Acute Kidney Injury in Cardiorenal Syndrome Type 1 Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: We evaluated the epidemiology and outcome of acute kidney injury (AKI) in patients with cardiorenal syndrome type 1 (CRS-1) and its subgroups: acute heart failure (AHF), acute coronary syndrome (ACS) and after cardiac surgery (CS). SUMMARY: We performed a systematic review and meta-analysis. CRS-1 was defined by AKI (based on RIFLE, AKIN and KDIGO), worsening renal failure (WRF) and renal replacement therapy (RRT). We investigated the three most common clinical causes of CRS-1: AHF, ACS and CS. Out of 332 potential papers, 64 were eligible - with AKI used in 41 studies, WRF in 25 and RRT in 20. The occurrence rate of CRS-1, defined by AKI, WRF and RRT, was 25.4, 22.4 and 2.6%, respectively. AHF patients had a higher occurrence rate of CRS-1 compared to ACS and CS patients (AKI: 47.4 vs. 14.9 vs. 22.1%), but RRT was evenly distributed among the types of acute cardiac disease. AKI was associated with an increased mortality rate (risk ratio = 5.14, 95% CI 3.81-6.94; 24 studies and 35,227 patients), a longer length of stay in the intensive care unit [LOSICU] (median duration = 1.37 days, 95% CI 0.41-2.33; 9 studies and 10,758 patients) and a longer LOS in hospital [LOShosp] (median duration = 3.94 days, 95% CI 1.74-6.15; 8 studies and 35,227 patients). Increasing AKI severity was associated with worse outcomes. The impact of CRS-1 defined by AKI on mortality was greatest in CS patients. RRT had an even greater impact compared to AKI (mortality risk ratio = 9.2, median duration of LOSICU = 10.6 days and that of LOShosp = 20.2 days). KEY MESSAGES: Of all included patients, almost one quarter developed AKI and approximately 3% needed RRT. AHF patients experienced the highest occurrence rate of AKI, but the impact on mortality was greatest in CS patients.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".