Incidence and Adverse Outcomes of Acute Kidney Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
RATIONALE & OBJECTIVE: Estimates of the incidence of acute kidney disease (AKD) and its associated adverse outcomes are inconsistent, which may be due in part to differences in prior studies' definitions of AKD. This study sought to summarize these reports and identify study-level characteristics, including the definition of AKD, that may explain the observed heterogeneity. STUDY DESIGN: Systematic review and meta-analysis. SETTING & STUDY POPULATIONS: Adults aged≥18 years and not receiving maintenance kidney replacement therapy. SELECTION CRITERIA FOR STUDIES: Observational studies that assessed the incidence of AKD and its association with adverse outcomes. DATA EXTRACTION: Two reviewers independently extracted data and assessed study quality. ANALYTICAL APPROACH: AKD definitions were classified as (1) Acute Disease Quality Initiative (ADQI) or ADQI-equivalent or (2) KDIGO (Kidney Disease: Improving Global Outcomes) or KDIGO-equivalent. A random-effects meta-analysis was used to calculate pooled estimates of incidence and the relationship between AKD and outcomes (mortality, kidney failure, onset of chronic kidney disease) summarized by ORs and 95% CIs. RESULTS: Among 1,883 identified studies, 59, involving nearly 6 million participants, met the inclusion criteria. Most studies were classified as being of good quality per the Newcastle-Ottawa scale (n=44). The pooled incidence of AKD was higher when defined by ADQI/ADQI-equivalent criteria compared with KDIGO/KDIGO-equivalent criteria (26.6% [95% CI, 20.3-34.9%] vs 11.1% [95% CI, 7.6-16.3%]; P<0.001). The pooled OR of all-cause mortality associated with AKD was similar whether defined with KDIGO/KDIGO-equivalent or ADQI/ADQI-equivalent criteria (3.8 [95% CI, 2.2-6.7] vs 3.0 [95% CI, 2.1-4.4]; P=0.5). After accounting for baseline acute kidney injury status, the incidence of AKD and its association with all-cause mortality were similar for the 2 definitions. The incidences of AKD were 13.6% and 11.1%, and the ORs for all-cause mortality were not different (4.2 [95% CI, 2.0-8.7] vs 3.8 [95% CI, 2.2-6.7]; P=0.8) using the ADQI/ADQI-equivalent and KDIGO/KDIGO-equivalent definitions, respectively. Similar results were observed for the association between AKD and the development of chronic kidney disease, but the association between AKD and kidney failure was stronger in studies that used the KDIGO/KDIGO-equivalent definition. LIMITATIONS: Heterogeneity persisted across most of the examined subgroups. CONCLUSIONS: Estimates for AKD incidence and AKD-associated risk for clinical outcomes vary by the definition used for AKD. These findings inform the assessment of the incidence and consequences of AKD in research and clinical settings. REGISTRATION: Registered at PROSPERO with identification number CRD42024515828. PLAIN-LANGUAGE SUMMARY: In this systematic review and meta-analysis of 59 studies involving nearly 6 million participants, we found that acute kidney disease (AKD) is globally prevalent and is associated with higher risks of adverse outcomes, including all-cause mortality, chronic kidney disease, and kidney failure. Estimates of AKD incidence and AKD-associated risks of clinical outcomes vary significantly depending on the definition of AKD used. The selection of the definition for AKD and the presence of baseline acute kidney injury influence the estimate of AKD incidence and its association with health consequences. The findings of this study should guide efforts to refine clinical guidelines and inform public health strategies to address the global burden of AKD more effectively.
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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 | 0.036 | 0.074 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".