Lithuanian University of Health Sciences Department of Nephrology Faculty of Medicine

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Lithuanian University of Health Sciences

Department of Nephrology

Faculty of Medicine

Assessment of kidney function: impact of different methods on

drug dosing

Master Thesis

Author: Jörn Jansen

Supervisor: Neda Kušleikaitė-Pere, MD, PhD

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1. SUMMARY

Author: Jörn Jansen

Title: Assessment of kidney function: impact of different methods on drug dosing Aim: To evaluate the accuracy and the clinical value of different estimated

glomerular filtration rate (eGFR) equations in regard to drug dosing adjustments.

Objectives: To compare precision and accuracy for Cockcroft-Gault (CG), CG with

adjusted body weight (CG AdjBW), CG with lean body weight (CG LBW), CG with ideal body weight (CG IBW), Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), Full Age Spectrum (FAS) and Berlin Initiative Study (BIS1) equations and their variations not scaled to a

standard body surface area (BSA) of 1,73 m². To compare difference of precision and accuracy in elderly and obese patients.

Methodology: Data of 150 patients of the Nephrology outpatient department of

Lithuanian University of Health Sciences Hospital was collected retrospectively. Inclusion criteria was the availability of mGFR by a 24h urine collection creatinine assay, which was used as reference.

Participants: 150 patients in total, 142 with sufficient data for CG, 136 with sufficient

data for CG AdjBW and CG LBW as well as 81 patients with age minimum 70 years suitable for BIS1.

Results: Median bias of CG/CG AdjBW/CG LBW/CG

IBW/MDRD/CKD-EPI/BIS1/FAS is -12,7 ± 21,2/-21,6 ± 21,1/-36,9 ± 23,8/27,1 ± 22,4/22,1 ± 23,4/-23,1 ± 22,7/17,3 ± 21,9/27,2 ± 23,0. P30 is 62,1/38,2/6,6/23,5/40,0/35,3/39,5/36,0

respectively. CG LBW performs significantly better in elderly (age≥70) with bias of -32,8 ± 21,4 vs. -47,7 ± 26,4 in the younger group. BIS1 performs significantly better in obese patients with bias of 35,4 ± 25,7 versus 11,3 ± 13,0 in non-obese. (p<0,05) MDRD, CKD-EPI, BIS1 and FAS had a higher P30 accuracy when multiplied by their BSA in compare to the standard version scaled to 1,73 m² BSA. CG IBW multiplied by BSA had the lowest bias and highest P30 accuracy of all tested equations.

Conclusion and Recommendations:

In clinical practice large diversions between eGFR and mGFR by 24h urine creatinine assay should be seen as indication not to use solely GFR for drug dosage

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2. ACKNOWLEDGEMENTS

I would like to thank my supervisor Neda Kušleikaitė-Pere for guidance and support. Also, a special thanks to all the staff members from the Kaunas clinic’s nephrology outpatient department who made this work possible.

3. CONFLICT OF INTERESTS

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4. PERMISSION ISSUED BY THE ETHICS COMMITTEE

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5. ABBREVIATIONS AND TERMS

GFR – glomerular filtration rate CKD – Chronic kidney disease CG – Cockroft-Gault equation

MDRD – Modification of Diet in Renal Disease equation eGFR – estimated glomerular filtration rate

CKD-EPI – Chronic Kidney Disease Epidemiology Collaboration equation mGFR – measured glomerular filtration rate

BIS1 – Berlin Initiative Study

FAS – Full Age Spectrum eGFR equation LMR – revised Lund Malmö equation eCcr – estimated creatinine clearance BSA – body surface area

FDA – Food and Drug Administration of the United States of America IBW – ideal body weight

AdjBW – adjusted body weight

MEAN-LMR+CAPA – variation of LMR for combined serum creatinine and cystatin C CKD-EPI-cr+cys – variation of CKD-EPI for combined serum creatinine and cystatin C

SI – Slope intercept SS – Single Sample

CG IBW – Cockcroft-Gault equation with IBW instead of real weight P30 – Percentage of results within 30% range of the reference RMSE – root mean square error

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6. INTRODUCTION

The Glomerular Filtration Rate (GFR) is the most superior marker in assessment of kidney function. [1] It is used in diagnosis and screening of renal disease, to adjust drug dosing in patients with renal disease, [2] to evaluate and classify end stage kidney disease [3] and to test kidney donor’s kidney function. [4] The GFR can be measured as well by endogenous as by exogenous markers.

Because of their advantage in cost, time and availability the endogenous markers are considerably more popular. [1] The most widely used endogenous marker is

creatinine, which can be measured in a 24 hour urine sample or in blood serum. This creates some issues, because creatinine is not only excreted by the glomerulus, but by the renal tubules as well. [5] Several factors influence the creatinine concentration in the blood serum: besides the kidney function the amount of skeletal muscle and the diet are the most significant ones. [6]

In 1959 [Kunin et al.] described reduced drug clearances in patients with renal impairment. [7-9] Today we know that even drugs that are not excreted by the kidneys can have different pharmacokinetics in patients with chronic Kidney disease (CKD). [10] GFR estimation is of high importance for drug dosing management and reduced kidney function is a major factor in hospital admissions for adverse drug reactions in the elderly. [11]

This lead to the development of the Cockcroft-Gault equation (CG) until 1976. [12] In 1999 the Modification of Diet in Renal Disease (MDRD) formula, based on a larger and more diverse patient sample, was introduced. [13, 14]

The current most used Glomerular Filtration Rate (eGFR) formula is from the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) from 2009. It was developed from a so far biggest and most diverse patient sample. [15]

Even though these equations use demographic modulators like sex, age, race and sometimes body weight to correct the results, the error is still significant in

underestimation and overestimation. [16] Also different equations have shown to provide quite different results, which leads to different medical decisions, especially in drug dosing. [17-20]

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But despite their known issues the eGFR determination is usually preferred about the measured Glomerular Filtration Rate (mGFR) due to a much higher convenience in clinical practice. [21]

Among newer creatinine based equations are the Berlin Initiative Study equation (BIS1) for the use in older adults [22], the Lund-Malmö revised equation developed in a predominantly Swedish population [23] and Schwartz equation for the use in

underage patients. [24] Due to severe problems with comparability and discontinuity even in the same patient, the full age spectrum (FAS) equation was developed under consideration of the BIS1 and the Schwartz equation. [25]

The drug dosing choices in clinical practice are highly influenced by the performance of creatinine based eGFR equations.

The aim of this study is to evaluate the performance of the most common and most promising eGFR formulas using serum creatinine in a Lithuanian population against a mGFR reference in regard to drug dosing choices.

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7. AIM AND OBJECTIVES

The aim of the study is the evaluation of creatinine based eGFR equations in regard to drug dosing choices in CKD patients of the Hospital of Lithuanian university of Health Sciences Nephrologyoutpatient department.

Objectives:

(1) To compare precision and accuracy of different eGFR equations.

(2) To compare precision and accuracy of MDRD, CKD-EPI, FAS, BIS1 and CG IBW not scaled to standard or ideal BSA.

(3) To compare precision and accuracy of different eGFR equations in elderly and obese patient groups.

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8. LITERATURE REVIEW

8.1 The Glomerular Filtration Rate

Filtration of blood and excretion of potentially toxic substances by urine is the main role of the kidneys and essential to sustain homeostasis. This function is

especially important in the safe administration of drugs and to diagnose and evaluate a possible kidney disease. [2, 3]

A prolonged or chronically decreased GFR occurs generally in company of other parameters of renal function, which can result in changes of electrolyte and volume balance, hypertension, decreased production of red blood cells or a change in bone mineral metabolism. [26] Therefore GFR is seen as the best marker to assess kidney function. [1] The stages of CKD were largely defined due to GFR by the National Kidney Foundation Kidney Disease and Quality Initiative, making it a suitable tool for detection, surveillance and staging of CKD. [27]

The GFR is the flow rate of all single nephrons combined in creating an ultrafiltrate from blood in the glomeruli. The blood enters the glomerulus by the afferent arteriole from arterial blood flow. The special anatomic arrangement of the glomerulus

provides a large surface area and its vessel walls have a tremendously high hydraulic permeability, so the ultrafiltrate can be created along a pressure gradient from inside to the outside of the glomerulus. Around 180 liter per day are created in an average human, which is ensured by the high amount of glomeruli, around 1 million per kidney. [28]

Most of the ultrafiltrate is reabsorbed back into the blood stream by the renal tubules, distal from the glomerulus.

There is no method to directly measure the GFR, so it can only be indirectly assessed and the „true” GFR can never be known with absolute certainty. Every measurement is either influenced by other factors or momentary fluctuations.

Therefore, leadings scientists suggest, the closest approximation to „true” GFR would be the average measured GFR of many results from a representative 1 to 2-day period. [29]

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The mGFR is obtained by collecting urine over a defined time span and measuring the concentration of a specific exogenous or endogenous marker. Wide clinical use has the creatinine concentration in a 24-hour urine collection. But in most clinical use the GFR is only estimated by the concentration of a marker in the blood serum (eGFR), again creatinine based eGFR is the most popular. [5]

8.2 Markers for GFR measurement

[Smith] defines the properties of a substance suitable for measurement of GFR to be fully filtrated by the glomerulus, neither synthesized, destroyed nor reabsorbed or excreted by the tubules and physiologically inert and to not interfere with the body’s function in another way. [30]

Common examples for such markers are Inulin, Iothalamate and Iohexol. And even though it is subject to current discussions which of these markers to favor as mGFR gold standard, [31] they are not used often.

While exogenous markers are more precise, they come with a high effort in time and cost, which is not practicable in normal diagnostic processes, especially not in broad patient screening.

Therefore, endogenous markers are much more popular, usually only the eGFR is ascertained. The first marker used was urea, the main waste product of nitrogen metabolism, which is eliminated almost entirely by urinary excretion. Blood Urea Nitrogen (BUN) quantification was developed and is a common practice in diagnostic medicine until today. Unfortunately, it is not very suitable for GFR estimation due to the fact that a lot of other reasons can change its plasma concentration. This is either due to tubular reabsorption or increased intake or production. It may be seen with a high protein intake, hypercatabolism, corticosteroid use or gastrointestinal bleeding. [26]

Nowadays the most popular endogenous marker is creatinine, the waste product of creatine in the muscle. It is formed and released to plasma in a relative constant rate, approximately proportional to muscle mass and mostly excreted by glomerular

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filtration. Therefore, an estimation of the GFR can be made under the consideration of specific patient data, usually age and sex.

Creatinine is not reabsorbed by renal tubules, but it is secreted by them in varying amounts, tampering the measurement of true GFR. Some drugs such as cimetidine and trimethoprim inhibit creatinine’s tubular secretion. But especially problematic is the kidneys tendency of relatively increasing tubular secretion rate to GFR in case of decreasing GFR. This means a declining kidney function may be not detected in the initial phase, due to compensation in creatinine elimination by tubular secretion. Only after a considerable decrease in kidney function the creatinine assay is suitable to detect it.

Other endogenous markers for GFR estimation are cystatin C, beta 2 microglobulin and beta trace protein. Especially cystatin C, a 122 amino acid low molecular weight protein and cysteine-proteinase inhibitor, that is constantly secreted by all cells with a nucleus [32] is suspected to be more reliable and thus a possible alternative to

creatinine. Combined methods of creatinine and cystatine C based GFR estimations are available as well.

8.3 Creatinine based formulas

The first vastly popular formula was the Cockcroft-Gault (CG) equation from 1976. It was developed from the data of 249 military veterans, by the majority male, with few obese patients. [12]

Figure 1 Estimated Creatinine Clearance (eCcr) by Cockcroft-Gault equation [12]

In opposite to newer equations, CG is considered to estimate solely renal creatinine clearance which is not only dependent on GFR but also on tubular secretion.

Therefore it is often called estimated creatinine clearance (eCcr) instead of eGFR in literature (Fig.1). [33]

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Until now CG is the most popular equation used by pharmacists to give drug dosage adjustment recommendations for patients. A beneficial feature of the equation is that it is not adjusted for body surface area (BSA), which is not recommended in drug dose adjustments. [34] Also it includes the patient’s weight. The USA’s Food and Drug Administration (FDA) used to recommend solely CG for new studies or dose adjustments until a few years ago. [35] Whether this is the right choice or not is an ongoing debate. [34]

In obese patients, CG equation will overestimate GFR. [36] In such cases, the CG formula with adjustment relative to ideal body weight (IBW) is considered more suitable. Adjusted body weight (AdjBW) is used in this CG variant instead of patient’s real weight. [37, 38]

IBW = 2.3 kg * (height (ft) -5) + (45.5 if female | 50 if

male)

Figure 2 Ideal Body weight (IBW) [39]

AdjBW = IBW + 0.4 (TBW – IBW)

Figure 3 Adjusted Body Weight (AdjBW). IBW: Ideal Body Weight in kg, TBW: Total Body Weight in kg [37]

Also lean body weight (LBW) was suggested as a surrogate for real body weight.

LBW for male = (9270 * AdjBW) / (6680 + 216 * BMI)

LBW for female = (9270 * AdjBW) / (8780 + 244 * BMI)

Figure 4 LBW: lean body weight in kg, AdjBW: adjusted body weight in kg, BMI: body mass index [40]

To improve estimation quality the Modification of Diet in Renal Disease (MDRD) formula, based on the data of 1,070 patients with 17% of them obese, was introduced in 1999. [13, 14]

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Up to date the recommended estimated Glomerular Filtration Rate formula is from the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) from 2009. It was developed in a diverse group of 8,254 people, with only a limited number of elderly and ethnic and racial minorities. [15]

Several studies showed the superiority of CKD-EPI over MDRD [41-46], most notably a 2018 meta-analysis of 48 studies and 26875 patients in which both equations underestimated the mGFR, but CKD-EPI equation had a higher accuracy. [47] In contrary a study in an Indian population showed superiority of MDRD over CKD-EPI. [48] This study had only 91 participants and used the 24h urine sample creatinine measurement as reference, which is a very biased way of determining the mGFR. [49, 50]

Unfortunately, all of these formulas have a significant error, for example for adjusting drug doses to renal function. [17-20]

While the CG equation estimates the Creatinine Clearance and thereby

systematically overestimates the GFR by neglection of tubular filtration, the MDRD and CKD-EPI equations try to estimate the actual GFR. [33]

8.4 New developments in creatinine based eGFR equations

During the last years several studies with varying designs regarding patient sample and reference method were conducted to test the different creatinine based eGFR equations. So far especially the FAS seem to be good a alternative for the previously leading CKD-EPI equation:

In 2016 an international team around [Pottel et al.] published a study to validate the FAS with 6870 white patients, including 735 children equal or below 18 years of age and 1764 adults ≥70 years of age. [25] The reference methods were Inulin, Iohexol and Iothalamate clearance. In compare to the Schwartz equation the result showed a superior prediction in accuracy and bias, in compare to the CKD-EPI equation a lower bias and comparable accuracy in young and middle-aged adults and superior values in bias and accuracy for older adults.

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FAS for Age group 2-40 years:

eGFR= 107,3/(Serum creatinine in mg/dL/Q)

FAS for Age group above 40 years:

eGFR = 107,3/(Serum creatinine in mg/dL/Q)*0,988

(Age-40)

Figure 5 FAS equation for adult patients. Q is an age related defined number given by the authors, for female above 20 years of age it is 0,7, for males above 20 years it is 0,9 [25]

[Da Silva Selistre et al.] conducted a single center cross sectional study in France with 2247 patients between 65 and 90 years of age published in 2019. [51] The reference method was inulin clearance. No superior diagnostic performance between the tested FAS, LMR and BIS1 against CKD-EPI was found, even though FAS had a significantly lower bias. But all equations had different errors, especially in the GFR range below 45 ml/min/1.73 m². The diverging results may be due to limitations of the study regarding the narrow patient sample in a single center study in France, very few patients with low GFRs below 30 ml/min/1.73 m² and the absence of information about race. Recent studies indicate that people of African ancestry have similar GFR values while Asian populations might have a reduced GFR. [52]

A team from the university of Berlin showed very good results in older individuals with their own developed BIS1 equation. [22] Unfortunately more recent studies showed a lot of conflicting results, rendering the BIS1 less accurate than FAS or CKD-EPI. [23, 25, 53-56]

BIS1:

eGFR = 3736 * (serum creatinine in mg/dL)

-0,87

*age

-0,95

_{(*0,82 if female)}

Figure 6 BIS1 eGFR equation [22]

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[Björk et al.] conducted a study with 3226 Caucasian participants from Germany, the United Kingdom and Sweden ≥70 years of age comparing FAS, revised Lund Malmö equation (LMR) and BIS 1 with CKD-EPI. [53] LMR and FAS showed superior GFR estimation capabilities than CKD-EPI. BIS 1 did not outperform CKD-EPI in this older age group in contrast to previous studies. [22] The possible main cause presented by the authors is the wider inclusions of patients, especially these with low levels of GFR, where BIS1 shows the biggest bias. Iohexolplasma clearance was used as reference method. Several cystatin C equations, which were also included in this study, showed similar bias and accuracy than the creatinine equations. Despite this the combination of creatinine and cystatine C equations called MEAN-LMR+CAPA equation and CKD-EPI-cr+cys equation, especially the first one, showed superior results. This coincides with previous results by [Fan et al.]. [56, 57]

Previously [Björk et al.] used data from an Icelandic cohort of 805 persons from 74 to 93 years of age to compare the LMR and FAS equation and could not find any

significant difference. [55]

Studies conducted in Chinese population’s older adults showed an overall good performance of BIS1 but a better performance of FAS. [58, 59]

A British study from 2019 including 1956 patients, from which 241 patients had a GFR equal or below 80 ml/min/1.73 m² compared the eGFR by MDRD with the reference methods Slope Intercept (SI) and Single Sample (SS) method to measure mGFR. [60] Due to the missing compare with other equations the results simply showed a general high error. The authors recommend the use of mGFR in case the eGFR is equal or below 40 ml/min/1.73 m².

8.5 Relevance in drug dosing

Reduced GFR is a serious cause of adverse drug reactions, that cause hospital admissions among elderly. [11] Drugs that are excreted renally will have a longer half-life in patients suffering from kidney failure. This requires dose

adjustment, which is usually based on eGFR or mGFR. The 2 main strategies to reduce the total drug dosage are either a prolonged interval or reduced doses with

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the same interval. Loading doses are required, if the Volume of Distribution is considerably increased in patients with CKD. [34, 61]

High error in eGFR can lead either to overdosation and toxicity or avoidable adverse reactions or underdosation with possible failure of treatment. In patients with renal impairment [Eppenga et al.] therefore recommend to prefer drugs which are not renally excreted and have a large therapeutic window. If there is uncertainty about the accuracy of eGFR, 24-hour urine collection should be performed. If the body surface area (BSA) diverges too widely from 1.73 m², the eGFR should be adjusted to patients BSA. [62]

[Guerville et al.] performed a study in a geriatric population, were 60% of patients needed dose adjustment or had contraindications to a specific drug from the

categories of anti-microbial drugs, anticoagulants and benzodiazepines due to a low GFR. In comparison of CG and CKD-EPI equations, eGFR was lower with CG in more than 80% of the patients, resulting in diverging recommendations in at least one of their drugs for 31% of patients. For absolute contraindications, 11 patients got a relatively contraindicated drug using CKD-EPI according to CG, in all of these cases it was enoxaparin. No patients got a relative contraindicated drug using CG according to CKD-EPI. No patient got an absolute contraindicated drug in either direction. While the dose adjustment for all 17 patients using benzodiazepines had no discrepancies, for anticoagulants and anti-microbial drugs they were 15% and 14% respectively. [63] In a study of [Gill et al.] concerning the impact of eGFR formula on drug dosing, less patients needed reduced doses of amantadine or digoxin with MDRD in compare to CG. The cumulative amount of amantadine with MDRD was significantly greater as well. [64] [Helldén et al.] examined the differences of CG and MDRD on the

prescription of Dabigatran, Gabapentin and Valaciclovir. Again, MDRD use would result in higher dosages. Therefore, the authors recommend to strictly use the same formula as the pharmacists giving dose adjustment recommendation. [65]

A 2017 study from Ethiopia with 422 patients came to similar results, but concluded that in adults below 70 years of age CG, MDRD and CKD-EPI equations can be used interchangeably due to a high correlation. [66] [Khanal et al.] come to similar

conclusions from their study with 2163 patients, but add the necessity of a long-term multi-center study in a diverse population. While the overall concordance of the

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equations is good, it can diverge massively in individuals. In these cases, they recommend additional use of mGFR. [20]

8.6 Measurement in special patient groups

A study with 24,516 adults with diabetes mellitus type 1 or type 2 showed superior results of MDRD over CKD-EPI, CG and CG with ideal body weight (CG-IBW). CKD-EPI performed best in patients below 60 years of age but the majority of patients were older (median age 72). MDRD was not only superior in the age group from 60 years of age and above but also in low ranges of eGFR (below 60

ml/min/1.73 m²). [67] 24h urine sample for creatinine excretion was used as a reference.

A 2014 study with 209 obese patients with CKD showed superiority of CKD-EPI scaled to body surface area (BSA) with ideal body weight in compare to BSA with real body weight. [68] CKD-EPI showed greater bias in obese than non-obese

patients, despite this the authors rate the CKD-EPI formula suitable to estimate GFR in obese patients with GFR ≤ 60 ml/min/1.73 m².

[Inker et al.] showed good performance of CKD-EPI among blacks and whites.[69] Studies in potential kidney donors have a younger and healthier patient sample, but show similar results:

A 2019 study of [Gaillard et al.] with 2733 potential living kidney donors compared the MDRD, CKD-EPI, LMR and FAS equations. [70] CKD-EPI and FAS showed the best results, nevertheless the authors recommend to prefer mGFR in the selection of living kidney donors as well as a similar study from Pakistan. [71]

Another study with 207 potential kidney donors is from 2016. 24h urine sample was used as reference method. [70] CKD-EPI was clearly superior over MDRD.

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8.7 Measurement of other endogenous markers for eGFR

A follow up to the 2014 study with obese patients including cystatin C equations showed the superiority of the combined creatinine cystatin C equation above both single ones. [72] Also the bias in the female patients was significantly lower, which, the authors assume, is due to a different waist circumference and body composition.

The Canadian Study of Longevity in Type 1 Diabetes compared MDRD, CKD-EPI, beta2-microglobulin and some cystatin C equations within a group of 66 type 1 diabetes patients and a control group of 73. [73] There was no significant difference found in the 2 creatinine based formulas, but the beta2-microglobulin performed worse in the diabetes group. Cystatin C only performed worse and the cystatin C – Creatinine combination equally to the creatinine only ones.

[White et al.] compared the alternative testing methods of the CKD-EPI equations Beta-Trace Protein, beta2-microglobulin and cystatin C with the widely used

creatinine and creatinine-cystatin (CKD-EPIcr+cys) method. [74] They concluded that the addition of these methods fit no benefit over the use of the combined creatinine-cystatin C method.

8.8 Conclusion and Limitations

So far, the most promising eGFR formula from the creatinine-based ones is the FAS, which also is clinically very practicable, because it is suited for all age groups. From the other endogenous markers, only the formulas for cystatin C in combination with creatinine seem to be useful replacements for creatinine only methods.

For drug dose adjustments FAS was not tested enough. The data on the superiority of CG, MDRD or CKD-EPI in this field is not clear as well.

Problematic when comparing of these studies is the absence of a common study design: While differing patient samples are not avoidable and even beneficial for proper evaluation of the most accurate eGFR method, huge differences in reference

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methods and tested formulas would be avoidable. Even recent studies often missed to include important formulas, even though the necessary patient data must have been collected and was available. Also, there is no single reference method, partially caused by different availability, practicability and cost efficiency. For the future it would be beneficial if researchers in this field would agree on a common study design. It could be considered to quantify actual serum levels or other effects of a renally excreted drug.

9. RESEARCH METHODOLOGY AND METHODS

Data of 150 patients, who visited a nephrologist in the outpatient department of the Hospital of Lithuanian university of Health Sciences Kauno klinikos, were collected retrospectively. Inclusion criteria were: diagnosis of chronic kidney disease, the presence of mGFR and serum creatinine data.

The following parameters were collected for each patient: • Age

• Sex

• Diagnosis of kidney disease • serum creatinine

• measured ClCrea from a 24-hour urine sample

Data regarding the patient’s weight were available for 140 patients, weight and height together for 136 patients.

All patients were of Caucasian race.

The data was collected between September of 2018 and October 2019. The measured ClCrea was used as reference value.

eGFR was calculated for each patient using MDRD, CKD-EPI and FAS equations. Additionally, BIS1 equation was used for all 81 patients with an age of 70 years and above. BSA was calculated in all 136 patients with sufficient data. MDRD, CKD-EPI,

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FAS and BIS1 were additionally multiplied with patients BSA to achieve a value not scaled for 1,73m² BSA.

CG was calculated for all 142 patients with known weight. BMI, IBW, CG IBW as well as CG AdjBW and CG LBW where calculated for all 136 patients with known weight and height. CG IBW was also additionally multiplied by BSA. The bias was calculated for each equation by the median difference to the mGFR and mean relative

difference in percent of the mGFR value.

Absolute bias is the average difference of one equation’s eGFR results to their corresponding mGFRs in ml/min. Relative bias is the average difference of one equation’s eGFR results to their corresponding mGFRs in percent. The important difference between these two parameters is, that the same absolute error in a high GFR is much less problematic than in low GFRs.

Accuracy and precision were evaluated by calculating the relative amount of results within 30% of the mGFR (P30). The P30 is considered to be a good tool in evaluating eGFR functionality. [75]

Root mean square error (RMSE) was calculated. RMSE in opposite to absolute bias does not consider if a value is above or below the prediction and instead includes only the deviation quantity, hence it shows the accuracy in regard to absolute differences of the prediction. [76]

Additionally,Bland-Altman plots were made. These are graphs to visualize the comparison of two methods of measurement. It can show a pattern of agreement, systematic differences and the dependence of the differences on the results, for example a larger difference in higher values of GFR than in lower ones. [77]

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10. RESULTS

10.1 Demographics

Of the 150 patients were 82 female (55%) and 68 males (45%).

Figure 7 Proportion of included male and female patients

In 136 patients the BMI was determinable. The mean BMI was 29,6 ± 5,9 kg/m². 44% had a BMI between 25 and 30kg/m², 32% between 30 and 35kg/m², 23% above 35 kg/m² and 1 patient had a BMI below 18.5kg/m².

Figure 8 Proportion of BMI (in kg/m²) among included patients Female 55% Male 45% Female Male BMI 25-30 44% BMI 30-35 32% BMI >35 23% BMI <18,5 1%

Body Mass Index

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The mean age was 66,3 ± 14,1 years.81 patients were 70 years of age and above (classified as elderly age group) and 69 patients below the age of 70 years (classified as non-elderly age group). For 74 of the elderly and 61 of the non-elderly patients the BMI was determinable (29,9 ± 6,2 vs. 27,3 ± 5,3 kg/m² respectively). No juveniles or children were included in the study, the youngest patient was 25 years old.

Table 1 Age groups of included patients

Age groups [years of age]

20 to 49 18

50 to 69 51

70 and above 81

The mean mGFR was 74,0 ± 34,5 ml/min.33% of patients had a mGFR above 89 ml/min24% between 60 and 89 ml/min (stage 2 CKD), 36% between 30 and 59 ml/min (stage 3 CDK), 7% between 15 and 29 ml/min (stage 4 CKD) and none of the patients had a mGFR below 15 ml/min (stage 5 CKD).

Figure 9 Kidney function of included patients

>89 33% 60 - 89 24% 30 - 59 36% 15 - 29 7%

Kidney function by mGFR

>89 60 - 89 30 - 59 15 - 29 < 15

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10.2 Overall Results

Table 2 Comparison of eGFR to mGFR for all included patients. BIS1 was only calculated for patients 70 years and older, CG, CG AdjBW and CG LBW only for patients with sufficient data

Equations Bias Bias % P 30 RMSE CG(n=140) -12,7 ± 21,2 28,5 ± 27,5 62,1 18,3 CG AdjBW(n=136) -21,6 ± 21,1 34,6± 22,0 38,2 26,1 CG LBW(n=136) -36,9 ± 23,8 54,3 ±16,1 6,6 41,0 CG IBW(n=136) 27,1 ± 22,4 64,6 ± 29,3 23,5 31,1 MDRD(n=150) -22,1 ± 23,4 73,6 ± 30,9 40,0 27,1 CKD-EPI(n=150) 23,09 ± 22,7 29,0 ± 20,4 35,3 27,9 BIS1(n=81) 17,3 ± 21,9 34,6 ± 17,7 39,5 24,2 FAS(n=150) 27,2 ± 23,0 30,8 ± 18,7 36,0 29,0 CG IBW*BSA (n=136) 9,2 ± 33,0 37,1 ± 58,8 63,6 23,8 MDRD*BSA (n=136) 20,9 ± 24,3 142,5 ± 61,8 51,5 25,7 CKD-EPI*BSA (n=136) 19,5 ± 25,7 41,4 ± 58,1 54,4 24,6 BIS1*BSA (n=74) 15,0 ± 16,7 37,8 ± 44,7 52,7 17,8 FAS*BSA (n=136) 14,9 ± 24,4 38,4 ± 53,3 59,7 22,3

For comparison of the different eGFR equations, absolute Bias (Bias), relative Bias (Bias %), P 30 and RMSE were calculated. (Table 1)

CG IBW*BSA has the smallest total bias, CG the smallest relative bias. In the P30 accuracy CG and CG IBW*BSA are the leading equations. Multiplied by their BSA and therefore not scaled to 1,73m² CG IBW, MDRD, CKD-EPI, FAS and BIS1

perform slightly to considerably better. Most prominent CG IBW and FAS (9,2 ± 33,0 vs 27,1 ± 22,4 and 14,9 ± 24,4 vs. 27,2 ± 23,0 in absolute bias respectively). P30 is tremendously higher without scaling to 1,73m² BSA.

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With scaled BSA MDRD, CKD-EPI, BIS1 and FAS all perform worse than the CG and CG AdjBW in P30 accuracy, where the highest one (MDRD) gets only 40,0%. They also perform worse against CG in all measured parameters. In absolute bias CG AdjBW performs similar to CKD-EPI (-21,6 ± 21,1 vs. 23,1 ± 22,7) and worse than BIS1 (-21,6 ± 21,1 vs. 17,3 ± 21,9). In P30 accuracy CG AdjBW performs slightly worse than MDRD (38,2% vs. 40,0%). CG LBW performs the worst in all parameters with a P30 of only 6,6%.

Multiplied by their BSA, the P30 increased prominently for MDRD (40,0% vs. 51,5%), CKD-EPI (35,3% vs. 54,4%), BIS1 (39,5% vs. 52,7%) and FAS (36,0% vs. 59,7%).

Figure 10 GFR distribution of common ranges by different equations where the vertical axis shows the number of patients in the range group, rounded to full numbers

In compare of common distribution ranges (Fig. 7) obvious differences between equations are seen. All formulas multiplied by BSA are more dominant in the higher ranges, which is partially explained by the fact that the median BSA is 1,93 ± 0,23 m², ranging from 1,35 m² to 2,69 m². The equations not normed to standard BSA (CG, CG AdjBW, CG IBW, CG LBW) are less concentrated in the higher GFR ranges. 0 20 40 60 80 100 120 140 GFR 10-30 GFR 31-50 GFR >50

GFR distribution by different equations

mGFR CG CG AdjBW CG LBW CG IBW CG IBW*BSA MDRD MDRD*BSA CKD-EPI CKD-EPI*BSA FAS FAS*BSA BIS1 BIS1*BSA

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The Bland-Altman plots (Fig. 8-15) show higher agreements in average GFR values around 70 ml/min/1.73 m². For the more extreme values the differences are larger, especially in high GFR ranges.

Figure 11 Bland-Altman plot (difference versus average) of CKD-EPI – mGFR with mean difference (middle line) with 95% confidence interval (CI), Limits of agreements (upper and lower dashed line) with 95% CI and regression line of differences (oblique dashed line) with 95% CI

-120 -100 -80 -60 -40 -20 0 20 40 60 0 50 100 150 200

Mean of CKD_EPI and mGFR

C KD _ EPI - m G F R Mean -25,7 -1.96 SD -70,2 +1.96 SD 18,8 -120 -100 -80 -60 -40 -20 0 20 40 60 0 50 100 150 200

Mean of FAS and mGFR

F AS - m G F R Mean -27,2 -1.96 SD -72,3 +1.96 SD 17,9

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Figure 12 Bland-Altman plot (difference versus average) of FAS – mGFR with mean difference (middle line) with 95% CI, Limits of agreements (upper and lower dashed line) with 95% CI and regression line of differences (oblique dashed line) with 95% CI

Figure 13 Bland-Altman plot (difference versus average) of BIS 1 – mGFR with mean difference (middle line) with 95% CI, Limits of agreements (upper and lower dashed line) with 95% CI and regression line of differences (oblique dashed line) with 95% CI

-120 -100 -80 -60 -40 -20 0 20 40 20 40 60 80 100 120

Mean of BIS_1 and mGFR

BI S_ 1 - m G F R Mean -22,4 -1.96 SD -65,4 +1.96 SD 20,5

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Figure 14 Bland-Altman plot (difference versus average) of MDRD – mGFR with mean difference (middle line) with 95% CI, Limits of agreements (upper and lower dashed line) with 95% CI and regression line of differences (oblique dashed line) with 95% CI

Figure 15 Bland-Altman plot (difference versus average) of CG – mGFR with mean difference (middle line) with 95% CI, Limits of agreements (upper and lower dashed line) with 95% CI and regression line of differences (oblique dashed line) with 95% CI

-350 -300 -250 -200 -150 -100 -50 0 50 100 -50 0 50 100 150 200 Mean of MDRD and mGFR M D R D - m G F R Mean -39,0 -1.96 SD -129,0 +1.96 SD 50,9 -80 -60 -40 -20 0 20 40 60 80 0 50 100 150 200 Mean of CG and mGFR C G - m G F R Mean -13,2 -1.96 SD -54,8 +1.96 SD 28,4

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Figure 16 Bland-Altman plot (difference versus average) of CG (IBW) – mGFR with mean difference (middle line) with 95% CI, Limits of agreements (upper and lower dashed line) with 95% CI and regression line of differences (oblique dashed line) with 95% CI

Figure 17 Bland-Altman plot (difference versus average) of CG (adjBW) – mGFR with mean difference (middle line) with 95% CI, Limits of agreements (upper and lower dashed line) with 95% CI and regression line of differences (oblique dashed line) with 95% CI

-120 -100 -80 -60 -40 -20 0 20 40 60 0 50 100 150 200

Mean of CG_IBW and mGFR

C G _ IBW - m G F R Mean -29,1 -1.96 SD -72,9 +1.96 SD 14,8 -100 -80 -60 -40 -20 0 20 40 60 80 0 50 100 150 200

Mean of CG_adjBW and mGFR

C G _ a d jBW - m G F R Mean -23,2 -1.96 SD -64,6 +1.96 SD 18,1

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Figure 18 Bland-Altman plot (difference versus average) of CG (LBW) – mGFR with mean difference (middle line) with 95% CI, Limits of agreements (upper and lower dashed line) with 95% CI and regression line of differences (oblique dashed line) with 95% CI

10.3 Comparison of age groups

In compare of elderly (age 70 years and above, table 3) to non-elderly

patients (age below 70 years, table 4), the absolute bias is higher in younger patients for all equations except CG. The strongest in CG LBW (-32,8 ± 21,4 vs. -47,7 ± 26,4), also prominent in CKD-EPI and FAS (-17,9 ± 20,5 vs. 33,7 ± 24,3 and 20,2 ± 21,4 vs. 33,9 ± 24,5 respectively). This is probably due to the fact that the average GFR is higher in younger patients as well. The differences in relative Bias (Bias %) are remarkably smaller than with the absolute bias for CKD-EPI and FAS.

When not scaled to standard BSA, the correspondent equations perform better in the elderly group, also in relative bias and P30. The average BMI was higher in the elderly than in the non-elderly group (29,9 ± 6,2 vs. 27,3 ± 5,3 respectively). In

comparison of P30 all equations perform better in the younger group except CKD-EPI which performs slightly better in the elderly. Median bias of CG IBW*BSA, CKD-EPI*BSA, FAS*BSA and CG LBW is significantly better in the elderly compared to the

-120 -100 -80 -60 -40 -20 0 20 40 0 20 40 60 80 100 120 140 160 Mean of CG_LBW and mGFR C G _ L BW - m G F R Mean -40,4 -1.96 SD -87,0 +1.96 SD 6,2

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younger group (p<0,05). None of the other equations’ performances have significant differences (p>0,05).

Table 3 Comparison of eGFR in Patients of 70 years and above

Equations Bias Bias % P 30 CG(n=77) -13,7 ± 17,4 29,4 ± 21,4 58,4 CG AdjBW(n=74) -21,0 ± 18,6 38,1± 14,0 64,8 CG LBW(n=74) -32,8 ± 21,4 57,7 ± 13,6 4,1 CG IBW(n=74) 24,9 ± 20,5 57,4 ± 18,9 13,5 MDRD(n=81) -15,0 ± 20,4 77,0 ± 28,6 48,8 CKD-EPI(n=81) -17,94 ± 20,5 34,6 ± 17,9 35,4 FAS(n=81) 20,2 ± 21,4 37,3 ± 16,8 35,4 CG IBW*BSA (n=74) 2,0 ± 17,0 24,3 ± 28,0 79,7 CKD-EPI*BSA (n=74) 14,8 ± 17,1 37,8 ± 44,9 54,1 FAS*BSA (n=74) 11,1 ± 16,5 31,9 ± 40,1 63,5

Table 4 Comparison of eGFR in patients of age 69 and below

Equations Bias Bias % P 30 CG(n=63) -11,6 ± 25,1 27,4 ± 33,6 66,7 CG AdjBW(n=62) -24,6 ± 23,8 30,6 ± 28,6 79,0 CG LBW(n=62) -47,7 ± 26,4 50,2 ± 18,0 9,7 CG IBW(n=62) 31,4 ± 24,5 73,2 ± 36,5 35,5 MDRD(n=69) -34,3 ± 25,0 69,6 ± 33,1 29,0 CKD-EPI(n=69) 33,7 ± 24,3 38,2 ± 22,9 34,8 FAS(n=69) 33,9 ± 24,5 36,6 ± 20,9 36,2 CG IBW*BSA (n=62) 22,1 ± 39,3 52,4 ± 79,2 45,2 CKD- 24,8 ± 32,0 45,7 ± 70,8 54,8

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10.4 Obese versus non-obese

In comparison of each equation’s performance in obese and non-obese patients (BMI 30 and above, table 5 versus BMI below 30, table 6), all equations that do not include the patient’s body weight (MDRD, CKD-EPI, FAS, BIS1) perform better in the non-obese group than they do in the obese group. CG performs slightly better in the obese group, CG AdjBW slightly worse. In both patient groups CG

IBW*BSA performs the best, CG LBW the worst. CKD-EPI, FAS and BIS1 all perform better in the obese group when not scaled to standard BSA. The difference of

median bias in BIS1 equation (35,4± 25,7 in obese versus 11,3 ± 13,0 in non-obese) is significant (p<0,05), all other differences are not significant (p>0,05).

Table 5 Comparison of eGFR in patients with BMI 30 and above

Equations Bias Bias % P 30 CG(n=58) -12,8± 23,5 29,2 ± 24,7 64,9 CG AdjBW(n=58) -29,3± 22,3 36,3± 16,6 40,4 CG LBW(n=58) -52,3± 23,8 57,6 ± 16,4 7,0 CG IBW (n=58) 42,8± 22,7 45,6 ± 15,9 15,8 MDRD(n=58) -34,0 ± 23,2 41,2 ± 17,0 24,6 CKD-EPI(n=58) 34,7± 23,1 42,2 ± 16,6 22,8 FAS(n=58) 37,3± 23,8 43,3 ± 16,1 24,6 BIS1(n=37) 35,4 ± 25,7 45,4 ± 15,7 9,5 CG IBW*BSA (n=58) 7,1 ± 33,0 35,1 ± 38,8 66,7 CKD-EPI*BSA (n=58) 16,7 ± 26,5 39,9 ± 52,3 61,4

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Table 6 Comparison of eGFR in patients with BMI below 30

Equations Bias Bias % P 30 CG(n=78) -13,2 ± 19,7 28,4 ± 30,0 62,3 CG AdjBW(n=78) -18,4 ± 19,9 34,2± 25,4 44,2 CG LBW(n=78) -30,6± 22,5 51,9 ± 15,6 6,5 CG IBW(n=78) 22,0 ± 20,3 37,7 ± 22,9 29,9 MDRD(n=78) -15,7 ± 22,4 31,1 ± 22,8 48,1 CKD-EPI(n=78) 18,1 ± 21,0 32,5 ± 22,3 42,9 FAS(n=78) 19,9 ± 20,7 33,0 ± 19,5 44,2 BIS1(n=36) 11,3 ± 13,0 26,1 ± 13,7 26,0 CG IBW*BSA (n=78) 10,4 ± 32,8 38,6 ± 70,3 63,6 CKD-EPI*BSA (n=136) 20,1 ± 25,1 42,4 ± 62,3 50,7 BIS1*BSA (n=78) 17,4 ± 11,6 37,5 ± 24,7 35,1 FAS*BSA (n=78) 15,8 ± 23,7 39,7 ± 57,1 59,6

11. DISCUSSION

CG LBW performs the worst with a bias of -36,9 ml/min in the general group and -52,3 ml/min in the obese group. The P30 accuracy is 6,6% and 7,0% in the general and obese group respectively. [Bouquegneau et al.] concluded in a 2016

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study about eGFR use in obese population, that CG LBW is highly inferior with a bias of -17,3 ml/min and a P30 of 52,8%. [38]

The highest P30 values were of CG IBW*BSA and CG with 63,6% and 62,1%

respectively. A large 2017 cohort study from [Nyman et al.] found similar results with a CG P30 of 62,8%. [78] Their median bias is 8,3 ml/min in compare to -12,7 ml/min in our study.

But except for CG the precision and accuracy of all eGFR formulas are significantly lower than in the leading studies to these subjects. An eGFR equation is viewed as suitable when the P30 is above 90%. [75]

Surprising is the relatively good performance of CG IBW multiplied by patient’s BSA, which is not a common equation. Other than MDRD, CKD-EPI, FAS and BIS1 the CG IBW equation includes the patient’s height and is therefore not considered to be scaled to 1,73m² BSA. But it could be considered as scaled to a height dependent ideal BSA due to the fact that the BSA is calculated by the parameters of patient’s weight and height. This might be worth to look into in further studies.

P30 of CKD-EPI is 35,3%, of FAS 36%, of BIS1 39,5% and of MDRD 40,0%. The bad performance is partially explained by the fact that the mGFR is not scaled to 1,73m² BSA as CKD-EPI, MDRD, FAS and BIS1 are. Scaling them for the patient’s BSA instead the P30 increases tremendously (54,4/59,7/52,7/51,5 % for CKD-EPI*BSA/FAS*BSA/BIS1*BSA/MDRD*BSA) but is still below comparable studies. In recent large studies evaluating these equations, the P30 accuracy is considerably higher. [Björk et al.] found the P30 even in the elderly for CKD-EPI, FAS and BIS1 to be 76,4%, 80,9% and 73,8% respectively.[53] A large 2019 study of [Gaillard et al.] found a P30 of MDRD, CKD-EPI and FAS of 90%, 94,4% and 93,1%. [70]

The bias for CKD-EPI, FAS and BIS1 is very large as well (23,1, 27,2 and 17,3 ml/min/1,73m² respectively), not scaled to standard BSA 19,5/14,9/15,0/20,9 ml/min for CKD-EPI/FAS/BIS1/MDRD.

In comparison the [Björk et al.] study from 2018 showed a median bias below 4 of CKD-EPI, FAS and BIS1 with 3,6, 0,6, 1,7 ml/min/1,73m² respectively. [53] [Gaillard

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A study in obese patients showed a bias of 25 and 1,6 ml/min for CG and CG AdjBW in compare to -12,8 and -29,3 ml/min in our obese patients.[38] Noticable is the relatively small bias of CG in these obese patients, while CG AdjBW has a much higher bias in opposite to its idea of a use in obese patients and in opposite to other study results. In our study P30 for CG and CGAdjBW in obese patients is 64,9% and 40,4%. [Bouquegneau et al.] got 56,6% and 79%.[38] Again the same pattern of a much better performance of CG over CG AdjBW in our study while contrary in the compared one.

The most likely explanation for the large biases and low accuracy is a lot of error in measurement with the reference method. Even though the creatinine assay from 24 hour urine collection is the common clinical way for assessment of mGFR, [79, 80] it is considered to have several errors. These are mainly from the process of inaccurate urine collection, but also analytical errors due to disease and drugs. [49] There is evidence that it is less accurate than eGFR on average. [50] Alternative explanations could be a strong deviation of physiological creatinine metabolism and kidney

function in the Lithuanian population or at least the selected patient sample or massive error of measurement in the serum creatinine assay. Both can be rendered unlikely.

The leading studies we compared our results to use recommended reference techniques as iohexol clearance. [53, 70, 78]

Also, to consider is that despite the sufficient sample size most differences are not significant due to the large interval of error.

Noticeable is also the quite large relative bias, especially in the eGFR equations multiplied by BSA. This is partially explained by the presentation of average in compare to the median in the absolute bias. Therefore, few values with an exceptional large deviation have a higher impact on the relative bias. But it also shows that the relative bias is especially high in patients with low GFR, where absolute errors have a higher risk for misjudgement.

To further evaluate the performance of eGFR equations in drug dosing adjustments, a study design including measured drug concentrations might be useful.

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12. CONCLUSIONS

1. CG IBW multiplied by BSA, CG and CG adjBW correlate the strongest with the reference method, while CG LBW performs the worst. Higher agreements in average GFR values were found around 70 ml/min and for the more extreme values the differences are larger, especially in the high ranges.

2. MDRD, CKD-EPI, FAS and BIS1 formulas perform better without scaling to 1,73m² BSA (i.e., accuracy of these formulas is higher when multiplied by actual BSA).

3. In the elderly patient group (70 years and above), CG IBW*BSA performed the best followed by CG AdjBW and FAS*BSA. In obese patient group (BMI 30 and above), CG IBW*BSA performs the best, while CG LBW - the worst. In obese patients the accuracy of BIS1 equation was significantly higher than in non-obese patients.

13. PRACTICAL RECOMMENDATIONS

In clinical diagnostics 24 hour urine creatinine assays should not be

considered as a reliable value, if it deviates harshly from eGFR equations. It is not an indicator of safe drug dosing in patients with suspected renal impairment and can only be viewed in relation to eGFR values. Instead repeated eGFR determinations, drug concentration assays and closely monitoring the drug’s effects and likely adverse effects should be considered.

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Lithuanian University of Health Sciences Department of Nephrology Faculty of Medicine