Post–Acute Kidney Injury Proteinuria and Subsequent Kidney Disease Progression
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".