#1090 Incidence and adverse outcomes of acute kidney disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background and Aims The estimated incidence of acute kidney disease (AKD) and its association with adverse outcomes are heterogeneous, which in part may be due to between-study differences in the definition of AKD. This study aims to summarize these estimates by definitions of AKD and to identify other study-level characteristics that may explain the observed heterogeneity. Method We searched MEDLINE, Embase, and Cochrane and references from eligible studies from inception until February 1, 2024 without language restriction for observational studies that assessed the incidence of AKD and its association with adverse outcomes including all-cause mortality, incident chronic kidney disease (CKD), and kidney failure. AKD definitions were classified as: 1) ADQI/ADQI-equivalent and 2) KDIGO/KDIGO-equivalent. We used random effects meta-analysis to calculate pooled estimates of incidence and outcomes, presented as incidence and odds ratios (ORs) with 95% confidence intervals (CIs). Results Of 1,883 identified reports, 59 studies involving nearly 6 million participants met inclusion criteria. Most studies were classified as having good quality per New-Castle Ottawa scale (n = 44). The pooled incidence of AKD was higher when defined with ADQI/ADQI-equivalent compared with KDIGO/KDIGO-equivalent criteria {27.1% (95% CI: 20.6–35.7%) and 11.2% (95% CI: 7.8–16.1%), respectively (p for difference <0.001)} (Fig. 1). The pooled odds ratio of all-cause mortality associated with AKD was similar whether defined with KDIGO/KDIGO equivalent or ADQI/ADQI-equivalent criteria {3.9 (95% CI: 2.2–6.8) vs. 2.9 (95%CI: 2.0–4.2), p for difference = 0.4} (Fig. 2). After accounting for the baseline AKI status in study populations, both incidence and odds risk of all-cause mortality were similar for the two definitions. For instance, the incidence of AKD was 13.7% and 11.2% and odds ratio of all-cause mortality was 4.2 and 3.9 per ADQI/ADQI-equivalent and KDIGO/KDIGO-equivalent definition, respectively, in cohorts that did not restrict study populations to patients with AKI. Similar results were observed for CKD and kidney failure, except that the association between AKD and kidney failure was stronger for studies using the KDIGO-equivalent definition. Conclusion Estimates for AKD incidence and AKD associated risk for clinical outcomes vary by the AKD definition. The application of standardized and harmonized definitions of AKD especially regarding a distinction between study population with and without baseline AKI is essential for establishing AKD burden and informing preventive and management approaches.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".