Comparison of Chronic Kidney Disease (CKD) Epidemiology Formula with Other Calculated Creatinine Formulas for the Determination of CKD in Cognitively Intact and Impaired Elderly Outpatients
Notice bibliographique
Résumé
Chronic kidney disease (CKD) is defined as a glomerular filtration rate (GFR) less than 60 mL/min per 1.73 m2 for three or more months. The prevalence of CKD increases with advancing age.1 Because serum creatinine is not an accurate measure for estimating renal function in elderly adults, creatinine clearance or GFR is used to assess CKD. The criterion standard methods for measuring GFR are based upon injection of a radioactive contrast agent such as iodothalamate or using a substance such as inulin. This method is impractical in regular clinical practice and is not cost effective, so calculated creatinine formulas are used instead. Various formulas for estimating the GFR such as the Cockroft-Gault adjusted for body surface area (CG/BSA), Modified Diet in Renal Disease (MDRD), Wright, and Mayo Clinic formulas have been suggested for calculating GFR from serum creatinine concentration.2-5 A new formula to estimate GFR from serum creatinine called the chronic kidney disease epidemiology (CKD-EPI) equation was recently developed and validated in some studies.6 CKD is associated with outcomes such as development of cardiovascular disease, progression to end-stage renal disease, hospitalization, and death in community-based populations.7, 8 Clinicians need a quick and reliable method to estimate renal function before determining proper doses of renally excreted medications. A recent study indicated that CKD-EPI estimated GFR (eGFR) is an independent predictor of incident adverse drug reactions in elderly adults.9 Little is known about the extent of the discrepancy between the CKD-EPI formulas and other calculated creatinine clearance formulas. The aim of this retrospective study was to compare a recently introduced CKD-EPI formula with other calculated creatinine clearance formulas in determining CKD in elderly outpatients. A chart review was conducted on consecutive adults aged 65 and older over a 9-month period. Data regarding age, sex, cognitive status, clock drawing, weight, height, and serum creatinine were collected. Serum creatinine was estimated using the isotope dilution mass spectrometry method. Pearson correlation coefficients, Bland-Altman plots, and kappa statistics were used for statistical analysis. Of 197 participants, 72% had mild cognitive impairment or dementia. Thirty-five percent of participants had Stage 3 renal disease according to the CKD-EPI formula, 36% according to the MDRD formula, 48% according to the CG/BSA formula, 48% according to the Wright formula, and 18% according to the Mayo clinic formula There was a high correlation between CKD-EPI and the MDRD (Pearson correlation coefficient (r) = 0.97, P < .001), CKD-EPI and the CG/BSA formulas (r = 0.91, P < .001), CKD-EPI and the Wright formula (r = 0.84, P < .001), and CKD-EPI and the Mayo Clinic formula (r = 0.84, P < .001). As shown in Bland-Altman plots (Figure 1), agreement between CKD-EPI and MDRD was better than agreement between CKD-EPI and the other formulas. In determining Stage 3 CKD, the kappa statistic was 0.91 between CKD-EPI and MDRD, 0.49 between CKD-EPI and the CG/BSA formula, 0.42 between CKD-EPI and the Mayo formula, 0.29 between CKD-EPI and the Wright formula, indicating low agreement between these last three formulas. The diagnosis of Stage 3 CKD in elderly adults with and without cognitive impairment depended on the formula used. Discordance of 1 mL/min was seen between CKD-EPI and MDRD, −9 mL/min between CKD-EPI and the CG/BSA, −12 mL/min between CKD-EPI and the Wright formulas, and 13 mL/min between CKD-EPI and the Mayo formulas for eGFR. The difference between CKD-EPI and MDRD was small, but the difference between CKD-EPI and the other formulas was significant. Similar differences were seen in the subgroup analysis comparing elderly adults with and without cognitive impairment. Even though there was strong correlation between the CKD-EPI and all four formulas studied, there was a clear discrepancy indicated by poor agreement between the CKD-EPI and the CG/BSA, Wright, and Mayo formulas. A limitation of the study was its cross-sectional nature and that eGFR was not correlated with definite or criterion standard measures of GFR, such as inulin clearance. In CKD screening, testing for proteinuria is also recommended in addition to eGFR. This was also a limitation, because this study did not have information on proteinuria. In conclusion, significant differences in eGFR were observed between the CKD-EPI equation and other calculated formulas in individuals with and without cognitive impairment. The most accurate method of estimating GFR and creatinine clearance in elderly adults is a topic of ongoing debate, and more research is needed to find an acceptable, validated formula. Conflict of Interest: None. Author Contributions: Dr. Alagiakrishnan: Study concept and design, acquisition of data, preparation of manuscript. Dr. Senthilselvan: Study design, analysis and interpretation of data, preparation of manuscript. Sponsor's Role: None.
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 | 0,009 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».