E

Ka

b

c

d

e

a

ARRA

KGLCWMC

1

E[aEaia

3

0d

Lung Cancer 76 (2012) 397– 402

Contents lists available at SciVerse ScienceDirect

Lung Cancer

j our na l ho me p age: www.elsev ier .com/ locate / lungcan

stimation of renal function in lung cancer patients

atja Trobeca, Lea Kneza, Pika Mesko Brguljanb, Tanja Cuferc, Mitja Lainscakd,e,∗

Pharmacy Department, University Clinic Golnik, Golnik 36, 4204 Golnik, SloveniaLaboratory for Clinical Biochemistry and Haematology, University Clinic Golnik, Golnik 36, 4204 Golnik, SloveniaDivision of Oncology, University Clinic Golnik, Golnik 36, 4204 Golnik, SloveniaDivision of Cardiology, University Clinic Golnik, Golnik 36, 4204 Golnik, SloveniaApplied Cachexia Research, Department of Cardiology, Charité Medical School, Campus Virchow-Klinikum, Berlin, Germany

r t i c l e i n f o

rticle history:eceived 3 August 2011eceived in revised form 28 October 2011ccepted 12 November 2011

eywords:lomerular filtration rateung cancerockcroft–Gault equationright equationDRD equation

KD-EPI equation

a b s t r a c t

Introduction: In lung cancer patients treated with chemotherapy, renal function is an important parameterto be monitored. Since measurement of renal function with either isotope or creatinine clearance istime consuming and expensive, we evaluated which of the following equations: Cockcroft–Gault (CG),Wright, modification of diet in renal disease equation (MDRD), MDRD adjusted for body surface area (BSA)and chronic kidney disease epidemiology collaboration (CKD-EPI) best resembles endogenous creatinineclearance (ECC) and could therefore replace its measurement in clinical practice.Methods: 218 lung cancer patients, who had their 24-h creatinine secretion in urine measured prior tothe start of any chemotherapy, were included. Estimation of renal function was calculated and comparedto ECC.Results: There were no major differences in the performance of the tested equations. Mean percentageerror of more than 20% and general underestimation was common to all equations. Wright equationperformed best although it describes only 43% of ECC variability. Mean measured ECC was 94 mL/min(95% confidence interval [CI]: 90–98 mL/min) and 90 mL/min for Wright equation (95% CI: 87–93 mL/min)(Supp. Fig. 3). MDRD and CKD-EPI equation performed poorest since they do not include any body sizedescriptor. Large deviations of differences were observed, with a median standard deviation of more than20% and deviations from ECC exceeding 100%. Wright equation performed best, whereas, despite theirleading role in the detection of renal diseases, the MDRD and CKD-EPI equation performed poorest sincethey do not include any body size descriptor. In the range of ECC < 50 mL/(min × 1.73 m2), the CG equation

most often detected a contraindication for cisplatin use. Differences between ECC and calculated valuescorrelated with patients’ weight, BSA and body mass index when these were not included in the equationitself.Conclusions: In evaluating the renal function of lung cancer patients, equations adjusted for body sizedescriptors should be preferred. Estimated renal function should be interpreted against the characteristicsof patient’s body size and special attention is needed when these are reaching the extremes.

. Introduction

Lung cancer is the most common cause of cancer death inurope, manifesting as small cell and non-small cell lung cancer1]. Although they differ in pathogenesis and prognosis, chemother-py presents a viable treatment option for both cancer types [2–4].ven though chemotherapy requires an adequate renal function

t baseline, drug-induced renal dysfunction is frequently observedn lung cancer patients treated with platinum-based chemother-py [5]. An accurate estimation of renal function is important to

∗ Corresponding author at: Division of Cardiology, University Clinic Golnik, Golnik6, 4204 Golnik, Slovenia. Tel.: +386 4 25 69 141; fax: +386 4 25 69 117.

E-mail address: mitja.lainscak@guest.arnes.si (M. Lainscak).

169-5002/$ – see front matter © 2011 Elsevier Ireland Ltd. All rights reserved.oi:10.1016/j.lungcan.2011.11.016

© 2011 Elsevier Ireland Ltd. All rights reserved.

allow proper dosing of cytotoxics (e.g. carboplatin) and to iden-tify a possible contraindication for the use of nephrotoxic drugs(e.g. cisplatin), minimising toxicity and maximising effectivenessof anticancer drugs [6,7].

Current practices of renal function determination differ consid-erably. While measurements of endogenous creatinine clearance(ECC) or isotope clearance are largely used for the evaluation ofrenal function prior to the first chemotherapy cycle, renal func-tion estimations based on serum creatinine concentration, usingdifferent equations are employed thereafter due to feasibility andresource related issues. Although these equations are widely used,

their performance in cancer patients is not well established as onlylimited data is available in cancer patients. Cancer patients areprone to muscle wasting and cachexia. Lower muscle mass gen-erally lowers serum creatinine levels, which in turn may cause

3 Cancer 76 (2012) 397– 402

oeCape(

i(WmCt

2

2

cpciis

••

•

•

•

t

2

lwpsimom[

E

Ecc

t

3 i i h

249 patie nts wit h lungcancer + ECC

measure d prior to th estart of chemotherapy 3 pa tients with

ECC < 30 mL/ (min x 1.73 m2)

13 patient s wit hECC > 150 mL/(min x 1 73

start of chemotherapy

ECC > 150 mL/(mi n x 1.73 m2)

233 patients

218 patients

15 patient s wit h UCr < 3000 μmol/ L

218 patient s

Fig. 1. Flow-chart of patients who met inclusion and exclusion criteria. ECC=

98 K. Trobec et al. / Lung

verestimation of renal function. To our best knowledge, the onlyquation evaluated in population of lung cancer patients is theockcroft–Gault (CG) equation [8,9]. There is no information avail-ble on whether the Wright equation, developed for use in canceratients, outperforms other established or emerging equations, forxample the chronic kidney disease epidemiology collaborationCKD-EPI) equation in the subset of cancer patients.

This analysis was initiated to investigate which of the follow-ng equations: CG, modification of diet in renal disease equationMDRD), MDRD adjusted for body surface area (MDRD-BSA),

right, or CKD-EPI, best resembles creatinine clearance as deter-ined by ECC measurements in lung cancer patients. The novel

KD-EPI equation was evaluated in cancer patients for the firstime.

. Patients and methods

.1. Study design and patient description

A retrospective study of renal function estimations in lung can-er patients, hospitalized at the University hospital Golnik, waserformed. The study protocol was approved by the local ethicsommittee. All ECC investigations ordered from the oncology wardn the year 2009 were retrieved from the laboratory electronicnformation system. The electronic database of medical records wasearched for additional information.

Patient’s data were included according to the following criteria:

a patient was diagnosed with lung cancer,an ECC measurement was performed prior to the start ofchemotherapy.

We excluded the following patients:

the measured ECC was below 30 mL/(min × 1.73 m2) as all equa-tions are expected to be inaccurate in this range of values,the measured ECC was above 150 mL/(min × 1.73 m2) as thesevalues do not confine with the expected physiological condition,the measured urine creatinine concentration was below3000 �mol/L which does not comply with the characteristics ofnormal human urine and may have resulted from inaccuratespecimen collection.

In total, 218 patients’ medical records were included in the fur-her analysis (Fig. 1).

.2. ECC measurement

In order to determine a patient’s ECC, their 24 h urine was col-ected and their serum creatinine concentration was measured

ithin the same day. Serum creatinine determinations have beenerformed using kinetic Jaffe reaction, rate-blanked and compen-ated on a Cobas 6000 analyzer (Roche Diagnostics). The methods standardized to Isotope Dilution Mass Spectrometry (IDMS)

ethod. Urine creatinine determinations have been performedn the Cobas 6000 analyzer (Roche Diagnostics) using the sameethod and calibrator as in serum. ECC was calculated by Eq. (1)

10,11].

CC (mL/min) = UCr × Uvol

1440 min × SCr(1)

CC = endogenous creatinine clearance, UCr = urine creatinine con-

entration (�mol/L), Uvol = volume of urine (mL), SCr = serumreatinine concentration (�mol/L).

ECC was used as a standard to which estimations of renal func-ion, calculated through the use of the different equations, were

endogenous creatinine clearance, UCr = urine creatinine concentration.

compared. ECC was chosen as a standard since, at the time ofthe study, its measurement was mandatory prior to the start ofchemotherapy treatment.

2.3. Equations for renal function estimation

Renal function estimates were calculated from four differentequations: CG, MDRD (basic equation and adjusted for BSA), Wrightand CKD-EPI.

The CG equation was developed back in the 1976 on a smallnumber of male patients (249) with renal disease of non-definedetiology, measuring creatinine concentration with a method thatis no longer in use and taking the ECC measurement as a refer-ence [12]. Thus, it estimates creatinine clearance whereas otherequations derived from the clearance of radiolabelled isotope, esti-mate GFR. Besides serum creatinine, the CG equation incorporatespatients’ age, body mass and gender.

The Wright equation was developed on only 62 patients withdiverse types of cancer, being the only one tackling the specificsof this patient population [13]. BSA, age and gender were found toinfluence GFR calculation and were therefore included into the finalequation.

The MDRD equation was developed on a bigger sample ofpatients (1070) with renal disease in 1999 [14]. There was no can-cer patient included. Due to its thorough validation, the MDRDequation is recommended by the NKDEP (National Kidney Dis-ease Epidemiology Group) to be used in routine practice [11,15,16].Since the original equation involves no patient’s size descriptor, theprovided estimation of renal function is standardised to the aver-age body surface area (1.73 m2). The modified equation, correctedfor patient’s actual BSA (MDRD-BSA), must therefore be employedin certain patient populations, like cancer patients.

The CKD-EPI equation was developed in 2009 in the largestnumber of patients (5504), including healthy people and patientswith different diseases that may lead to an impaired renal func-tion [17]. Again, no cancer patients were included. The equationapplies different correction factors in the specific ranges of serumcreatinine concentration. However, no details on patient’s height

or weight are included and, as for the MDRD equation, results arereported relative to the average BSA.

K. Trobec et al. / Lung Cance

Table 1Patients’ characteristics.

Characteristic n Median (range) %

Number of patients 218 100Gender

Male 152 70Female 66 30

Age in years 67.5 (40–89)Weight in kg 73 (47–126)Height in cm 169 (143–188)BMI 25.7 (16.5–39.8)

BMI < 18.5 6 2.7518.5 < BMI < 25 91 41.7BMI > 25 121 55.5BMI > 30 39 17.9

BSA in m2 1.84 (1.43–2.41)ECC in mL/(min × 1.73 m2) 86 (31–149)

Ba

2

f(cm

S

R

M

M

Scofi

a(aefqi

as

(tPc

(

3

6oA(

MI = body mass index, BSA = body surface area, ECC = endogenous creatinine clear-nce.

.4. Data calculation and statistical analysis

Calculated values were compared to ECC results through theollowing parameters: (1) absolute and relative differences (Eqs.2) and (3)); (2) median, range and standard deviation (SD) of cal-ulated differences; (3) mean percentage error (MPE; Eq. (4)) andean absolute percentage error (MAPE; Eq. (5)).

E = calculated value − measured ECC (2)

SE = (calculated value − measured ECC)measured ECC

(3)

PE = 1N

×∑ (calculated value − measured ECC)

measured ECC× 100% (4)

APE = 1N

×∑ ∣∣(calculated value − measured ECC)

∣∣measured ECC

× 100% (5)

E = standard error, RSE = relative standard error, MPE = mean per-entage error, MAPE = mean absolute percentage error, N = numberf data, ECC = endogenous creatinine clearance, GFR = glomerularltration rate.

Q–Q plots of calculated and measured ECC values were used tosses Pearson correlation. Additionally, the relative standard errorRSE) was plotted against measured ECC to identify the tendency ofn equation to under- or over-estimate renal function in the differ-nt ECC ranges. Finally, the fraction of calculated values differingrom ECC for more than ±10% was calculated to estimate the fre-uency of the differences that may lead to a potentially clinically

mportant alteration of carboplatin dose [18].Measurements of ECC below 50 mL/(min × 1.73 m2), presenting

contraindication for cisplatin treatment, are also presented in aeparate analysis.

Finally, RSEs were associated with patients’ characteristicsweight, BSA and BMI). A single characteristic was correlated withhe RSE only if it was not already included in the equation itself.earson correlation was used and values of p below 0.01 wereonsidered statistically significant.

All statistical analyses were performed by SPSS version 16.0SPSS Inc., Chichago, IL).

. Results

The study included 218 patients with lung cancer (mean age

7.5 years, 70% male; Table 1). Patients’ median BMI was in theverweight category, ranging from underweight to obese class II.lso, median BSA of 1.84 m2 was higher than the standardised BSA

1.73 m2), ranging from 1.43 m2 to as high as 2.41 m2. The median

r 76 (2012) 397– 402 399

of measured ECCs indicates a normal kidney function; however, themeasured values extended to the limits of the exclusion criteria.

3.1. Estimation of renal function

A significant correlation between calculated and measuredvalues of renal function was noted for all equations (Pearson cor-relation, p < 0.01 for all equations; Fig. 2, Supp. Fig. 1 and Table II).Square of Pearson correlation coefficient was the highest for Wrightand CG equation, explaining over 40% of ECC variability, and lowestfor MDRD-BSA (9.2%).

Relatively high median differences between calculated andmeasured renal function (highest for MDRD and CG equation:−14 mL/min and −13 mL/min, respectively) with large SDs (highestagain in MDRD equation, 27 ml/min) were observed (Table 2). Over-all, all equations shared similar statistical parameters but Wrightequation performed best: its differences were characterized bythe lowest MPE (0.8%), MAPE (21.1%) and median (−6.7 mL/min).Among all equations, Wright’s estimates less often differed fromECC for more than ±10% (Table 3). When comparing mean valueswith 95% confidence intervals, ECC obtained values were sig-nificantly different from those obtained by others except whencompared to Wright equation (Supp. Fig. 3). The difference betweenWright and other equations was also significant (p < 0.001, pairedsample t-test).

3.1.1. Estimation of renal function in patients with an ECC below50 mL/min

A separate analysis was performed for the 18 patients with ECCvalue below 50 mL/(min × 1.73 m2). The CG equation detected ECCunder this limit in 72% of cases, whereas other equations detected itin less than 40% (MDRD 39%, MDRD-BSA 39%, Wright 33%, CKD-EPI22%). Mean values and respective 95% CIs (in mL/min) in this rangeof renal function were as follows: ECC (45, 41–49), CG (53, 43–64),MDRD (58, 48–67), MDRD-BSA (59, 49–70), Wright (62, 50–74),CKD-EPI (63, 53–73).

3.2. Correlation between the accuracy of renal function estimatesand patient’s weight, BMI and BSA

When the impact of various patients’ characteristics was inves-tigated, neither BMI nor BSA influenced the accuracy of CGcalculations (Table 4). However, weight and BSA correlated with thedifferences found when applying the MDRD and CKD-EPI equations.Interestingly, BMI was significantly associated with deviations ofCKD-EPI, MDRD, MDRD-BSA and Wright equation, despite BSA isincorporated in the last two equations.

4. Discussion

Four different equations for the estimation of renal functionwere evaluated against ECC in a large, uniform group of 218 lungcancer patients prior any cytotoxic therapy. Although no majordifferences were noted among the different equations, our resultssuggest the use of the Wright equation in the specific populationof lung cancer patients. Most importantly, our results emphasizethe need to interpret any estimation against patient characteris-tics such as weight, BSA and BMI, if not already incorporated in theequation.

4.1. Estimation of renal function

In general, no major differences were noted in the performanceof the tested equations when compared to ECC in the overall rangeof renal function.

400 K. Trobec et al. / Lung Cancer 76 (2012) 397– 402

Table 2Statistical analysis of the differences between the estimation of renal function, measured by ECC and calculated through the respective equation.

MPE (%) MAPE (%) Median (mL/min) SD (mL/min) Min (mL/min) Max (mL/min) 95% CI for MPE (%)

CG −9.2 22.0 −12.6 22.5 −99.8 60.1 −12.6 to −5.8MDRD −8.9 25.8 −13.8 27.4 −94.5 81.1 −13.0 to −4.7MDRD-BSA −4.5 22.4 −11.2 24.5 −88.2 89.0 −8.4 to −0.7Wright 0.80 21.1 −6.7 23.4 −87.6 87.7 −3.0 to 4.7CKD-EPI −8.6 24.4 −12.4 25.7 −91.3 57.0 −12.6 to −4.5

MPE = mean percentage error, MAPE = mean absolute percentage error, SD = standard deviation of differences, min = maximal negative deviation, max = maximal positivedeviation, CI = confidence interval; CG = Cockcroft–Gault equation, MDRD = modification of diet in renal disease equation, MDRD-BSA = modification of diet in renal diseaseadjusted for body surface area equation, CKD-EPI = chronic kidney disease epidemiology collaboration equation.

Table 3Distribution of the relative differences between the estimation of renal function, measured by ECC and calculated through the respective equation.

CG (%) MDRD (%) MDRD-BSA (%) Wright (%) CKD-EPI (%)

eGFR−ECCECC < −10% 57.3 60.6 51.8 43.6 59.6

−10% < eGFR−ECCECC < +10% 24.8 19.7 25.2 28.0 22.0

eGFR−ECCECC > +10% 17.9 19.7 22.9 28.4 18.8

eGFR = estimated glomerular filtration rate, ECC = measured endogenous creatinine clearance; CG = Cockcroft–Gault equation, MDRD = modification of diet in renal diseaseequation, MDRD-BSA = modification of diet in renal disease adjusted for Body Surface Area equation, CKD-EPI = chronic kidney disease epidemiology collaboration equation.

Table 4Correlation of relative differences (between estimated renal function and measured ECC) and patients’ characteristic.

Weight BSA BMI

Pearson coefficient p-Value Pearson coefficient p-Value Pearson coefficient p-Value

CG 0.063 0.358 0.098 0.148MDRD −0.466 <0.01 −0.454 <0.01 −0.387 <0.01MDRD-BSA −0.210 <0.01Wright −0.227 <0.01CKD-EPI −0.242 <0.01 −0.261 <0.01 −0.157 <0.01

BSA = body surface area, BMI = body mass index, UCr = urine creatinine concentration, CG = Cockcroft–Gault equation, MDRD = modification of diet in renal disease equation,M = chros

s1t

srhtf5si

piEiW[ao

inomtmpC

DRD-BSA = modification of diet in renal disease adjusted for BSA equation, CKD-EPIignificant correlation at the 0.01 level (2-tailed).

Large deviations of differences were observed, with a mediantandard deviation of more than 20% and maximal errors exceeding00%. Differences were in-between ±10% of measured ECC in lesshan one third of cases.

All equations performed poorly at the extremes of the inclu-ion interval. As reported in previous studies, all overestimatedenal function at lower ranges of ECC and underestimated it atigher ranges [19–22]. Especially in patients with poor renal func-ion, inaccuracies of the used equations (MPE ranging from 21%or CG and 44% for CKD-EPI equation in the ECC range below0 mL/(min × 1.73 m2), data not shown) can misguide clinical deci-ions with important implications for patient care; special attentions required in these patients.

Wright equation showed best correlation with ECC which sup-orts it as the best replacement for use in clinical practice. However,

t should not be neglected that the equation describes only 43% ofCC variability and is prone to large deviations. Former studies aren agreement with our findings, showing the best accuracy of the

right equation for estimation of renal function in cancer patients20,21,23]. This suggests that equations perform best when they arepplied in patients resembling those included in the developmentf the equation itself.

In our study, the CG equation performed reasonably well. Thiss not surprising since it is the only equation estimating creati-ine clearance and not GFR. Although a previous study shares ourbservation [19], other studies, comparing the CG estimates to GFR,easured with isotope, found poorer performance of CG compared

o other equations [18,22,23]. The MDRD equation did worse inimicking ECC in our study. Earlier studies demonstrated its poorer

erformance against Wright equation [23] but better accuracy thanG equation [22].

nic kidney disease epidemiology collaboration equation. Bolded numbers represent

Our study tested the CKD-EPI in cancer patients for the firsttime. This equation is emerging as the equation of choice to esti-mate GFR, since it confers less underestimation of GFR in subjectsof normal renal function and has been proposed as the most appro-priate formula for GFR estimation for drug dosing decisions [24,25].However, our study, performed in lung cancer patients, could notconfirm the advantages seen in the population with chronic kidneydisease. The poor performance of MDRD and CKD-EPI equationsin lung cancer patients may be explained. Both equations weredeveloped primarily to detect renal failure in patients with kidneydisease but this population is poorly represented in our study (only14% of ECC measurements were below 60 mL/(min × 1.73 m2).Moreover, none of them includes a descriptor of body weight andsize, which seems an obligatory requirement for the application incancer patients [11,14,17].

4.2. Estimation of renal function in patients with an ECC below50 mL/min

In the range of ECC below 50 mL/min, where cisplatin is con-traindicated and precise dosing of carboplatin becomes even moreimportant, all equations overestimated ECC. The CG equation out-performed others, identifying a contraindication for cisplatin usein approximately two thirds of patients. Moreover, the lower trendof CG formula towards overestimation of renal function in rangesof low creatinine clearance can be seen in Fig. 2 and Supp. Fig. 2.

Other studies performed in cancer patients with impaired renalfunction found either MDRD to be a better predictor than CG and/orWright equation [18] or similar accuracy was shared between allthree equations [23].

K. Trobec et al. / Lung Cance

Fig. 2. Presentation of renal function estimation in comparison to measuredendogenous creatinine clearance (ECC) for selected equations. Full circles (•) rep-resent male patients; empty circles (о) represent female patients. (a) Correlationbetween estimation of glomerular filtration rate (GFR) by Wright equation and mea-sured ECC, with the presentation of the 95% confidence interval (dotted lines) andPearson’s coefficient (R Sq Linear). (b) Relative differences between estimation ofrenal function by Wright equation and measured ECC, plotted against measuredECC. (c) Relative differences between estimation of renal function by Cockcroft-Gaultequation and measured ECC, plotted against measured ECC.

r 76 (2012) 397– 402 401

However, as calculated values were compared to ClCr deter-mined by ECC, the high contribution of creatinine tubular secretionto the overall CrCl in this range of renal function should be con-sidered. This could also explain the better performance of the CGequation, which is the only equation that actually describes crea-tinine excretion. Therefore, the estimation of an impaired kidneyfunction remains challenging in cancer patients; both, an estima-tion of a low renal function or a significant raise in serum creatinineconcentration should serve as an alarming signal for the practi-tioner to critically evaluate the calculated value and to considerECC measurement.

4.3. Correlation between the accuracy of renal function estimatesand patient’s weight, BMI and BSA

Weight, BSA and BMI significantly correlated with the accuracyof renal function estimations provided by equations, not includingthem. When estimating renal function in the extremes of weight,BSA or BMI, it is important to use an equation including one ormore weight descriptors (Wright, MDRD-BSA or CG equation) andinterpret the estimation against these patient’s characteristics. Esti-mation of renal function from serum creatinine is influenced bybody constitution, which is indeed changed in cancer patients,prone to cachexia and muscle wasting [26,27]. However, not all can-cer patients are the same: lung cancer patients are characterized bylower BMIs and by marked muscle mass loss which is independentof a patient’s BMI. Our results may therefore not be directly trans-posed to other cancers [28,29]. Weight, BSA and BMI are not theonly surrogate markers for muscle mass; its determination by non-invasive measurements of body composition in future researchesmay improve the estimation of renal function in the whole rangeof cancer patients.

4.4. Limitations of our study

Our study used ECC as a reference for renal function, which couldcause potential bias: (1) it overestimates GFR for creatinine tubularsecretion and (2) it is prone to errors in the pre-analytical phaseof urine collection. These limitations were accepted since ECC isstill the method employed in routine clinical practice. Finally, wecollected data in a retrospective manner and could not control forpotential confounders or repeat the testing if sampling was inap-propriate.

5. Conclusions and clinical implications

Estimations from the Wright equation correlated best with mea-sured ECC and may replace it in routine clinical practice whendecisions about chemotherapy initiation, continuation and dosingare made. In view of cancer patient characteristics, particularlybody wasting and altered composition, equations adjusting forbody size descriptors, e.g. Wright, CG or MDRD-BSA should be pre-ferred. Estimated renal function should be interpreted against thesepatients’ characteristics and special attention is needed when theseare reaching the extremes.

Conflict of interest statement

None declared.

Acknowledgements

Mitja Lainscak has received HFA research fellowship. Study wassupported by Slovenian Research Agency (grant number J3-2394).

4 Cance

A

t

R

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

02 K. Trobec et al. / Lung

ppendix A. Supplementary data

Supplementary data associated with this article can be found, inhe online version, at doi:10.1016/j.lungcan.2011.11.016.

eferences

[1] Ferlay J, Parkin DM, Steliarova-Foucher E. Estimates of cancer incidence andmortality in Europe in 2008. Eur J Cancer 2010;46:765–81.

[2] Crinò L, Weder W, van Meerbeeck J, Felip E. Early stage and locally advanced(non-metastatic) non-small-cell lung cancer: ESMO Clinical Practice Guidelinesfor diagnosis, treatment and follow up. Ann Oncol 2010;21:v103–15.

[3] Addario GD, Früh M, Baumann P, Klepetko W, Felip E. Metastatic non-small-cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment andfollow-up. Ann Oncol 2010;21:v116–9.

[4] Sørensen M, Pijls-johannesma M, Felip E. Small-cell lung cancer: ESMO Clin-ical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol2010;21:v120–5.

[5] Launay-Vacher V, Etessami R, Janus N, Spano JP, Ray-Coquard I, Oudard S,et al. Renal Insufficiency Anticancer Medications (IRMA) Study Group. Lungcancer and renal insufficiency: prevalence and anticancer drug issues. Lung2009;187(1):69–74.

[6] Calvert AH. Carboplatin dosage: prospective evaluation of a simple equationbased on renal function. J Clin Oncol 1989;7:1748–56.

[7] Hanigan MH, Devarajan P. Cisplatin nephrotoxicity: molecular mechanisms.Cancer Ther 2003;1:47–61.

[8] Hubner RA, Goldstein R, Mitchell S, Jones A, Ashley S, O’Brien ME, et al. Influenceof co-morbidity on renal function assessment by Cockcroft–Gault calculation inlung cancer and mesothelioma patients receiving platinum-based chemother-apy. Lung Cancer 2011. doi:10.1016/j.lungcan.2011.01.013 [E-published aheadof print].

[9] Chang GC, Yang TY, Shih CM, Lin LY, Lee HS, Chiang CD. Serial-measuredversus estimated creatinine clearance in patients with non-small celllung cancer receiving cisplatin-based chemotherapy. J Formos Med Assoc2003;102:257–61.

10] Camara AA, Arn KD, Reimer A, Newburgh LH. The twenty fourly ECC as a clin-ical measure of the functional state of the kidneys. J Lab Clin Med 1951;37:743–63.

11] National Kidney Foundation. K/DOQI clinical practice guidelines for chronickidney disease: evaluation, classification and stratification. Am J Kidney Dis2002;39:S1–266.

12] Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum crea-tinine. Nephron 1976;16:31–41.

13] Wright JG, Boddy AV, Highley M, Fenwick J, McGill A, Calvert AH. Esti-mates of glomerular filtration rate in cancer patients. Brit J Cancer 2001;84:452–9.

14] Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accuratemethod to estimate glomerular filtration rate from serum creatinine: a new

[

r 76 (2012) 397– 402

prediction equation. Modification of Diet in Renal Disease Study Group. AnnIntern Med 1999;130:461–70.

15] National Kidney Disease Epidemiology Group. Laboratory Professionals: esti-mating GFR. http://www.nkdep.nih.gov/.labprofessionals/estimating gfr.htm#mdrd [accessed 25.01.11].

16] National Kidney Disease Epidemiology Group. Chronic Kidney Disease and DrugDosing: Information for Providers. http://www.nkdep.nih.gov/professionals/drug-dosing-information.htm [accessed 15.07.11].

17] Levey AS, Lesley AS, Schmid CH, Zhang YL, Castro AF, Feldman HI, et al.A new equation to estimate glomerular filtration rate. Ann Intern Med2009;150:604–12.

18] Faluyi OO, Masinhghe SP, Hayward RL. Accuracy of GFR estimation by the Cock-roft and Gault, MDRD, and Wright equations in oncology patients with renalimpairment. Med Oncol 2011. doi:10.1007/s12032-011-9824-0 [E-publishedahead of print].

19] Hahn T, Yao S, Dunford LM, Thomas J, Lohr J, Arora P, et al. A comparison ofmeasured creatinine clearance versus calculated glomerular filtration rate forassessment of renal function before autologous and allogeneic BMT. Biol BloodMarrow Transplant 2009;15:574–9.

20] Marx GM, Blake GM, Galani E, Steer CB, Harper SE, adamson KL, et al. Evalua-tion of the Crockcroft–Gault, Jeliffe and Wright equatione in estimating renalfunction in elderly cancer patients. Ann Oncol 2004;15:291–5.

21] Poole SG, Dooley MJ, Rischin D. a comparison of bedside renal function esti-mates and measured glomerular filtration rate (Tc99mDTPA clearance) incancer patients. Ann Oncol 2002;13:399–402.

22] de Lemos ML, Hsieh T, Hamata L, Levin A, Swenerton K, Djurdjev O, et al.Evaluation of predictive equatione for glomerular filtration rate for car-boplatin dosing in gynaecological malignancies. Gynaecol Oncol 2006;103:1063–9.

23] Barraclough LH, Field C, Wieringa g, Swindell R, Livsey JE, Davidson SE. Esti-mation of renal function—what is appropriate in cancer patients? Clin Oncol2008;20:721–6.

24] Florkowski CM, Chew-Harris JSC. Methods of estimating GFR—different equa-tions including CKD-EPI. Clin Biochem Rev 2011;32:75–9.

25] Jones GRD. Estimating renal function for drug dosing decisions. Clin BiochemRev 2011;32:81–8.

26] von Haehling S, Anker SD. Cachexia as a major underestimated and unmetedical need: facts and numbers. J Cachex Sarcopenia Muscle 2010;1:1–5.

27] Argilés JM, López-Soriano FJ, Toledo M, Betancourt A, Serpe R, Busquets S. Thecachexia score (CASCO): a new tool for staging cachectic cancer patients. JCachex Sarcopenia Muscle 2011;2:87–93.

28] Rosseau MC, Parent ME, Siemiatycki J. Comparison of self-reported height andweight by cancer type among men from Montreal, Canada. Eur J Cancer Prev2005;14:431–8.

29] Baracos VE, Reiman T, Mourtzakis M, Gioulbasanis I, Antoun S. Body com-position in patients with non-small cell lung cancer: a contemporaryview of cancer cachexia with the use of computed tomography imageanalysis. Am J Clin Nutr 2010;91:1133S–7S. doi:10.3945/ajcn.2010.28608C[E-published].