Prevalence of acute kidney injury after cardiac surgery:A systematic review and meta-analysis on risk factors and different diagnostic criterias(AKIN,RIFLE,KDIGO)
Bibliographic record
Abstract
Abstract Objective: This systematic review and meta-analysis aimed to determine the incidence and some of risk factors of AKI after cardiac surgery using all three diagnostic criteria (AKIN,RIFLE, and KIDGO). Method: We searched for published literature in the English language in MEDLINE via PubMed, EMBASETM via Ovid, The Cochrane Library, and Trip database. For literature published in other languages, we searched national databases (Magiran and SID) , KoreaMed and LILACS, and we searched OpenGrey (www.opengrey.eu/) and the World Health Organization Clinical Trials Registry (who.int/ictrp) for unpublished literature and ongoing studies.To ensure the literature saturation, the list of the included research references or the relevant reviews found by searching was studied(MS). The keywords used in the search strategy were Acute kidney injury,acute renal failure, creatinine,cardiac surgery,heart surgury, Coronary artery bypass grafting (CABG),valve replacement,RIFLE (risk, injury, failure, loss, end-stage renal disease) ,Acute Kidney Injury Network (AKIN),KDIGO (Kidney Disease: Improving Global Outcomes) , which were combined using the AND, OR, and NOT operators. Results: A total of 33298 patients who had undergone the cardiac surgery were studied. Based on the random effect model the total prevalence of AKI in 33298 patients undergone the heart surgery was 26.3% (95% confidence interval[CI]:26.1%,26.6%, I 2 =99.5%). the total prevalence of AKI in patients undergone cardiac surgery based on AKIN criteria was 21.6% (95% CI –21.2%, 22.1%,I 2 =98.5%) of whom 21.6% (95% CI-21.2%,22.1%,I 2 =98.5%) were classified as AKIN stage 1, whereas 3% (95% CI-2.7%%,3.4%%,I 2 =90.6%) were classified as AKIN stage 2, and 3.2% (95% CI-3.0%%,3.4%%,I 2 =97.2%) were classified as AKIN stage 3. the total prevalence of AKI in patients undergone cardiac surgery based on RIFLE criteria was 26.0%% (95% CI –25.6%, 26.5%,I 2 =99.4%) . total prevalence of AKI in patients undergone cardiac surgery based on KDIGO criteria was 34.7% (95% CI –33.8%, 35.7%,I 2 =98.4%). the highest prevalence of AKI in patients undergone cardiac surgery was in Brazil 45.7% (95% CI –43.6%, 47.8%) based on 3 articles included followed by USA with a prevalence of 29.6%(95% CI –28.7%, 29.4%) based on 7 articles included, Uruguay with a prevalence of 36.1%(95% CI –35%, 37.2%) based on 1 article, Canada with a prevalence of 16.7%(95% CI –15.7%, 17.6%) based on 4 articles included and Italy with a prevalence of 10.6%(95% CI –9.8%, 11.3%) based on 3 articles included. Conclusion: AKI after cardiac surgery is a common symptom, although most often more severe in elderly patients. The prevalence of AKI after cardiac surgery based on KDIGO criteria was found to be higher than RIFLE and AKIN. The prevalence of AKI regardless of the definition used showed a decreasing trend from 2009 to 2019. Our findings pointed to the superiority of the KDIGO criterion over RIFLE and AKIN for diagnosing and evaluating AKI after cardiac surgery. However, the widespread acceptance of consensus definitions ( RIFLE and AKIN criteria) for AKI is still reflected in the studies. In order to progress further, establishment of a uniform definition for AKI seems necessary.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| 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".