Monitoring residual kidney function in haemodialysis patients using timed urine collections: validation of the use of estimated blood results to calculate GFR

Evidence of the benefits of preserved residual kidney function (RKF) in people on dialysis has been accruing for some years. The importance of RKF was first recognised in those treated with peritoneal dialysis, (PD), most notably in the Canada–USA study (Bargman et al 2001) where survival was 36% higher for each increment of 250 ml in urine volume, but subsequent studies have found similar benefits in the haemodialysis (HD) population. The Netherlands Cooperative Study of Adequate Dialysis (NECOSAD-2) observed that mortality was up to 17 times higher for anuric HD patients than those with measurable RKF, and more recently a US study found that a faster decline in RKF loss during the first year of HD was associated with increased risk of mortality (Termorshuizen 2004, Obi et al 2016). When routinely measured, the time for which clinically significant RKF can be maintained in HD patients has been shown to be comparable with those treated with PD (McKane et al 2002). Other advantages of preserved RKF include improved wellbeing, better quality of life (Merkus et al 1997) and the reduced need to remove high fluid volumes during dialysis sessions with the associated risks of intra-dialytic hypotension and cardiac stunning (Burton et al 2009).

Although HD guidelines do now allow dialysis dose to be adjusted if patients have sufficient renal clearance, it is still common practice for HD to be initiated at 'full-dose', typically 12 h/week in three sessions. An important barrier to a more individualised care in haemodialysis patients is that RKF is often not monitored in HD patients (Dasgupta et al 2016). While timed urine collections are carried out routinely in PD patients to measure RKF, they appear to be widely considered as impractical or inaccurate in HD patients, in part due to the cycling of plasma solute levels necessitating the taking of multiple blood samples. As well as impeding the implementation of incremental haemodialysis and the best use of resources, it is likely that this has hindered the conduct of research into interventions to help maintain RKF in HD patients.

Fluid depletion is known to accelerate loss of RKF, so interventions to improve fluid assessment could lead to better preservation of residual function. The BISTRO study (Bio-Impedance Spectroscopy to maintain Renal Output) was a randomized, controlled trial designed to determine whether using bioimpedance technology in setting the target post-dialysis weight can slow the loss of RKF in incident HD patients [Protocol: BMC Nephrol 2017] (Davies et al 2017). Although the primary outcome was time to anuria, of equal importance was the rate of decline of RKF, calculated as a glomerular filtration rate (GFR). The BISTRO protocol involved up to 15 GFR measurements per patient, each requiring a timed interdialytic urine collection and a set of blood samples from which to calculate the average plasma solute levels during the collection interval. This study provided the opportunity to evaluate the use of estimated plasma levels to calculate GFR.

Determination of residual GFR

In dialysis patients, GFR is usually calculated from the mean of the renal urea and creatinine clearances (K_r U and K_r C), normalised to a body surface area (BSA) of 1.73 m²:

$$\begin{eqnarray}&&\mathrm{GFR}=\left(\frac{{K}_{{\rm{r}}}U+{K}_{{\rm{r}}}C}{2}\right)\times \frac{1.73}{\mathrm{BSA}}\mathrm{ml}/\min /1.73{{\rm{m}}}^{2}.\end{eqnarray} \tag{ 1 }$$

The renal clearance for a solute from a timed urine collection is calculated using:

$$\begin{eqnarray}&&{K}_{{\rm{r}}}=\frac{{C}_{\mathrm{urine}}\times {V}_{\mathrm{urine}}}{{C}_{\mathrm{p\_TAC}}\times {T}_{\mathrm{urine}}}\mathrm{ml}/\min ,\end{eqnarray} \tag{ 2 }$$

where C_urine is the concentration of the solute in the urine and C_{p_TAC} is the time-averaged concentration of the solute in the plasma during the collection. All concentrations are in mmol l⁻¹. V_urine and T_urine are the volume and duration of the timed urine collection (in ml and minutes respectively).

If a linear increase in solute concentration is assumed, C_{p_TAC} is simply the concentration at the midpoint of the collection. For a full interdialytic urine collection, C_{p_TAC} is the mean of the concentration of the solute at the end of session before the collection (C_reb) and the concentration at the start of the next session (C_preNext)

$$\begin{eqnarray}&&{C}_{\mathrm{p\_TAC}}=\frac{{C}_{\mathrm{reb}}+{C}_{\mathrm{preNext}}}{2}.\end{eqnarray} \tag{ 3 }$$

Pre- and post-dialysis plasma levels (C_pre and C_post) are required for the session before the collection to allow correction for rebound using the Tattersall formula (Tattersall et al 1996):

$$\begin{eqnarray}&&{C}_{\mathrm{reb}}={C}_{\mathrm{pre}}\times {\left(\frac{{C}_{\mathrm{post}}}{{C}_{\mathrm{pre}}}\right)}^{\frac{{T}_{{\rm{d}}}}{{T}_{{\rm{d}}}-{T}_{{\rm{p}}}}},\end{eqnarray} \tag{ 4 }$$

where T_d is the dialysis time in minutes and t_p is the 'patient clearance time' for the solute (35 and 66 min for urea and creatinine respectively). Figure 1 shows the concentration profile during the interdialytic interval.

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The standard protocol used to collect the data needed to calculate K_r U and K_r C in BISTRO was a full interdialytic collection which required patients to collect their urine throughout the interdialytic interval. A modified protocol was introduced to accommodate patients, for example those working, who wished to remain in the study but were not able to carry out the urine collections as specified above. For a 'partial interdialytic' collection, the three blood samples were taken as for the full interdialytic collection but the patient collected urine for a minimum of 24 h at the most convenient time during the interval between the sessions. They recorded the time they emptied their bladder before starting the collection and the last time they collected their urine in the canister. The equation used to calculate C_{p_TAC} for a partial interdialytic collection is given in supplementary materials appendix A (available online at stacks.iop.org/PMEA/43/08NT01/mmedia).

Estimation of plasma solute levels using the steady state assumption

Validation of the use of estimated compared to measured plasma levels in GFR calculations

An audit of the procedure to monitor GFR during the BISTRO study provided the data required to assess the effect of using estimated plasma levels in two scenarios. For scenario A, the pre- and post-dialysis plasma levels were measured for the session before the collection and used to estimate the pre-dialysis levels for the next session. For scenario B, pre-dialysis plasma levels were measured for the session after the collection and used, together with pre- and post-dialysis levels from another session, to estimate the post rebound levels for the last session. For each patient with sufficient data, GFR was calculated for one collection with a complete set of urine data and measured plasma levels (GFR_meas), then recalculated using the estimated plasma levels for each scenario. The equations are for a patient on thrice weekly dialysis. The derivation of these equations, and the equivalent equations for twice weekly dialysis, can be found in supplementary materials appendix B.

Scenario A

For the scenario where pre- and post-dialysis plasma levels (C_pre and C_post) were available for the session before the collection, the pre-dialysis levels for the next session were estimated using:

$$\begin{eqnarray}&&{C}_{\mathrm{preNext}}={C}_{\mathrm{pre}}\times {R}_{\mathrm{HD}}+{G}_{{\rm{c}}}\times {T}_{\mathrm{int}},\end{eqnarray} \tag{ 5 }$$

where T_int is the interdialytic interval during which the collection took place. R_HD, the concentration reduction fraction during HD, is the ratio of the post-dialysis solute concentration corrected for rebound (using equation (4)) to C_pre:

$$\begin{eqnarray}&&{R}_{\mathrm{HD}}=\frac{{C}_{\mathrm{reb}}}{{C}_{\mathrm{pre}}}={\left(\frac{{C}_{\mathrm{post}}}{{C}_{\mathrm{pre}}}\right)}^{\frac{{T}_{{\rm{d}}}}{{T}_{{\rm{d}}}-{T}_{{\rm{p}}}}}.\end{eqnarray} \tag{ 6 }$$

For thrice weekly dialysis G_c, the concentration gradient, is calculated from C_pre and R_HD using:

$$\begin{eqnarray}&&{G}_{{\rm{c}}}=\frac{{C}_{\mathrm{pre}}\times \left(\,1-{{R}_{\mathrm{HD}}}^{3}\right)}{{T}_{\mathrm{int}}\times {{R}_{\mathrm{HD}}}^{2}+{T}_{\mathrm{intNext}}\times {R}_{\mathrm{HD}}+{T}_{\mathrm{intLast}}},\end{eqnarray} \tag{ 7 }$$

where T_intNext and T_intLast are, respectively, the interdialytic intervals after and before the interval during which the collection took place. C_{p_TAC} and the clearances for urea and creatinine were then calculated with the estimated C_preNext and used to determine GFR_estA.

Scenario B

For the scenario where the pre-dialysis plasma levels (C_preNext) were available for the session after the collection, the post-rebound levels for the last session were estimated using:

$$\begin{eqnarray}&&\begin{array}{l}{C}_{\mathrm{reb}}={C}_{\mathrm{preNext}}\times {{R}_{\mathrm{HD}}}^{3}+{G}_{{\rm{c}}}\times {T}_{\mathrm{intNext}}\\ \,\,\,\,\,\,\,\,\,\,\,\,\times \,{{R}_{\mathrm{HD}}}^{2}+{G}_{{\rm{c}}}\times {T}_{\mathrm{intLast}}\times {R}_{\mathrm{HD}}.\end{array}\end{eqnarray} \tag{ 8 }$$

As the BISTRO protocol did not include post-dialysis blood samples for the session after the collection, for scenario B, R_HD was determined using pre- and post-dialysis solute concentrations for the session before another collection, ideally with the same treatment time (a 'matched' session).

In scenario B, G_c is calculated from C_preNextI and R_HD from the matched session using:

$$\begin{eqnarray}&&{G}_{{\rm{c}}}=\frac{{C}_{\mathrm{preNext}}\times \left(1-{{R}_{\mathrm{HD}}}^{3}\right)}{{T}_{\mathrm{intNext}}\times {{R}_{\mathrm{HD}}}^{2}+{T}_{\mathrm{intLast}}\times {R}_{\mathrm{HD}}+{T}_{\mathrm{int}}}.\end{eqnarray} \tag{ 9 }$$

C_{p_TAC} and the clearances for urea and creatinine were then calculated with the estimated C_reb and used to determine GFR_estB.

Selection of the validation dataset

Statistical analysis

Data from 346 patients recruited by 29 of the 32 BISTRO participating centres from across the UK (England, Wales, Northern Ireland and Scotland) were audited for data quality checks in 2019. The QC data included patients who did not have sufficient residual function to be randomised when screened, as well as patients who had been followed up for more than a year.

316 patients (median age 63, range 23–89 years; 71% male; 91% white) had at least one admissible interdialytic collection when their data was submitted. The admissible collection closest to month 3 of the study was selected for the validation dataset, as described in the methods. This was the month 3 collection for 142 patients. An earlier collection was selected for 77 patients who had not reached month 3 (including 20 who were not randomised). The closest collection to month 3 was selected for 97 patients who did not have an admissible collection at month 3 due to issues with the urine collection (25), lack of documentation or records showing a disrupted dialysis regime (15), missing blood results (40) and implausible plasma levels (17). The majority of the collections included in the validation dataset (93%) were full interdialytic collections and 95% of these were carried out over 2 d. The remaining 7% were partial interdialytic collections. The matched session required for the simulation in scenario B was available for 275 of the 316 patients.

For the selected collections, the mean GFR_meas was 3.7 ml min⁻¹/1.73 m² (range 0.1–20.0 ml min⁻¹/1.73 m²). Tables 1 and 2 show the mean and standard deviation for the solute levels, time averaged concentrations, renal clearances and GFR in the two scenarios. There was a tendency to underestimate the pre-dialysis concentration for the session after the urine collection and to overestimate the post-rebound concentration for the session before. The discrepancy was halved in the time averaged concentration as would be expected when the estimate is combined with a measured value.

For scenario A, the mean difference between GFR_estA and GFR_meas as a percentage of GFR_meas was 2.0% (95% limits of agreement −10.7% to +14.7%). For scenario B, the mean difference between GFR_estB and GFR_meas was −1.2% (limits of agreement −10.3% to +7.9%). Only 9 (3%) of GFR_estA and 6 (2%) of GFR_estB differed from GFR_meas by more than 0.5 ml min _⁻¹ /1.73 m² (or 10% if this was larger).

Figures 2 and 3 show the correlation and differences between the GFRs calculated with estimated and measured blood levels for scenarios A and B. A subsequent review of the outliers (difference >10%) showed changes in creatinine levels that suggested that these patients had missed a session or changed shifts and were not in steady state.

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Although there was no requirement to ensure that BISTRO participants were in a stable dialysis regime we found good agreement, over a wide range of RKF, between GFR calculated using the measured plasma levels and using levels estimated by assuming the patients were in steady state. The difference between GFR_meas and GFR calculated with estimated levels was clinically acceptable for 97% of patients when C_preNext was estimated (scenario A) and 98% when C_reb was estimated (scenario B).

This validation exercise supports the use of protocols for urine collections where blood samples are taken at only one session. The logical choice would be to take blood samples at the session when the patient returns the canister having completed the collection. The agreement with GFR_meas was slightly better for scenario B, even though GFR_estB relied on data from a matched session at least a month earlier or later. Closer agreement might be expected if the HD reduction fraction had been determined using pre- and post-dialysis levels for the session after the collection, which would be the case if the collection was timed to end with the routine bloods.

Reducing the requirement for blood samples is an important step towards a pragmatic protocol for measuring RKF in HD patients. The other step is to reduce the collection time. The preference for full interdialytic collections for HD patients is based on concerns that the GFR is suppressed by haemodialysis, although there are few publications to support this. The most frequently cited paper indicating an increase in GFR during the interdialytic interval is from 1995 (van Olden et al 1995), in an era when target weight was routinely set to the lowest weight that the patient could tolerate and post-dialysis fluid depletion would have been the norm. To enable patients who could not collect for the full interdialytic interval to remain in the study, urine collections of at least 24 h were accepted for BISTRO. We were not able to make a comparison of partial and full interdialytic collections (this would require patients to switch to a new canister at a known time during the collection). However, collecting for a convenient period may be more accurate than collecting for the full interval as the patient can empty their bladder naturally and they are less likely to forget to use the canister or overfill it. If acceptable to the patient, it is preferable to carry out the collection towards the end of the interdialytic interval as this minimises the time for which the urine is stored and, where blood samples are taken after the collection, the impact of any error in estimating the post rebound levels for the previous session. In patients with substantial RKF, collecting for the shorter interdialytic interval reduces the possible overestimation of GFR due to the non-linear (asymptotic) concentration rise (see supplementary materials appendix A).

In incremental dialysis, adjustments to the dialysis prescription are made using K_r U, rather than GFR. The agreement between K_r U, determined using equation (2), with the time averaged urea concentration calculated using measured and estimated plasma levels was similar to that for GFR. An alternative method for estimating C_{p_TAC} for urea was published by Daugirdas (Daugirdas 2016) shortly after the BISTRO protocol was finalised. In this method, urine is collected for a minimum of 24 h ending just before the HD session where a pre-dialysis (and ideally post-dialysis) blood sample is taken. C_{p_TAC} for urea is estimated using an empirical (best-fit) equation derived using formal urea kinetic modelling to construct the interdialytic plasma urea profile for a range of hypothetical dialysis regimes (Daugirdas et al 2009).

$$\begin{eqnarray}&&{C}_{\mathrm{p\_TAC}}={{\rm{C}}}_{\mathrm{preNext}}\times \left[1.075{\rm{\mbox{--}}}\left(0.0038\times \mathrm{URR}+0.059\right)\times {T}_{\mathrm{urine}}/{T}_{\mathrm{int}}\right],\end{eqnarray} \tag{ 10 }$$

where the urea reduction ratio, URR, is (C_pre—C_post)/C_pre expressed as a percentage.

For the full interdialytic collections used to validate scenario B, the Daugirdas equation gave a slightly higher C_{p_TAC} (and a slightly lower K_r U) than we obtained using the steady state assumption where URR was high (over 0.75).

The Daugirdas method offers a quick way to obtain K_r U from a urine collection timed to coincide with the end of the interdialytic interval. For collections carried out earlier in the interval, or when GFR is required, the equations in this paper can be used. Our data suggest that urine volume should not be used as a surrogate for GFR or K_r U. For the 60 patients with GFR_meas between 3 and 4 ml min⁻¹/1.73 m² in the validation dataset (typically with K_r U 2 to 3 ml min⁻¹) the mean urine output was 680 ml d⁻¹ but this varied from 220 to 1470 ml d⁻¹. The urine urea concentration in this cohort varied from 25 to 181 mmol l⁻¹. The factors contributing to this variation include hydration and fluid intake, which can vary for an individual as well as between patients, making residual urine output a poor indicator of RKF.

The good agreement between GFR_meas and GFR calculated with estimated plasma levels for the session before or after the urine collection validates the steady state assumption, and the use of estimated levels when calculating GFR from timed interdialytic urine collections. This allows the protocol for urine collections to be simplified and, for the BISTRO study, justifies the use of estimates where blood samples have been missed.

To monitor RKF in HD patients, we suggest using timed collections of about 24 h in the dialysis interval before routine blood samples are scheduled. If this is not convenient, a pre-dialysis blood sample from the session after the collection can be used, together with pre- and post-dialysis plasma levels from a matched session. Other than noting the start and end times, the collection procedure is the same as for PD patients. Ideally, patients should keep to the same timing (which will usually be towards the end of the interdialytic interval) and not undertake collections if their dialysis regime has recently been changed or disrupted.

If a pragmatic protocol such as this is adopted in routine care, it will underpin awareness of the preservation of RKF and facilitate the implementation of incremental haemodialysis (Lindley and Tattersall 2022) For research studies looking at changes in GFR, such as BISTRO, this type of protocol will make the organisation of collections considerably easier.

David Coyle, Patient representative, NIHR Devices for Dignity, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK. Fergus Caskey, School of Social and Community Medicine, University of Bristol, UK. Jamie Macdonald, School of Sport, Health and Exercise Sciences, Bangor University, Bangor, North Wales, UK. Sandip Mitra, Renal Medicine, Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK. Paula Ormandy, School of Nursing, Midwifery, Social Work and Social Science, University of Salford, Manchester, UK. Martin Wilkie, Renal Medicine, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK. Andrew Davenport, Renal Medicine, Royal Free Hampstead NHS Trust, London, UK. Ken Farrington, Renal Medicine, East and North Hertfordshire NHS Trust, Hertfordshire, UK. Indranil Dasgupta, Renal Medicine, Heartland Hospital, Birmingham; Lazaros Andronis, Centre for Health Economics, Warwick University, UK. Ivonne Solis Trapala and Julius Sim, School of Medicine, Keele University, UK.

Julie Wessels, University Hospitals of North Midlands, Stoke-on-Trent, UK

Arfi Khwaja, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK.

Indranil Dasgupta, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK

Andrew Davenport, Royal Free Hampstead NHS Trust, London, UK

Enric Vilar, East and North Hertfordshire NHS Trust, Stevenage, UK

Sandip Mitra, Manchester University NHS Foundation Trust, Manchester, UK

Albert Power, North Bristol NHS Trust, Bristol, UK

David Meredith, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK

Michael Schulz, Royal Liverpool and Broadgreen Hospitals NHS Trust, Liverpool, UK.

Michael Bedford, East Kent Hospitals University NHS Trust, Canterbury, UK.

Kieran McCafferty, Barts Health NHS Trust, London, UK.

Punit Yadav, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.

Sally Pugh, Gloucestershire Hospital NHS Foundation Trust, Gloucester, UK.

Stewart Lambie, NHS Highland, Raigmore, Scotland, UK.

Beng So, Lancashire Teaching Hospitals, Preston, UK

James Burton, University Hospitals of Leicester NHS Trust, Leicester, UK

Janet Hegarty, Salford Royal NHS Foundation Trust, Salford, UK.

Timothy Shipley, South Tees NHS Trust, Middlesbrough, UK.

Neal Morgan, Southern Health and Social Care Trust, Newry, Northern Ireland, UK

Linda Bisset, Nottingham University Hospitals NHS Trust, Nottingham, UK

Thomas Connor, Oxford University Hospital NHS Trust, Oxford, UK.

James Chess, Abertawe Bro Morgannwg University Health Board, Swansea, Wales, UK.

Colin Jones, York Teaching Hospital NHS Foundation Trust, York, UK.

Azra Nache, Aintree University Hospital NHS Foundation Trust, Aintree, UK

Rajan Patel, NHS Greater Glasgow and Clyde, Glasgow, Scotland, UK.

Neill Duncan, Imperial College Healthcare NHS Trust, London, UK

Johann Nicholas, Shrewsbury and Telford Hospital NHS Trust, Shrewsbury, UK

Sharlene Greenwood, King's College Hospital NHS Foundation Trust, London, UK.

Kanwaljit Sandhu, The Royal Wolverhampton NHS Trust, Wolverhampton, UK.

Subash Somolanka, Epsom and St Helier University Hospitals NHS Trust, UK

Kate Shiell, NHS Fife, Kirkaldy, Scotland, UK.

Stephanie Bolton, Northern Health and Social Care Trust, Antrim, Northern Ireland, UK.

Abdulfattah Alejmi, Betsi Cadwalader University Health Board, Bangor, Wales, UK.

This research is funded by the National Institute for Health, Research Health Technology Assessment Programme under assigned Grant Number: 14/216/01. DK received a Career Development Award from the National Institute for Health Research, ICA-CL-2017-03-017. The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the Health Technology Assessment programme, NIHR, NHS or the Department of Health.