The impact of acute kidney damage in the community
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: The aim was to assess incidence of acute kidney disease and disorders (AKD) and acute kidney injury (AKI) episodes and their impact on progression of renal dysfunction and risk of all-cause mortality in the community. METHODS: Community of 1 863 731 aged >23 years with at least two serum creatinine measurements. eGFR was calculated using the chronic kidney disease (CKD)-EPI formula. CKD, AKD and AKI were defined according to the harmonized KDIGO criteria (Lameire 2021). The sCr values and Risk, Injury, Failure, Loss, End-stage (RIFLE) scale was used to classify episodes. Progression of renal dysfunction and mortality were evaluated. RESULTS: A total of 56 850 episodes of AKD in 47 972 patients over 4.8 years were identified. AKD incidence of AKD was 3.51 and 12.56/1000 patients/year in non-CKD and CKD, respectively. One AKD episode was observed in 87.3% patients, two in 9.3%, and three or more in 3.4%. A second episode was less common in patients without CKD (10.3%) compared to those with CKD (18.4%). Among patients without CKD a total of 43.8% progressed to CKD, and those with previous CKD 63.1% had eGFR decline of >50%. The risk of progression to CKD was higher in women, older, overweight-obesity, and heart failure, as was the risk of eGFR decline >50% in CKD patients, although the number of AKD episodes was also a risk factor. AKI episodes were observed in 5646 patients with or without CKD. Of these, 12.7% progressed to CKD and of those with pre-existing CKD, 43.2% had an eGFR decline of >20%. In the total population, mortality within 3 months of detection of AKD episode occurred in 7% patients, and was even higher in patients with AKI at 30.1%. CONCLUSION: Acute elevations in serum creatinine in the community may pose a health risk and contribute to the development of CKD. Identification of therapeutic targets and provision of appropriate follow-up for those who survive an episode is warranted.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".