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Post–Acute Kidney Injury Proteinuria and Subsequent Kidney Disease Progression

2020· article· en· W3000897988 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJAMA Internal Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoSickKids Foundation
FundersNational Center for Advancing Translational SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of HealthU.S. Department of Veterans Affairs
KeywordsMedicineRenal functionAcute kidney injuryKidney diseaseHazard ratioProteinuriaInternal medicineCreatinineAlbuminuriaProspective cohort studyCohort studyUrologyNephrologyKidneyConfidence interval

Abstract

fetched live from OpenAlex

Importance: Among patients who had acute kidney injury (AKI) during hospitalization, there is a need to improve risk prediction such that those at highest risk for subsequent loss of kidney function are identified for appropriate follow-up. Objective: To evaluate the association of post-AKI proteinuria with increased risk of future loss of renal function. Design, Setting, and Participants: The Assessment, Serial Evaluation, and Subsequent Sequelae in Acute Kidney Injury (ASSESS-AKI) Study was a multicenter prospective cohort study including 4 clinical centers in North America included 1538 patients enrolled 3 months after hospital discharge between December 2009 and February 2015. Exposures: Urine albumin-to-creatinine ratio (ACR) quantified 3 months after hospital discharge. Main Outcomes and Measures: Kidney disease progression defined as halving of estimated glomerular filtration rate (eGFR) or end-stage renal disease. Results: Of the 1538 participants, 769 (50%) had AKI durring hospitalization. The baseline study visit took place at a mean (SD) 91 (23) days after discharge. The mean (SD) age was 65 (13) years; the median eGFR was 68 mL/min/1.73 m2; and the median urine ACR was 15 mg/g. Overall, 547 (37%) study participants were women and 195 (13%) were black. After a median follow-up of 4.7 years, 138 (9%) participants had kidney disease progression. Higher post-AKI urine ACR level was associated with increased risk of kidney disease progression (hazard ratio [HR], 1.53 for each doubling; 95% CI, 1.45-1.62), and urine ACR measurement was a strong discriminator for future kidney disease progression (C statistic, 0.82). The performance of urine ACR was stronger in patients who had had AKI than in those who had not (C statistic, 0.70). A comprehensive model of clinical risk factors (eGFR, blood pressure, and demographics) including ACR provided better discrimination for predicting kidney disease progression after hospital discharge among those who had had AKI (C statistic, 0.85) vs those who had not (C statistic, 0.76). In the entire matched cohort, after taking into account urine ACR, eGFR, demographics, and traditional chronic kidney risk factors determined 3 months after discharge, AKI (HR, 1.46; 95% CI, 0.51-4.13 for AKI vs non-AKI) or severity of AKI (HR, 1.54; 95% CI, 0.50-4.72 for AKI stage 1 vs non-AKI; HR, 0.56; 95% CI, 0.07-4.84 for AKI stage 2 vs non-AKI; HR, 2.24; 95% CI, 0.33-15.29 for AKI stage 3 vs non-AKI) was not independently associated with more rapid kidney disease progression. Conclusions and Relevance: Proteinuria level is a valuable risk-stratification tool in the post-AKI period. These results suggest there should be more widespread and routine quantification of proteinuria after hospitalized AKI.

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.

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)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.657
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.009
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.345
Teacher spread0.323 · 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