Prevalence of acute kidney injury after cardiac surgery:A systematic review and meta-analysis on risk factors and different diagnostic criterias(AKIN,RIFLE,KDIGO)
Notice bibliographique
Résumé
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we searched OpenGrey (www.opengrey.eu/) and the World Health Organization Clinical Trials Registry (who.int/ictrp) for unpublished literature and ongoing studies.To ensure the literature saturation, the list of the included research references or the relevant reviews found by searching was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.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,009 | 0,004 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».