Camiel L.M. de Roij van Zuijdewijn; Isabelle Chapdelaine; Menso J. Nubé; Peter J. Blankestijn; Michiel L. Bots; Constantijn J.A.M. Konings; Ton Kremer Hovinga; Femke M. Molenaar; Neelke C. van der Weerd; Muriel P.C. Grooteman

Submitted

Chapter 4.4

Feasibility of achieving high convection volumes in post-dilution online hemodiafiltration

172

Chapter 4.4

aBSTraCT

Introduction

Available evidence suggests a survival benefit for patients treated with high volume post-dilution HDF, compared to hemodialysis. As the magnitude of the convection volume depends on treatment-related factors rather than patient-related charac-teristics, we prospectively investigated whether a high volume (≥22L/session) is feasible in the majority of patients (>75%).

methods

A multicenter cohort study was performed in adult, prevalent end-stage kidney disease (ESKD) patients. Participants were eligible if treated 3x/week, compliant to dialysis prescription and had no life expectancy <3 months due to non-renal disease. Non-participating eligible patients formed a reference group. A stepwise protocol was designed to optimize treatment time (up to 4 hours), blood flow rate (up to 400 mL/min) and filtration fraction (up to 33%). At the end of this optimiza-tion phase and 4 and 8 weeks thereafter, the convection volume was determined.

results

Baseline characteristics were comparable in participants (n=86) and non-partici-pating subjects (n=58). At the end of the optimization and eight weeks thereafter, 71/86 (83%) and 66/83 (80%) patients achieved high-volume HDF (mean 25.5±3.6 and 26.0±3.4L/session, respectively). While treatment time remained unaltered during optimization, mean blood flow rate increased 27% and filtration fraction 23%. 17/83 Patients (20%) did not achieve a convection volume ≥22L/session, half of which was caused by drop-out due to general discomfort. Patients < and ≥22L/session had similar clinical profiles.

Conclusions

High-volume HDF can be achieved in the vast majority of ESKD patients. As treat-ment time remained unaltered, these findings indicate that high-volume HDF is feasible by increasing blood flow rate and filtration fraction.

173

4.4

Feasibility of High-Volume HDF

INTroduCTIoN

Although dialysis is a life-saving treatment in patients with end-stage kidney dis-ease (ESKD), both morbidity and mortality remain unacceptably high.1 Despite the introduction of high permeable (high-flux) dialysers, which are capable of removing middle molecular weight (MMW) substances, large randomized controlled trials (RCTs) failed to show survival differences between hemodialysis (HD) with pure diffusive transport, as occurring in HD with low permeable dialyzers (low-flux), and combined diffusive combined with a small amount of convective transport, as occurring in HD with high-flux devices.2,3

In hemodiafiltration (HDF), diffusion is combined with a surplus of convection by extracting an excess of plasma water on top of the net ultrafiltration (UF) needed to correct for the interdialytic weight gain. The availability of online produced sterile replacement fluid enabled the performance of HDF with high convection volumes on a large scale. As the infusion of replacement fluid after the dialyser (so-called post-dilution HDF) has been shown to be more efficient than before (pre-dilution) or within the dialyser (mid-dilution),4 post-dilution HDF is currently the preferred modality in clinical practice.

In recent years, three large RCTs were published comparing post-dilution online HDF with HD. Neither the Dutch CONvective TRAnsport STudy (CONTRAST)5 nor the Turkish HDF study (THDFS)6 found a survival difference between the two treatment arms. Investigators from the Catalonian HDF study ESHOL, however, in which the highest mean convection volume per session was achieved (mean 23.4L versus 19.8L in THDFS and 20.7L in CONTRAST), did find a significant difference in mortality risk in favor of HDF.7 Post hoc analyses of all three RCTs demonstrated a positive association between high-volume HDF (roughly defined as ≥22L/session) and survival, as previously reported in several observational studies.8-11 Hence, it seems justified to consider the magnitude of the convection volume as the dose of HDF.12 In this respect, it is interesting to note that the high convection volumes in the ESHOL study were not coincidentally obtained, but, according to the authors, the result of a stringent and intensive training program for the nursing staff to optimize its magnitude.7 Furthermore, it should be mentioned that observational results, including post hoc analyses, are limited by possible residual confounding. Previously, we found that treatment time, blood flow rate and filtration fraction determine the magnitude of the convection volume rather than individual patient characteristics.13-15 As these determinants are largely modifiable, we prospectively investigated whether high-volume HDF (≥22L/session) is long-lasting feasible in

174

Chapter 4.4

everyday clinical practice (>75% of participants), irrespective of patient profiles at baseline.

meTHodS

Study design

This prospective cohort study (clinicaltrials.gov identifier NCT01877499) included patients from 6 dialysis facilities (3 university hospitals, 2 community based hos-pitals and 1 private dialysis clinic) across the Netherlands between May 28th, 2013 and March 6th, 2015. Adult ESKD patients (≥18 years) were eligible if treated chronically with intermittent low-flux HD, high-flux HD or HDF three times per week for at least six weeks. Furthermore, participants should understand the study procedures and provide informed consent. Exclusion criteria were severe incompli-ance to dialysis treatment or a life expectancy <3 months due to non-renal disease. This study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice Guidelines and its protocol was approved by the central medical ethics review board of VU University Medical Center, Amsterdam, the Netherlands.

Patient characteristics

Various demographic, biochemical and clinical participant characteristics were collected at baseline. Biochemical parameters were assessed prior to dialysis at baseline. To assure that a representative sample of the dialysis population was investigated, we compared baseline data of the participants with various dialysis cohorts. First, aggregated data on eligible patients not participating in the present study, for example due to moving to a non-participating center or a language barrier, were collected (reference group). Second, aggregated cross-sectional data on all HD patients in the Netherlands were studied from the national dialysis registry RENINE and compared to our study population. Lastly, both the study and reference groups were compared with the complete cohort from the CONvective TRAnsport STudy (CONTRAST).

dialysis treatment and step-up protocol

In a stepwise fashion, treatment time, blood flow rate and filtration fraction were optimized as shown in figure 1. Dialysate flow rate and temperature remained unaltered during the study. During the first dialysis session, treatment time and blood flow rate remained similar to the previous prescriptions, as was the filtration fraction of participants previously treated with HDF. For participants not previously treated with HDF (n=63), filtration fraction was set at 25%. Before the second

175

4.4

Feasibility of High-Volume HDF

figure 1. Flow chart for optimization of the convection volume.

176

Chapter 4.4

session, participants with a treatment time less than 4 hours were advised to increase the duration up to 4 hours. Thereafter, blood flow rate was increased with 50 mL/min/session to a maximum of 400 mL/min or as high as possible within safety limits (fig. 1). After optimization of the blood flow rate, the filtration fraction was increased with 2 percent point per session up to a maximum of 33% or as high as possible within safety limits. As can be seen from figure 1, sugges-tions were provided to optimize blood flow rate and filtration fraction when the maximum values were not primarily reached. However, it is important to note that such treatment-related adjustments were not mandatory, nor was the increase in treatment time. All decisions were made by the dialysis staff in close deliberation with the participant.

dialysis equipment

Different types of dialysis machines, dialysers and anticoagulation were used across the centers (table 1). While treatment time and blood flow rate can be easily adjusted in all machines, none provided the possibility to set a convection volume goal (substitution volume + net UF) or filtration fraction. Instead, in every machine, a different filtration fraction-related parameter could be set. To ensure a comparable study approach in all participating centers, filtration fraction was used as parameter in the stepwise protocol. Therefore, an algorithm was built in which treatment time (hours, minutes), blood flow rate (mL/min), filtration fraction (%) and net UF (mL) could be entered. After selecting the appropriate machine, the

Table 1. HDF equipment of participating dialysis centers.

Center 1 Center 2 Center 3 Center 4 Center 5 Center 6

Dialysis machine(s)

Fresenius 5008

Nikkiso DBB-05

Nikkiso DBB-07

Gambro AK 200Gambro Artis

Nikkiso DBB-07

Fresenius 5008

Dialyser(s) Polyflux 210HKUF*: 85Surface#: 2.1

Xenium XPH 210KUF: 82Surface: 2.1

Xenium Ultra 22HKUF: 89Surface: 2.2

FxCordiax 1000KUF: 76Surface: 2.3

Polyflux 210HKUF: 85Surface: 2.1

FxCordiax 1000KUF: 76Surface: 2.3

Elisio 210HKUF: 82Surface: 2.1

FxCordiax 1000KUF: 76Surface: 2.3

Anticoagula-tion

Dalteparin Dalteparin Dalteparin Nadroparin Dalteparin Nadroparin

Abbreviations: HDF = hemodiafiltration, KUF = dialyzer ultrafiltration coefficient* KUF values in mL/(h*mmHg)# Surface areas in m2

177

4.4

Feasibility of High-Volume HDF

algorithm calculated the filtration fraction-related parameter for the concerning machine (i.e. either substitution ratio, substitution rate or substitution volume), as is shown in supplementary figure 1.

follow-up

The study was divided in two phases. In phase one (‘optimization phase’), the aforementioned stepwise protocol was used and patients were monitored every dialysis session. After the optimization phase, treatment time, blood flow rate and filtration fraction were set and the second phase (‘maintenance phase’) started. Here, data were collected 4 (T4) and 8 (T8) weeks after the end of the optimiza-tion phase to investigate the consistency of the data. Various data were collected before each study session (i.e. anticoagulation type and dose, set treatment time, blood flow rate and filtration fraction), during the session (arterial, venous, trans-membrane and filter-entrance pressures) and after the session (real treatment time, reached UF and substitution volume).

recirculation

Recirculation occurs when blood from the venous needle does not enter the systemic circulation, but re-enters the extracorporeal circuit through the arterial needle. Consequently, recirculation results in a high blood flow rate and thus convection volume, which, however, does not contribute to the clearance of toxins. Therefore, its monitoring is indicated to assess whether the magnitude of the convection volume is ‘real’ or not. In our study, recirculation was measured with an ultrasound dilution technique in 3 centers to determine the scope of this potential problem. Measurements were performed after reaching the maximum blood flow rate. When recirculation was ≥10%, it was also measured at lower blood flow rates until it was <10%. Then, the blood flow rate was set and used in the study until the problem was solved (i.e. by angioplasty of the vascular access).

Statistical analysis

At the end of the optimization phase and 4 and 8 weeks thereafter, the proportion of patients reaching ≥22L of convection volume per session was calculated. Fur-thermore, the mean reached values of the determinants of the convection volume and the convection volume per patient per session were noted, as were the various pressure values during the dialysis session. Data are shown as intention to treat. The relative change between baseline (first dialysis session in the study) and end of the optimization phase was calculated as [(value at end of stepwise protocol – value at baseline)/value at baseline]*100. Differences in patient characteristics between patients that reached ≥22L/treatment or those that did not reach 22L/

178

Chapter 4.4

treatment were calculated with independent t-tests, Mann-Whitney U-tests or χ2 tests, when appropriate. Potential center differences in mean reached convection volume were analyzed with ANOVA with Tukey’s HSD test as post-hoc analysis to correct for multiple comparisons. All statistical analyses were performed using the software package IBM SPSS Statistics version 20.0 (SPSS Inc, Chicago, IL, USA).

reSuLTS

Clinical data

Participants and reference groupsAs can be seen in figure 2, 144 eligible patients were identified of which 86 partici-pated in the study. All other patients (n=58) were included in the reference group. Mean age of participants was 59.9±17.7 years and 61.6% were male. Residual kidney function as reflected in a diuresis of ≥100mL/24h was present in 47.7%. As can be seen from table 2, the groups of participants and controls, as well as the CONTRAST cohort are largely comparable. Although some minor differences exist, these appear well balanced between groups (e.g. controls more diabetics, but less cardiovascular disease). In comparison with the national registry data, however, the mean age of patients was lower in investigated subjects (60 versus 68 years).

Convection volumeResults are shown in table 3 and figure 3. After completing the optimization phase, 71 out of 86 participants (83%) reached a convection volume of ≥22L/session, the average being 25.5±3.6L/session. In this period, 9 patients discontinued HDF treatment due to various reasons, varying from general discomfort to chest pain (fig. 2). Four weeks thereafter (T4), 64 participants (74%) reached a mean con-vection volume of ≥22L per treatment. Eight weeks after optimization (T8), two participants had died and one had received a renal transplant (fig. 2). Out of the remaining 83, 66 (80%) participants achieved a mean convection volume of ≥22L/session. At T4 and T8, the mean convection volumes achieved were 26.2±3.6 and 26.0±3.4L/session, respectively. An ANOVA analyzing potential center differences in mean achieved convection volumes was significant (p = 0.02). This finding, however, disappeared after application of Tukey’s HSD test for multiple testing. Except for a difference in baseline predialysis systolic blood pressure, patient char-acteristics were similar between participants that reached < or ≥22L/session, as can be seen in supplementary table 1.

179

4.4

Feasibility of High-Volume HDF

Vascular accessOf the 12 of participants with a central venous catheter (CVC) at baseline, 6 (50%) reached a convection volume of ≥22L/session at T8. While a CVC was present in 9.1% of individuals reaching ≥22L/session, this amounted to 29.4% in patients <22L/session. One patient with a CVC died during follow-up, 2 patients did not reach 22L/session due to an inadequate blood flow rate (275 and 290 mL/min, respectively) and 3 quit during the optimization phase (one due to an unknown cause, two due to general discomfort).

Technical and practical issues

Treatment time, blood flow rate and filtration fractionMean treatment time, blood flow rate and filtration fraction at baseline, end of the optimization phase, T4 and T8 are shown in table 3. These values changed between the start of the study and the end of the optimization phase by 0%, 27% and 23%, respectively. For patients < and ≥ 22L/session, see supplementary tables 1 and 2. From the 17 participants not reaching 22L/session at T8, 9 discontinued HDF

figure 2. Flow chart of eligible patients.

180

Chapter 4.4

Table 2. Baseline characteristics of participants and reference group.

DeterminantParticipants (n=86)

Controls (n=58)

CONTRAST (n=714)

RENINE (n=5345)

Demographic characteristics Age (years) Male gender BMI (kg/m2) Caucasian ethnicity

60 (18)53 (61.6%)26.4 (5.7)57 (66.3%)

57 (22)34 (58.6%)26.0 (5.9)43 (74.1%)

64 (14)445 (62.3%)25.4 (4.8)600 (84.0%)

683130 (58.6%)

Clinical data Diabetes Hypertension Coronary Heart Disease Predialysis SBP (mmHg) RKF* CCI (points) Dialysis vintage (years)

29 (33.7%)61 (70.9%)24 (27.9%)139 (22)41 (47.7%)3.0 (2.0-5.0)2.0 (1.0-4.3)

26 (44.8%)42 (72.4%)8 (13.8%)142 (20)30 (51.7%)4.0 (2.0-5.0)2.0 (1.0-5.0)

170 (23.8%)

148 (22)376 (52.7%)

2.0 (1.0-4.0)

Laboratory values Hematocrit (L/L) Phosphate (mmol/L) Albumin (g/L) Cholesterol (mmol/L) Urea (mmol/L)

0.35 (0.04)1.53 (0.45)37.9 (5.0)4.15 (1.05)21.0 (7.6)

0.34 (0.05)1.60 (0.57)37.5 (4.4)4.06 (1.07)20.6 (9.0)

0.36 (0.04)1.64 (0.49)40.4 (3.8)3.68 (0.96)

Medication Beta-blocker Calcium antagonist RAS inhibitor§ Statin Platelet inhibitor ESA

53 (61.6%)24 (27.9%)29 (33.7%)28 (32.6%)38 (44.2%)80 (93.0%)

32 (55.2%)11 (19.0%)16 (27.6%)16 (27.6%)22 (37.9%)53 (91.4%)

381 (53.4%)230 (32.2%)351 (49.2%)369 (51.7%)240 (33.6%)633 (88.7%)

Treatment characteristics Treatment time (minutes) Set blood flow (mL/min) Vascular access AV fistula Graft Access flow (mL/min)¶

CVC spKt/Vurea

UF (mL)

236 (14)314 (36)

68 (79.0%)6 (7.0%)1242 (844-1798)12 (14.0%)1.42 (0.30)2069 (1527-2683)

234 (21)311 (40)

46 (79.3%)4 (6.9%)1128 (871-1580)8 (13.8%)1.43 (0.30)2223 (1398-2725)

226 (23)301 (40)

567 (79.4%)97 (13.6%)

46 (6.4%)1.40 (0.22)1900 (1267-2492)

241

1563 (72.8%)119 (5.5%)

296 (13.8%)1.44

Data are shown as mean (standard deviation), median (interquartile range) or number (percentage), when appropriate* defined as >100 mL/24h§ use of either an ACE inhibitor or an ATII antagonist¶ For either AV fistula or graft; if available (n=72 for participants, n=50 for non-participants)Abbreviations: SBP = systolic blood pressure, RKF = residual kidney function, CCI = Charlson Co-morbidity Index, RAS = renin-angiotensin system, ESA = erythropoietin stimulating agents, AV = arteriovenous, CVC = central venous catheter, UF = ultrafiltration, ACE = angiontensin converting enzyme, ATII = angiotensin type II

181

4.4

Feasibility of High-Volume HDF

treatment due to various reasons (see fig. 2). The remaining eight patients that did not reach 22L/session at T8 were treated, on average, 23 minutes shorter, with a 61 mL/min lower blood flow rate and a 1% lower filtration fraction.

RecirculationRecirculation was measured in 39 participants (45.3%) in 3 centers after reaching their maximal blood flow rate and was present in 2 patients (5.1%). One patient had a recirculation percentage of 6%, which was accepted and did not increase

Table 3. Results.

Baseline*

End of stepwise protocol

Relative change between baseline and end protocol (%)

Four weeks after optimization

Eight weeks after optimization

Number of patients 86 86 NA 86 83&

Number of patients treated with HDF$

86 84 NA 77 74

Number of patients ≥22L/session#

NA§ 71 (82.6%) NA 64 (74.4%) 66 (79.5%)

Mean convection volume(L/session)+

19.2 (3.1) 25.5 (3.6) +33% 26.2 (3.6) 26.0 (3.4)

Mean ultrafiltration(L/session)+

2.1 (1.4-2.7) 2.2 (1.8-2.7) 5% 2.1 (1.6-2.7) 2.0 (1.6-2.6)

Mean reached treatment time (min)+

236 (15) 235 (16) 0% 238 (13) 238 (14)

Mean set blood flow rate (mL/min)+

300 (300-350) 380 (350-400) +27% 380 (350-400) 380 (350-400)

Mean set filtration fraction (%)+

26 (2) 32 (2) +23% 32 (2) 32 (2)

Mean prescribed dose extracorporeal anticoagulation(IU/session)^

4482 (1344) 4922 (1703) +10% 4959 (1700) 4989 (1680)

Data are shown as mean (standard deviation) or number (percentage), when appropriate* Defined as the first dialysis session in the study& Between T4 and T8, two patients died and one received a renal transplant$ Number of patients who continued with the study at the given time point# Percentage is calculated out of the total number of patients § not applicable as most patients (n=63) were not previously treated with HDF + Mean values for patients treated with HDF at a specific time point^in the present study, only low molecular weight heparin (dalteparin/nadroparin) was used as ex-tracorporeal anticoagulationAbbreviations: HDF = hemodiafiltration, NA = not applicable, IU = International Units

182

Chapter 4.4

during follow-up. The other patient reached a blood flow rate of 400 mL/min during the optimization phase, while his access flow was only 233 mL/min. Measurement revealed a recirculation percentage of 40%. After angioplasty, his access flow increased up to 580 mL/min, and treatment was continued with a blood flow rate of 400 mL/min without recirculation (0%).

Arterial, venous, trans-membrane and filter-entrance pressure measurementsAs shown in supplementary table 3, arterial pressure decreased between start of the study and end of the optimization phase by 17% over time, while venous pressure increased by 14%. Whereas filter-entrance pressure increased on average by 21%, TMP increased up to >60% (fig 4).

dISCuSSIoN

The present study prospectively investigated the feasibility of high-volume HDF (≥22L/session) in a representative sample of 86 dialysis patients. A number of major conclusions can be drawn from this study: (1) high volume HDF (≥22L/ses-

figure 3. Reached convection volume at different time points.

183

4.4

Feasibility of High-Volume HDF

sion) is feasible in approximately 80% of dialysis patients, (2) the average volume per patient is 26L/session and (3) as none of the patients agreed with a longer treatment time, both an increase in blood flow rate (+27%) and filtration fraction (+23%) are responsible for this effect. These settings were accompanied by a higher venous, filter-entrance and trans-membrane pressure and a more negative arterial pressure. Altogether, these results indicate that high-volume HDF is feasible in everyday clinical practice when pursuing a stringent and structured approach.

From several observational studies and post hoc analyses of three large RCTs comparing post-dilution online HDF with HD, an inverse association between the magnitude of the convection volume and mortality has been demonstrated.5-11 Although these studies are limited by their observational design, including the post hoc analyses of the RCTs, all results point into the same direction. As such,

figure 4. Reached trans-membrane pressure at various time points during diaysis at different time points in the study.

184

Chapter 4.4

it seems justified to conclude that the convection volume is a key parameter for the adequacy of HDF, as was previously stated in a consensus meeting from the EUDIAL working group.12 The feasibility of high-volume HDF in the vast majority of ESKD patients, as demonstrated in the present study, is the next step in HDF research and paves the way for a new RCT to definitively answer the question whether high volume HDF results in a better survival than standard HD.

It has been repeatedly demonstrated that treatment-related parameters, such as time and blood flow rate, rather than patient-related parameters, such as albumin or hematocrit, determine the magnitude of the convection volume.13-15 The present study indeed shows that the convection volume in HDF is largely independent of clinical characteristics, given the vast majority that reached high-volume HDF for a prolonged period of time (80%) when adhering to a structured step-up protocol.

In this respect, it should be noted that 9 participants discontinued the study be-cause of general discomfort. Furthermore, 8 (9.3%) did not reach high convection volumes at the end of the study. As mentioned in the results section, this was either due to a short treatment time (n=4) or to a low blood flow rate (n=2). Although the baseline clinical characteristics of these patients were not different from the study group, the number of patients with a CVC was relatively high (6/12). Whether vascular access is an important determinant of the convection volume is not completely clear from previous studies. While in CONTRAST the average volume did not differ between patients with a fistula and a CVC,13 a more recent study showed that in only 33% of sessions with a CVC high-volume HDF was achieved.16 In the present study, 50% of patients with a CVC reached ≥22L/session in the long term. Thus, although a CVC is not a contra-indication per se for high volume HDF, a fistula seems preferable.17

Our findings are largely in line with a recent retrospective analysis, showing that 79% of 4176 sessions could be classified as high-volume HDF (≥21L substitution volume/session, corresponding to 23.4L of convection volume/session).16 However, as it seems reasonable to assume that especially subjects who reach high-volume HDF over a prolonged period of time benefit from this treatment, as recently sug-gested by Canaud et al,18 it appears more rational to investigate the feasibility of high-volume HDF on the patient level than on the session level.

Importantly, the high convection volumes were not associated with undesirable or extreme pressure changes. In this respect, it is noteworthy to mention that various manufacturers of dialysis equipment are currently investigating automatic

185

4.4

Feasibility of High-Volume HDF

optimization of the convection volume based on, for example, the trans-membrane pressure.19,20 Such software can automatically adjust the filtration fraction during treatment and thus, theoretically, achieve the highest possible volume on an indi-vidual basis. However, as of yet, it is unclear whether this equipment is as effective as our structured, manual approach, and could be subject of future research.

Our study has both strengths and limitations. The most important limitation is that selection bias cannot be fully excluded, despite its prospective design. On the other hand, patient characteristics did not differ between the study group, the group of reference patients and the CONTRAST cohort. Furthermore, a relative large proportion of the investigated participants came from university hospitals, which may differ from community based hospitals as far as the type of patients and treatment strategies are concerned. However, testing for differences in convection volume did not yield marked variations between centers. Important strengths of the present study include its multicenter design, in which different dialysers and dialysis machines were used, and the structured, individual approach to optimize the convection volume. Furthermore, extending the study to eight weeks after optimization and the exclusion of recirculation as a cause of high-volume HDF in a substantial subset of the patients increases the robustness of our findings. Lastly, the monitoring of various treatment-related pressures assures the safety of the volumes achieved during the study.

In brief, in this prospectively designed study, using a step-up protocol for the optimization of treatment time, blood flow rate and filtration fraction, high-volume HDF (≥22L/session) appeared feasible in 80% of ESKD patients. Moreover, 8 weeks after the end of the optimization phase, the mean convection volume was 26L/session, irrespective of age, body size, blood pressure and co-morbidity. Clinical characteristics of patients who did not reach 22L/session were not different from other participants, while the proportion CVCs was considerably higher. To provide definitive evidence for a survival benefit of high volume HDF over standard HD, an RCT comparing high volume HDF with HD is awaited.

186

Chapter 4.4

SuPPLemeNTary TaBLeS aNd fIGureS

Supplementary table 1. Baseline characteristics of participants reaching high volume HDF at T8 versus participants not achieving high volume HDF at T8.

DeterminantParticipants ≥22L (n=66)

Participants <22L (n=17) p for difference

Demographic characteristics Age (years) Male gender BMI (kg/m2) Caucasian ethnicity

58.4 (18.6)41 (62.1%)26.5 (5.4)45 (68.2%)

64.5 (13.1)9 (52.9%)25.3 (7.3)9 (52.9%)

0.120.490.510.56

Clinical data Diabetes Hypertension Coronary heart disease Mean predialysis SBP (mmHg) Residual kidney function* Charlson Comorbidity Index (points) Dialysis vintage (years)

21 (31.8%)46 (69.7%)19 (28.8%)136 (21)30 (45.5%)3.5 (2.0-5.0)2.0 (1.0-4.0)

7 (41.2%)4 (23.5%)5 (29.4%)154 (19)10 (58.8%)3.0 (2.0-5.0)2.0 (1.0-6.5)

0.470.580.960.0020.220.880.51

Laboratory values Hemotocrit (L/L) Phosphate (mg/dL) Albumin (g/dL) Cholesterol (mg/dL)

0.35 (0.03)1.49 (0.38)38.3 (4.4)4.09 (1.06)

0.33 (0.05)1.59 (0.65)36.5 (5.6)4.35 (0.99)

0.080.550.210.38

Medication Beta-blocker Calcium antagonist RAS inhibitor# Statin Platelet aggregation inhibitor ESA

40 (60.6%)20 (30.3%)22 (33.3%)22 (33.3%)27 (40.9%)60 (90.9%)

11 (64.7%)4 (23.5%)6 (35.3%)5 (29.4%)10 (58.8%)17 (100.0%)

0.760.580.880.760.190.20

Treatment characteristics Treatment time (minutes) Blood flow (mL/min) Vascular access AV fistel graft Access flow (mL/min)¶

central venous catheter spKt/Vurea

Net UF (L)

240 (7)318 (37)

56 (84.8%)4 (6.1%)1262 (859-1820)6 (9.1%)1.45 (0.31)2.2 (1.5-2.7)

222 (26)302 (33)

11 (64.7%)1 (5.9%)1162 (683-1591)5 (29.4%)1.33 (0.28)1.7 (1.3-2.5)

0.070.100.52

0.190.36

Data are shown as mean (standard deviation), median (interquartile range) or number (percentage), when appropriate.* defined as diuresis >100 mL/24h# use of either an ACE inhibitor or an ATII antagonist¶ for either an AV fistula or a graftAbbreviations: BMI = Body Mass Index, SBP = Systolic Blood Pressure, RAS = Renin Angiontenis System, ESA = Erythropoetin Stimulating Agent, AV = arteriovenous, UF = ultrafiltration

187

4.4

Feasibility of High-Volume HDF

Supp

lem

enta

ry t

able

2. T

reat

men

t ch

arac

teris

tics

stra

tified

for

patie

nts

belo

w a

nd o

ver

22L/

sess

ion

(mea

sure

d at

T8)

.

Base

line*

End

of o

ptim

izat

ion

phas

eRe

lativ

e ch

ange

be

twee

n ba

selin

e an

d en

d of

pro

toco

l (%

)

Four

wee

ks a

fter

op

timiz

atio

n$Ei

ght

wee

ks a

fter

op

timiz

atio

n$

Patie

nts

≥22

L/se

ssio

n (n

=66

)

Conv

ectio

n vo

lum

e (L

)

UF

(L)

Re

ache

d tr

eatm

ent

time

(min

)

Set

bloo

d flo

w r

ate

(mL/

min

)

Set

filtr

atio

n fr

actio

n (%

)

Pres

crib

ed d

ose

antic

oagu

latio

n

(I

E/se

ssio

n)

19.6

(3.

0)2.

2 (1

.4-2

.8)

239

(10)

300

(300

-350

)26

(2)

4494

(12

50)

26.5

(3.

0)2.

2 (1

.8-2

.7)

238

(9)

400

(350

-400

)32

(2)

4935

(16

47)

+35

%0% 0% +

33%

+23

%+

10%

26.8

(3.

2)2.

2 (1

.6-2

.8)

239

(10)

400

(350

-400

)32

(1)

4918

(16

20)

26.8

(2.

6)2.

2 (1

.6-2

.7)

241

(10)

390

(350

-400

)32

(2)

5006

(16

00)

Patie

nts

<22

L/se

ssio

n (n

=17

)

Conv

ectio

n vo

lum

e (L

)

UF

(L)

Re

ache

d tr

eatm

ent

time

(min

)

Set

bloo

d flo

w r

ate

(mL/

min

)

Set

filtr

atio

n fr

actio

n (%

)

Pres

crib

ed d

ose

antic

oagu

latio

n (I

E/se

ssio

n)

16.5

(2.

8)1.

8 (1

.1-2

.2)

223

(26)

300

(300

-350

)25

(1)

4283

(17

79)

20.9

(2.

6)2.

0 (1

.6-2

.4)

218

(26)

340

(325

-400

)30

(3)

4617

(18

86)

+27

%+

11%

-2%

+13

%+

20%

+8%

20.

7 (3

.3)

1.9

(1.6

-2.1

)22

2 (2

3)32

8 (3

13-3

67)

31 (

3)48

11 (

2323

)

19.4

(1.

4)1.

7 (1

.1-2

.1)

218

(27)

329

(293

-350

)31

(3)

4863

(23

69)

Dat

a ar

e sh

own

as m

ean

(sta

ndar

d de

viat

ion)

or

med

ian

(inte

rqua

rtile

ran

ge),

whe

n ap

prop

riate

* D

efine

d as

the

firs

t di

alys

is s

essi

on in

the

stu

dy$ T

hese

val

ues

are

for

the

patie

nts

<22

L/se

ssio

n on

ly a

vaila

ble

for

thos

e st

ill p

artic

ipat

ing

at t

his

poin

t in

the

stu

dy (

n=8)

Abbr

evia

tions

: U

F =

ultr

afiltr

atio

n

188

Chapter 4.4

Supp

lem

enta

ry t

able

3. P

ress

ure

mea

sure

men

ts d

urin

g th

e op

timiz

atio

n an

d m

aint

enan

ce p

hase

s.

Base

line*

End

of s

tepw

ise

prot

ocol

Rela

tive

chan

ge

betw

een

base

line

and

end

prot

ocol

(%

)

Four

wee

ks a

fter

op

timiz

atio

nEi

ght

wee

ks a

fter

op

timiz

atio

n

30 M

inut

es a

fter

sta

rt d

ialy

sis

sess

ion

Se

t bl

ood

flow

rat

e (m

L/m

in)

Ar

teria

l pre

ssur

e (m

mH

g)

Veno

us p

ress

ure

(mm

Hg)

Tr

ans-

mem

bran

e pr

essu

re (

mm

Hg)

Fi

lter

entr

ance

pre

ssur

e (m

mH

g)

300

(300

-350

)-1

12 (

47)

159

(35)

122

(36)

308

(57)

380

(350

-400

)-1

31 (

46)

181

(34)

185

(61)

372

(84)

+27

%-1

7%+

14%

+52

%+

21%

378

(350

-400

)-1

28 (

46)

177

(27)

187

(55)

373

(65)

375

(350

-400

)-1

31 (

48)

175

(32)

183

(55)

363

(76)

2 H

ours

aft

er s

tart

dia

lysi

s se

ssio

n

Set

bloo

d flo

w r

ate

(mL/

min

)

Arte

rial p

ress

ure

(mm

Hg)

Ve

nous

pre

ssur

e (m

mH

g)

Tran

s-m

embr

ane

pres

sure

(m

mH

g)

Filte

r en

tran

ce p

ress

ure

(mm

Hg)

300

(300

-350

)-1

14 (

43)

154

(36)

140

(60)

318

(78)

378

(350

-400

)-1

34 (

46)

177

(36)

224

(77)

389

(94)

+26

%-1

8%+

15%

+60

%+

22%

375

(350

-400

)-1

33 (

46)

171

(33)

222

(72)

396

(84)

375

(350

-400

)-1

32 (

48)

173

(35)

220

(61)

381

(84)

Just

bef

ore

endi

ng t

he d

ialy

sis

sess

ion

Se

t bl

ood

flow

rat

e (m

L/m

in)

Ar

teria

l pre

ssur

e (m

mH

g)

Veno

us p

ress

ure

(mm

Hg)

Tr

ans-

mem

bran

e pr

essu

re (

mm

Hg)

Fi

lter

entr

ance

pre

ssur

e (m

mH

g)

300

(300

-350

)-1

14 (

47)

158

(38)

168

(80)

351

(98)

364

(350

-400

)-1

36 (

48)

175

(42)

240

(93)

423

(106

)

+21

%-1

9%+

11%

+43

%+

21%

367

(350

-400

)-1

33 (

45)

172

(33)

230

(97)

420

(101

)

362

(350

-400

)-1

27 (

49)

169

(34)

229

(76)

391

(96)

Resu

lts a

re s

how

n as

mea

n (s

tand

ard

devi

atio

n) o

r m

edia

n (in

terq

uart

ile r

ange

), w

hen

appr

opria

te

189

4.4

Feasibility of High-Volume HDF

Supplementary figure 1. Screenshot of the algorithm calculating the appropriate filtration fraction given a certain type of dialysis machine.

190

Chapter 4.4

refereNCe LIST

1. U.S.Renal Data System. USRDS 2013 Annual Data Report: Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, 2013

2. Locatelli F, Martin-Malo A, Hannedouche T, Loureiro A, Papadimitrou M, Wizemann V, Jacobson SH, Czekalski S, Ronco C, Vanholder R, Membrane Permeability Outcome (MPO) Study Group: Effect of membrane permeability on survival of hemodialysis patients. J Am Soc Nephrol 20:645-654, 2009

3. Eknoyan G, Beck GJ, Cheung AK, Daugirdas JT, Greene T, Kusek JW, Allon M, Bailey J, Deimez JA, Depner TA, Dwyer JT, Levey AS, Levin NW, Milford E, Ornt DB, Rocco MV, Schul-man G, Schwab SJ, Teehan BP, Tot R, Hemodialysis (HEMO) Study Group: Effect of dialysis dose and membrane flux in maintenance hemodialysis. N Engl J Med 347:2010-2019, 2010

4. Leypoldt JK: Solute fluxes in different treatment modalities. Nephrol Dial Transplant 15 Suppl 1:3-9, 2000

5. Grooteman MP, van den Dorpel MA, Bots ML, Penne EL, van der Weerd NC, Mazairac AH, den Hoedt CH, van der Tweel I, Lévesque R, Nubé MJ, ter Wee PM, Blankestijn PJ, CONTRAST investigators: Effect of online hemodiafiltration on all-cause mortality and cardiovascular outcomes. J Am Soc Nephrol 23:1087-1096, 2012

6. Ok E, Asci G, Toz H, Ok ES, Kircelli F, Yilmaz M, Hur E, Demirci MS, Dermici C, Duman S, Basci A, Adam SM, Isik IO, Zengin M, Suleymanlar G, Yilmaz ME, Ozkahya M Turkish Online Haemodiafiltration Study: Mortality and cardiovascular events in online haemodiafiltration (OL-HDF) compared with high-flux dialysis: results from the Turkish OL-HDF Study. Nephrol Dial Transplant 28:192-202, 2013

7. Maduell F, Moreso F, Pons M, Ramos R, Mora-Marcià J, Carreras J, Soler J, Torres F, Campistol JM, Martinez-Castelao A, ESHOL Study Group: High-efficiency postdilution online hemodiafiltration reduces all-cause mortality in hemodialysis patients. J Am Soc Nephrol 24:487-497, 2013

8. Siriopol D, Canaud B, Stuard S, Mircescu G, Nistor I, Covic A: New insights into the effect of haemodiafiltration on mortality: the Romanian experience. Nephrol Dial Transplant 30:294-301, 2015

9. Panichi V, Rizza GM, Paoletti S, Bigazzi R, Alosi M, Barsotti G, Rindi P, Donati G, Antonelli A, Panicucci E, Tripepi G, Tetta C, Palla R, RISCAVID Study Group: Chronic inflammation and mortality in haemodialysis: effect of different renal replacement therapies. Results from the RISCAVID study. Nephrol Dial Transplant 23:2337-2343, 2008

10. Canaud B, Bragg-Gresham JL, Marshall MR, Desmeules S, Gillespie BW, Depner T, Klas-sen P, Port FK: Mortality risk for patients receiving hemodiafiltration versus hemodialysis: European results from the DOPPS. Kidney Int 69:2087-2093, 2006

11. Imamovic G, Hrvacevic R, Kapun S, Marcelli D, Bayh I, Grassmann A, Scatizzi L, Maslovarić J, Canaud B: Survival of incident patients on high-volume online hemodiafiltration com-pared to low-volume online hemodiafiltration and high-flux hemodialysis. Int Urol Nephrol 46:1191-1200, 2014

12. Tattersall JE, Ward RA: Online haemodiafiltration: definition, dose quantification and safety revisited. Nephrol Dial Transplant 28:542-550, 2013

13. Chapdelaine I, Mostovaya IM, Blankestijn PJ, Bots ML, van den Dorpel MA, Lévesque R, Nubé MJ, ter Wee PM, Grooteman MP, CONTRAST investigators: Treatment Policy rather

191

4.4

Feasibility of High-Volume HDF

than Patient Characteristics Determines Convection Volume in Online Post-Dilution Hemo-diafiltration. Blood Purif 37:229-237, 2014

14. Penne EL, van der Weerd NC, Bots ML, van den Dorpel MA, Grooteman MP, Lésvesque R, Nubé MJ, ter Wee PM, Blankestijn PJ, CONTRAST investigators: Patient- and treatment-related determinants of convective volume in post-dilution haemodiafiltration in clinical practice. Nephrol Dial Transplant 24:3493-3499, 2009

15. Marcelli D, Kopperschmidt P, Bayh I, Jirka T, Merello JI, Ponce P, Ladanyi E, Di Benedetto A, Dovc-Dimec R, Rosenberger J, Stuard S, Scholz C, Grassmann A, Canaud B: Modifiable factors associated with achievement of high-volume post-dilution hemodiafiltration: results from an international study. Int J Artif Organs 38:244-250, 2015

16. Marcelli D, Scholz C, Ponce P, Sousa T, Kopperschmidt P, Grassmann A, Pinto B, Canaud B: High-Volume Postdilution Hemodiafiltration Is a Feasible Option in Routine Clinical Practice. Artif Organs 39:142-149, 2015

17. Chapdelaine I, de Roij van Zuijdewijn CL, Mostovaya IM, Lésvesque R, Davenport A, Blankestijn PJ, Wanner C, Nubé MJ, Grooteman MP, EUDIAL Group: Optimization of the convection volume in online post-dilution haemodiafiltration: practical and technical issues. Clin Kidney J 8:191-198, 2015

18. Canaud B, Barbieri C, Marcelli D, Bellocchio F, Bowry S, Mari F, Amato C, Gatto E: Optimal convection volume for improving patient outcomes in an international incident dialysis cohort treated with online hemodiafiltration. Kidney Int 88:1108-1116, 2015

19. Teatini U, Steckiph D, Romei LG: Evaluation of a new online hemodiafiltration mode with automated pressure control of convection. Blood Purif 31:259-267, 2011

20. Maduell F, Rodriguez N, Sahdala L, Coronel D, Arias Giullén M, Ojeda R, Vera M, Fontseré N, Cases A, Campistol JM: Impact of the 5008 monitor software update on total convective volume. Nefrologia 34:599-604, 2014