P153
PREDICTION OF RESIDUAL KIDNEY FUNCTION IN HAEMODIALYSIS USING
BETA-TRACE PROTEIN AND BETA-2 MICROGLOBULIN
Jonathan Wong1, 2, Sivakumar Sridharan1, 2, Enric Vilar1, 2& Ken Farrington1, 2
1
Lister Hospital, Stevenage, Hertfordshire, 2University of Hertfordshire, Hatfield
BACKGROUND: Residual kidney function (RKF) can contribute significantly to overall
solute clearance in haemodialysis (HD)patients and provides numerous clinical benefits.
KDOQI guidelines suggest that Kt/V targets maybe reduced in those with urea clearance
(KRU)>2ml/min/1.73m2. Measurement of RKF requires cumbersome inter-dialytic urine
collections. Serum biomarkers that could accurately predict RKF and replace inconvenient urine
collections would be desirable. Serum beta-trace protein (BTP), a 23kDa glycoprotein, and beta2 microglobulin (B2M) are potential candidates. We determined if BTP and B2M could
accurately estimate RKF in HD patients.
METHODS: We measured KRU, GFR (mean of urea and creatinine clearance from interdialytic urine collection), serum BTP and B2M in 127 HD patients (test cohort). Co-morbidity
and demographic data were collected to determine independent predictors of BTP and B2M.
Linear regression equations were constructed to define predictors of GFR and KRU based on
BTP, B2M levels and other relevant variables. Measured and estimated GFR and KRU (from
equations) were compared in the test cohort and a validation cohort of 22 patients using BlandAltman analysis. Levels of agreement (kappa statistic) were determined for cut-off values of
>2ml/min/1.73m2 for measured and estimated KRU.
RESULTS: The test cohort consisted of 127 patients (85 male; mean age 62±15.7) and the
validation cohort consisted of 22 patients (13 male; mean age 66.6±15.4). Median GFR was
0ml/min (IQR 0-1.7) in the test cohort and 0ml/min (IQR 0-1.67) in the validation cohort.
Reciprocal values of BTP and B2M were closely correlated with GFR (r2=0.57 and 0.55
respectively). The best model of GFR contained terms for 1/BTP, 1/B2M, dialysis vintage,
convective volume and body mass index and explained 72.4% of the variance (r2=0.72).
𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 𝐺𝐹𝑅 = −0.952 +
16.373 52.557
+
− 0.093 × 𝑑𝑖𝑎𝑙𝑦𝑠𝑖𝑠 𝑣𝑖𝑛𝑡𝑎𝑔𝑒 − 0.04 × 𝑐𝑜𝑛𝑣𝑒𝑐𝑡𝑖𝑣𝑒 𝑣𝑜𝑙𝑢𝑚𝑒 − 0.04 × 𝐵𝑀𝐼
𝐵𝑇𝑃
𝐵2𝑀
The best model of KRU contained the same terms and explained 70% of the variance (r2= 0.70,
equation not shown).In Bland-Altman analysis, for KRU, bias was 0.24ml/min/1.73m2 (limits of
agreement -1.6 to 2.1) for the test cohort and -0.42ml/min/1.73m2 (-2.0 to 1.1) for the validation
cohort. For GFR, bias was 0.25ml/min/1.73m2 (-2.5 to 3.0) for the test cohort and 0.85ml/min/1.73m2 (-3.5 to 1.8) for the validation cohort.There was substantial agreement
between measured and estimated KRU for cut-off point <2ml/min/1.73m2 (test cohort,
kappa=0.736: p<0.001) and in the validation cohort (kappa=0.776:p<0.001). The regression
equation correctly distinguished patients with KRU above 2ml/min/1.73m2 in92.1% and 95.5%
in test and validation cohorts respectively. KRU was incorrectly predicted KRU above cut-off in
two patients (1.6%) in the test cohort and one patient (4.5%) in the validation cohort.
CONCLUSION: Reciprocal values of serumBTP and B2M may provide reasonable estimates
of RKF, however incorporating both the reciprocal of B2M and BTP into the regression
equation improved accuracy. If the modelled equation were applied to HD patients following
the KDOQI algorithm to allow reduction of Kt/V targets in those with KRU >2ml/min/1.73m2,
between 1.6-4.5% of patients would be inappropriately prescribed a lower Kt/V target, risking
under-dialysis. The limitations of this study were the relatively small number of patients in the
test and validation cohort and a significant proportion did not have RKF. BTP and B2M are
potential markers that could be used estimate RKF in HD but modelling equations will need to
be derived and validated in a larger group of patients with RKF.