Urinary Epidermal Growth Factor and CKD Progression: The ASSESS-AKI Study
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) and chronic kidney disease (CKD) are interconnected syndromes with AKI recognized as a risk factor for CKD incidence or progression. However, biomarkers of repair or resilience, such as epidermal growth factor (EGF), may help better inform this risk, given the limitations of serum creatinine (sCr) in the setting of AKI. Methods: We enrolled 1,538 hospitalized patients prospectively in the multicenter Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury (ASSESS-AKI) Study. We measured urinary epidermal growth factor (uEGF) from samples at 3 months post-discharge. The primary outcome was a composite of CKD incidence, progression, or development of end-stage kidney disease (ESKD). We also evaluated change in estimated glomerular filtration rate (eGFR) over time by EGF quartile. Results: 299 (20%) patients developed the primary outcome at a median of 4.3 years follow-up. Patients in the fourth quartile of uEGF had higher eGFR at baseline and at 3-month follow-up compared to those in quartiles 1-3, as well as significantly lower albuminuria. Each 2-fold higher uEGF level was significantly associated with decreased risk of the composite outcome (HR 0.65; 95% CI: 0.59-0.71). This association remained robust after adjustment for demographic factors, baseline kidney function, urinary albumin, and other urinary biomarkers of injury and inflammation (aHR 0.65, 95% CI: 0.54-0.79). Patients in uEGF quartile 1 had the fastest decline in eGFR (-5.6% per year), compared to patients in uEGF quartiles 2-4 (-3.2, -2.8, -2.3% per year, respectively). Conclusions: Urinary EGF is a marker of repair after kidney injury, and higher levels of uEGF after discharge are associated with reduced risk of CKD and progression to ESKD in hospitalized patients with and without AKI. Funding: NIDDK Support
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".