A systematic review of ethnic differences in the rate of renal progression in CKD patients
Notice bibliographique
Résumé
Chronic kidney disease (CKD) is a public health problem of increasing importance, consuming a growing proportion of health care resources. It is estimated that, in the United States, there are 19.2 million individuals with CKD [1], and this figure is expected to increase in parallel to the rising prevalence of hypertension and diabetes. By 2010, the projected number of end-stage renal disease (ESRD) patients will climb to over 650 000 [2], with a further predicted increase to 2.24 million by 2030 [3]. Therefore, improved understanding of the predictors of GFR decline is essential. Information about predictors would allow nephrologists to accurately predict those CKD patients at risk of progressing to ESRD, which would help to individualize patient care, allowing for optimal planning for renal replacement therapy (RRT), reduce the need for urgent dialysis and ultimately allow for more efficient allocation of scarce health care resources. In addition, the recognition of novel risk factors for progression may lead to the development of new therapies capable of altering the trajectory of the disease. It has been recognized that the incidence of ESRD is higher in ethnic minorities; however, the reasons for this have not been well defined. For example, American blacks are four times more likely to require dialysis than whites [4]. An increased burden of ESRD has also been shown to affect Hispanics and Asians [5]. Conversely, ethnic minorities tend, paradoxically, to have improved outcomes once started on haemodialysis [5,6]. Several mechanisms have been suggested to explain such differences, though these have not been proven. For example, a higher prevalence of co-morbidities, lower socioeconomic status and comparatively worse access to health care among ethnic minorities have been cited as reasons for the higher incidence of ESRD [7–9]. Such reasoning holds that the incidence of ESRD is dependent on the number of CKD patients at risk of progressing, and the higher prevalence of diabetes and hypertension in blacks may result in greater CKD [4]. Similarly, lower socioeconomic status in minorities may create inequalities in access to health care resources and thus reduce delivery of medical management known to slow the progression of renal dysfunction [10,11]. A second explanation is that a higher death rate amongst CKD patients in one ethnic group would leave fewer patients alive to require RRT, thus affecting the observed incidence rate of ESRD. Different thresholds for starting RRTwould also affect the measured incidence of ESRD. Finally, the higher incidence of ESRD amongst ethnic minorities may in fact be due to a faster rate of GFR decline and more rapid progression of renal disease. The purpose of this review is to summarize the available evidence on ethnic differences in the rates of CKD progression towards ESRD. Ideally, available studies would directly observe rates of GFR decline in CKD cohorts of different races. This would avoid such confounders as CKD prevalence, the competing risk of death and varying thresholds for starting RRT. Alternatively, in studies examining the incidence rates of ESRD, inferences can be made regarding progression rates only if attempts are made to account for differences in the baseline prevalence of CKD and in longitudinal mortality rates.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».