The Risk of AKI in the Elderly With Advanced CKD
Bibliographic record
Abstract
Background: AKI in the elderly is associated with short- and long-term mortality, increased risk of ESKD, and functional decline. Estimating the incidence of AKI in advanced CKD is challenging, given that the 0.3 mg/dl threshold might not be of clinical significance. Using the KDIGO definitions, we assessed AKI frequency and recovery time in the elderly with advanced CKD as well as its predictors. Methods: We included all patients ≥ 70 years of age and followed for ≥3 months at the Sacré-Coeur kidney protection clinic from 2012 to 2020. All AKI episodes, defined by an increase of 0.3 mg/dL over 48 hours or 1.5x increase over 7 days during the followup period were recorded. Given the elevated baseline creatinine, we also identified subpopulations of patients who also reached 0.5 and 1.0 mg/dL elevation in creatinine. We assessed differences between age groups. AKI recovery was defined as a return to within 0.3 mg/dL from baseline, within 2 or 7 days. Results: We included 462 patients, of which 46 % were female, with an initial eGFR of 20 ± 8 mL/min/1.73m2, and followed for a median of 21 [9-38] months. The rate of AKI was 36 events/100 patient-years, with 39 % experiencing at least one episode. AKI incidence was similar across age strata (Table), but recovery using the 1.5x criteria in those ≥ 90 years was lower (43 % vs. 79 %, p = 0.04). CKD etiologies were not associated with the risk or recovery of AKI. Predisposing risk factors of AKI were a history of congestive heart failure (26 % vs. 13 %, p = 0.005) and liver disease (44 % vs 15 %, p = 0.02). Conclusions: AKI frequency is elevated in the elderly with severe CKD, with an increased risk of non-recovery in the very old. Funding: Private Foundation 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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".