Bibliographic record

VenueThe Annals of Thoracic Surgery · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsHospital for Sick Children
FundersNational Center for Advancing Translational SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsMedicineAcute kidney injuryBiomarkerCreatinineCardiac surgeryInternal medicineNatriuretic peptideGalectin-3RifleCardiorenal syndromeRisk stratificationCardiologyCardiopulmonary bypassBrain natriuretic peptideHeart failure

Abstract

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BackgroundChildren undergoing a cardiac surgical procedure are at increased risk for acute kidney injury (AKI). Novel biomarkers are needed to improve risk stratification of AKI after cardiac surgery.MethodsWe enrolled children aged 1 month to 18 years old from July 2007 to December 2010 undergoing cardiopulmonary bypass. Three United States Food and Drug Administration-approved plasma biomarkers of cardiac stretch, N-terminal pro B-type natriuretic peptide (NTproBNP), inflammation (ST2), or fibrosis (galectin-3), were measured preoperatively and postoperatively within 6 hours of cardiac surgery. All analyses were stratified by age (<2 or ≥2 years old) to account for changing biomarker distributions during childhood and due to a significant interaction between biomarker and age for galectin-3 and NTproBNP (P < .05).ResultsPostoperatively, AKI, defined by a doubling of baseline serum creatinine, was diagnosed in 51 of 194 children <2 years and in 28 of 201 children ≥2 years. After multivariable adjustment, for children <2 years, none of the biomarkers were independently associated with AKI, whereas for children ≥2 years, the highest tertile of preoperative galectin-3 and NTproBNP as well as the postoperative galectin-3 and ST2 were associated with AKI.ConclusionsPreoperative plasma galectin-3 and NTproBNP and the first postoperative galectin-3 and ST2 levels were independently associated with AKI in children ≥2 years old. The performance of cardiac biomarkers after cardiac surgical procedure is affected by age, and research is required to develop biomarkers for children <2 years old. Children undergoing a cardiac surgical procedure are at increased risk for acute kidney injury (AKI). Novel biomarkers are needed to improve risk stratification of AKI after cardiac surgery. We enrolled children aged 1 month to 18 years old from July 2007 to December 2010 undergoing cardiopulmonary bypass. Three United States Food and Drug Administration-approved plasma biomarkers of cardiac stretch, N-terminal pro B-type natriuretic peptide (NTproBNP), inflammation (ST2), or fibrosis (galectin-3), were measured preoperatively and postoperatively within 6 hours of cardiac surgery. All analyses were stratified by age (<2 or ≥2 years old) to account for changing biomarker distributions during childhood and due to a significant interaction between biomarker and age for galectin-3 and NTproBNP (P < .05). Postoperatively, AKI, defined by a doubling of baseline serum creatinine, was diagnosed in 51 of 194 children <2 years and in 28 of 201 children ≥2 years. After multivariable adjustment, for children <2 years, none of the biomarkers were independently associated with AKI, whereas for children ≥2 years, the highest tertile of preoperative galectin-3 and NTproBNP as well as the postoperative galectin-3 and ST2 were associated with AKI. Preoperative plasma galectin-3 and NTproBNP and the first postoperative galectin-3 and ST2 levels were independently associated with AKI in children ≥2 years old. The performance of cardiac biomarkers after cardiac surgical procedure is affected by age, and research is required to develop biomarkers for children <2 years old.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.327
Teacher spread0.276 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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Citations20
Published2020
Admission routes1
Has abstractyes

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