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Record W2461565061 · doi:10.1097/ccm.0000000000001827

Urinary Tissue Inhibitor of Metalloproteinase-2 and Insulin-Like Growth Factor-Binding Protein 7 for Risk Stratification of Acute Kidney Injury in Patients With Sepsis

2016· article· en· W2461565061 on OpenAlexaff
Patrick M. Honoré, H. Bryant Nguyen, Michelle N. Gong, Lakhmir S. Chawla, Sean M. Bagshaw, Antonio Artigas, Jing Shi, Olivier Joannès-Boyau, Jean‐Louis Vincent, John A. Kellum

Bibliographic record

VenueCritical Care Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of Alberta
FundersCytoSorbents EuropeNational Institutes of HealthGrifolsAstute MedicalAlereGeorge Washington UniversityLa Jolla Pharmaceutical CompanyPfizer
KeywordsMedicineAcute kidney injuryTissue inhibitor of metalloproteinaseSepsisBiomarkerKidney diseaseInternal medicinePopulationInsulin-like growth factor-binding proteinInsulin-like growth factorKidneyGastroenterologyEndocrinologyGrowth factorMatrix metalloproteinaseBiologyReceptor

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine the performance of the urinary biomarker panel tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 in patients with sepsis at ICU admission. To investigate the effect of nonrenal organ dysfunction on tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 in this population. METHOD: In this ancillary analysis, we included patients with sepsis who were enrolled in either of two trials including 39 ICUs across Europe and North America. The primary endpoint was moderate-severe acute kidney injury (equivalent to Kidney Disease Improving Global Outcome stage 2-3) within 12 hours of enrollment. We assessed biomarker performance by calculating the area under the receiver operating characteristic curve, sensitivity, specificity, and negative and positive predictive values at three cutoffs: 0.3, 1.0, and 2.0 (ng/mL)/1,000. We also calculated nonrenal Sequential Organ Failure Assessment scores for each patient on enrollment and compared tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 results in patients with and without acute kidney injury and across nonrenal Sequential Organ Failure Assessment scores. Finally, we constructed a clinical model for acute kidney injury in this population and compared the performance of the model with and without tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7. RESULTS: We included 232 patients in the analysis and 40 (17%) developed acute kidney injury. We observed significantly higher urine tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 in patients with acute kidney injury than without acute kidney injury in both patients with low and high nonrenal Sequential Organ Failure Assessment scores (p < 0.001). The area under the receiver operating characteristic curve (95% CI) of tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 was 0.84 (0.73-0.92) and 0.85 (0.76-0.94), in low and high nonrenal Sequential Organ Failure Assessment score subgroups. Performance of the tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 test was not modified by nonrenal Sequential Organ Failure Assessment (p = 0.70). In multivariate analysis, the addition of tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 significantly improved the performance of a clinical model for predicting acute kidney injury (p = 0.015). CONCLUSION: Urinary tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 accurately predicts acute kidney injury in septic patients with or without other organ failures.

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)0.000
metaresearch head score (Gemma)0.004
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.327
Teacher spread0.308 · 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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Citations124
Published2016
Admission routes1
Has abstractyes

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