Comparison of formulas for calculating estimated glomerular filtration rate and its diagnostic thresholds for chronic kidney disease in older adults: a cross-sectional study

Abstract

Objective

To evaluate and compare the CKD-EPI, BIS1 and MDRD formulas and diagnostic thresholds of 45 and 60 ml/min/1.73m² in older patients.

Method

A cross-sectional, descriptive, analytical observational study was conducted. Patients aged ≥65 years treated at a referral outpatient clinic between January 2020 and June 2022, were assessed. Patients with only one creatinine level or with transient GFR abnormalities were excluded.

Results

The GFR estimates using the CKD-EPI formula were higher than both the BIS1 and MDRD formulas in patient groups aged 65-74 years and 75-84 years. In the group of patients aged 85-94 years, the CKD-EPI showed no difference when compared with the MDRD, yielding higher estimates only compared with the BIS1. Greater dispersion was found between the CKD-EPI and BIS1, showing less agreement between these formulas, as confirmed by the Kappa test (76.7%), while there was almost perfect agreement between the CKD-EPI and MDRD.

Conclusion

The BIS1 formula showed stronger correlation of the decrease in eGFR with advancing age, reflecting the physiological renal aging process and serving as a potentially useful tool for estimating GFR in older adults. The formula can help provide a more accurate diagnosis of CKD and aid planning of interventions to slow the progression of CKD and predict the risk of mortality from cardiovascular diseases.

Keywords
Chronic Renal Failure; Glomerular Filtration Rate; Aged; Diagnosis

Resumo

Objetivo

Avaliar e comparar as fórmulas CKD-EPI, BIS1 e MDRD e o limiar diagnóstico de 45 e 60 ml/min/1,73m² em pacientes idosos.

Método

Estudo observacional do tipo transversal, de caráter descritivo e analítico. Foram incluídos pacientes atendidos em ambulatório de referência entre janeiro/2020 e junho/2022, com idade ≥65 anos. Foram excluídos pacientes com apenas uma dosagem de creatinina ou com alterações transitórias da TFG.

Resultados

Observou-se que entre o grupo de pacientes com idade entre 65-74 anos e 75-84 anos as estimativas da TFG feitas pela fórmula CKD-EPI foram superiores às fórmulas BIS1 e MDRD. Enquanto, no grupo de pacientes com 85-94 anos a CKD-EPI não apresentou diferença quando comparada com MDRD, permaneceu com estimativas maiores apenas quando comparada com a BIS1. Observamos maior dispersão entre CKD-EPI e BIS1, mostrando menor concordância entre essas fórmulas, confirmada pelo teste Kappa (76,7%) e a concordância quase perfeita entre CKD-EPI e MDRD.

Conclusão

A fórmula BIS1 apresentou melhor correlação da diminuição da TFGe conforme o avanço das faixas etárias, podendo-se relacionar com o processo de envelhecimento renal fisiológico, podendo ser uma ferramenta auxiliar na estimativa da TFG em idosos, auxiliando no diagnóstico mais preciso da DRC, assim como, no planejamento de intervenções que possam retardar a progressão da DRC e prever risco de mortalidade por doenças cardiovasculares.

Palavras-Chave:
Insuficiência Renal Crônica; Taxa de Filtração Glomerular; Idoso; Diagnóstico

INTRODUCTION

Chronic kidney disease (CKD) is defined by structural change in the kidney or a glomerular filtration rate (GFR) <60 ml/min/1.73m² for a period of ≥3 months¹. The detection and timely management of decline in kidney function can slow the progression of CKD to more severe forms, such as end-stage kidney disease.

Estimated GFR (eGFR) is often employed in clinical practice as a standardized measure, with several biomarkers used for this purpose, such as insulin or iohexol levels (exogenous markers), and creatinine or cystatin C (endogenous markers). Currently, creatinine is the most commonly used measure due to its accuracy and low cost, and is estimated with formulas such as the Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations²-⁶.

Diagnosing CKD in older adults can be challenging because, besides aging-related physiological decline in GFR and changes in body composition (lower muscle mass and bone density plus increased adipose tissue)⁷, there is a growing incidence of type 2 diabetes mellitus (DM2), systemic arterial hypertension (SAH), other conditions, and non-communicable diseases contributing to a rise in the prevalence of CKD⁸,⁹.

In addition, the classification of CKD across different age groups using the MDRD and CKD-EPI formulas has limitations, raising doubts as to whether the changes in GFR are due to the aging process alone¹⁰.. In an effort to address this limitation, the Berlin Initiative Equation (BIS) was recently developed and specifically adapted for older individuals¹¹. Validated equations for estimating GFR in this age group were lacking, especially in cases of normal or only moderately-reduced kidney function, where an age-calibrated formula can reduce incorrect classification of individuals with GFR <60 mL/min/1.73m² or >60 mL/min/1.73m² 12.

Assessing GFR is vital in clinical practice and has always posed a challenge. It is paramount to interpret signs, symptoms and laboratory anomalies which may be an indication of kidney disease and to determine drug dosing and help detect, control and estimate CKD progression and prognosis. Thus, early recognition of high-risk populations with decreased kidney function is important in CKD. Assessment based on 24-hour creatinine clearance has practical limitations and can lead to errors in classification due to pre-analytical and analytical interference and limitations. In an attempt to attain an estimate as close as possible to the true GFR, a number of different formulas for estimating GFR have been developed, including the CKD-EPI, MDRD and BIS1 equations¹³,¹⁴.

Therefore, the objective of the present study was to assess and compare the CKD-EPI, BIS1 and MDRD equations for calculating GFR in older adults and the diagnostic threshold of 45 and 60 ml/min/1.73m² for CKD in these patients.

METHOD

An analytical, descriptive, cross-sectional study was conducted based on data from medical records of patients treated at the nephrology outpatient clinic of the Center for Medical Specialties of CESUPA (CEMEC) in Belém city, Pará state, Brazil between January 2020 and June 2022. The study population was selected by convenience sampling.

The sample size was calculated for a 95% confidence interval and 5% margin of error. The ideal sample size for identifying the association investigated was estimated at 231 patients for a more heterogeneous sample with a statistical power of 50%, and at 173 patients for a more homogenous population with a statistical power of 80%.

The study included patients of both genders, aged ≥65 years (matching age for validating BIS1formula and used in previous studies¹²,¹⁵), who had at least 2 serum creatinine measurements registered in their medical records, with a time interval of ≥ 3 months between readings. Patients that had transient changes in GFR were excluded.

The variables collected (age, gender, self-reported race, and creatinine values) and comorbidities were analyzed according to data from medical records (Systemic Arterial Hypertension - SAH; type 2 Diabetes Mellitus – DM2; Cardiovascular Disease – CVD; Obesity and Dyslipidemia).

Laboratory serum creatinine values were determined using the kinetic colorimetric method, while estimated GFR (eGFR) were calculated using the MDRD, CKD-EPI (CKD EPI 2021) and BIS 1 equations³,⁵,¹¹.

The variables were extracted, summarized and compiled into tables and/or figures using descriptive statistics according to the nature of the variables and distributions. The normality of the variables was assessed using the Shapiro-Wilk test. Difference between the groups with and without comorbidities for numeric variables was tested using the Mann-Whitney test, whereas age groups were evaluated using the Kruskall-Wallis test, with application of Dunn´s post-hoc test in the event of rejection of the null hypothesis. Related (same age for each formula) groups were compared using Friedman´s test, with Dunn´s post-hoc test when p<0.05.

The association between independent groups (with and without comorbidities; dichotomous diagnostic thresholds) and dichotomous variables was investigated using Fisher´s Exact test and, where these were polychotomous (diagnostic thresholds with > 2 categories), the association was investigated using the G test followed by Residuals Analysis for chi-squared when independence was rejected.

Agreement among the results of the formulas investigated was assessed using Fleiss´ Kappa test (categorical variable) or the Intraclass Correlation Coefficient (numeric variable). Also, Bland Altman plots were employed to illustrate the relationship between the GFRs estimated by the formulas analyzed. The level of statistical significance adopted for the statistical tests was set at 5% (0.05) for rejection of the null hypothesis.

Data were collected from medical records after approval of the project by the research ethics committee of the Centro Universitário do Estado do Pará (CESUPA), under Permit no. 5.308.765.

RESULTS

A total of 574 medical records of patients treated at the nephrology outpatient clinic were initially analyzed, with selection of 254 patients for inclusion in the study. Participants had a mean age of 75?7.6 years (range 65-103 years) (95%CI: 74.1–76.1), mean creatinine level of 1.6?1.0 mg/dl (95%CI: 1.4–1.7) and 61.8% were female (95%CI: 55.7–67.6). With regard to comorbidities presented by the patients studied, 88.6% (n=225) had systemic arterial hypertension (SAH); 48.8% (n=124) type II Diabetes Mellitus (DM2); 67.7% (n=172) dyslipidemia; 30.7% (n=78) obesity and 23.2% cardiovascular disease (CVD).

Figure 1 depicts the distribution of individual measurements and median values for GFR estimated using the formulas CKD-EPI (Median=47.3; IQR= 34.4–61.9), BIS1 (Median=42.9; IQR=34.1–53.8) and MDRD (Median=44.4; IQR=32.0–57.5), providing a comparison of formulas and revealing significant differences between the values obtained by the CDK-EPI versus the BIS1 (p<0.001) and the CDK-EPI versus the MDRD (p<0,001), but no difference for the MDRD versus the BIS1 (p=0.889).

Figure 1
Median values (median +P95) and comparison of GFR values (mL/min/1.73m²) estimated by CDK-EPI, BIS1 and MDRD formulas. Belém, Pará state, 2023.

A comparison of estimated GFR (eGFR) by age group and formula is depicted in Figure 2. The 65-74 years age group contains a higher number of patients and exhibits a statistically significant difference among all 3 formulas. In the 75-84 years group, a statistical difference was found only for the CKD-EPI vs. BIS1 and the CKD-EPI vs. the MDRD. The GFRs estimated by the CKD-EPI formula were higher across all age groups than the values obtained using the other formulas.

Figure 2
Comparison of eGFR (mL/min/1.73 m²) obtained by CKD-EPI, BIS1 and MDRD formulas in 65-74 years (1A), 75-84 years (1B) and 85-94 years (1C) age groups. Belém, Pará state, 2023.

Regarding the distribution of patients according to the cut-off points for GFR thresholds, there was high agreement of values estimated by the CKD-EPI and BIS1 (Agreement=76.7%; Kappa=0.629) formulas and almost perfect agreement between the CKD-EPI and MDRD (Agreement=87.8%; Kappa=0.808) and between the BIS1 and MDRD (Agreement=82.6%; Kappa=0.712) formulas.

The analysis of the graphical pattern showing dispersion of differences among the formulas (Figure 3) suggests lower agreement between the CKD-EPI and BIS1 formulas, with higher mean difference from zero relative to the other comparisons (Mean of differences=4.71, 95%CI= -7.71 to 17.13; Figure 3A). Furthermore, the results of comparing the CKD-EPI versus BIS1 (Figure 3A) and the CKD-EPI versus MDRD (Figure 3B) show that the positive difference between the formulas widens with increasing mean, the opposite pattern to that seen when comparing the BIS1 versus the MDRD (Figure 3C).

Figure 3
Bland-Altman plot for mean and 95% Confidence Interval of agreement of GFR values (mL/min/1.73m²) estimated by CKD-EPI, BIS1 and MDRD formulas. Belém, Pará state, 2023.

Examining estimated crude values, the intraclass correlation coefficient (ICC) of comparing CKD-EPI versus BIS1 was equal to 0.956 (95%CI=0.846–0.980; p<0.001). CKD-EPI versus MDRD was 0.978 (95%CI=0.962–0.986; p<0.001) and MDRD versus BIS1 was 0.964 (95%CI=0.948–0.974; p<0.001).

Comparison of GFR (mL/min/1.73m²) estimated using the CKD-EPI, BIS1 and MDRD formulas and the comorbidities analyzed showed significantly lower values for all formulas in patients with SAH, DM2 and CVD, and no difference in those with and without dyslipidemia or across different BMI categories (Table 1).

Table 1
Comparison of eGFR (mL/min/1.73m²,) according to comorbidity and formula. Belém, Pará state, 2023.

Condition	CKD-EPI Median (IQR)	p-value*	BIS 1 Median (IQR)	p-value*	MDRD Median (IQR)	p-value*
SAH
Yes (n=225)	45.8 (31.9 – 59.6)	<0.001	40.5 (33.3 – 52.3)	<0.001	43.3 (30.9 – 55.9)	<0.001
No (n=29)	60.7 (48.8 – 74.2)		54.1 (43.9 – 61.4)		59.1 (47.5 – 67.9)
DM 2
Yes (n=124)	41.6 (30.6 – 59.5)	0.008	40.0 (31.9 – 53.1)	0.026	39.1 (29.1 – 54.2)	0.004
No (n=130)	51.8 (40.0 – 65.4)		46.1 (37.6 – 54.7)		49.0 (38.1 – 59.7)
Dyslipidemia
Yes (n=172)	47.2 (34.8 – 62.0)	0.905	42.5 (34.5 – 54.1)	0.472	43.9 (32.2 – 57.5)	0.787
No (n=82)	48.1 (31.4 – 63.5)		43.3 (31.4 – 51.6)		46.0 (31.1 – 58.9)
CVD
Yes (n=59)	40.8 (28.4 – 53.0)	0.006	38.3 (29.4 – 47.8)	0.005	38.4 (28.5 – 48.9)	0.005
No (n=195)	48.4 (36.3 – 65.6)		44.4 (36.2 – 54.7)		45.9 (34.1 – 60.7)
Normal BMI
Yes (n=176)	47.6 (34.7 – 62.0)	0.736	43.4 (34.2 – 53.9)	0.992	45.5 (32.2 – 57.5)	0.618
No (n=78)	46.0 (33.0 – 63.9)		41.8 (33.8 – 54.0)		43.2 (31.1 – 58.7)

• *Mann-Whitney Test. IQR: interquartile range. SAH: systemic arterial hypertension. DM2: type 2 diabetes mellitus. CVD: cardiovascular disease. BMI: body mass index. Source: study protocol, 2023.

Comparing the difference in frequency of CKD diagnoses using the current criteria of eGFR <60 mL/min/1.73m² versus eGFR <45 mL/min/1.73m², fewer patients were diagnosed by all formulas when applying the lower limit, with 28.7% of cases defined as not having CKD using the CKD-EPI, 29.1% by the MDRD and 29.5% by the BIS1 equation.

Regarding the eGFR thresholds of 45 and 60 mL/min/1.73m², both calculated by the CKD-EPI, BIS1 and MDRD formulas, a comparison of patients classified above and below the defined cut-off points by comorbidity is presented in Table 2. For estimates using the BIS1 formula, there was a significant association between the presence of SAH and CVD in patients with eGFR <45 (p=0.002 and p=0.036, respectively) and <60 mL/min/1.73m² (p=0.024 and p=0.012, respectively) and between the presence of DM2 and eGFR <45 mL/min/1.73m² (p=0.044).

Table 2
Distribution of patients according to diagnostic thresholds for eGFR of 45 and 60 mL/min/1.73m², and presence of comorbidities. Belém, Pará state, 2023.

Formula and variable	eGFR		p-value*	eGFR		p-value*
	<45	≥45		<60	≥60
CKD-EPI	(N=112)	(N=142)		(N=184)	(N=70)
SAH	106 (94.6%)	119 (83.8%)	0.009	171 (92.9%)	54 (77.1%)	0.001
DM2	68 (60.7%)	56 (39.4%)	0.001	95 (51.6%)	29 (41.4%)	0.162
Dyslipidemia	79 (70.5%)	93 (65.5%)	0.420	124 (67.4%)	48 (68.6%)	0.882
CVD	32 (28.6%)	27 (19.0%)	0.099	49 (26.6%)	10 (17.0%)	0.046
Abnormal BMI	38 (33.9%)	40 (28.2%)	0.340	56 (30.4%)	22 (31.4%)	0.880
BIS 1	(N=141)	(N=113)		(N=215)	(N=39)
SAH	133 (94.3%)	92 (81.2%)	0.002	195 (90.7%)	30 (76.9%)	0.024
DM2	77 (54.6%)	47 (41.6%)	0.044	108 (50.2%)	16 (41.0%)	0.303
Dyslipidemia	94 (66.7%)	78 (69.0%)	0.787	146 (67.9%)	26 (66.7%)	0.855
CVD	40 (28.4%)	19 (16.8%)	0.036	56 (26.1%)	3 (7.7%)	0.012
Abnormal BMI	45 (31.9%)	33 (29.2%)	0.683	67 (31.2%)	11 (28.2%)	0.851
MDRD	(N=128)	(N=126)		(N=199)	(N=55)
SAH	122 (95.3%)	103 (81.8%)	<0.007†	183 (92.0%)	42 (76.4%)	0.003
DM2	76 (59.4%)	48 (38.1%)	<0.001	101 (50.8%)	23 (29.5%)	0.002
Dyslipidemia	90 (70.3%)	82 (65.1%)	0.421	135 (67.8%)	37 (67.3%)	>0.999
CVD	36 (28.1%)	23 (18.3%)	0.075	54 (27.1%)	5 (9.1%)	0.004
Abnormal BMI	43 (33.6%)	35 (27.8%)	0.343	61 (30.7%)	17 (30.9%)	>0.999

• *Fisher´s Exact test. IQR: interquartile range. SAH: systemic arterial hypertension. DM2: Type 2 Diabetes Mellitus. CVD: cardiovascular disease. BMI: body mass index. Source: study protocol, 2023.

DISCUSSION

The results of the present study showed the overall relationships among the 3 formulas for calculating eGFR, revealing reassignments in CKD diagnoses based on eGFR and reclassifications in staging of some patients, with greater dispersion between CKD-EPI and BIS1 indicating lower agreement of these equations.

This lower agreement is likely explained by the fact that the BIS1 formula yielded lower estimated values across all analyses that decreased with advancing age, due to different levels of influence of comorbidities and to intrinsic factors such as sex and muscle mass between these formulas⁶,⁹,¹⁰,¹⁶-²⁰. This result may point to greater utility of this formula for differentiating between a physiological or pathological decline in eGFR among older patients and is consistent with a previous systematic review suggesting the BIS1 formula may be more accurate than the CKD-EPI for calculating eGFR in older adults²¹,²² .

A 2020 cohort study of 7,845 outpatients aged >70 years with CVD showed reclassification of CKD stages in approximately 35% of participants, with CKD prevalence increasing from 35.4% to 55.5% when using the BIS-1 compared to the CKD-EPI⁶.

Similar studies have also shown that BIS1 values were lower in older adults compared with GFR calculated using the CKD-EPI and MDRD formulas¹⁶,¹⁸. It is important to note that the BIS1 formula was developed and validated in a sample of people aged over 70 years⁶,²³, whereas the serum creatinine-based CKD-EPI included only a small percentage of older adults⁵.

By contrast, in participants aged 85-94 years, no statistically significant differences between the MDRD and CDK-EPI equations were found. Thus, almost perfect agreement occurs predominantly in this age group, a finding replicated in previous studies¹⁴,²⁴. Other studies of individuals aged > 85 years have also shown that the BIS1 offered greater accuracy than both the CKD-EPI and MDRD¹⁴.

Given that different stages of CKD can require different therapeutic approaches, not only in the context of CKD but also its comorbidities, these disagreements can further complicate the health care needs found for older patients, such as incorrect dosing of drugs owing to the need to adjust for renal function, the use of diagnostic mediums such as contrast, and the start of renal replacement therapy²⁵-²⁷.

In addition to comparing different formulas for estimating GFR, this study analyzed different thresholds for defining CKD in older adults. Results showed a stronger association for eGFR <45 mL/min/1.73m², calculated using the BIS1 formula, with the comorbidities SAH, DM2 and CVD. This threshold led to a higher percentage of people with comorbidities diagnosed with CKD, particularly when determined by increased mortality or greater risk of progression to end-stage renal failure, since the association of CKD with SAH, DM2 and CVD is clear¹,²⁸-³¹.

A 2021 cohort study of a Canadian population reported that the use of the same eGFR threshold (60 mL/min/1.73m²) for all ages can lead to overestimation of diagnosed cases of CKD in older adults and result in unnecessary interventions³². A systematic cohort review conducted in 2010 showed that eGFR < 60 mL/min/1.73m² was associated with increased mortality and higher risk for end-stage renal disease³³. However, many authors have cited that the reference group used to support this result was inadequate, concluding that the mortality risk in older patients with eGFR of 45–60 and >60 mL/min/1.73m² was similar, with the former being slightly higher, where significant change in mortality of this group only occurs when eGFR is < 45 mL/min/1.73m^{2 14},²²,³⁴,³⁵.

Accurate assessment of eGFR is useful in clinical practice for identifying possible cardiovascular outcomes. A study of a general population in China found that the use of serum creatinine-based eGFR, combined with albuminuria or otherwise, improved cardiovascular prediction, particularly for cardiovascular mortality and heart failure²⁵. Thus, correctly calculating eGFR in older adults is paramount, where existing comorbidities and commonly used drugs should also be taken into account³¹ and different classifications of renal function in older adults have direct important clinical implications¹⁵,²⁸.

The present study has some limitations. First, the analysis was based on a single center and relatively small sample. Second, equations that use cystatin C for calculating GFR were not adopted, and direct measurements of GFR to serve as a gold standard for comparing the eGFR derived from the formulas were not performed.

CONCLUSION

The BIS1 formula yielded lower eGFR values, favoring its use in the older population. The formula can be used in conjunction with the diagnostic threshold of 45 ml/min/1.73m² for reclassification of CKD patients, leading to more accurate diagnosis of CKD in older patients. Adoption of this approach can have a major impact on the public health system and on patient prognosis, given that the criteria of 45 ml/min/1.73m² showed stronger associations with comorbidities, particularly SAH, DM2 and CVD. Long-term studies should be conducted to validate the BIS1 for the Brazilian population and compare morbidity-mortality outcomes in patients diagnosed using different thresholds and equations.

• There was no funding for the execution of this work.

• DATA AVAILABILITY

  The full dataset underpinning the study results is available upon request from the corresponding author Lucas Lobato Acatauassu Nunes.

REFERÊNCIAS

• ¹
  Levin A, Stevens PE, Bilous RW, Coresh J, De Francisco AL, De Jong PE, et al. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney international supplements. 2013 Jan 1;3(1):1-50. Disponível em: 10.1038/kisup.2012.73
• ²
  Levey AS, Stevens LA, Schmid CH, Zhang Y, Castro III AF, Feldman HI et al. A new equation to estimate glomerular filtration rate. Annals of internal medicine. 2009 May 5;150(9):604-12. Disponível em: 10.7326/0003-4819-150-9-200905050-00006
• ³
  Michels WM, Grootendorst DC, Verduijn M, Elliott EG, Dekker FW, Krediet RT. Performance of the Cockcroft-Gault, MDRD, and new CKD-EPI formulas in relation to GFR, age, and body size. Clinical Journal of the American Society of Nephrology. 2010 Jun 1;5(6):1003-9. Disponível em: 10.2215/CJN.06870909
• ⁴
  Inker LA, Eneanya ND, Coresh J, Tighiouart H, Wang D, Sang Y et al. New creatinine-and cystatin C–based equations to estimate GFR without race. New England Journal of Medicine. 2021 Nov 4;385(19):1737-49. Disponível em: 10.1056/NEJMoa2102953
• ⁵
  Levey AS, Coresh J, Greene T, Stevens LA, Zhang Y, Hendriksen S et al, Chronic Kidney Disease Epidemiology Collaboration*. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Annals of internal medicine. 2006 Aug 15;145(4):247-54. Disponível em: 10.7326/0003-4819-145-4-200608150-00004
• ⁶
  Koppe L, Klich A, Dubourg L, Ecochard R, Hadj-Aissa A. Performance of creatinine-based equations compared in older patients. Diabetes. 2013;49:21-9. Disponível em: 10.5301/jn.5000297
• ⁷
  St-Onge MP, Gallagher D. Body composition changes with aging: the cause or the result of alterations in metabolic rate and macronutrient oxidation? Nutrition. 2010;26(2):152-155. Disponível em: 10.1016/j.nut.2009.07.004
• ⁸
  James MT, Hemmelgarn BR, Tonelli M. Early recognition and prevention of chronic kidney disease. The Lancet. 2010 Apr 10;375(9722):1296-309. Disponível em: 10.1016/S0140-6736(09)62004-3
• ⁹
  Turgut F, Balogun RA, Abdel-Rahman EM. Renin-angiotensin-aldosterone system blockade effects on the kidney in the elderly: benefits and limitations. Clinical Journal of the American Society of Nephrology. 2010 Jul 1;5(7):1330-9. Disponível em: 10.2215/CJN.08611209
• ¹⁰
  Denic A, Glassock RJ, Rule AD. Structural and functional changes with the aging kidney. Advances in chronic kidney disease. 2016 Jan 1;23(1):19-28. Disponível em: 10.1053/j.ackd.2015.08.004
• ¹¹
  Alshaer IM, Kilbride HS, Stevens PE, et al. External validation of the Berlin equations for estimation of GFR in the elderly. Am J Kidney Dis. 2014;63(5):862-865 Disponível em: 10.1053/j.ajkd.2014.01.013
• ¹²
  Schaeffner, E. S. Et al. Two novel equations to estimate kidney function in persons aged 70 years or older. Ann. Intern. Med., v. 157, n. 7, p. 471–481, out. 2012. Disponível em: 10.7326/0003-4819-157-7-201210020-00003
• ¹³
  Maioli C, Mangano M, Conte F, Del Sole A, Tagliabue L, Alberici F et al. The ideal marker for measuring GFR: what are we looking for? Acta Biomed. 2020 Dec 15;91(4):e2020132. Disponível em: 10.23750/abm.v91i4.9304
• ¹⁴
  Bustos-Guadaño F, Martín-Calderón JL, Criado-Álvarez JJ, Muñoz-Jara R, Cantalejo-Gutiérrez A, Mena-Moreno MC. Glomerular filtration rate estimation in people older than 85: Comparison between CKD-EPI, MDRD-IDMS and BIS1 equations. Nefrologia. 2017 Mar-Apr;37(2):172-180. Disponível em: 10.1016/j/nefroe.2017.04.011
• ¹⁵
  Ma Y, Shen X, Yong Z, Wei L, Zhao W. Comparison of glomerular filtration rate estimating equations in older adults: A systematic review and meta-analysis. Arch Gerontol Geriatr. 2023 Nov;114:105-107. Disponível em: 10.1016/j.archger.2023.105107
• ¹⁶
  Delanaye P, Jager KJ, Bökenkamp A, Christensson A, Dubourg L, Eriksen BO et al. CKD: a call for an age-adapted definition. Journal of the American Society of Nephrology. 2019 Oct 1;30(10):1785-805. Disponível em: 10.1681/ASN.2019030238
• ¹⁷
  Glassock R, Delanaye P, El Nahas M. An age-calibrated classification of chronic kidney disease. Jama. 2015 Aug 11;314(6):559-60. Disponível em: 10.1001/jama.2015.6731
• ¹⁸
  Lengnan X, Aiqun C, Ying S, Chuanbao L, Yonghui M. The effects of aging on the renal function of a healthy population in Beijing and an evaluation of a range of estimation equations for glomerular filtration rate. Aging (Albany NY). 2021 Mar 15;13(5):6904. Disponível em: 10.18632/aging.202548
• ¹⁹
  Hall JE, do Carmo JM, da Silva AA, Wang Z, Hall ME. Obesity-induced hypertension: interaction of neurohumoral and renal mechanisms. Circulation research. 2015 Mar 13;116(6):991-1006. Disponível em: 10.1161/CIRCRESAHA.116.305697
• ²⁰
  Neumann J, Ligtenberg G, Klein II, Koomans HA, Blankestijn PJ. Sympathetic hyperactivity in chronic kidney disease: pathogenesis, clinical relevance, and treatment. Kidney international. 2004 May 1;65(5):1568-76. Disponível em: 10.1111/j.1523-1755.2004.00552.x
• ²¹
  Han Q, Mesmo Norris KC, Smoyer KE, Rolland C, Van der Vaart J, Grubb EB. Albuminuria, serum creatinine, and estimated glomerular filtration rate as predictors of cardio-renal outcomes in patients with type 2 diabetes mellitus and kidney disease: a systematic literature review. BMC nephrology. 2018 Dec;19(1):1-3. Disponível em: 10.1186/s12882-018-0821-9
• ²²
  Zhang D, Zhao Y, Liu L, Li J, Zhang F et al. The practicality of different eGFR equations in centenarians and near-centenarians: which equation should we choose?. PeerJ. 2020 Feb 21;8:e8636. Disponível em: 10.7717/peerj.8636
• ²³
  Inker LA, Eneanya ND, Coresh J, Tighiouart H, Wang D, Sang Y et al. New creatinine-and cystatin C–based equations to estimate GFR without race. New England Journal of Medicine. 2021 Nov 4;385(19):1737-49. Disponível em: 10.1056/NEJMoa2102953
• ²⁴
  Wu J, Jia J, Li Z, Pan H, Wang A, Guo X et al. Association of estimated glomerular filtration rate and proteinuria with all-cause mortality in community-based population in China: A Result from Kailuan Study. Scientific reports. 2018 Feb 1;8(1):1-8. Disponível em: 10.1038/s41598-018-20554-3
• ²⁵
  Casal MA, Nolin TD, Beumer JH. Estimation of Kidney Function in Oncology: Implications for Anticancer Drug Selection and Dosing. Clin J Am Soc Nephrol. 2019 Apr 5;14(4):587-595. doi: 10.2215/CJN.11721018. Disponível em: 10.2215/CJN.11721018
• ²⁶
  Rudnick MR, Wahba IM, Leonberg-Yoo AK, Miskulin D, Litt HI. Risks and Options With Gadolinium-Based Contrast Agents in Patients With CKD: A Review. Am J Kidney Dis. 2021 Apr;77(4):517-528. Disponível em: 10.1053/j.ajkd.2020.07.012
• ²⁷
  Malavasi VL, Valenti AC, Ruggerini S, Manicardi M, Orlandi C, Sgreccia D et al. Kidney Function According to Different Equations in Patients Admitted to a Cardiology Unit and Impact on Outcome. J Clin Med. 2022 Feb 8;11(3):891. Disponível em: 10.3390/jcm11030891
• ²⁸
  Liu P, Quinn RR, Lam NN, Elliott MJ, Xu Y, James MT et al. Accounting for age in the definition of chronic kidney disease. JAMA Internal Medicine. 2021 Oct 1;181(10):1359-66. Disponível em: 10.1001/jamainternmed.2021.4813
• ²⁹
  Aguiar LK, Prado RR, Gazzinelli A, Malta DC. Fatores associados à doença renal crônica: inquérito epidemiológico da Pesquisa Nacional de Saúde. Revista Brasileira de Epidemiologia. 2020 Jun 5;23. Disponível em: 10.1590/1980-549720200044
• ³⁰
  Norris KC, Smoyer KE, Rolland C, Van der Vaart J, Grubb EB. Albuminuria, serum creatinine, and estimated glomerular filtration rate as predictors of cardio-renal outcomes in patients with type 2 diabetes mellitus and kidney disease: a systematic literature review. BMC nephrology. 2018 Dec;19(1):1-3. Disponível em: 10.1186/s12882-018-0821-9
• ³¹
  Ji B, Zhang S, Gong L, Wang Z, Ren W, Li Q et al.The risk factors of mild decline in estimated glomerular filtration rate in a community-based population. Clinical biochemistry. 2013 Jun 1;46(9):750-4. Disponível em: 10.1016/j.clinbiochem.2013.01.011
• ³²
  Chronic Kidney Disease Prognosis Consortium. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. The Lancet. 2010 Jun 12;375(9731):2073-81. Disponível em: 10.1016/S0140-6736(10)60674-5
• ³³
  Wu J, Jia J, Li Z, Pan H, Wang A, Guo X et al. Association of estimated glomerular filtration rate and proteinuria with all-cause mortality in community-based population in China: A Result from Kailuan Study. Scientific reports. 2018 Feb 1;8(1):1-8. Disponível em: 10.1038/s41598-018-20554-3
• ³⁴
  Corsonello A, Pedone C, Bandinelli S, Ferrucci L, Antonelli Incalzi R. Predicting survival of older community-dwelling individuals according to five estimated glomerular filtration rate equations: The InChianti study. Geriatrics & gerontology international. 2018 Apr;18(4):607-14. Disponível em: 10.1111/ggi.13225
• ³⁵
  Gansevoort RT, Correa-Rotter R, Hemmelgarn BR, Jafar TH, Heerspink HJ, Mann JF et al. Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention. The Lancet. 2013 Jul 27;382(9889):339-52. Disponível em: 10.1016/S0140-6736(13)60595-4

Edited by

Edited by: Isac Davidson S. F. Pimenta

Data availability

The full dataset underpinning the study results is available upon request from the corresponding author Lucas Lobato Acatauassu Nunes.

Publication Dates

• Publication in this collection
  08 July 2024
• Date of issue
  2024

History

• Received
  04 Jan 2024
• Accepted
  30 Apr 2024