Iron metabolism-related indicators as predictors of the incidence of acute kidney injury after cardiac surgery: a meta-analysis
Bibliographic record
Abstract
Some studies have found that ferroptosis plays an important role in the incidence of acute kidney injury (AKI) after cardiac surgery. However, whether iron metabolism-related indicators can be used as predictors of the incidence of AKI after cardiac surgery remains unclear. We aimed to systematically evaluate whether iron metabolism-related indicators can be used as predictors of the incidence of AKI after cardiac surgery via meta-analysis. Search methods: The PubMed, Embase, Web of Science, and Cochrane Library databases were searched from January 1971 to February 2023 to identify prospective observational and retrospective observational studies examining iron metabolism-related indicators and the incidence of AKI after cardiac surgery among adults. Data Extraction and Synthesis: The following data were extracted by two independent authors (ZLM and YXY): date of publication, first author, country, age, sex, number of included patients, iron metabolism-related indicators, outcomes of patients, patient types, study types, sample, and specimen sampling time. The level of agreement between authors was determined using Cohen’s κ value. The Newcastle–Ottawa Scale (NOS) was used to evaluate the quality of studies. Statistical heterogeneity across the studies was measured by the I2 statistic. The standardized mean difference (SMD) and 95% confidence interval (CI) were used as effect size measures. Meta-analysis was performed using Stata 15. After applying the inclusion and exclusion criteria, 9 articles on iron metabolism-related indicators and the incidence of AKI after cardiac surgery were included in this study. Meta-analysis revealed that after cardiac surgery, baseline serum ferritin (μg/L) (I2 = 43%, fixed effects model, SMD = −0.3, 95% CI:-0.54 to −0.07, p = 0.010), preoperative and 6-hour postoperative fractional excretion (FE) of hepcidin (%) (I2 = 0.0%, fixed effects model, SMD = −0.41, 95% CI: −0.79 to −0.02, p = 0.038; I2 = 27.0%, fixed effects model, SMD = −0.49, 95% CI: −0.88 to −0.11, p = 0.012), 24-hour postoperative urinary hepcidin (μg/L) (I2 = 0.0%, fixed effects model, SMD = −0.60, 95% CI: −0.82 to −0.37, p < 0.001) and urine hepcidin/urine creatinine ratio (μg/mmoL) (I2 = 0.0%, fixed effects model, SMD = −0.65, 95% CI: −0.86 to −0.43, p < 0.001) were significantly lower in patients who developed to AKI than in those who did not. After cardiac surgery, patients with lower baseline serum ferritin levels (μg/L), lower preoperative and 6-hour postoperative FE of hepcidin (%), lower 24-hour postoperative hepcidin/urine creatinine ratios (μg/mmol) and lower 24-hour postoperative urinary hepcidin levels (μg/L) are more likely to develop AKI. Therefore, these parameters have the potential to be predictors for AKI after cardiac surgery in the future. In addition, there is a need for relevant clinical research of larger scale and with multiple centers to further test these parameters and prove our conclusion. Trial Registration: PROSPERO identifier: CRD42022369380.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.072 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".