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Record W2953492042 · doi:10.1681/asn.2018101036

Impact of AKI on Urinary Protein Excretion: Analysis of Two Prospective Cohorts

2019· article· en· W2953492042 on OpenAlexafffund
Chi‐yuan Hsu, Raymond K. Hsu, Kathleen D. Liu, Jingrong Yang, Amanda Anderson, Jing Chen, Vernon M. Chinchilli, Harold I. Feldman, Amit X. Garg, L. Lee Hamm, Jonathan Himmelfarb, James S. Kaufman, John W. Kusek, Chirag R. Parikh, Ana C. Ricardo, Sylvia E. Rosas, Georges Saab, Daohang Sha, Edward D. Siew, James Sondheimer, Jonathan J. Taliercio, Wei Yang, Alan S. Go

Bibliographic record

VenueJournal of the American Society of Nephrology · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsWestern University
FundersNational Center for Advancing Translational SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Center for Research ResourcesNational Institute of General Medical SciencesCanadian Institutes of Health ResearchUniversity of Illinois at ChicagoUniversity of MarylandMichigan Institute for Clinical and Health ResearchNational Institutes of HealthKaiser Permanente
KeywordsMedicineCreatinineProteinuriaInterquartile rangeRenal functionProspective cohort studyInternal medicineKidney diseaseCohortAcute kidney injuryCohort studyUrologyUrineKidney

Abstract

fetched live from OpenAlex

Significance Statement Studies of the adverse renal consequences of AKI have almost exclusively focused on eGFR changes, whereas few studies have examined AKI’s effects on proteinuria. The authors analyzed data from two prospective cohort studies that assessed urine protein-to-creatinine ratio, BP, eGFR, medication use and other important covariates annually per research protocol and tracked interim episodes of hospitalization for AKI. They found that an episode of hospitalized AKI was independently and significantly associated with increased proteinuria. Further research is needed to examine worsening proteinuria as a potential mechanism by which AKI leads to accelerated loss of renal function. The authors’ findings also suggest that routine monitoring of proteinuria after AKI may be warranted, and highlight the need for research to determine how to best manage proteinuria post-AKI. Background Prior studies of adverse renal consequences of AKI have almost exclusively focused on eGFR changes. Less is known about potential effects of AKI on proteinuria, although proteinuria is perhaps the strongest risk factor for future loss of renal function. Methods We studied enrollees from the Assessment, Serial Evaluation, and Subsequent Sequelae of AKI (ASSESS-AKI) study and the subset of the Chronic Renal Insufficiency Cohort (CRIC) study enrollees recruited from Kaiser Permanente Northern California. Both prospective cohort studies included annual ascertainment of urine protein-to-creatinine ratio, eGFR, BP, and medication use. For hospitalized participants, we used inpatient serum creatinine measurements obtained as part of clinical care to define an episode of AKI ( i.e. , peak/nadir inpatient serum creatinine ≥1.5). We performed mixed effects regression to examine change in log-transformed urine protein-to-creatinine ratio after AKI, controlling for time-updated covariates. Results At cohort entry, median eGFR was 62.9 ml/min per 1.73 m 2 (interquartile range [IQR], 46.9–84.6) among 2048 eligible participants, and median urine protein-to-creatinine ratio was 0.12 g/g (IQR, 0.07–0.25). After enrollment, 324 participants experienced at least one episode of hospitalized AKI during 9271 person-years of follow-up; 50.3% of first AKI episodes were Kidney Disease Improving Global Outcomes stage 1 in severity, 23.8% were stage 2, and 25.9% were stage 3. In multivariable analysis, an episode of hospitalized AKI was independently associated with a 9% increase in the urine protein-to-creatinine ratio. Conclusions Our analysis of data from two prospective cohort studies found that hospitalization for an AKI episode was independently associated with subsequent worsening of proteinuria.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.440
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.361
Teacher spread0.345 · 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 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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Citations40
Published2019
Admission routes2
Has abstractyes

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