Baseline Characteristics and Representativeness of Participants in the BEST-Fluids Trial: A Randomized Trial of Balanced Crystalloid Solution Versus Saline in Deceased Donor Kidney Transplantation : Transplantation Direct

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Kidney Transplantation

Baseline Characteristics and Representativeness of Participants in the BEST-Fluids Trial: A Randomized Trial of Balanced Crystalloid Solution Versus Saline in Deceased Donor Kidney Transplantation

Collins, Michael G. PhD1,2,3,4; Fahim, Magid A. PhD4,5; Pascoe, Elaine M. MBiostats4; Hawley, Carmel M. MMedSc4,5,6; Johnson, David W. PhD4,5,6; Varghese, Julie BHMS4; Hickey, Laura E. BSc4; Clayton, Philip A. PhD1,3,7; Gill, John S. MD8; Dansie, Kathryn B. MBiostats3,7; McConnochie, Rachael C. MN9; Vergara, Liza A PhD4; Kiriwandeniya, Charani BSc4; Reidlinger, Donna MPH4; Mount, Peter F. PhD10,11; Weinberg, Laurence PhD12,13; McArthur, Colin J. MBChB9; Coates, P. Toby PhD1,3; Endre, Zoltan H. PhD14,15; Goodman, David PhD16; Howard, Kirsten PhD17,18; Howell, Martin PhD17,18; Jamboti, Jagadish S. DM19,20; Kanellis, John PhD21,22; Laurence, Jerome M. PhD23; Lim, Wai H. PhD20,24; McTaggart, Steven J. PhD25; O’Connell, Philip J. PhD26,27; Pilmore, Helen L. MD2,28; Wong, Germaine PhD17,26,27; Chadban, Steven J. PhD29,30;  on behalf of the BEST-Fluids Investigators and the Australasian Kidney Trials Network

Author Information
Transplantation Direct 8(12):p e1399, December 2022. | DOI: 10.1097/TXD.0000000000001399
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Abstract

INTRODUCTION

Delayed graft function (DGF) is a frequent complication of deceased donor kidney transplantation1,2 associated with inferior outcomes and higher costs.3-5 DGF occurs when ischemia-reperfusion injury sustained during transplantation results in poor kidney function and the need for dialysis treatment during the first week after transplantation.6,7 Intravenous (IV) fluids administered during and after transplantation are critical for maintaining intravascular volume and kidney perfusion and play an important role in minimizing the risk of DGF.8 Saline (0.9% sodium chloride) is the most commonly used IV fluid.9,10 However, the supraphysiological chloride concentration in saline may increase the risk of DGF by promoting the development of hyperchloremic metabolic acidosis and acute kidney injury.11-13 Balanced crystalloids with a lower chloride concentration avoid this acidosis and may improve kidney function.14-16 Whether using a balanced crystalloid instead of saline is safe and improves kidney function after deceased donor kidney transplantation is unknown.15

The Better Evidence for Selecting Transplant Fluids (BEST-Fluids) trial was a pragmatic randomized controlled trial (RCT) to test the hypothesis that, compared with 0.9% saline, IV fluid therapy with a balanced low-chloride crystalloid solution, Plasma-Lyte 148, would reduce DGF incidence in deceased donor kidney transplant recipients.10 BEST-Fluids incorporated broad eligibility criteria and closely aligned interventions and follow-up procedures with standard transplant practice to maximize the applicability of trial findings to a broad range of transplant recipients. The primary outcome of DGF was defined as receiving dialysis in the first 7 d after transplantation.

A pragmatic registry-based RCT design was chosen to maximize internal validity and minimize bias while ensuring that external validity was optimized and could be directly assessed. Data on all kidney transplant recipients in Australia and New Zealand are routinely reported to the Australia and New Zealand Dialysis and Transplant (ANZDATA) Registry for clinical quality purposes. In BEST-Fluids, trial enrollment, randomization, and most data collection were embedded within the ANZDATA Registry.10 Participant characteristics, the primary outcome, and many key secondary outcomes used in the trial were already collected routinely in the registry for all kidney transplant recipients. Thus, to fully inform the interpretation of the findings of BEST-Fluids once they become available, we sought to use registry data to compare the characteristics of trial participants with those of the population who would have met the trial eligibility criteria but were not enrolled in the study.

This article reports the baseline demographic, clinical, donor, and transplant characteristics, including those associated with the risk of DGF, for participants in the BEST-Fluids trial. The primary aim was to determine whether the trial participants were representative of deceased donor kidney transplant recipients in Australia and New Zealand during the recruitment period. To explore potential international generalizability, we also compared the trial participants with a contemporary US transplant cohort.

MATERIALS AND METHODS

Study Design

BEST-Fluids trial participants were compared with (1) all other patients who received deceased donor kidney transplants in Australia and New Zealand during the trial period and met the eligibility criteria but were not enrolled in the trial and (2) a contemporary cohort of deceased donor kidney-only transplant recipients in the United States.

Details of the BEST-Fluids study design and protocol have been published previously.10 Briefly, BEST-Fluids was an investigator-initiated, pragmatic, registry-based, multicenter, double-blind RCT that enrolled participants at hospitals in Australia (12 sites) and New Zealand (4 sites), representing 13 out of 17 adult transplant hospitals and 3 out of 8 children’s hospitals. The trial was prospectively registered (Australia New Zealand Clinical Trials Registry ACTRN12617000358347). A total of 808 participants who received a kidney transplant between January 31, 2018, and August 10, 2020, were recruited.

Study Population

Participants were eligible for the BEST-Fluids trial if they had kidney failure, were admitted to a participating hospital for a deceased donor kidney transplant, and provided written informed consent. Participants were excluded if they (1) were receiving a multiorgan transplant (a kidney combined with another organ transplant), (2) were a child that weighed <20 kg or were considered by their physician too small for a blinded fluid study, or (3) had known hypersensitivity to the trial fluid preparations or packaging. Participants who were enrolled and randomized but had their kidney transplant surgery canceled were excluded from the analysis.

The comparison cohort included all deceased donor kidney-only recipients in Australia and New Zealand transplanted during the trial recruitment period (January 31, 2018‚ until August 10, 2020) who met the eligibility criteria but were not enrolled in the BEST-Fluids trial. Transplant recipients were excluded from the comparison cohort if they had enrolled in BEST-Fluids, received a multiorgan transplant, or were a child that weighed <20 kg at transplant. The US comparison cohort included all recipients of a deceased donor kidney-only transplant aged at least 18 y from January 1, 2018, to December 31, 2020, excluding recipients of dual or pediatric en bloc kidneys.

Data Sources

The ANZDATA Registry provided baseline demographic, clinical, and transplant characteristic data for trial participants and the comparison cohort from Australia and New Zealand. The Australia and New Zealand Organ Donor Registry, administered and managed by the ANZDATA Registry, provided data on donor characteristics linked to each transplant recipient’s record. Data from the US Scientific Registry of Transplant Recipients (SRTR) were also used for this analysis; the SRTR data system includes data on all donors, wait-listed candidates, and transplant recipients in the United States, submitted by the members of the Organ Procurement and Transplantation Network.17 The Health Resources and Services Administration, US Department of Health and Human Services‚ provides oversight to the activities of the Organ Procurement and Transplantation Network and SRTR contractors.

Data Variables

For transplant recipients in Australia and New Zealand, the collected demographics included age, gender, and ethnicity. Clinical characteristics (collected at the time of transplantation) included participant height, weight, body mass index (BMI), smoking status, cause of kidney failure, comorbidities (coronary artery disease, chronic lung disease, peripheral vascular disease, cerebrovascular disease, diabetes, and malignancy), and dialysis treatment history before transplantation. Transplant characteristics included the year of transplantation, graft number, peak panel reactive antibody (PRA), number of HLA mismatches, total ischemic time, and initial immunosuppression at the time of transplantation.

Donor characteristics collected included demographics (age, gender), donor type (brain or circulatory death), cause of death, cardiopulmonary resuscitation, comorbidities (treated hypertension, diabetes, smoking), preterminal kidney function (creatinine), preretrieval biopsy, the number of kidneys transplanted, and expanded criteria donor status.18 The kidney donor risk index (KDRI) scores for each donor were calculated using the Australian modification of this score.19,20 We also collected data on the treatment of donor kidneys with hypothermic machine perfusion before transplantation for trial participants from ANZDATA and directly as aggregate (not patient-level) data from the only hospital that used this technology during the trial period (Princess Alexandra Hospital, Brisbane).

To determine the differences in the location and type of kidney replacement therapy (KRT) between trial participants and nonparticipants before transplantation, we also collected data on transplant country and region, transplant center status of the hospital providing KRT, patient geographic location (metropolitan, regional, or remote for Australian participants), and the location of dialysis treatment received (home, satellite, in-center hemodialysis, or peritoneal dialysis).

For US recipients, data variables equivalent to those described above were used, where these were available in the SRTR dataset and were comparable to those available in ANZDATA.

There were very few (1% or less) missing data for most variables provided by ANZDATA, whereas data obtained from the SRTR for the US cohort had significant missing data for some variables. A complete case analysis approach was used for the comparative analyses.

Statistical Analysis

Continuous variables were expressed as means and SD or medians with interquartile ranges. Categorical variables were expressed as frequencies with percentages. Differences between the groups were quantified using standardized differences (d), where values of 0.2, 0.5, and 0.8 represented small, medium, and large differences, respectively.21,22 Between-group hypothesis tests were conducted using independent sample t-tests, Wilcoxon rank-sum tests, or chi-squared tests. Linear, binomial, or multinomial logistic regression models with robust standard errors were used to compare donor characteristics for the Australian and New Zealand cohorts to account for clustering due to paired donors, that is, where 2 recipients received kidneys from the same donor. Donor characteristics were compared with those of the US cohort by using aggregate data from unique donors. P values of <0.05 were considered statistically significant. Statistical analyses were performed using Stata‚ version 17‚ and SAS‚ version 9.4.

Ethical Considerations

All the BEST-Fluids trial participants provided written informed consent. The BEST-Fluids trial received ethical approval from the Northern A Health and Disability Ethics Committee (approval number 17/NTA/62) for New Zealand and the Sydney Local Health District Human Research Ethics Committee, Royal Prince Alfred Hospital (approval numbers X17-0201 and HREC/17/RPAH/308)‚ for Australia.

This study was deemed of negligible risk and exempted from ethics review by the University of Queensland Research Ethics and Integrity Committee (project numbers 2021/HE000022, approved February 1, 2021 [ANZDATA]‚ and 2021/HE002522, approved November 24, 2021 [US SRTR]). The ANZDATA Registry provided deidentified participant-level data for this comparative study. All analyses of the SRTR dataset to produce the summary data were performed in Vancouver, Canada.

RESULTS

Over the 31-mo recruitment period, 1350 potential participants were screened for enrollment, 856 were randomized, and 808 received a deceased donor kidney transplant and continued in the trial (Figure 1). Forty-eight participants did not receive transplants and were therefore excluded from the trial. Participants were recruited from 13 adult and 3 children’s hospitals in Australia and New Zealand. During the same period, 2178 patients eligible for the trial received deceased donor kidney transplants at 25 hospitals (17 adult and 8 children’s hospitals) in Australia and New Zealand (Figure 2). Accordingly, the ANZDATA comparison cohort included 1370 (2178 less 808) transplant recipients who did not participate in the trial.

F1
FIGURE 1.:
Screening and enrollment of participants in the BEST-Fluids trial. *Participants were considered screened for the trial if they were admitted for a potential transplant at a participating hospital after recruitment had been initiated at that site. **Other reasons for exclusion reported by sites included no translator (7), medical reasons (5), an overseas patient (5), developmental delay/intellectual disability (4), physician decision (4), patient anxiety (3), transplanted at a participating hospital for another nonparticipating hospital during COVID-19 (2), followed for safety reasons only (because of being given study fluids but not enrolled or randomized) (1), interstate logistics (1), a nonadherent patient (1), and transplant at another hospital (1). BEST-Fluids‚ Better Evidence for Selecting Transplant Fluids.
F2
FIGURE 2.:
Selection and inclusion of participants in the ANZDATA Registry comparison cohort. #Other reasons for exclusion were as follows: participants were enrolled and randomized in BEST-Fluids, did not receive kidney transplant at the time, and were transplanted later but not reenrolled in the trial (3); participants were enrolled, randomized, and transplanted but withdrawn because of invalid or missing consent (2). ANZDATA, Australia and New Zealand Dialysis and Transplant Registry. ANZDATA‚ Australia and New Zealand Dialysis and Transplant; BEST-Fluids‚ Better Evidence for Selecting Transplant Fluids.

Characteristics of Trial Participants Compared With Other Australian and New Zealand Transplant Recipients

The baseline demographic, clinical, and transplant characteristics of the recipients are shown in Table 1. BEST-Fluids participants were similar to the ANZDATA cohort of non-trial participants in terms of age, gender, BMI, smoking status, cause of kidney failure, comorbidities (diabetes, chronic lung disease, peripheral vascular disease, and cerebrovascular disease), history of cancer, graft number, dialysis modality, number of HLA mismatches, peak PRA >0%, machine perfusion, and number of transplanted kidneys (|d| < 0.12; P > 0.05 for all comparisons). Comparing trial participants and other transplant recipients, there were differences in the number of children (0.5% versus 2.6%; d = −0.17, P = 0.001), and ethnicity (P < 0.001), with more New Zealand Māori (5.8% versus 1.4%; d = 0.24) and Pacific Islanders (7.8% versus 4.2%; d = 0.16) and fewer Asians (9.7% versus 13.3%; d = −0.10). Compared with other transplant recipients, the trial participants had higher preoperative weights (mean ± SD, 81.4 ± 18.4 versus 77.9 ± 18.9 kg; d = 0.19, P < 0.001), slightly more coronary artery disease (24% versus 20%; d = 0.09, P = 0.03), and a slightly longer dialysis duration (median [interquartile range] 31 [17–52] mo versus 28 [14–47] mo; d = 0.18, P < 0.001). There were also differences in the proportion of trial participants who received any induction immunosuppression (99% versus 93%; d = 0.30, P < 0.001), basiliximab (89% versus 83%; d = 0.18, P < 0.001), maintenance glucocorticoids (99% versus 95%; d = 0.23, P < 0.001), and cyclosporin (15% versus 3%; d = 0.43, P < 0.001). Trial participants were more likely to have been transplanted in 2019 than nonparticipants (d = 0.25, P < 0.001).

TABLE 1. - Baseline characteristics of BEST-Fluids trial participants compared with other Australian and New Zealand deceased donor kidney transplant recipients in the ANZDATA Registry
Characteristic Total (N = 2178) ANZDATA (N = 1370) BEST-Fluids (N = 808) Standardized difference (d) (BEST-Fluids—ANZDATA) P
Age (y)
 Mean (SD) 52.0 (14.1) 51.8 (14.4) 52.5 (13.5) 0.05 0.27
Gender 0.52
 Male 1361 (62.5%) 849 (62.0%) 512 (63.4%) 0.03
 Female 817 (37.5%) 521 (38.0%) 296 (36.6%) −0.03
Children <16 39 (1.8%) 35 (2.6%) 4 (0.5%) −0.17 0.001
Ethnicity <0.001
 Asian 245 (11.9%) 168 (13.3%) 77 (9.7%) −0.1
 Arab 54 (2.6%) 34 (2.7%) 20 (2.5%) −0.005
 Australian Aboriginal or Torres Strait Islander 122 (5.9%) 74 (5.9%) 48 (6.0%) −0.007
 White/European 1232 (59.9%) 761 (60.3%) 471 (59.2%) −0.02
 Indian Subcontinent 93 (4.5%) 58 (4.6%) 35 (4.4%) −0.001
 New Zealand Māori 64 (3.1%) 18 (1.4%) 46 (5.8%) 0.24
 Pacific peoples 115 (5.6%) 53 (4.2%) 62 (7.8%) 0.16
 Other 132 (6.4%) 95 (7.5%) 37 (4.6%) −0.12
 Not recorded/missing 121 109 12
Weight presurgery (kg)
 Mean (SD) 79.2 (18.8) 77.9 (18.9) 81.4 (18.4) 0.19 <0.001
Body mass index (kg/m2)
 Mean (SD) 28.1 (13.2) 27.8 (13.4) 28.8 (12.9) 0.08 0.09
Smoking status 0.11
 Never 1224 (57.6%) 789 (59.3%) 435 (54.8%) −0.09
 Former 698 (32.8%) 423 (31.8%) 275 (34.6%) 0.06
 Current 203 (9.6%) 119 (8.9%) 84 (10.6%) 0.06
 Missing 53 39 14
Cause of kidney failure 0.07
 Diabetic nephropathy 454 (22.1%) 286 (22.4%) 168 (21.6%) −0.02
 Hypertension 162 (7.9%) 90 (7.1%) 72 (9.3%) 0.08
 Glomerulonephritis 794 (38.7%) 489 (38.4%) 305 (39.2%) 0.02
 Reflux nephropathy 111 (5.4%) 66 (5.2%) 45 (5.8%) 0.03
 Polycystic kidney disease 256 (12.5%) 153 (12.0%) 103 (13.2%) 0.04
 Other 276 (13.4%) 191 (15.0%) 85 (10.9%) −0.12
 Missing 125 95 30
Coronary artery disease 462 (21.3%) 270 (19.9%) 192 (23.8%) 0.09 0.03
Chronic lung disease 221 (10.2%) 130 (9.6%) 91 (11.3%) 0.05 0.21
Peripheral vascular disease 258 (11.9%) 169 (12.5%) 89 (11.0%) −0.04 0.32
Cerebrovascular disease 129 (6.0%) 82 (6.0%) 47 (5.8%) −0.01 0.83
Diabetes 0.78
 None 1525 (70.3%) 1525 (70.3%) 1525 (70.3%) 0.01
 Type 1 65 (3.0%) 41 (3.0%) 24 (3.0%) −0.002
 Type 2 580 (26.7%) 371 (27.2%) 209 (25.9%) −0.03
History of any malignancy 282 (12.9%) 170 (12.4%) 112 (13.9%) 0.04 0.33
History of nonskin malignancy 184 (8.4%) 115 (8.4%) 69 (8.5%) 0.01 0.91
Year of transplant <0.001
 2018 865 (39.7%) 581 (42.4%) 284 (35.1%) −0.15
 2019 923 (42.4%) 518 (37.8%) 405 (50.1%) 0.25
 2020 390 (17.9%) 271 (19.8%) 119 (14.7%) −0.13
Graft number 0.16
 1 1930 (88.6%) 1219 (89.0%) 711 (88.0%) −0.03
 2 207 (9.5%) 121 (8.8%) 86 (10.6%) 0.06
 3+ 41 (1.9%) 30 (2.2%) 11 (1.4%) −0.06
Dialysis modality before transplant 0.18
 Hemodialysis 1439 (66.1%) 889 (64.9%) 550 (68.1%) 0.07
 Peritoneal dialysis 702 (32.2%) 460 (33.6%) 242 (30.0%) −0.08
 None (preemptive transplant) 37 (1.7%) 21 (1.5%) 16 (2.0%) 0.03
Dialysis duration (mo)
 Mean (SD) 37.3 (32.5) 35.1 (29.5) 41.0 (36.8) 0.18 <0.001
 Median (IQR) 29 (15–49) 28 (14–47) 31 (17–52) 0.003
Peak panel reactive antibody (%)
 Mean (SD) 18 (33) 17 (32) 20 (34) 0.09 0.03
 Median (IQR) 0 (0–22) 0 (0–10) 0 (0–30) 0.02
 0% 1465 (67.3%) 946 (69.1%) 519 (64.4%) −0.10 0.07
 1%–98% 644 (29.6%) 382 (27.9%) 262 (32.5%) 0.10
≥99% 67 (3.1%) 42 (3.1%) 25 (3.1%) 0.00
 Missing 2 0 2
Number of HLA mismatches 0.48
 0 70 (3.2%) 42 (3.1%) 28 (3.5%) 0.02
 1–2 613 (28.2%) 394 (28.8%) 219 (27.2%) −0.04
 3–4 652 (30.0%) 396 (28.9%) 256 (31.8%) 0.06
 5–6 841 (38.6%) 538 (39.3%) 303 (37.6%) −0.03
Total ischemic time (h)
 Mean (SD) 10.7 (4.8) 10.5 (4.8) 11.0 (4.8) 0.01 0.02
 Median (IQR) 10 (7–13) 10 (7–13) 10 (7–13) 0.01
 Missing 65 63 2
Number of kidneystransplanted into recipient 0.34
 1 2125 (97.6%) 1340 (97.8%) 785 (97.2%) −0.04
 2 53 (2.4%) 30 (2.2%) 23 (2.8%) 0.04
Machine perfusion of kidney a 45 (2.1%) 25 (1.8%) 20 (2.5%) 0.19 0.30
Any induction immunosuppression (Y/N) 2068 (94.9%) 1270 (92.7%) 798 (98.8%) 0.30 <0.001
Induction immunosuppression reported b
 Basiliximab 1851 (85.0%) 1132 (82.6%) 719 (89.0%) 0.18 <0.001
 T-cell depletion 234 (10.7%) 138 (10.1%) 96 (11.9%) 0.06 0.19
 B-cell depletion 2 (0.1%) 2 (0.1%) 0 −0.05 0.28
 Immunoglobulin 42 (1.9%) 30 (2.2%) 12 (1.5%) −0.05 0.25
 Eculizumab 2 (0.1%) 1 (0.1%) 1 (0.1%) 0.02 0.71
Maintenance immunosuppression reported c
 Glucocorticoid 2103 (96.6%) 1303 (95.1%) 800 (99.0%) 0.23 <0.001
 Cyclosporin 155 (7.1%) 37 (2.7%) 118 (14.6%) 0.43 <0.001
 Tacrolimus 1940 (89.1%) 1260 (92.0%) 680 (84.2%) −0.24 <0.001
 Mycophenolic acid derivative 2094 (96.1%) 1303 (95.1%) 791 (97.9%) 0.15 0.001
 mTOR inhibitor 16 (0.7%) 6 (0.4%) 10 (1.2%) 0.09 0.03
 Azathioprine 4 (0.2%) 3 (0.2%) 1 (0.1%) −0.02 0.62
 Other 1 (0.0%) 1 (0.1%) 0 −0.04 0.44
aEthnicity was categorized as reported in the ANZDATA Registry; this may be either self-reported or clinician-reported.
bPrincess Alexandra Hospital, Brisbane only; no other centers in Australia or New Zealand used machine perfusion during the trial period.
cFor both induction and maintenance immunosuppression, the proportions are out of the total number in each group reported at baseline.
ANZDATA, Australia and New Zealand Dialysis and Transplant Registry; BEST-Fluids‚ Better Evidence for Selecting Transplant Fluids; IQR, interquartile range; mTOR, mammalian target of rapamycin.

The location and characteristics of KRT received before transplantation are shown in Table 2. Compared with the ANZDATA cohort, a higher proportion of trial participants were transplanted in New Zealand (23% versus 5%; d = 0.54, P < 0.001) and the Australian state of Queensland (42% versus 9%, d = 0.81, P < 0.001). Furthermore, more trial participants were cared for at a nontransplanting hospital before transplantation (61% versus 38%; d = 0.46, P < 0.001) or were from regional areas (29% versus 22%, d = 0.15, P < 0.01). The proportions of patients receiving home-based dialysis and those not yet on dialysis before transplantation did not differ.

TABLE 2. - Location and characteristics of KRT provided to BEST-Fluids trial participants compared with other Australian and New Zealand deceased donor kidney transplant recipients in the ANZDATA Registry
Characteristic Total (N = 2178) ANZDATA (N = 1370) BEST-Fluids (N = 808) Standardized difference (BEST-Fluids—ANZDATA) P
Transplant country <0.001
 Australia 1920 (88.2%) 1301 (95.0%) 619 (76.6%) −0.54
 New Zealand 258 (11.8%) 69 (5.0%) 189 (23.4%) 0.54
Transplant state (AU) <0.001
 New South Wales 555 (28.9%) 409 (31.4%) 146 (23.6%) −0.18
 South Australia 158 (8.2%) 95 (7.3%) 63 (10.2%) 0.10
 Queensland 381 (19.8%) 122 (9.4%) 259 (41.8%) 0.80
 Victoria 627 (32.7%) 500 (38.4%) 127 (20.5%) −0.40
 Western Australia 199 (10.4%) 175 (13.5%) 24 (3.9%) −0.35
Center providing KRT before transplant <0.001
 Non-transplanting hospital 1013 (46.5%) 524 (38.2%) 489 (60.5%) 0.46
 Transplanting hospital 1165 (53.5%) 846 (61.8%) 319 (39.5%) −0.46
Location of patient domicile at transplant a 0.01
 Major city 1370 (72.1%) 951 (74.1%) 419 (68.0%) −0.13
 Regional 472 (24.9%) 292 (22.8%) 180 (29.2%) 0.15
 Remote 57 (3.0%) 40 (3.1%) 17 (2.8%) −0.02
 Not recorded/missing 279 87 192
Location of dialysis treatment at transplant 0.66
 Home 1058 (48.6%) 672 (49.1%) 386 (47.8%) −0.03
 In-center 1083 (49.7%) 677 (49.4%) 406 (50.2%) 0.02
 Not on dialysis 37 (1.7%) 21 (1.5%) 16 (2.0%) 0.03
aThese data were derived from postcodes and were only available for Australian patients; all New Zealand patients were recorded as having missing data.
ANZDATA, Australia and New Zealand Dialysis and Transplant Registry; AU, Australia; BEST-Fluids‚ Better Evidence for Selecting Transplant Fluids; KRT, kidney replacement therapy.

The characteristics of the deceased donors for these recipients are presented in Table 3. The groups did not differ in donor age, cause of death, receipt of cardiopulmonary resuscitation, weight, BMI, diabetes, smoking status, admission, terminal creatinine, or expanded criteria donor status (|d| < 0.1, P > 0.05, for all comparisons). The trial participant group had slightly fewer male donors (56% versus 62%, d = −0.11, P =0.03), fewer donors after circulatory determination of death (25% versus 31%, d = −0.14, P =0.008), and fewer donors with treated hypertension (22% versus 27%, d = −0.11, P =0.03) than the other transplant recipients. The mean KDRI was slightly lower for trial participants than for other transplant recipients (1.33 versus 1.39; d = −0.14, P =0.007). However, there were similar proportions of both groups that received higher- and lower-risk KDRI tertile kidneys. More trial participants had kidneys that had been biopsied before transplantation (16% versus 12%; d = 0.13, P = 0.01).

TABLE 3. - Characteristics of deceased donors for BEST-Fluids trial participants compared with donors for other Australian and New Zealand kidney transplant recipients in the ANZDATA Registry
Characteristic Total (N = 2178) ANZDATA (N = 1370) BEST-Fluids (N = 808) Standardized difference (BEST-Fluids—ANZDATA) P
Age (y)
 Mean (SD) 46.6 (16.6) 47.0 (16.5) 46.0 (16.5) −0.06 0.22
Gender 0.03
 Male 1301 (59.7%) 847 (61.8%) 454 (56.2%) −0.11
 Female 877 (40.3%) 523 (38.2%) 354 (43.8%) 0.11
Donor type 0.008
 Donor after circulatory death 628 (28.8%) 427 (31.2%) 201 (24.9%) −0.14
 Donor after brain death 1550 (71.2%) 943 (68.8%) 607 (75.1%) 0.14
Cause of donor death 0.70
 Cerebral hypoxia/ischemia 829 (38.1%) 514 (37.5%) 315 (39.0%) 0.03
 Cerebral infarct 120 (5.5%) 85 (6.2%) 35 (4.3%) −0.08
 Intracranial hemorrhage 752 (34.5%) 473 (34.5%) 279 (34.5%) 0.0001
 Traumatic brain injury 377 (17.3%) 235 (17.2%) 142 (17.6%) 0.01
 Other neurological condition 45 (2.1%) 27 (2.0%) 18 (2.2%) 0.02
 Non-neurological condition 55 (2.5%) 36 (2.6%) 19 (2.4%) −0.02
Donor received CPR 1053 (48.4%) 651 (47.5%) 402 (49.9%) 0.05 0.36
Weight (kg)
 Mean (SD) 81.9 (22.8) 81.9 (22.9) 81.9 (22.6) 0.0001 1.00
Body mass index (kg/m2)
 Mean (SD) 27.7 (6.7) 27.7 (6.7) 27.7 (6.6) 0.01 0.90
Diabetes 162 (7.4%) 111 (8.1%) 51 (6.3%) −0.07 0.19
Treated hypertension 534 (24.7%) 360 (26.5%) 174 (21.7%) −0.11 0.03
Smoking status 0.69
 Never 806 (37.1%) 517 (37.8%) 289 (35.9%) −0.04
 Former 457 (21.0%) 281 (20.6%) 176 (21.8%) 0.03
 Current 909 (41.9%) 568 (41.6%) 341 (42.3%) 0.01
Admission serum creatinine (µmol/L) 0.71
 Mean (SD) 90.2 (46.1) 90.5 (50.2) 89.7 (38.3) −0.02
 Median (IQR) 81 (63–105) 80 (62–105) 83 (64–105)
Terminal serum creatinine (µmol/L) 0.33
 Mean (SD) 94.7 (90.3) 96.3 (89.9) 91.8 (91.1) −0.05
 Median (IQR) 69 (56–94) 69 (56–97) 68.5 (56–91)
Preretrieval biopsy performed 296 (13.6%) 164 (12.0%) 132 (16.3%) 0.13 0.01
Expanded criteria donor 658 (30.2%) 430 (31.4%) 228 (28.2%) −0.07 0.18
Kidney donor risk index (KDRI)
 Mean (SD) 1.37 (0.46) 1.39 (0.46) 1.33 (0.44) −0.14 0.007
KDRI tertile a 0.06
 First 721 (33.3%) 444 (32.6%) 277 (34.5%) 0.04
 Second 721 (33.3%) 434 (31.9%) 287 (35.7%) 0.08
 Third 721 (33.3%) 482 (35.4%) 239 (29.8%) −0.12
KDRI tertile here refers to the distributions of the individual KDRI scores in the combined Australian and New Zealand cohorts (n = 2178).
ANZDATA, Australia and New Zealand Dialysis and Transplant Registry; BEST-Fluids‚ Better Evidence for Selecting Transplant Fluids; CPR‚ cardiopulmonary resuscitation; IQR‚ interquartile range; KDRI‚ kidney donor risk index.

Comparison of Trial Participants With US Transplant Recipients 2018 to 2020

Tables S1 and S2 (SDC, https://links.lww.com/TXD/A469) show the baseline recipient and donor characteristics of the 808 BEST-Fluids participants (585 deceased donors) and 46 050 US transplant recipients (26 864 deceased donors), respectively. Age (52 ± 13 versus 54 ± 13 y, d = −0.15, P < 0.001), proportion of White ethnicity (59% versus 57%, d = 0.05, P < 0.001), BMI (29 ± 13 versus 28 ± 13 kg/m2, d = 0.08, P < 0.001), and peak PRA (20 ± 34% versus 26 ± 37%, d = −0.16, P < 0.001) showed small but significant differences. A lower proportion of trial participants had diabetic nephropathy, hypertension, or “other” as the primary cause of kidney failure (22% versus 30%, d = −0.23; 9% versus 23%, d = −0.61; and 11% versus 21%, d = −0.42, respectively, P < 0.001). BEST-Fluids participants were more likely to have glomerulonephritis or polycystic kidney disease as the primary cause of kidney failure (39% versus 16%, d = 0.67; and 13% versus 8%, d = 0.30, respectively; P < 0.001). Compared with recipients in the United States, BEST-Fluids participants had less type 2 diabetes (26% versus 35%, d = −0.24, P < 0.001), shorter total dialysis duration (41 ± 37 versus 55 ± 40 mo, d = −0.35, P < 0.001), shorter total ischemic time (11 ± 5 versus 18 ± 9 h, d = −0.78, P < 0.001), fewer HLA mismatches (Table S1, SDC, https://links.lww.com/TXD/A469, P < 0.001), and less commonly received T-cell depleting induction immunosuppression (12% versus 76%, d = −1.73, P < 0.001). Both groups predominantly used calcineurin inhibitor-based triple immunosuppression after transplantation, but there were some differences, notably‚ a higher proportion of glucocorticoids and cyclosporin in Australia and New Zealand (99% versus 72%, d = 2.01; and 15% versus 0.6%, d = 1.86, respectively, P < 0.001). Deceased donor kidneys in Australia and New Zealand were much less likely to be machine-perfused before transplantation (3% versus 30%, d = −1.48, P < 0.001).

The 585 donors for the 808 BEST-Fluids participants were significantly older than the 26 864 US donors during the study period (46 ± 17 versus 39 ± 16 y, d = 0.44, P < 0.001). Furthermore, BEST-Fluids donors were more likely to have died of cerebral anoxia (38% versus 11%, d = 0.88) but less likely to have died after a cerebrovascular event (40% versus 56%, d = −0.36) or head trauma (18% versus 27%, d = −0.29) (all P < 0.001) and were more likely to be smokers (42% versus 17%, d = 0.70, P < 0.001) and expanded criteria donors (29% versus 15%, d = 0.46, P < 0.001). However, BEST-Fluids donors had lower admission and terminal serum creatinine concentrations (0.99 ± 0.42 versus 1.79 ± 1.44 mg/dL, d = −0.56; and 1.03 ± 1.02 versus 1.33 ± 1.28 mg/dL, d = −0.24, respectively, both P < 0.001), less diabetes (7% versus 9%, d = −0.14, P = 0.01), and less hypertension (22% versus 29%, d = −0.20, P < 0.001) than US deceased donors. No significant differences were observed between the groups in terms of gender or donor type (donation after brain death versus donation after circulatory death [DCD]).

DISCUSSION

To maximize the applicability of trial findings to clinical transplantation, BEST-Fluids was a pragmatic RCT closely aligned with clinical practice, with broad eligibility criteria. We sought to recruit a trial population representative of the general population of patients with kidney failure who received deceased donor kidney transplants. We found that a significant majority of all deceased donor kidney transplant recipients in Australia and New Zealand during the trial enrollment period (2178 of 2373; 92%) met the trial eligibility criteria, confirming that these were broad and pragmatic. During active recruitment at the participating sites, 1350 were screened for participation, and a high proportion (63%) of these were enrolled in the trial. Of those who were not enrolled, only 95 (7%) were ineligible, 55 (4%) declined to participate, and the remainder (344; 25%) were not enrolled because of factors largely unrelated to the trial design. Indeed, BEST-Fluids participants made up a substantial proportion (808 of 2178; 37%) of all eligible deceased donor kidney transplants performed during the trial period in Australia and New Zealand. Compared with nonparticipants, trial participants had no clinically meaningful differences in baseline characteristics, including those relevant to the risk of the primary trial outcome of DGF; thus, significant selection bias was absent.

Minor differences in characteristics between trial participants and nonparticipants were identified. First, the trial participants were ethnically and geographically different from the other recipients, reflecting differences in trial participation, recruitment rates, and durations at different hospitals. For example, recruitment rates were higher in New Zealand and the Australian state of Queensland than in other Australian states, reflecting the fact that the participating transplant hospitals in New Zealand and Queensland began recruitment early in the trial and maintained high recruitment throughout‚ whereas other hospitals joined later or did not participate in the study. In addition, the proportion of New Zealand Māori and Pacific Islanders enrolled was higher‚ and the proportion of Asian ethnicity was lower. Trial participants were less likely to be under the care of a transplanting hospital before transplantation, likely because of differences in the organization of transplant and dialysis services in different jurisdictions. Nevertheless, trial participants were recruited from metropolitan and regional areas and a range of hospitals (both small and large centers) across Australia and New Zealand, including Aboriginal and Torres Strait Islander Australians and New Zealand Māori, 2 groups that experience a high burden of kidney failure and poor access to transplantation compared with other population groups.23-25

Second, the clinical characteristics differed slightly between the groups, although these are unlikely to affect the generalizability of the trial. The trial participants had a slightly higher burden of comorbid conditions at baseline (coronary artery disease, body weight, and longer dialysis duration). Conversely, the trial cohort received slightly fewer kidneys from donors with hypertension and at higher risk of DGF (ie, DCD). More trial participants also received induction immunosuppression, glucocorticoids, or cyclosporin. These small differences (all values of d < 0.20, except for induction immunosuppression [0.30], glucocorticoids [0.23], and cyclosporin [0.34]) likely reflected the variability between higher and lower recruiting jurisdictions and participating and non-participating hospitals in transplant waiting times, organ donation rates, donor acceptance practices, and immunosuppression use,26,27 rather than systematic differences between those recruited at a given site and those who were not. The observed differences in cyclosporin use were due to 1 high-enrolling hospital (Auckland City Hospital) that used cyclosporin in standard risk recipients and would not be expected to affect DGF incidence.28 However, the slightly lower proportion of DCD kidneys (absolute difference 6%, d = −0.14) received by the trial participants might be expected to slightly reduce the risk of DGF.

We observed several differences between the BEST-Fluids trial participants and the US cohort of transplant recipients. Population characteristics and transplant practices differ between countries and have been documented previously.29 Given the size of the US dataset, small differences in characteristics (that may not be clinically meaningful) between the groups were associated with a high degree of statistical significance. Hence, it is important to consider standardized differences in these comparisons. In the United States, there was a higher proportion of patients with diabetes (either as a cause of kidney failure or as a comorbidity), greater use of T-cell–depleting induction immunosuppression, longer total ischemic times, and greater use of machine perfusion. T-cell depletion use varies within and between countries,30-32 and although it has been postulated to be associated with a lower risk of DGF,33 this has not been confirmed in randomized trials.34-36 The longer the ischemic time, the higher the expected risk of DGF,6 whereas increased use of machine perfusion would be expected to lower the risk of DGF.37 Meanwhile, deceased donors in Australia and New Zealand were older and more likely to be expanded criteria donors, albeit with lower terminal serum creatinine values. It is beyond the scope of this study to explain these differences. However, the main question remains whether they are associated with a significantly different risk of DGF in these 2 populations. In a multicenter prospective study of DGF in a US cohort with similar characteristics to the SRTR cohort described here, the incidence of DGF was 38%.1 This is similar to the postulated DGF incidence of 36% used for the sample size calculations in BEST-Fluids, which was confirmed by a blinded review of the event rate for the first 113 trial participants,10 and to the DGF incidence reported in other settings.38,39 Thus, BEST-Fluids results should be generalizable to the US deceased donor kidney transplant population and other populations with similar DGF risk.

BEST-Fluids was the first large multicenter kidney transplant trial to prospectively assess participant representativeness by using a registry-based design. The importance of representativeness when considering the applicability of trial results is increasingly recognized,40 and we argue that the need to comprehensively evaluate the external validity of trials41 makes a strong case for the routine separate publication of the types of analyses reported here to fully inform clinicians. However, most currently published trial reports are limited to reporting data on the number of patients screened, which provides limited information about the underlying population who might have been eligible for the trial. Although registries have been used to retrospectively determine the representativeness of trial participants in nephrology42 and are increasingly being used in other medical disciplines,43 they have not been used in transplantation trials despite their widespread use and availability.29

The major strengths of this study were the completeness and reliability of the data used for comparisons and the very low amount of missing data for the Australian and New Zealand patients. Trial enrollment and key data collection for BEST-Fluids were embedded into ANZDATA, a population-based clinical quality registry that collects and reports demographics, comorbidity, and outcome data on all kidney transplants in Australia and New Zealand.44 By adopting this approach, we could use the same data collection approaches and definitions to directly assess the representativeness of the trial participants compared with other transplant recipients during the trial period. Although this analysis was performed retrospectively, the implementation plan was prespecified,10 and all data were collected prospectively.

This study has some limitations. ANZDATA does not collect all factors that might have been relevant to representativeness, such as socioeconomic status, distance from the transplanting hospital, primary language spoken, or severity of comorbidities. Similarly, we did not have information on laboratory data (other than donor creatinine) or other recipient factors that may have affected DGF risk (eg, preoperative fluid status, dialysis requirement, or plasma exchange). The comparison with the US cohort was limited to summary data, and data on several important characteristics (including comorbidities, dialysis modality, and smoking) were either unavailable or unreliable because of very high levels of missing data.45 Several variables included in the US cohort comparison also had notable missing data, particularly machine perfusion (21% missing) and cause of donor death (25%), which might have affected the results for these and other (eg, proportions of expanded criteria donors) comparisons. In addition, although there have been previous comparisons of data from the US SRTR and ANZDATA,29 the validity of such comparisons has not been rigorously audited. Finally, although these comparisons were intended to explore generalizability, we acknowledge that it is difficult to draw firm conclusions until full trial results including subgroup analyses are available.

The highest priority for a RCT is to generate unbiased results with high internal validity to draw causal inferences about the tested intervention. This often comes at the expense of external validity, and it is well recognized that trial populations, as identified by Kennedy-Martin et al, are often “highly selected and have a lower risk profile than real-world populations, with the frequent exclusion of elderly patients and patients with comorbidities.”41 Pragmatic trial designs allow for the evaluation of the effectiveness of interventions because they are used in routine clinical care.46,47 We have shown that‚ by combining this with the use of a population-based disease registry for trial data collection and follow-up,29 representativeness assessments can be incorporated into routine trial reporting to readily determine the applicability of trial findings, thereby facilitating rapid implementation.48

In conclusion, participants in the BEST-Fluids trial broadly represented the Australian and New Zealand kidney transplant recipient population. Importantly, the trial included representative proportions of Aboriginal and Torres Strait Islander Australians, New Zealand Māori, and Pacific peoples, groups who are disproportionately affected by kidney failure in these 2 countries. The trial participants had a slightly higher comorbidity burden and received fewer high-risk donor kidneys. These small differences were likely due to the different recruitment rates in the 2 countries and at different hospitals and reflect variability in approaches to care in these settings rather than systematic differences between recruited and nonrecruited participants. In addition, the trial participants were generally similar to US transplant recipients, with most differences being small or because of known underlying population differences. The results of the BEST-Fluids trial should be applicable to most deceased donor kidney transplant recipients.

ACKNOWLEDGMENTS

The authors are grateful to Doris Chang (Providence Health Research Institute, Vancouver, British Columbia, Canada) for assistance in obtaining the US SRTR summary data.

BEST-Fluids Trial Steering Committee

Steven J. Chadban (Co-chair), Royal Prince Alfred Hospital and University of Sydney, Sydney, Australia; Michael G. Collins (Co-chair), Auckland City Hospital, Auckland, New Zealand, and Royal Adelaide Hospital and University of Adelaide, Adelaide, Australia; Philip A. Clayton, ANZDATA Registry, Royal Adelaide Hospital and University of Adelaide, Adelaide, Australia; P. Toby Coates, Royal Adelaide Hospital and University of Adelaide, Adelaide, Australia; Zoltan Endre, Prince of Wales Hospital and University of New South Wales, Sydney, Australia; Magid Fahim, Princess Alexandra Hospital and University of Queensland, Brisbane, Australia; Carmel M. Hawley, Princess Alexandra Hospital and University of Queensland, Brisbane, Australia; Kirsten Howard, Sydney School of Public Health, University of Sydney, Sydney, Australia; Martin Howell, Sydney School of Public Health, University of Sydney, Sydney, Australia; Kathy Kable, Westmead Hospital, Sydney, Australia; Jerome Laurence, Royal Prince Alfred Hospital and University of Sydney, Sydney, Australia; Wai H. Lim, Sir Charles Gairdner Hospital and University of Western Australia, Perth, Australia; Colin McArthur, Department of Critical Care Medicine, Auckland City Hospital, Auckland, New Zealand; Rachael McConnochie, Auckland City Hospital, Auckland, New Zealand; Steven McTaggart, Children’s Health Queensland Hospital and Health Service and The University of Queensland, Australia; Peter Mount, Austin Health & University of Melbourne, Melbourne, Australia; Elaine M. Pascoe, University of Queensland, Brisbane, Australia; Helen Pilmore, Auckland City Hospital and University of Auckland, Auckland, New Zealand; Donna Reidlinger, University of Queensland, Brisbane, Australia; Laura E. Hickey, University of Queensland, Brisbane, Australia; Charani Kiriwandeniya, University of Queensland, Brisbane, Australia; Julie Varghese, University of Queensland, Brisbane, Australia; Liza A. Vergara, University of Queensland, Brisbane, Australia; Laurence Weinberg, Austin Health and University of Melbourne, Melbourne, Australia; Germaine Wong, Westmead Hospital and University of Sydney, Sydney, Australia.

Trial Management Committee

Steven J. Chadban (Co-chair), Michael G. Collins (Co-chair), Magid A. Fahim, Carmel M. Hawley, David W. Johnson, Charani Kiriwandeniya, Rachael McConnochie, Elaine M. Pascoe, Donna M. Reidlinger, Laura E Hickey, Julie Varghese, Liza A. Vergara, Hayley Candler, Justin Scott.

Collaborating Sites and Investigators

Australia: New South Wales: Royal Prince Alfred Hospital (Steven J. Chadban, Beatriz Habijanec, Hee-eun Yeo, Lin Lin, Brenda Rosales, Julia Hudaly, Vera Rodrigues, Tracey Ying, David Gracey, Leyla Aouad, Kate Wyburn), Westmead Hospital (Germaine Wong, Philip O’Connell, Penelope Murie), Prince of Wales Hospital (Zoltan Endre, Zuzana Gray), Sydney Children’s Hospital (Rebecca Spicer), Children’s Hospital Westmead (Anne Durkan); Queensland: Princess Alexandra Hospital (Magid A. Fahim, Dev Jegatheesan, Diana Leary, Yujing [Sarah] Guo, Amanda Coburn), Queensland Children’s Hospital (Steven McTaggart, Aimee Crawford); South Australia: Royal Adelaide Hospital (Patrick T. Coates, Bronwyn Hockley, Karen Fischer); Victoria: Austin Health (Laurence Weinberg, Peter Mount, Sarah Baulch, Gayle Claxton, Saskia Harris, Sofia Sidiropoulos, Marieke Veenendal), Monash Medical Center (John Kanellis, Rita Barbis), St Vincent’ Hospital Melbourne (David Goodman, Anjalee Brahmbhatt), Monash Children’s Hospital (Amelia Le Page); Western Australia: Fiona Stanley Hospital (Jagadish Jamboti, Anna Chiam, Anne Warger), Sir Charles Gairdner Hospital (Wai H. Lim); New Zealand: Auckland City Hospital (Michael Collins, Helen Pilmore, Ian Dittmer, Paul Manley, Jafar Ahmed, Rachael McConnochie, Lynette Newby, Yan Chen, Catherine Simmonds), Christchurch Hospital (John Irvine, Jenny Usher), Wellington Hospital (Carolyn Clark, Claire Beckett), Starship Children’s Hospital (Chanel Prestidge, Robin Erickson, Miriam Rea, Claire Sherring).

Australasian Kidney Trials Network (AKTN) Scientific Committee

Carmel M. Hawley, Department of Nephrology, Princess Alexandra Hospital, Woolloongabba, Australia, the Translational Research Institute and Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; David W. Johnson, Department of Nephrology, Princess Alexandra Hospital, Woolloongabba, Australia, the Translational Research Institute and Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; Elaine M. Pascoe, Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; Sunil V. Badve, Department of Renal Medicine, St George Hospital, Sydney, Australia‚ and Renal and Metabolic Division, The George Institute for Global Health, University of New South Wales Medicine, Sydney, Australia; Katrina Campbell, Department of Nephrology, Princess Alexandra Hospital, Brisbane, Australia; Neil Boudville, Sir Charles Gairdner Hospital, Perth, Australia‚ and Division of Internal Medicine, Medical School, University of Western Australia, Perth, Australia; Meg J. Jardine, NHMRC Clinical Trials Center, Faculty of Medicine and Health, University of Sydney, Australia, Concord Repatriation and General Hospital, Concord, Australia‚ and The George Institute for Global Health, University of New South Wales, Sydney, Australia; Yeoungjee Cho, Department of Nephrology, Princess Alexandra Hospital, Woolloongabba, Australia, and Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; Martin Gallagher, The George Institute for Global Health and Faculty of Medicine, University of New South Wales, Sydney, Australia; Wai H. Lim, Sir Charles Gairdner Hospital and University of Western Australia, Perth, Australia; Suetonia C. Palmer, Department of Medicine, University of Otago, Christchurch, New Zealand; Nigel Toussaint, Department of Nephrology, The Royal Melbourne Hospital and The University of Melbourne, Victoria, Australia; Robert J. Walker, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand; Stephen P. McDonald, ANZDATA Registry, SA Health and Medical Research Institute, Adelaide, Australia; Jonathan C. Craig, Flinders University, Adelaide, Australia; Matthew Jose, Royal Hobart Hospital and School of Medicine, University of Tasmania, Tasmania, Australia; Josephine Chow, Liverpool Renal Clinical Research Center, Liverpool Hospital, Sydney, Australia; Rathika Krishnasamy, Sunshine Coast University Hospital, Australia, and Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; Vlado Perkovic, The George Institute for Global Health, University of New South Wales, Sydney, Australia; Magid A. Fahim, Department of Nephrology, Princess Alexandra Hospital, Woolloongabba, Australia‚ and Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; Matthew A. Roberts, Eastern Health Clinical School, Monash University, Melbourne, Australia; Michael Collins, Auckland City Hospital and University of Auckland, Auckland, New Zealand; Donna Reidlinger, Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; Laura E. Hickey, Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; Fabian Marsden, AKTN Scientific Committee.

The Australasian Kidney Trials Network Executive Operations Secretariat

Carmel Hawley (Chair), Department of Nephrology, Princess Alexandra Hospital, Woolloongabba, Australia, and the Translational Research Institute and Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; David W. Johnson (Deputy Chair), Department of Nephrology, Princess Alexandra Hospital, Woolloongabba, Australia, the Translational Research Institute and Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia‚ and Translational Research Institute, Brisbane, Australia; Matthew Roberts, Eastern Health Clinical School, Monash University, Melbourne, Australia; Michael Collins, Auckland City Hospital, Auckland, New Zealand, and Royal Adelaide Hospital and University of Adelaide, Adelaide, Australia; Neil Boudville, Sir Charles Gairdner Hospital, Perth, Australia and Division of Internal Medicine, Medical School, University of Western Australia, Perth, Australia; Meg J. Jardine, NHMRC Clinical Trials Center, Faculty of Medicine and Health, University of Sydney, Australia, Concord Repatriation and General Hospital, Concord, Australia‚ and The George Institute for Global Health, University of New South Wales, Sydney, Australia; Magid A. Fahim, Department of Nephrology, Princess Alexandra Hospital, Woolloongabba, Australia and Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; Yeoungjee Cho, Department of Nephrology, Princess Alexandra Hospital, Woolloongabba, Australia‚ and Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; Rathika Krishnasamy, Sunshine Coast University Hospital, Australia, and Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; Donna Reidlinger, Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; Elaine Pascoe, Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; Laura E. Hickey, Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; Sunil V. Badve, Department of Renal Medicine, St George Hospital, Sydney, Australia‚ and Renal and Metabolic Division, the George Institute for Global Health, University of New South Wales Medicine, Sydney, Australia; Andrea K. Viecelli, Department of Nephrology, Princess Alexandra Hospital, Woolloongabba, Australia‚ and Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; Liza Vergara, Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; Vlado Perkovic, The George Institute for Global Health, University of New South Wales, Sydney, Australia; Alicia Morrish, Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; Peta-Anne Paul-Brent, Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia; Hayley Candler, Australasian Kidney Trials Network, The University of Queensland, Brisbane, Australia.

BEST-Fluids Data and Safety Monitoring Board

Bruce Neal (Chair), The George Institute for Global Health, Sydney, Australia, The University of Sydney; Andrew Forbes, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia; David C. Wheeler, Department of Renal Medicine, University College, London, England; and Christoph Wanner, Department of Medicine, University Hospital, Wuerzburg, Germany.

The investigators also wish to acknowledge Peta-Anne Paul-Brent and Stephanie Smith (AKTN, University of Queensland) for their contributions to developing the protocol; Ross Francis (Princess Alexandra Hospital, Brisbane) and Katherine Barraclough (Royal Melbourne Hospital, Melbourne)‚ who contributed to the study design as members of the AKTN Transplant Working Group; and Stephen McDonald, Matilda D’Antoine, and Kylie Hurst (ANZDATA Registry) for their assistance in developing the trial-specific module within ANZDATA.

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