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. 2020 Jul 29;15(7):e0236662. doi: 10.1371/journal.pone.0236662

Improving outcomes for donation after circulatory death kidney transplantation: Science of the times

Michèle J C de Kok 1, Alexander F M Schaapherder 1, Ian P J Alwayn 1, Frederike J Bemelman 2, Jacqueline van de Wetering 3, Arjan D van Zuilen 4, Maarten H L Christiaans 5, Marije C Baas 6, Azam S Nurmohamed 7, Stefan P Berger 8, Esther Bastiaannet 9, Rutger J Ploeg 1,10, Aiko P J de Vries 11, Jan H N Lindeman 1,*
Editor: Frank JMF Dor12
PMCID: PMC7390443  PMID: 32726350

Abstract

The use of kidneys donated after circulatory death (DCD) remains controversial due to concerns with regard to high incidences of early graft loss, delayed graft function (DGF), and impaired graft survival. As these concerns are mainly based on data from historical cohorts, they are prone to time-related effects and may therefore not apply to the current timeframe. To assess the impact of time on outcomes, we performed a time-dependent comparative analysis of outcomes of DCD and donation after brain death (DBD) kidney transplantations. Data of all 11,415 deceased-donor kidney transplantations performed in The Netherlands between 1990–2018 were collected. Based on the incidences of early graft loss, two eras were defined (1998–2008 [n = 3,499] and 2008–2018 [n = 3,781]), and potential time-related effects on outcomes evaluated. Multivariate analyses were applied to examine associations between donor type and outcomes. Interaction tests were used to explore presence of effect modification. Results show clear time-related effects on posttransplant outcomes. The 1998–2008 interval showed compromised outcomes for DCD procedures (higher incidences of DGF and early graft loss, impaired 1-year renal function, and inferior graft survival), whereas DBD and DCD outcome equivalence was observed for the 2008–2018 interval. This occurred despite persistently high incidences of DGF in DCD grafts, and more adverse recipient and donor risk profiles (recipients were 6 years older and the KDRI increased from 1.23 to 1.39 and from 1.35 to 1.49 for DBD and DCD donors). In contrast, the median cold ischaemic period decreased from 20 to 15 hours. This national study shows major improvements in outcomes of transplanted DCD kidneys over time. The time-dependent shift underpins that kidney transplantation has come of age and DCD results are nowadays comparable to DBD transplants. It also calls for careful interpretation of conclusions based on historical cohorts, and emphasises that retrospective studies should correct for time-related effects.

Introduction

In the past decades, organs retrieved from donation after brain death (DBD) donors have provided the majority of solid organ transplants globally. Due to the medical success of transplantation as an effective therapy for patients with end stage organ failure, the increased need of donor organs created a persistent shortage which has resulted in the death of many patients while waiting for a transplant.

For many years now, kidneys donated after circulatory death (DCD) have been proposed as an effective means of addressing this severe organ shortage [1, 2]. Despite emerging reports indicating that mid-term and long-term outcomes of DCD procedures are better than commonly thought [3–5], only some countries have fully embraced this opportunity [6]. For various reasons, others have been reluctant or even outspoken adverse towards the introduction of a controlled DCD programme that could alleviate the shortage and save many lives within a healthcare system [7, 8]. While for some countries reasons to not or only slowly allow DCD programmes relate to ethical issues, legal restrictions or logistical concerns [9], for the majority of countries the reticent attitude generally reflects medical concerns that are based on reported high incidences of early graft loss, delayed graft function (DGF), and an assumed impaired graft survival for DCD kidneys.

Since the concerns regarding the inferior DCD outcomes are mainly based on historical analyses, they are prone to time-related effects as time-varying confounding and effect modification by time [10, 11]. Time-varying confounding is the phenomenon that the values of confounding variables, such as donor and recipient age, change over time [10]. Effect modification by time occurs when the effect of donor type on outcomes is modified by time (e.g. due to changes in procedural characteristics and/or medical decision-making over time) [11]. Therefore, assumptions as regards inferior outcomes of DCD procedures may not apply anymore to our current timeframe.

To test whether conclusions with regard to the outcomes of DCD kidney transplant procedures are influenced by time, and to objectify the current results achieved when utilising DCD donor kidneys, we performed a longitudinal time-dependent comparative analysis of the outcomes of DBD and DCD kidney transplant procedures performed in The Netherlands, as country with a longstanding tradition of the use of DCD donor kidneys.

Materials and methods

Patient population

This national outcome evaluation was approved by the Ethics Committee of the Leiden University Medical Center, and the clinical and research activities being reported are consistent with the Principles of the Declaration of Istanbul as outlined in the 'Declaration of Istanbul on Organ Trafficking and Transplant Tourism’. Data was fully anonymized prior to access and analysis.

In this study, we collected data of all 11,415 deceased-donor kidney transplant procedures performed in The Netherlands between 1990 and 2018. Combined organ procedures (n = 635), procedures with grafts donated after uncontrolled circulatory death (i.e. Maastricht Category I: dead on arrival and II: unsuccessful resuscitation) (n = 212), and procedures in recipients younger than 12 years old (n = 261) were excluded.

To explore a possible time-related effect, the incidence of early graft loss was mapped for the years 1990 to 2018. In this analysis, only primary kidney transplant procedures (n = 8,511) were included since early graft loss after re-transplantation is potentially interfered with accumulation of recipient-related risk factors [12]. Based on the early graft loss incidences, two timeframes were defined for the time-dependent comparative analysis. This analysis included all (primary transplantations and re-transplantations) transplantations performed between 1998–2008 (n = 3,499) and 2008–2018 (n = 3,781).

Data was retrieved from the Dutch National Organ Transplant Registry, which is a mandatory registry that contains granular data of all eight Dutch kidney transplant centres.

Definitions

Early graft loss was defined as graft loss within 90 days after transplantation. Patients who died within 90 days after transplantation with a functioning graft were not considered as early graft loss recipients. DGF was defined as the need for dialysis in the first postoperative week(s). The Modification of Diet in Renal Disease (MDRD) equation was used to estimate the glomerular filtration rate (eGFR) in the recipient. The non-scaled, donor-only version of the Kidney Donor Risk Index (KDRI) was calculated as described by Rao et al. [13]. The following definitions were used for ischaemic periods of the donor kidneys. The first warm ischaemic period is the time following the no touch period after circulatory arrest and asystole in the DCD donor, until cold flush-out in the donor is commenced. The cold ischaemic period is the time from start of cold flush-out until the start of the vascular anastomosis in the recipient. The graft anastomosis time is defined as the time from kidney removal from static cold storage or hypothermic machine perfusion until reperfusion in the recipient.

Data analysis

IBM SPSS Statistics 23.0 (IBM Corp., Armonk, NY, USA) was used for statistical analysis. Comparisons between DBD and DCD procedures were performed using the independent t-test for normal-distributed data, the Mann-Whitney rank test for non-parametric data, and the Chi-Square test for categorical data.

The KDRI was reported to facilitate comparison of the Dutch donor cohort with that of other countries. However, in the Dutch National Organ Transplant Registry donor hypertension and diabetes—which are included in the KDRI—are only registered from 2000 respectively 2002 onwards. As such, there was a high proportion of missing data for the 1998–2018 interval (26.6% for diabetes and 10.0% for hypertension) and multiple imputation of missing data of variables included in the KDRI was applied.

Logistic and linear regression analyses were used to examine the association between outcomes (DGF, early graft loss and 1-year eGFR) and donor type. Cox proportional hazards analyses were performed to evaluate differences in patient survival and death censored graft survival. All the multivariate models were adjusted for variables statistically relevant (p-value <0.10) in the univariate analysis (S1 Table). To avoid overcorrection the KDRI was not included in the multivariate models (as the KDRI also comprises donor age and donor type which are already included separately in the univariate and multivariate analyses). Also the type of preservation solution was not included as the inter-relationship between variables (the selection of preservation solution depends on donor type) would substantially impact the validity of the model. Results are represented as beta coefficient (β), Odds Ratio (OR) or Hazard Ratio (HR) with the corresponding 95% Confidence Interval (CI).

An interaction (Wald) test was used to explore the presence of effect modification by time. In other words, to test whether the effect of donor type on outcomes is modified by time. To specifically determine the association (R2) between KDRI and 5-year graft survival, logistic regression analysis was performed using the data from 2002 to 2018 (non-imputed). P-values <0.05 were considered statistically significant.

Results

To explore a possible time-dependent effect on transplant outcomes, we mapped the incidence of early graft loss, as an unambiguous outcome parameter, for the years 1990 to 2018 in The Netherlands (Fig 1). Analyses of 5,895 DBD and 2,616 DCD primary kidney transplants performed in this period indicated clear time-related effects with 1998 (DBD) and 2008 (DCD) as clear transition years, after which the incidence of early graft loss dropped and stabilized at an incidence of approximately 6% (Fig 1).

Fig 1. Time-related incidences of early graft loss in 8,511 primary kidney transplant recipients according to deceased donor type in The Netherlands.

Fig 1

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* The small number of DCD kidney transplant procedures performed in these years (n < 15), does not justify adequate power for analysis. DBD, donation after brain death; DCD, donation after circulatory death; EGL, early graft loss.

Based on the transition years for early graft loss, two timeframes (1998–2008 and 2008–2018) were defined, and the outcomes of all 7,280 DBD and DCD procedures performed in these periods were compared accordingly (Tables 1 and 2). This comparison showed a marked increase in donor and recipient age, and in KDRI over time: donors and recipients were respectively 6.5 and 6 years older in the recent (2008–2018) timeframe and the KDRI increased from 1.23 to 1.39 for DBD donors and from 1.35 to 1.49 for DCD donors (Table 1). Although the KDRI for this Dutch cohort was significantly associated with 5-year graft survival (p<0.001), the Nagelkerke R2 for the KDRI and 5-year graft loss was only 1.7% suggesting a limited impact of donor characteristics on graft survival.

Table 1. Baseline characteristics of deceased-donor kidney transplants in The Netherlands for the 1998–2008 and the 2008–2018 interval.

01/1998–12/2007 01/2008–12/2017
DBD DCD p-value DBD DCD p-value p for interaction
n = 2361 (67.5%) n = 1138 (32.5%) n = 1929 (51.0%) n = 1852 (49.0%)
BASELNIE Donor age 46.2 ± 15.3 45.4 ± 15.4 0.16 52.7 ± 14.6 51.6 ± 14.5 0.03 <0.001
Recipient age 48.6 ± 14.3 50.7 ± 13.3 <0.001 55.0 ± 14.2 55.9 ± 12.9 0.03 <0.001
Time on dialysis (years) 4.1 ± 2.5 4.2 ± 2.0 0.24 3.8 ± 2.5 3.6 ± 2.1 0.001 <0.001
First warm ischaemic period (min.) NA 19.0 [15.0–25.0] - NA 16.0 [13.0–19.0] - -
Cold ischaemic period (hours) 20.0 [15.6–24.6] 20.0 [16.0–23.8] 0.32 15.4 [11.8–19.8] 14.0 [11.5–17.6] <0.001 <0.001
≤24 hours 1705 (74.3%) 889 (79.9%) 1589 (92.2%) 1575 (96.8%)
>24 hours 589 (25.7%) 224 (20.1%) 135 (7.8%) 52 (3.2%)
Graft anastomosis time (min.) 34.0 [27.0–41.0] 33.0 [27.0–40.0] 0.32 33.0 [26.0–41.0] 32.0 [25.0–40.0] 0.01 0.01
KDRI 1 1.23 [1.0–1.5] 1.35 [1.1–1.6] <0.001 1.39 [1.1–1.7] 1.49 [1.2–1.8] <0.001 <0.001
Preservation solution <0.001 <0.001 <0.001
- HTK 325 (13.9%) 967 (86.1%) 463 (24.4%) 719 (39.3%)
- University of Wisconsin 2001 (85.4%) 152 (13.5%) 1383 (72.8%) 1058 (57.8%)
- Other 17 (0.7%) 4 (0.4%) 55 (2.9%) 53 (2.9%)
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Data are respectively presented as mean ± standard deviation, as number (%), as median [interquartile range] or as beta coefficient (β), odds ratio or hazard ratio with the corresponding 95% confidence interval.

1 Multiple imputation was applied for missing data of variables included in the KDRI.

DBD, donation after brain death; DCD, donation after circulatory death; HTK, histidine-tryptophan-ketoglutarate; KDRI, kidney donor risk index; min., minutes; NA, not applicable.

Table 2. Deceased-donor kidney transplant outcomes in The Netherlands for the 1998–2008 and the 2008–2018 interval.

01/1998–12/2007 01/2008–12/2017
DBD DCD p-value DBD DCD p-value p for interaction
n = 2361 (67.5%) n = 1138 (32.5%) n = 1929 (51.0%) n = 1852 (49.0%)
OUTCOME Delayed graft function 420 (17.8%) 511 (44.9%) <0.001 321 (16.6%) 736 (39.7%) <0.001
Crude OR (95% CI) Ref. 4.04 (3.42–4.78) <0.001 Ref. 3.85 (3.28–4.53) <0.001 <0.001
Adjusted OR (95% CI) Ref. 4.17 (3.45–5.04) <0.001 Ref. 4.78 (3.99–5.70) <0.001
Early graft loss (<day 90) 194 (8.2%) 151 (13.3%) <0.001 110 (5.7%) 114 (6.2%) 0.56
Crude OR (95% CI) Ref. 1.71 (1.36–2.14) <0.001 Ref. 1.09 (0.83–1.42) 0.56 0.002
Adjusted OR (95% CI) Ref. 1.77 (1.40–2.23) <0.001 Ref. 1.24 (0.92–1.68) 0.16
- Primary non-function 44 (22.7%) 58 (38.4%) 42 (38.2%) 43 (37.7%)
- Rejection 58 (29.9%) 25 (16.6%) 23 (20.9%) 20 (17.5%)
- Thrombosis or infarction 38 (19.6%) 36 (23.8%) 14 (12.7) 24 (21.1%)
- Other 54 (27.8%) 32 (21.2%) 31 (28.2%) 27 (23.7%)
1-year eGFR DGF - 52.6 ± 19.7 49.5 ± 18.1 0.02 51.5 ± 19.7 52.3 ± 20.0 0.44
Crude β (95% CI) Ref. -3.07 (-5.67 - -0.47) 0.02 Ref. 0.84 (-1.29–2.96) 0.44 0.93
Adjusted β (95% CI) Ref. -4.21 (-6.57 - -1.86) <0.001 Ref. -0.11 (-2.04–1.82) 0.91
1-year eGFR DGF + 44.1 ± 19.0 44.4 ± 18.4 0.81 44.8 ± 18.9 44.6 ± 17.5 0.89
Crude β (95% CI) Ref. 0.32 (-2.19–2.82) 0.81 Ref. -0.18 (-2.81–2.45) 0.89 0.70
Adjusted β (95% CI) Ref. -0.69 (-3.02–1.63) 0.56 Ref. -1.63 (-4.05–0.79) 0.19
1-year graft loss (10.9%) (15.0%) (8.0%) (7.9%)
Crude HR (95% CI) 1.0 1.41 (1.16–1.71) 0.001 1.0 0.98 (0.78–1.23) 0.84 0.001
Adjusted H R (95% CI) 1.0 1.45 (1.19–1.77) <0.001 1.0 1.09 (0.85–1.40) 0.49
5-year graft loss (20.0%) (22.8%) (14.0%) (13.6%)
Crude HR (95% CI) 1.0 1.18 (1.01–1.37) 0.04 1.0 0.96 (0.80–1.15) 0.66 <0.001
Adjusted HR (95% CI) 1.0 1.25 (1.07–1.46) 0.01 1.0 1.04 (0.85–1.27) 0.69
1-year patient survival (95.0%) (94.4%) (95.2%) (94.8%)
Crude HR (95% CI) 1.0 1.14 (0.84–1.54) 0.41 1.0 1.08 (0.82–1.43) 0.58 0.60
Adjusted HR (95% CI) 1.0 1.08 (0.80–1.46) 0.62 1.0 1.08 (0.81–1.42) 0.61
5-year patient survival (82.8%) (82.4%) (82.3%) (82.5%)
Crude HR (95% CI) 1.0 1.04 (0.88–1.23) 0.66 1.0 1.00 (0.85–1.17) 0.96 0.81
Adjusted HR (95% CI) 1.0 1.08 (0.89–1.30) 0.44 1.0 1.02 (0.85–1.22) 0.83
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Data are respectively presented as mean ± standard deviation, or as number (%), and beta coefficient (β), odds ratio or hazard ratio with the corresponding 95% confidence interval.

95% CI, 95% confidence interval; DBD, donation after brain death; DCD, donation after circulatory death; DGF, delayed graft function; eGFR, estimated glomerular filtration rate; HR, hazard ratio; min., minutes; OR, odds ratio.

In contrast to the increase in donor and recipient age over time, there was a clear decrease in the cold ischaemic period from a mean value of 20 hours in the 1998–2008 era to approximately 15 hours in the 2008–2018 era (Table 1). Also the proportion of procedures with excessive cold ischaemic periods (>24 hours) substantially decreased over time (from 25.7% to 7.8% and from 20.1% to 3.2% for DBD respectively DCD donors).

Furthermore, the type of preservation solution changed over time. Whereas histidine-tryptophan-ketoglutarate (HTK) solution was most common for DCD donor kidneys between 1998 and 2008 (86.1%), University of Wisconsin solution was most commonly used between 2008 and 2018 (57.8%) (Table 1).

The comparative outcome analysis showed clear time-related effects (Table 2). Whereas the 1998–2008 era associates with compromised outcomes for DCD procedures in comparison to DBD procedures (i.e. higher incidences of DGF and early graft loss, impaired 1-year renal function in patients without DGF, and inferior 1- and 5-year graft survival rates), the 2008–2018 era shows outcome equivalence for DBD and DCD procedures. Only the incidence of DGF for DCD grafts remained high in the current era (Table 2), but this did not impact graft and patient survival. Yet, for both timeframes and for both donor types, DGF resulted in a 15% reduced 1-year renal function (Table 2).

To explore whether the shift towards outcome equivalence reflects effect modification by time, an interaction (Wald) test was performed. As expected, the interaction test confirmed a significant difference in the effect of donor type on outcomes (DGF, early graft loss, 1- and 5-year graft survival) between the two time eras (p for interaction: <0.001, 0.002, 0.001 and <0.001, respectively) (Table 2).

Discussion

This national study demonstrates a major improvement in outcomes of transplanted DCD kidneys over time. Whereas the 1998–2008 era associates with inferior outcomes for DCD kidney transplant procedures, the 2008–2018 era shows outcome equivalence between DBD and DCD kidney transplants. This shift underpins that DCD kidney transplantation has come of age and results are nowadays comparable to DBD kidney transplants. It also emphasises that conclusions based on retrospective data (i.e. based on timeframes in which outcomes of DCD procedures were inferior to DBD procedures) are interfered by time-varying confounding and effect modification, and are therefore no longer justified.

Patient- and graft survival equivalence for DBD and DCD procedures occurred despite a persistent high incidence of DGF in DCD grafts. This apparent paradox can be explained by differential impacts of DGF on DBD and DCD outcomes, with a negligible impact of DGF on patient- and graft survival in recipients with DCD grafts [4, 14]. A phenomenon that presumably relates to donor type-specific molecular differences in organ resilience [14].

A clear, univocal explanation for the improved outcomes is missing. In the context of more adverse donor and recipient risk profiles in the more recent timeframe, the improvement in DCD outcomes over time presumably involves a complex interplay of factors that includes optimized surgical procedures and immunosuppressive regimens, altered organ preservation techniques, and enhanced transport logistics [15–19]. Certainly, a significant impact has been the profound reduction in cold ischaemic time [16, 20]. Several studies have shown that a prolonged cold ischaemic time is more deleterious in recipients receiving kidney transplants from DCD donors than in recipients from DBD donors [21–23]. This finding, with DCD grafts being more ‘vulnerable’ to cold ischaemia than DBD grafts, has recently been confirmed by colleagues in The Netherlands [20], and might also explain why graft survival rates have improved to a greater extent for recipients of DCD donors than for recipients of DBD donors. Another possible explanation is that—as cold ischaemic periods of more than 24 hours in DCD kidneys are associated with worse graft survival—the proportion of procedures with excessive cold ischaemic periods (>24 hours) decreased over time with the increasing awareness of avoiding long cold ischaemic times in the Netherlands [4]. A potential contribution of hypothermic machine perfusion (HMP) to improved outcomes, however, is limited in this Dutch cohort since HMP was only fully implemented in the year 2016, and as the available data indicate that although HMP reduces the risk of DGF, it has a limited impact on the other outcome data [24].

Improved outcomes over time may further reflect advances in immunosuppressive therapies including the conversion from cyclosporine to tacrolimus as standard maintenance regime [25, 26]. Also, the introduction of more sensitive techniques to detect anti-human leukocyte antigen antibodies, such as the LUMINEX technique may have resulted in increased graft survival [18].

An alternative and non-exclusive explanation for the improved outcomes is the presence of a learning curve that involves the intangible and often intuitive aspects of medical decision-making processes in the context of donor and recipient selection, and organ allocation. Existence of a learning curve phenomenon is supported by the observation that outcomes of transplanted DBD kidneys improved significantly in a similar way as for DCD transplant procedures, but at an earlier (1990–1998) time era, and by the dynamics of the incidence of early graft loss over time (Fig 1). To be more specific, data for DBD procedures show a steep decline and stabilization of the incidence of early graft loss after 1998. A similar—albeit postponed—pattern is seen for the DCD procedures, for which the early graft loss incidence rate dropped and stabilized following 2008. Thus, it is likely that countries initiated a controlled DCD programme, may also experience some form of learning curve with transient inferior outcomes for this type of transplant procedure.

Remarkably, the time-related improvements in DCD outcomes occurred despite considerable increases in donor and recipient age, and in KDRI over time (Table 1). The apparent paradox of increasing KDRI values but improving graft survival rates suggests that, after the medical decision to accept a kidney for donation, there is a limited impact of donor characteristics on graft survival. This is illustrated by the remarkably low Nagelkerke R2 for the association between KDRI and 5-year graft survival [13]. Hence, these data illustrate that graft survival reflects an interplay of donor, procedural and recipient factors [15].

This study has some limitations. Firstly, it is a registry-based study, which is associated with inherent design limitations. Secondly, this is a country-specific study as outcomes are influenced by national guidelines and decision-making policies. However, considering the liberal attitude towards DCD kidneys in The Netherlands (reflected by an equal distribution in DBD and DCD procedures, comparable donor ages and KDRI values) it is unlikely that the results reflect a high threshold in accepting DCD grafts.

In conclusion, this registry-based study shows a major improvement in outcomes of transplanted DCD kidneys over time, with DBD and DCD outcome equivalence in the current timeframe. The time-related improvements for DCD outcomes not only show that DCD kidneys can be fully embraced, but also emphasise that careful interpretation is required for conclusions that are based on historical cohorts.

Supporting information

S1 Table. Multivariate analyses of posttransplant outcomes.

In total 16 multivariate analyses were performed. Each row represents a single multivariate analysis. The multivariate models were adjusted for variables that were statistically relevant in the univariate analysis (p-value <0.1). (-) Indicates that the variable was not included in the multivariate analysis. (*) p-value <0.05, (**) p-value <0.005. 95% CI, 95% confidence interval; DGF, delayed graft function; eGFR, estimated glomerular filtration rate; HR, hazard ratio; OR, odds ratio.

(DOCX)

Click here for additional data file. (17.3KB, docx)

Acknowledgments

We thank the Dutch Transplant Foundation (Nederlandse Transplantatie Stichting) for providing the data.

Data Availability

Because the data contain potentially identifying and sensitive patient information, they cannot be made publicly available. Access to the data can be requested through the Dutch Transplant Foundation (Nederlandse Transplantatie Stichting) (info@transplantatiestichting.nl).

Funding Statement

The authors received no specific funding for this work.

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Decision Letter 0

Frank JMF Dor

9 Jun 2020

PONE-D-20-14599

Improving outcomes for donation after circulatory death kidney transplantation: Science of the times.

PLOS ONE

Dear Dr. Lindeman,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

ACADEMIC EDITOR:

Interesting and important registry study on DCD kidney transplantation in the Netherlands, a high volume DCD KTx country. Two time periods have been compared, with a significant improvement in outcomes over time, which is indeed novel data.

Three expert reviewers have shown interest, but also recommended major revisions to the MS, and I agree with this assessment. They include concerns regarding methods, clarifications on data, the use of KDRI, the use of HMP, and discrepancies in the total numbers of DD transplants performed, along with additional queries.

These will need to be addressed meticulously, and I am looking forward to your revised MS.

Please ensure that your decision is justified on PLOS ONE’s publication criteria and not, for example, on novelty or perceived impact.

==============================

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Reviewer #1: Partly

Reviewer #2: Yes

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

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**********

5. Review Comments to the Author

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Reviewer #1: In general, this is an important and interesting topic: it has been notable that transplantation from DCD donors has been common in the Netherlands, and the early post-transplant results have been poor historically. This is a large cohort study that demonstrates how outcomes have improved.

I found it very difficult to interpret the results for a few reasons:

1. How much data was missing? The authors describe imputing missing data points, but without some sense of the scale of the problem, it is impossible to comment on how appropriately this was done.

2. What variables were considered for inclusion in the multivariate models? What were actually included? What was the size of the effects of these variables?

3. I am unclear as to how KDRI was used. This includes both donor age (as the most important factor) and DCD/DBD status. How can you use KDRI to risk-adjust and then present data for DCD and DBD? Was there a double adjustment for donor age? (I may have mis-interpreted how it was used). Is KDRI appropriate for the local population?

4. The abstract contains no results. It would be helpful to have some numbers in the abstract.

Reviewer #2: The manuscript contains a register analysis. The register is the mandatory kidney transplant registry of the Netherlands. Therefore, the data completeness is granted. The question is whether kidney transplantation after donation from brain death donor (DBD) or cardiac defined death donor (DCD) have a difference in early graft loss and in particularly whether there were changes over time. Two periods of time were compared 1998-2007 and 2008-2017 with a total of 10307 deceased donor transplants, see below. The question is relevant and clear. The observation number is sufficient. The result is that there is no longer a difference between DBD and DCD concerning early graft loss during the second time period.

The clear-cut question gets an answer that is robust due to the large number of observations and rather simple and reliable statistics employed. The message is interesting information for people involved in kidney transplantation.

In the discussion the authors speculate on possible factors causing this improvement. The cold ischemia time is one of the mentioned mechanisms. In the data table the mean values for cold ischemia time are given for both periods. To further explore whether cold ischemia is a critical factor a multivariate analysis including the available parameters such as time period, cold ischemia time, kidney donor risk index KDRI, time on dialysis etc. might help to shed more light on this question.

A major critical point is the description of the observed transplantations. In the abstract 10307 deceased donor transplants between 1990 and 2018 were included. In material and methods section (line 90) it is 11415 deceased donor transplants between 1990 and 2018. However, the real observation periods were from 1/1998 to 12/2007 3499 transplants and from 1/2008 to 12/2017 3781 transplants, see table 1. This discrepancy between the total number of transplants has to be resolved.

In the abstract the numbers should be limited to the cases evaluated for the presented analysis.

Reviewer #3: the authors present a well-written analysis of post KT outcomes in the Netherlands. They compare DBD and DCD recipients in two sequential time cohorts and not a significant improvement in outcome especially for the DCD cohort. This is very interesting and has not previously been reported. I have the following comments.

1. The most significant difference is a nearly 5 hour reduction in cold ischemia time. I do think this is potentially a more important observation which I would recommend highlighting in the abstract, as well as somewhat more strongly in the discussion.

2. I would also like to know if there is any other technical change that occurred that could contribute to this-- like the type of preservation fluid in DCD, or use of heparin in DCD, or not using DCD donors who took more than a certain amount of time to expire (this is reflected in the first warm ischemia time which is slightly short in the recent time cohort-- was there an upper cut off instituted as a national standard?)

3. It is also not clear the use of kidney pumps-- I think this is what you mean by hypothermic machine perfusion (HMP) but I also know there is a practice for some European centers to pump the donor via ECMO, so could you clarify if kidneys are routinely place on pumps after organ recovery, and if so, did this really only start in 2016 and is it applied only for high KDRI or only for DCD.

**********

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Reviewer #2: No

Reviewer #3: No

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PLoS One. 2020 Jul 29;15(7):e0236662. doi: 10.1371/journal.pone.0236662.r002

Author response to Decision Letter 0

18 Jun 2020

Academic editor

Interesting and important registry study on DCD kidney transplantation in the Netherlands, a high volume DCD KTx country. Two time periods have been compared, with a significant improvement in outcomes over time, which is indeed novel data.

Three expert reviewers have shown interest, but also recommended major revisions to the MS, and I agree with this assessment. They include concerns regarding methods, clarifications on data, the use of KDRI, the use of HMP, and discrepancies in the total numbers of DD transplants performed, along with additional queries.

These will need to be addressed meticulously, and I am looking forward to your revised MS.

Thank you for reviewing our work and your valuable comments, which have helped us to improve the manuscript. A detailed response to your and the reviewers’ comments, as well as the revised manuscript, can be found below.

Reviewer #1

In general, this is an important and interesting topic: it has been notable that transplantation from DCD donors has been common in the Netherlands, and the early post-transplant results have been poor historically. This is a large cohort study that demonstrates how outcomes have improved.

We thank the reviewer for his or her supportive comments.

I found it very difficult to interpret the results for a few reasons:

1. How much data was missing? The authors describe imputing missing data points, but without some sense of the scale of the problem, it is impossible to comment on how appropriately this was done

We agree with the reviewer that the text is unclear with respect to this point. Multiple imputation was only performed for the KDRI in order to facilitate comparison of the Dutch donor cohort with that of other countries for the 1998-2018 interval. However, donor hypertension and diabetes are only registered in The Dutch National Organ Transplant Registry from 2000 respectively 2002 onwards. As such, the proportion of missing data (26.6% for diabetes, and 10.0% for hypertension) prompted our decision to apply multiple imputation for the variables included in the KDRI. To better illustrate the scale of missing data, we have now expanded the information in the methods section (Methods page 6, line 124-129).

2. What variables were considered for inclusion in the multivariate models? What were actually included? What was the size of the effects of these variables?

These are important questions and we agree with the reviewer that this information is missing. Variables included in the multivariate analyses, were all found statically relevant in the univariate analysis (p-value <0.1) (Methods page 7, line 133-134). In total, 16 multivariate models were performed, and following the reviewer’s advice we have now included a table illustrating the variables included in the multivariate models and their respective effect sizes (S1 Table).

3. I am unclear as to how KDRI was used. This includes both donor age (as the most important factor) and DCD/DBD status. How can you use KDRI to risk-adjust and then present data for DCD and DBD? Was there a double adjustment for donor age? (I may have mis-interpreted how it was used). Is KDRI appropriate for the local population?

We apologize for being unclear. In this study, we presented the imputed-KDRI in Table 1 (Baseline characteristics) to facilitate comparison of the Dutch donor cohort with that of other countries. We have deliberately chosen to exclude the KDRI in the multivariate models in order to avoid overcorrection (as the KDRI also comprises the variables donor age and donor type which are already included separately in the univariate and multivariate analyses). This has now been clarified in the methods section (Methods page 7, line 134-136).

With regard to the last question, whether the KDRI is appropriate for the local population, is an interesting question and has been previously investigated. In fact, the KDRI as proposed by Rao et al. was externally validated in the Dutch cohort and the authors concluded that the KDRI performs equally well in the Dutch population [Peters-Sengers et al]. As such, we consider the KDRI appropriate for the local population.

Peters-Sengers H, et al. “Validation of the Prognostic Kidney Donor Risk Index Scoring System of Deceased Donors for Renal Transplantation in the Netherlands.” Transplantation vol. 102,1 (2018): 162-170.

4. The abstract contains no results. It would be helpful to have some numbers in the abstract.

We agree with the reviewer that it would be helpful to have some numbers in the abstract. With the permitted number of words in mind, we have tried to maximize the information in the abstract. (Abstract page 2, line 34, 42-45).

Reviewer #2

The manuscript contains a register analysis. The register is the mandatory kidney transplant registry of the Netherlands. Therefore, the data completeness is granted. The question is whether kidney transplantation after donation from brain death donor (DBD) or cardiac defined death donor (DCD) have a difference in early graft loss and in particularly whether there were changes over time. Two periods of time were compared 1998-2007 and 2008-2017 with a total of 10307 deceased donor transplants, see below. The question is relevant and clear. The observation number is sufficient. The result is that there is no longer a difference between DBD and DCD concerning early graft loss during the second time period. The clear-cut question gets an answer that is robust due to the large number of observations and rather simple and reliable statistics employed. The message is interesting information for people involved in kidney transplantation.

The reviewer’s supportive comments are highly appreciated.

1. In the discussion the authors speculate on possible factors causing this improvement. The cold ischemia time is one of the mentioned mechanisms. In the data table the mean values for cold ischemia time are given for both periods. To further explore whether cold ischemia is a critical factor a multivariate analysis including the available parameters such as time period, cold ischemia time, kidney donor risk index KDRI, time on dialysis etc. might help to shed more light on this question.

We agree with the reviewer that this needs further elaboration. To address this point, we have now included a table that includes all multivariate models which also includes the effect of cold ischaemia time on outcome (S1 Table). The KDRI is not presented in this table as we have deliberately chosen to exclude this variable from the multivariate analyses to avoid overcorrection. This relevant point has also been addressed in question 2 of reviewer 1, and has been clarified in the methods section (Methods page 7, line 134-136).

2. A major critical point is the description of the observed transplantations. In the abstract 10307 deceased donor transplants between 1990 and 2018 were included. In material and methods section (line 90) it is 11415 deceased donor transplants between 1990 and 2018. However, the real observation periods were from 1/1998 to 12/2007 3499 transplants and from 1/2008 to 12/2017 3781 transplants, see table 1. This discrepancy between the total number of transplants has to be resolved.

We truly apologize for being unclear. We have now clarified the numbers of included patients in the methods section (Methods page 5, line 90 and 95-101).

3. In the abstract the numbers should be limited to the cases evaluated for the presented analysis.

We agree with the reviewer that the number of cases evaluated in this study should be presented in the abstract. We have therefore added this information in the abstract (Abstract page 2, line 33-35).

Reviewer #3

The authors present a well-written analysis of post KT outcomes in the Netherlands. They compare DBD and DCD recipients in two sequential time cohorts and not a significant improvement in outcome especially for the DCD cohort. This is very interesting and has not previously been reported.

We thank the reviewer for these valuable comments.

I have the following comments.

1. The most significant difference is a nearly 5 hour reduction in cold ischemia time. I do think this is potentially a more important observation which I would recommend highlighting in the abstract, as well as somewhat more strongly in the discussion.

We fully agree with the author that this is an important observation and can be stated more clearly in the abstract and the discussion. In order to emphasize this, we have now added this information in the abstract (Abstract page 2, line 44-45) and expanded the discussion section (Discussion page 14, line 233-236). To be more specific, another possible explanation for improved outcomes over time is that - as cold ischaemic periods of more than 24 hours in DCD kidneys are associated with worse graft survival [Schaapherder et al.]- the proportion of procedures with excessive cold ischaemia times (>24 hours) decreased over time with the increasing awareness of avoiding long cold ischaemic times in the Netherlands. In order to emphasize this, we have added this information in the baseline characteristics table, results section and discussion section (Table 1; Results page 8, line 170-172; Discussion page 14, line 233-236).

Schaapherder, A et al. “Equivalent Long-term Transplantation Outcomes for Kidneys Donated After Brain Death and Cardiac Death: Conclusions From a Nationwide Evaluation.” EClinicalMedicine vol. 4-5 25-31. 9 Oct. 2018).

2. I would also like to know if there is any other technical change that occurred that could contribute to this-- like the type of preservation fluid in DCD, or use of heparin in DCD, or not using DCD donors who took more than a certain amount of time to expire (this is reflected in the first warm ischemia time which is slightly short in the recent time cohort-- was there an upper cut off instituted as a national standard?).

The reviewer brings up an interesting point. In the Eurotransplant organ donation protocol, the DBD donor receives heparin intravenously before the start of the cold perfusion. In the case of the DCD donor, heparin is added to the preservation fluid. This aspect of the protocol has not changed over time.

The reviewer is right that there is a national upper cut-off in the context of kidney donation. To be more specific, in The Netherlands, the time from withdrawal of life sustaining therapy to circulatory death (also known as the agonal period) has been limited to 2 hours for kidney donation, and this maximum time period did not change over time. Although it would be interesting to compare the mean values over time, this is not possible as the agonal time is not registered in The Dutch National Organ Transplant Registry. Furthermore it should be noted that the agonal period is not reflected in the first warm ischaemic period, as the first warm ischaemic period begins after the agonal phase and no touch period (Methods page 6, line 112-114).

Fortunately, the type of preservation fluid is included in The Dutch National Organ Transplant Registry and indeed there is a change in type of preservation fluid in DCD donor kidneys over time. This aspect has now been included in Table 1, the results and discussion (Table 1; Results page 9, line 173-176; and Discussion page (results page 8-9, line 162-165) and discussion (discussion, page 13, line 225). We have deliberately chosen not to include the type of preservation in the univariate and multivariate analyses, as this variable is collinear (the selection of preservation solution depends on donor type) and would therefore impact the validity of the model (Methods page 7, line 136-139)

3. It is also not clear the use of kidney pumps-- I think this is what you mean by hypothermic machine perfusion (HMP) but I also know there is a practice for some European centers to pump the donor via ECMO, so could you clarify if kidneys are routinely place on pumps after organ recovery, and if so, did this really only start in 2016 and is it applied only for high KDRI or only for DCD.

It is correct that hypothermic machine perfusion (HMP) preserves kidney grafts after retrieval (i.e. ex-vivo) by administration of cold preservation solution with the use of a pump. In The Netherlands, the use of HMP is implemented from the year 2016 onwards for all donated donor kidneys regardless of the donor type or donor quality (KDRI). In previous years, HMP was only incidentally performed in the research setting.

Preservation of organs with normothermic regional perfusion (NRP), which is based on the use of ECMO devices, is performed in the donor (in-vivo) and applied for the first time in October 2018 in the Netherlands in research setting. As such the number of transplantation performed after NRP is neglectable in this study population (1990-2018). Nevertheless, we agree that it would definitely be interesting to evaluate the effect of NRP on outcomes in the future.

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Decision Letter 1

Frank JMF Dor

13 Jul 2020

Improving outcomes for donation after circulatory death kidney transplantation: Science of the times.

PONE-D-20-14599R1

Dear Dr. Lindeman,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Frank JMF Dor, M.D., Ph.D., FEBS, FRCS

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for addressing my comments. The manuscript is much clearer and more explicit about methodology.

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Dirk L. Stippel

Acceptance letter

Frank JMF Dor

15 Jul 2020

PONE-D-20-14599R1

Improving outcomes for donation after circulatory death kidney transplantation: Science of the times.

Dear Dr. Lindeman:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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on behalf of

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Multivariate analyses of posttransplant outcomes.

    In total 16 multivariate analyses were performed. Each row represents a single multivariate analysis. The multivariate models were adjusted for variables that were statistically relevant in the univariate analysis (p-value <0.1). (-) Indicates that the variable was not included in the multivariate analysis. (*) p-value <0.05, (**) p-value <0.005. 95% CI, 95% confidence interval; DGF, delayed graft function; eGFR, estimated glomerular filtration rate; HR, hazard ratio; OR, odds ratio.

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    Submitted filename: Response to Reviewers.docx

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    Data Availability Statement

    Because the data contain potentially identifying and sensitive patient information, they cannot be made publicly available. Access to the data can be requested through the Dutch Transplant Foundation (Nederlandse Transplantatie Stichting) (info@transplantatiestichting.nl).

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