Temporal Trends in Acute Kidney Injury in Hospitalizations From 2009-2018 in Alberta: A Retrospective Population-Based Cohort Study
Bibliographic record
Abstract
Rationale & Objective: Studies have reported an increase in acute kidney injury (AKI) incidence; however, they are limited by administrative codes. We aimed to identify trends in AKI incidence, severity, and mortality using Kidney Disease: Improving Global Outcomes (KDIGO)-based definitions. Study Design: This is a retrospective, population-based cohort study. Setting & Population: Hospitalized adult patients in Alberta, Canada from 2009-2018. Exposures: AKI episodes were identified using validated KDIGO definitions. Outcomes: We assessed in-hospital and 90-day all-cause mortality. Analytical Approach: Generalized linear models with a Gaussian family were used to determine absolute rates of AKI and mortality. Rates of AKI and mortality were adjusted for patient demographics and comorbid conditions. Results: There were 339,986 hospitalizations with an episode of AKI (12.7%, 2,668,954 hospitalizations) with a median age of 70 years (56, 82) and 152,115 (44.7%) women. AKI rates increased by an unadjusted relative increase of 5.5% (95% confidence interval [CI], 4.2-6.9). When fully adjusted, a relative decrease of 11.2% (95% CI, 9.2-13.2) was seen in rates of AKI. Stage 1 AKI was most common (unadjusted mean rate, 659 per 100,000 person-years [95% CI, 655-662]). In-hospital mortality decreased across all stages of AKI with the greatest decrease noted in stage 3 AKI requiring kidney replacement therapy (unadjusted relative decrease 29.9% [95% CI, 20-38.6]). Similar trends were identified in 90-day mortality. Limitations: The primary strength of this paper is that it involves a large cohort of patients from a diverse population. The use of KDIGO definition of AKI is limited by the reliance on serum creatinine values. Conclusions: Although rates of AKI appear to be increasing, this seems to be largely driven by patient comorbid condition with the highest rates seen in stage 1 AKI. Furthermore, there was an overall increase in rates of AKI in patients aged younger than 60 and a decrease in the most elderly of patients in both the crude and adjusted data, suggesting potential changes in practice patterns and patient characteristics. Despite this increase, there was an overall decrease in mortality, especially in severe forms 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".