Iron metabolism-related indicators as predictors of the incidence of acute kidney injury after cardiac surgery: a meta-analysis
Notice bibliographique
Résumé
Background 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.Objectives 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.Results 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.Conclusion 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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,019 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,020 | 0,064 |
| Bibliométrie | 0,010 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».