Proteinuria and reduced glomerular filtration rate as risk factors for acute kidney injury
Bibliographic record
Abstract
PURPOSE OF REVIEW: Acute kidney injury (AKI) is a major public health concern, and preexisting kidney disease may be one of the most important risk factors. We review recent epidemiologic evidence supporting baseline proteinuria and reduced glomerular filtration rate as risk factors for AKI. RECENT FINDINGS: In 2008, a case-control study of over 600 000 patients in an integrated healthcare system in California first quantified a graded association between reduced baseline estimated glomerular filtration rate (eGFR) and risk of dialysis-requiring AKI; it also showed proteinuria as an independent predictor for AKI. In 2010, a cohort study consisting of 1235 adults undergoing coronary artery bypass graft in Taiwan demonstrated that mild and heavy degrees of proteinuria detected by dipstick were associated with increasingly higher odds ratio of postoperative AKI, independent of chronic kidney disease stage. A US cohort study in 2010 of over 11 000 patients determined that elevated urine albumin-to-creatinine ratio (UACR) was an independent risk factor for hospitalizations with AKI; this association started with the submicroalbuminuric range (UACR 11-29 mg/g) and increased stepwise along severity of albuminuria, after adjustment for eGFR. A cohort study in 2010 of over 900 000 adults in Alberta demonstrated increased rates of hospital admissions with AKI for patients with mild and moderate dipstick proteinuria across all values of eGFR. SUMMARY: The presence of baseline proteinuria and reduced baseline eGFR are powerful independent predictors for AKI and should be taken into account in clinical practice to identify high-risk patients for receipt of aggressive preventive measures to reduce risk of AKI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".