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• W2274612293 abstract "Residual kidney function (RKF) contributes significant solute clearance in hemodialysis patients. Kidney Diseases Outcomes Quality Initiative (KDOQI) guidelines suggest that hemodialysis dose can be safely reduced in those with residual urea clearance (KRU) of 2 ml/min/1.73 m2 or more. However, serial measurement of RKF is cumbersome and requires regular interdialytic urine collections. Simpler methods for assessing RKF are needed. β-trace protein (βTP) and β2-microglobulin (β2M) have been proposed as alternative markers of RKF. We derived predictive equations to estimate glomerular filtration rate (GFR) and KRU based on serum βTP and β2M from 191 hemodialysis patients based on standard measurements of KRU and GFR (mean of urea and creatinine clearances) using interdialytic urine collections. These modeled equations were tested in a separate validation cohort of 40 patients. A prediction equation for GFR that includes both βTP and β2M provided a better estimate than either alone and contained the terms 1/βTP, 1/β2M, 1/serum creatinine, and a factor for gender. The equation for KRU contained the terms 1/βTP, 1/β2M, and a factor for ethnicity. Mean bias between predicted and measured GFR was 0.63 ml/min and 0.50 ml/min for KRU. There was substantial agreement between predicted and measured KRU at a cut-off level of 2 ml/min/1.73 m2. Thus, equations involving βTP and β2M provide reasonable estimates of RKF and could potentially be used to identify those with KRU of 2 ml/min/1.73 m2 or more to follow the KDOQI incremental hemodialysis algorithm. Residual kidney function (RKF) contributes significant solute clearance in hemodialysis patients. Kidney Diseases Outcomes Quality Initiative (KDOQI) guidelines suggest that hemodialysis dose can be safely reduced in those with residual urea clearance (KRU) of 2 ml/min/1.73 m2 or more. However, serial measurement of RKF is cumbersome and requires regular interdialytic urine collections. Simpler methods for assessing RKF are needed. β-trace protein (βTP) and β2-microglobulin (β2M) have been proposed as alternative markers of RKF. We derived predictive equations to estimate glomerular filtration rate (GFR) and KRU based on serum βTP and β2M from 191 hemodialysis patients based on standard measurements of KRU and GFR (mean of urea and creatinine clearances) using interdialytic urine collections. These modeled equations were tested in a separate validation cohort of 40 patients. A prediction equation for GFR that includes both βTP and β2M provided a better estimate than either alone and contained the terms 1/βTP, 1/β2M, 1/serum creatinine, and a factor for gender. The equation for KRU contained the terms 1/βTP, 1/β2M, and a factor for ethnicity. Mean bias between predicted and measured GFR was 0.63 ml/min and 0.50 ml/min for KRU. There was substantial agreement between predicted and measured KRU at a cut-off level of 2 ml/min/1.73 m2. Thus, equations involving βTP and β2M provide reasonable estimates of RKF and could potentially be used to identify those with KRU of 2 ml/min/1.73 m2 or more to follow the KDOQI incremental hemodialysis algorithm. Residual kidney function (RKF) is of significant prognostic importance to patients on hemodialysis (HD).1Vilar E. Wellsted D. Chandna S.M. Greenwood R.N. Farrington K. Residual renal function improves outcome in incremental haemodialysis despite reduced dialysis dose.Nephrol Dial Transplant. 2009; 24: 2502-2510Crossref PubMed Scopus (115) Google Scholar It has many clinical advantages including improved nutrition,2Sudha T. Hiroshige K. Ohta T. The contribution of residual renal function to overall nutritional status in chronic haemodialysis patients.Nephrol Dial Transplant. 2000; 15: 396-401Crossref PubMed Scopus (105) Google Scholar anemia, and phosphate control.3Penne E. van der Weerd N. Grooteman M. Role of residual renal function in phosphate control and anemia management in chronic hemodialysis patients.Clin J Am Soc Nephrol. 2011; 6: 281-289Crossref PubMed Scopus (73) Google Scholar Even small amounts of RKF can provide significant benefit. The Kidney Diseases Outcomes Quality Initiative (KDOQI) suggests that minimum dialysis Kt/V targets may be reduced in those with residual urea clearance (KRU) ≥2 ml/min/1.73 m2. The European Best Practice Guidelines (EBPG) recommend measuring RKF in HD patients using the mean of urea and creatinine clearances and offer suggestions to incorporate this into the HD prescription to allow individual adjustment of dialysis prescription to meet minimum dialysis adequacy targets.4Hemodialysis Adequacy 2006 Work GroupClinical practice guidelines for hemodialysis adequacy, update 2006.Am J Kidney Dis. 2006; 48: S2-90PubMed Google Scholar, 5European Best Practice GuidelinesII.3 Haemodialysis dose and residual renal function (Kr).Nephrol Dial Transplant. 2002; 17: 24Crossref Google Scholar However, measurement of urea and creatinine clearances requires an interdialytic urine collection,6European Best Practice GuidelinesSection I. Measurement of renal function, when to refer and when to start dialysis.Nephrol Dial Transplant. 2002; 17: 7-15Google Scholar which can be difficult and inconvenient for patients because RKF has to be monitored at least every 1 to 3 months for incremental HD to be practiced safely.7Wong J. Vilar E. Davenport A. Farrington K. Incremental haemodialysis.Nephrol Dial Transplant. 2015; 30: 1639-1648Crossref PubMed Scopus (62) Google Scholar Serum biomarkers that obviate the need for regular urine collections would be desirable. Urea and creatinine are imperfect biomarkers of kidney function because of external influences by factors such as muscle mass, gender, diet, and nutritional status. Hence there has been interest in novel alternative serum biomarkers, especially cystatin C, β-trace protein (βTP), and β2-microglobulin (β2M).8Filler G. Priem F. Lepage N. β-trace protein, cystatin C, β2-microglobulin, and creatinine compared for detecting impaired glomerular filtration rates in children.Clin Chem. 2002; 48: 729-736PubMed Google Scholar, 9Juraschek S.P. Coresh J. Inker L.A. et al.Comparison of serum concentrations of β-trace protein, β2-microglobulin, cystatin C, and creatinine in the US population.Clin J Am Soc Nephrol. 2013; 8: 584-592Crossref PubMed Scopus (49) Google Scholar, 10Bhavsar N. Appel L. Kusek J. Contreras G. Bakris G. Comparison of measured GFR, serum creatinine, cystatin C, and beta-trace protein to predict ESRD in African Americans with hypertensive CKD.Am J Kidney Dis. 2012; 59: 653-662Abstract Full Text Full Text PDF PubMed Scopus (123) Google Scholar, 11Poge U. Gerhardt T. Stoffel-Wagner B. Beta-trace protein-based equations for calculation of GFR in renal transplant recipients.Am J Transplant. 2008; 8: 608-615Crossref PubMed Scopus (39) Google Scholar Use of cystatin C in dialysis patients is limited because nonrenal clearance of cystatin C is significant and greatly exceeds its renal clearance in this setting.12Sjostrom P. Tidman M. Jones I. Determination of the production rate and non-renal clearance of cystatin C and estimation of glomerular filtration rate from the serum concentration of cystatin C in humans.Scand J Clin Lab Invest. 2005; 65: 111-124Crossref PubMed Scopus (162) Google Scholar, 13Vilar E. Boltiador C. Viljoen A. Machado A. Farrington K. Removal and rebound kinetics of cystatin C in high-flux hemodialysis and hemodiafiltration.Clin J Am Soc Nephrol. 2014; 9: 1240-1247Crossref PubMed Scopus (19) Google Scholar βTP is a 23 kDa glycoprotein, also known as lipocalin type prostaglandin D synthase, and is expressed in a number of organs including the brain, retina, testes, heart, and kidney.14Orenes-Pinero E. β-trace protein: from GFR marker to cardiovascular risk predictor.Clin J Am Soc Nephrol. 2013; 8: 873-881Crossref PubMed Scopus (34) Google Scholar It is virtually exclusively excreted by the kidneys,15Olsson J.E. Link H. Nosslin B. Metabolic studies on 125I-labelled beta-trace protein, with special reference to synthesis within the central nervous system.J Neurochem. 1973; 21: 1153-1159Crossref PubMed Scopus (45) Google Scholar and serum levels of βTP concentration correlate well with residual urine volumes in HD patients,16Gerhardt T. Poge U. Stoffel-Wagner B. Serum levels of beta-trace protein and its association to diuresis in haemodialysis patients.Nephrol Dial Transpl. 2008; 23: 309-314Crossref PubMed Scopus (31) Google Scholar though its ability to predict RKF in the HD setting has not been explored. β2-microglobulin (β2M) has a molecular weight of 11.8 kDa and accumulates in kidney failure. β2M levels have a close relationship with RKF in HD17Fry A.C. Singh D.K. Chandna S.M. Farrington K. Relative importance of residual renal function and convection in determining beta-2-microglobulin levels in high-flux haemodialysis and on-line haemodiafiltration.Blood Purif. 2007; 25: 295-302Crossref PubMed Scopus (62) Google Scholar and peritoneal dialysis.18López-Menchero R. Miguel A. García-Ramón R. Pérez-Contreras J. Girbés V. Importance of residual renal function in continuous ambulatory peritoneal dialysis: its influence on different parameters of renal replacement treatment.Nephron. 1999; 83: 219-225Crossref PubMed Scopus (39) Google Scholar, 19Amici G. Virga G. Da Rin G. et al.Serum beta-2-microglobulin level and residual renal function in peritoneal dialysis.Nephron. 1993; 65: 469-471Crossref PubMed Scopus (49) Google Scholar RKF is the most significant determinant of β2M levels in HD patients and has a greater influence on these levels than the convective clearance provided by hemodiafiltration (HDF).17Fry A.C. Singh D.K. Chandna S.M. Farrington K. Relative importance of residual renal function and convection in determining beta-2-microglobulin levels in high-flux haemodialysis and on-line haemodiafiltration.Blood Purif. 2007; 25: 295-302Crossref PubMed Scopus (62) Google Scholar, 20Penne E.L. van der Weerd N.C. Blankestijn P.J. et al.Role of residual kidney function and convective volume on change in beta2-microglobulin levels in hemodiafiltration patients.Clin J Am Soc Nephrol. 2010; 5: 80-86Crossref PubMed Scopus (69) Google Scholar Hence βTP and β2M are promising candidates as predictors of RKF in the HD setting. Both have limitations though; β2M levels may increase with conditions such as lupus and malignancy,21Evrin P. Wibell L. Serum β2-microglobulin in various disorders.Clin Chim Acta. 1973; 43: 183-186Crossref PubMed Scopus (125) Google Scholar, 22Maury C. Helve T. Serum beta 2-microglobulin, sialic acid, and C-reactive protein in systemic lupus erythematosus.Rheumatol Int. 1982; 2: 145-149Crossref PubMed Scopus (24) Google Scholar and the clinical factors that influence βTP are not well understood, although factors such as gender,9Juraschek S.P. Coresh J. Inker L.A. et al.Comparison of serum concentrations of β-trace protein, β2-microglobulin, cystatin C, and creatinine in the US population.Clin J Am Soc Nephrol. 2013; 8: 584-592Crossref PubMed Scopus (49) Google Scholar ethnicity,23Tin A. Astor B.C. Boerwinkle E. Hoogeveen R.C. Coresh J. Kao W.H.L. Genome-wide significant locus of beta-trace protein, a novel kidney function biomarker, identified in European and African Americans.Nephrol Dial Transplant. 2013; 28: 1497-1504Crossref PubMed Scopus (18) Google Scholar atherosclerosis,24Inoue T. Eguchi Y. Matsumoto T. et al.Lipocalin-type prostaglandin D synthase is a powerful biomarker for severity of stable coronary artery disease.Atherosclerosis. 2008; 201: 385-391Abstract Full Text Full Text PDF PubMed Scopus (49) Google Scholar and inflammation9Juraschek S.P. Coresh J. Inker L.A. et al.Comparison of serum concentrations of β-trace protein, β2-microglobulin, cystatin C, and creatinine in the US population.Clin J Am Soc Nephrol. 2013; 8: 584-592Crossref PubMed Scopus (49) Google Scholar have been implicated. The aim of this study was to evaluate the usefulness of βTP and β2M as estimates of RKF in HD patients. Clinical determinants of βTP and β2M in the HD setting were explored, and prediction equations to estimate residual urea clearance (KRU) and glomerular filtration rate (GFR) in HD patients were constructed based on serum levels of βTP and β2M. The predictive equations were compared with KRU and GFR measured using interdialytic urea and creatinine clearances in a separate validation cohort of HD patients. We also explored the ability of predictive equations to identify HD patients with KRU ≥2 ml/min/1.73 m2 to follow the KDOQI incremental HD algorithm. The study cohort consisted of 231 prevalent HD patients based at the East & North Herts NHS Trust; 191 patients were randomly selected into a modeling group for derivation of equations for predicting parameters of RKF based on serum levels of βTP and β2M, and the remaining 40 patients were used for validation of the final constructed equations (Table 1). There were no significant differences between the modeling and validation cohorts in terms of age, anthropometric parameters, ethnicity, blood pressure, dialysis adequacy, diabetes prevalence, primary renal disease, and Charlson co-morbidity index. Serum βTP and β2M concentrations were similar in both groups. The modeling cohort had a higher GFR than the validation cohort (1.72 vs. 0.74 ml/min/1.73 m2), whereas the validation cohort had a higher prevalence of malignant disease and a higher median C-reactive protein level.Table 1Baseline characteristics of patientsDescriptiveModeling group (n = 191)Validation group (n = 40)PDemographicsAge (y)67 (IQR 53–77)68 (IQR 50–77)0.910Male (%)69.6600.235Dry weight (kg)73.6 (IQR 64.5–88.5)85.2 (IQR 68.1–96.4)0.159Height (cm)170 (IQR 161–177)170 (IQR 163–178)0.698BMI25.4 (IQR 22.6–31.1)27.6 (IQR 23.0–32.3)0.205BSA (m2)1.86 (IQR 1.7–2.0)1.95 (IQR 1.73–2.11)0.128Watson volume (l)38.7 (IQR 34.2–43.2)40 (IQR 32.3–46.7)0.355Ethnicity (%)0.106White73.382.5Black8.412.5Asian15.22.5Other0.52.5Primary renal disease (%)0.157Diabetes26.722.5Glomerulonephritis12.627.5Polycystic kidney disease8.412.5Tubulointerstitial disease2.60Hypertension or renovascular disease16.815.0Other33.022.5Mean weekly systolic BP (mmHg)150 (IQR 135–162)152 (IQR 133–171)0.802Mean weekly diastolic BP (mmHg)75 (IQR 66–84)69 (IQR 63–80)0.163KRU (ml/min/1.73 m2)1.29 (IQR 0–2.38)0.62 (IQR 0–1.51)0.042∗Denotes statistical significance (P < 0.05).Residual GFR (ml/min/1.73 m2)1.72 (IQR 0–3.51)0.74 (IQR 0–2.02)0.016∗Denotes statistical significance (P < 0.05).Interdialytic urine volume (ml)675 (0–1510)295 (IQR 0–865)0.026∗Denotes statistical significance (P < 0.05).Anuric patients (%)34.042.50.309Diuretic use (%)30.927.50.671Diuretic dose (milligram of furosemide)0 (IQR 0–80)0 (IQR 0–40)0.714Dialysis parametersHDF/high flux HD (%)82.7/17.380/200.682Convective volume (l)16.7 (IQR 13.1–19.5)18.2 (IQR 13.4–21.5)0.191Ultrafiltration volume (l)1.7 (IQR 0.98–2.4)1.8 (IQR 0.9–2.3)0.986Mean IDWG (kg)1.5 (IQR 0.8–2)1.3 (IQR 0.7–2)0.655Dialysis vintage (y)1.9 (IQR 0.8–4.8)2.2 (IQR 0.95–4.35)0.882Equilibrated Kt/V (dialyzer)1.1 (IQR 0.9–1.3)1.2 (IQR 1.0–1.4)0.194Total Kt/V (renal + dialyzer)1.3 (IQR 1.2–1.5)1.4 (IQR 1.2–1.5)0.914Co-morbidityCharlson co-morbidity index4.0 (IQR 2–5)3.0 (IQR 2–6)0.623Presence of atheromatous disease (%)49.752.50.751Presence of malignant disease (%)8.920.00.013∗Denotes statistical significance (P < 0.05).Presence of diabetes (%)36.625.00.159CRP (mg/l)6 (IQR 5.0–13.0)10 (IQR 5.0-20.8)0.019∗Denotes statistical significance (P < 0.05).βTP (mg/l)6.51 (IQR 5.34–9.35)6.86 (IQR 5.86–7.71)0.909β2M (mg/l)24.3 (IQR 19.2–29.1)24.4 (IQR 20.3–28.4)0.645BMI, body mass index (calculated from dry weight); BP, blood pressure; BSA, body surface area (calculated from dry weight); βTP, β-trace protein; β2M, β2-microglobulin; CRP, C-reactive protein; GFR, glomerular filtration rate; HD, hemodialysis; HDF, hemodiafiltration; IDWG, interdialytic weight gain; IQR, interquartile range; KRU, residual urea clearance.Atheromatous disease indicates the presence of any of the following: coronary artery disease, cerebrovascular disease, renovascular disease and peripheral vascular disease. Diuretic dose is give as milligrams of furosemide. Anuric patients are patients with interdialytic urine volume < 200 ml.∗ Denotes statistical significance (P < 0.05). Open table in a new tab BMI, body mass index (calculated from dry weight); BP, blood pressure; BSA, body surface area (calculated from dry weight); βTP, β-trace protein; β2M, β2-microglobulin; CRP, C-reactive protein; GFR, glomerular filtration rate; HD, hemodialysis; HDF, hemodiafiltration; IDWG, interdialytic weight gain; IQR, interquartile range; KRU, residual urea clearance. Atheromatous disease indicates the presence of any of the following: coronary artery disease, cerebrovascular disease, renovascular disease and peripheral vascular disease. Diuretic dose is give as milligrams of furosemide. Anuric patients are patients with interdialytic urine volume < 200 ml. Clinical determinants of serum βTP and β2M levels in HD were sought using univariable and multivariable regression analysis of clinical and demographic data from the modeling cohort. Independent predictors of βTP and β2M are shown in Table 2 and Table 3, respectively.Table 2Determinants of βTP: univariable and multivariable regression analysis (R2 of multivariable model = 0.552)DeterminantUnivariable modelMultivariable modelBetaStandard errorSignificanceBetaStandard errorSignificanceDemographic/Clinical dataAge (y)–0.0410.0130.001∗Denotes statistical significance (P < 0.05).–0.0360.009<0.001Dry weight (kg)–0.0190.010.069Post HD weight (kg)–0.0180.010.081Height (cm)0.0030.0180.886BMI (kg/m2)–0.0410.0240.087Male gender0.6100.4270.1551.3940.326<0.001BSA (m2)–1.2170.8310.145–2.2050.627<0.001Watson volume (ml)–0.0000100.671Ethnicity (white)–1.770.427<0.001∗Denotes statistical significance (P < 0.05).Systolic BP (mmHg)–0.0020.0090.806Diastolic BP (mmHg)0.0010.0130.932GFR (ml/min/1.73 m2)–0.8130.066<0.001∗Denotes statistical significance (P < 0.05).–0.80.061<0.001KRU (ml/min/1.73 m2)–1.2080.1<0.001∗Denotes statistical significance (P < 0.05).ComorbiditiesCCI–0.140.090.124Atheromatous disease0.9590.3890.015∗Denotes statistical significance (P < 0.05).0.8570.2830.003Diabetes mellitus–0.0720.410.861Malignancy–1.8350.680.008∗Denotes statistical significance (P < 0.05).CRP–0.0110.0110.35Diuretic use–1.4840.413<0.001∗Denotes statistical significance (P < 0.05).Diuretic dose–0.0020.0020.179Dialysis parametersHD Modality (HDF/high-flux HD)0.5590.5210.2850.8960.3530.012Vintage (y)0.0350.0260.168UF volume (l)0.001<0.001<0.001∗Denotes statistical significance (P < 0.05).Convective volume (l)0.0240.0270.359Dialyser Kt/V2.4620.740.001∗Denotes statistical significance (P < 0.05).Mean IDWG (kg)0.9480.224<0.001∗Denotes statistical significance (P < 0.05).BMI, body mass index; BP, blood pressure; BSA, body surface area; βTP, β-trace protein; CCI, Charlson comorbidity index; CRP, C-reactive protein; GFR, glomerular filtration rate; HD, hemodialysis; HDF, hemodiafiltration; IDWG, interdialytic weight gain; KRU, residual urea clearance; UF, ultrafiltration volume.The diuretic dose is given in milligrams furosemide.∗ Denotes statistical significance (P < 0.05). Open table in a new tab Table 3Determinants of β2M: univariable and multivariable regression analysis (R2 of multivariable model = 0.484)DeterminantUnivariable modelMultivariable modelBetaStandard errorSignificanceBetaStandard errorSignificanceDemographic dataAge (y)–0.0630.0370.088Dry weight (kg)–0.0590.0290.044∗Denotes statistical significance (P < 0.05).Post HD weight (kg)–0.0580.0290.045∗Denotes statistical significance (P < 0.05).Height (cm)–0.0210.0520.688BMI (kg/m2)–0.1180.0680.085Male gender–2.431.2230.048∗Denotes statistical significance (P < 0.05).BSA (m2)–4.3632.3830.069Watson volume (ml)000.079Race, Caucasian–2.4121.2720.059Systolic BP (mmHg)–0.0130.0260.616Diastolic BP (mmHg)0.040.0390.303GFR (ml/min/1.73 m2)–2.4030.184<0.001∗Denotes statistical significance (P < 0.05).–2.360.183<0.001KRU (ml/min/1.73 m2)–3.4760.287<0.001∗Denotes statistical significance (P < 0.05).ComorbiditiesCCI–0.3390.2610.195Atheromatous disease1.1781.1330.3Diabetes mellitus−3.1031.1570.008∗Denotes statistical significance (P < 0.05).–2.0310.8490.018∗Denotes statistical significance (P < 0.05).Malignancy0.1291.9950.949CRP0.0010.0330.976Diuretic use–4.4251.187<0.001∗Denotes statistical significance (P < 0.05).Diuretic dose–0.0040.0050.417Dialysis parametersHD modality (high-flux HD/HDF)–0.5971.5020.691Vintage (y)0.160.0730.03∗Denotes statistical significance (P < 0.05).UF volume (l)0.0020.0010.003∗Denotes statistical significance (P < 0.05).Convective volume (l)0.1260.0760.101Dialyzer Kt/V9.4132.08<0.001∗Denotes statistical significance (P < 0.05).Mean IDWG (kg)1.780.6630.008∗Denotes statistical significance (P < 0.05).BMI, body mass index; BP, blood pressure; BSA, body surface area; β2M, β2-microglobulin; CCI, Charlson comorbidity index; CRP, C-reactive protein; GFR, glomerular filtration rate; HD, hemodialysis; HDF, hemodiafiltration; IDWG, interdialytic weight gain; KRU, residual urea clearance; UF, ultrafiltration volume.The diuretic dose is given in milligrams furosemide.∗ Denotes statistical significance (P < 0.05). Open table in a new tab BMI, body mass index; BP, blood pressure; BSA, body surface area; βTP, β-trace protein; CCI, Charlson comorbidity index; CRP, C-reactive protein; GFR, glomerular filtration rate; HD, hemodialysis; HDF, hemodiafiltration; IDWG, interdialytic weight gain; KRU, residual urea clearance; UF, ultrafiltration volume. The diuretic dose is given in milligrams furosemide. BMI, body mass index; BP, blood pressure; BSA, body surface area; β2M, β2-microglobulin; CCI, Charlson comorbidity index; CRP, C-reactive protein; GFR, glomerular filtration rate; HD, hemodialysis; HDF, hemodiafiltration; IDWG, interdialytic weight gain; KRU, residual urea clearance; UF, ultrafiltration volume. The diuretic dose is given in milligrams furosemide. In multivariable analysis, significant positive associations with βTP were found for male gender and the prevalence of atheromatous disease (Table 2). There were inverse associations for age, body surface area, GFR, and treatment with HDF. Caucasian ethnicity, prevalence of malignant disease, ultrafiltration volume, dialyzer Kt/V, diuretic use, and mean interdialytic weight gain were associated with βTP in univariable analysis only. In multivariable analysis, significant associations with β2M were found with GFR and diabetic status (Table 3). Weight, male gender, dialysis vintage, ultrafiltration volume, mean interdialytic weight gain, dialyzer Kt/V, and diuretic use were associated in univariable analysis only. No significant associations were found with C-reactive protein, HDF treatment, or convective volume. Linear regression models for KRU and GFR were determined using the modeling cohort in three phases: (i) using βTP alone, (ii) using β2M alone, and (iii) using both βTP and β2M. Other relevant covariates were used in each case. The best constructed models are shown in Table 4. Integrated Discrimination Improvement analysis25Kerr K.F. McClelland R.L. Brown E.R. Lumley T. Evaluating the incremental value of new biomarkers with integrated discrimination improvement.Am J Epidemiol. 2011; 174: 364-374Crossref PubMed Scopus (132) Google Scholar was used to assess the predictive accuracy of the equation that incorporated both βTP and β2M (model 3) compared with the best model using a single biomarker (model 1 or 2) for cut-off levels 1 to 5 ml/min for both KRU and GFR. This demonstrated that predictive equations that use both βTP and β2M had greater accuracy than the best equation using a single protein (β2M) at cut-off levels of measured clearance ranging from 1 to 5 ml/min. This was true for both estimated GFR and KRU. For instance, at a cut-off KRU >2 ml/min/1.73 m2, the integrated discrimination improvement index was 0.216 for the combined equation compared with 0.171 for the equation using β2M alone (P = 0.001). Likewise, at a cut-off GFR >2 ml/min/1.73 m2 the corresponding values for the integrated discrimination improvement index were 0.200 and 0.125 (P < 0.001).Table 4Linear regression equations for KRU and GFRModelBiomarker usedParametersβ coefficientSignificanceR2KRUβTP1/βTP14.985<0.0010.5691/creatinine682.73<0.0011/urea11.4210.03Male gender0.5210.001β2M1/ β2M50.022<0.0010.5971/creatinine596.149<0.0011/urea–14.6180.004Caucasian ethnicity0.4830.003βTP and β2M1/βTP9.097<0.0010.6251/β2M37.568<0.001Caucasian ethnicity0.4020.01GFRβTP1/βTP23.968<0.0010.6331/creatinine1230.716<0.001Age–0.0160.019Gender0.938<0.001β2M1/β2M78.247<0.0010.6651/creatinine1143.816<0.0011/urea–20.40.003Caucasian ethnicity0.4690.033βTP and β2M1/βTP13.471<0.0010.7001/β2M52.379<0.0011/creatinine782.909<0.001Male gender0.5190.012βTP, β-trace protein; β2M, β2-microglobulin; GFR, glomerular filtration rate; KRU, residual urea clearance. Open table in a new tab βTP, β-trace protein; β2M, β2-microglobulin; GFR, glomerular filtration rate; KRU, residual urea clearance. The best modeled equation of GFR (Equation 1) explained 70% of the variance (R2 = 0.70).Estimated GFR=13.471βTP+52.379β2M+782.909creatinine+0.519×gender factor−3.939Equation 1 Where for gender, male = 1, female = 0 The best model of KRU (Equation 2) explained 63% of the variance (R2 = 0.63).Estimated KRU=9.097βTP+37.568β2M+0.402×ethnicity factor−2.049Equation 2 Where for ethnicity, Caucasian = 1, Non-Caucasian = 0 Leave-out one-cross validation for estimated GFR and KRU demonstrated a pseudo-R2 of 0.66 and 0.60, respectively, which were similar to the performance of the above regression equations in the modeling cohort (R2 = 0.70 and 0.63, respectively). The best modeled predictive equations for estimating KRU and GFR were compared with measured KRU and GFR (using urinary urea and creatinine clearances) in the modeling and validation cohorts using correlation and Bland-Altman analysis.26Bland J.M. Altman D.G. Statistical methods for assessing agreement between two methods of clinical measurement.Lancet. 1986; 1: 307-310Abstract PubMed Scopus (39381) Google Scholar Level of agreement for different cut-off levels of residual kidney function was assessed using the kappa statistic (κ). Estimated and measured values of both parameters correlated significantly in both modeling (correlation coefficients for KRU and GFR were 0.781 and 0.801, respectively [P < 0.001]) and validation cohorts (correlation coefficient for KRU and GFR were 0.783 and 0.762, respectively [P < 0.001]). Mean bias between measured and estimated KRU in the validation cohort was –0.50 ml/min [95% CI –0.25 to –0.75] with 95% limits of agreement from –2.03 to 1.04 ml/min. For GFR, mean bias was –0.64 ml/min [95% CI –0.89 to –0.39] with 95% limits of agreement from –2.84 to 1.57 ml/min (Figure 1). Level of agreement using the kappa statistic (κ)27Viera A.J. Garrett J.M. Understanding interobserver agreement: the kappa statistic.Fam Med. 2005; 37: 360-363PubMed Google Scholar between the proportions of patients with measured and predicted levels of GFR above cut-offs in the range 1 to 3 ml/min/1.73 m2 was substantial in the modeling cohort (κ = 0.65–0.67, all P < 0.001) and ranged from moderate to substantial in the validation cohort (κ = 0.43–0.77, all P < 0.01). Similarly, level of agreement for KRU above cut-offs in the range 1 to 3 ml/min/1.73 m2 was moderate to substantial in the modeling cohort (κ = 0.51–0.66, all P < 0.001) and fair to substantial in the validation cohort (κ = 0.36–0.65, all P < 0.02). For both GFR and KRU, the level of agreements deteriorated outside of these ranges. The diagnostic accuracy of predictive equations to identify those with KRU >2 ml/min/1.73 m2, which might allow safe reduction of minimum dialysis Kt/V targets as suggested in the KDOQI Hemodialysis Adequacy guidelines, was assessed in modeling and validation groups. Receiving operator characteristic analyses were performed for prediction of various cut-off levels of measured GFR or KRU using the prediction equations in both the modeling and validation cohorts. The prediction equations demonstrated a high degree of accuracy with area under curve values between 0.900 and 0.948 (Table 5). For instance, identifying patients in the modeling cohort with measured levels of KRU >2 ml/min/1.73 m2, using cut-off predicted KRU levels >2 ml/min/1.73 m2 yielded an area under curve of 0.903, a sensitivity of 58%, and a specificity 92%, while in the validation cohort corresponding values were area under curve 0.948, sensitivity 71%, and specificity 94%.Table 5Receiver operating characteristic analyses for identification of patients with RKF above defined levelsPopulationRKF measure(ml/min/1.73 m2)nCut-off RKF level to be identified(ml/min/1.73 m2 of GFR or KRU as appropriate)AUCPSensitivity at predicted cutoff (%)Specificity at predicted cutoff (%)ModelingGFR114>10.909<0.001946989>20.908<0.001768858>30.941<0.001729242>40.937<0.001559722>50.930<0.0015997KRU104>10.906<0.001887862>20.903<0.001589232>30.900<0.001509610>40.930<0.0013099ValidationGFR17>10.903<0.001945210>20.910<0.00170774>30.9440.00410094KRU14>10.942<0.001100697>20.948<0.0017194AUC, area under curve; GFR, glomerular filtration rate; KRU, residual urea clearance, RKF, residual kidney function.In these receiving operator characteristic analyses, the cut-off level of measured GFR or KRU to be identified is shown in column 2. Identification of patients with GFR/KRU above these levels is with predicted KRU/GFR from equations (1) and 2 above this same level. Open table in a new tab" @default.
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• W2274612293 title "Predicting residual kidney function in hemodialysis patients using serum β-trace protein and β2-microglobulin" @default.
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