Document Type : Systematic Review/ Meta-analysis
Authors
1 Department of Urology, Zahedan University of Medical Sciences, Zahedan-Iran
2 Research Center in Emergency and Disaster Health, University of Social Welfare and Rehabilitation Sciences (USWR), Tehran, Iran
Abstract
Highlights
Keywords
Main Subjects
Introduction
Among renal replacement therapies for End-Stage Renal Disease (ESRD) patients, kidney transplantation is a selective treatment due to improved quality of life, increased survival, and reduced financial costs (1, 2). The most important risk factors of kidney transplant rejection reported in various studies are: Early Graft Function (3, 4), Delayed Graft Function (5, 6), mismatch HLA (7),(8), blood group compatibility (9), cold ischemia time (10), acute rejection (6, 11), age (12, 13), Donor-recipient sex mismatch (14, 15), BMI of donor and recipient (16, 17) and immunosuppressive regime (18-20). A remarkable increase in ESRD incidence in developed and developing countries and long waiting lists for transplantation led to the revision and development of guidelines to use the deceased donor for transplantation (21, 22). Therefore, the donor type is an essential factor in the graft- and patient survival due to the different mechanisms of tissue life in these two types of donors. Nonetheless, the results of studies on the role of the transplant from the deceased are inconsistent (23-25). In several studies, compared to living donors, it has been shown that kidney transplanted from deceased donors is less likely to survive due to immunological and hormonal changes (26, 27). In some other studies, researchers reported desirable results from the allograft function (28-30). Numerous single-center or multi-center studies conducted in different parts of the world have produced a variety of different survival rates, and the role of a kidney transplant from the deceased is unclear in rejecting or surviving the graft. The current meta-analysis was performed to determine the short-term and long-term survival rate of kidney transplantation from the deceased donor and determine the factors influencing it, using all observational and registry-based studies.
Method
In this systematic review and meta-analysis, all prospective, retrospective, and registry-based studies worldwide that examined the survival rate of kidney transplantation from deceased donors were included without any restriction.
Inclusion Criteria for Studies
All stages of this study were performed in accordance with PRISMA guidelines. The criteria for the inclusion of the study were: study design (prospective, retrospective, or registry-based studies), report of the patient, graft survival rate, or report of Hazard Ratio (HR) for determining the effect of DD-related factors on graft rejection. Articles that met at least two of the inclusion criteria envisaged were included in this study. The outcome measures included the 1-, 5- or 10-year patient or kidney transplantation survival rates from DD and risk factors of rejection related to characteristics of DD such as age and sex, weight, type of DD, donation after brain death (DBD), donation after circulatory death (DCD), expanded-criteria donors (ECDs) or standard-criteria donors (SCDs) and history of chronic disease.
Figure 1. Flow diagram of information through the different phases of the systematic review.
In this study, using the search strategy shown in Table 1. PubMed and Scopus databases were searched to obtain relevant studies in March 2019. In order to obtain more articles and to ensure proper search of databases, references of selected articles were reviewed. Search strategy in Scopus and PubMed to 4/2019 is presented in supplementary file 1.
Data Extraction and Statistical Methods
To ensure the correct selection of articles in terms of their pertinence to the research topic and in accordance with the inclusion criteria, two researchers (B.N. and M.S.) reviewed the articles independently. The names of the authors, the names of the journals, and their results were not hidden from the reviewers. The Kappa percentage for the interviewer agreement was 85%. The variables taken into consideration in this study were the name of the first author, year of publication, country of the study, the mean age of the deceased donor, gender, study design, weight of DD, type of DD (DBD or cardiac DCD), ECD or SCD and history of chronic disease. For quality assessment in terms of methodology and reporting, the study used the STROBE checklist for cohort studies. To determine the heterogeneity of the studies, Cochran's Q-test of heterogeneity was used at a 5% confidence level. Because survival rates vary between zero and one and have no negative values, all studies are located to the right of the vertical line, and publication bias cannot be determined. The measures in this study could be summarized as the survival rate and the HR of graft rejection and patient death in the presence of DD-related risk factors; these were calculated along with 95% confidence intervals (CIs) for a 2-tailed distribution. Data analysis was done using the Stata (version 11, Stata Corp, College Station, Texas) and applying the random-effects model.
Results
We identified 16968 articles, after removing the duplicate papers, the titles and abstracts of 10786 articles were reviewed. In the next step, with respect to the exclusion criteria, 9986 articles were excluded and 923 full-text articles were studied. According to our objectives, and quality assessment of selected articles with PRISMA guidelines, 725 papers were withdrawn, and finally, 75 articles were included in the final analysis. Figure 1 and Table 1 represent author, country, design, sex, male donor (%), donor mean age (year) ± SE, type of deceased donor (DBD or DCD), criteria (ECD or SCD), donor BMI, sample size, patient survival rate, and graft survival rate.
Table 1. Characteristics of the studies included in this meta-analysis
Author (year) |
Country |
Design |
Sex |
Donor Male (%) |
Donor Mean Age ± SE(years) |
Type Deceased Donor |
Criteria (%) |
Donor BMI |
SS |
Survival % |
|||||
Patient |
Graft |
||||||||||||||
First Year |
Third Year |
Fifth Year |
First Year |
Third Year |
Fifth Year |
||||||||||
Kute 2014(31) |
India |
R.cohort |
Both |
61.5 |
45.9 |
DBD |
- |
- |
294 |
81.6 |
- |
- |
92.5 |
- |
- |
Augliene 2017(32) |
Lithuania |
R.cohort |
Both |
61.1 |
44.1±15.8 |
DBD & DCD |
ECD & SCD |
- |
186 |
- |
- |
- |
93.5 |
90.0 |
- |
Cardinal 2005(33) |
Canada |
R.cohort |
Both |
- |
42.0 |
DBD & DCD |
- |
- |
256 |
93.0 |
- |
74.0 |
88.0 |
- |
67.0 |
Centellas-Perez 2019(34) |
Spain |
R.cohort |
Both |
62.8 |
60.5±13.6 |
DBD: 92.9 DCD: 7.1 |
ECD & SCD |
- |
183 |
- |
- |
- |
86.9 |
- |
- |
Cho 2014(35) |
Korea |
R.cohort |
Both |
- |
42.0±14.0 |
DBD & DCD |
- |
- |
136 |
- |
- |
- |
99.2 |
97.3 |
95.5 |
Terasaki 1998)(36) |
United States |
R.cohort |
Both |
- |
- |
DBD & DCD |
- |
- |
DBD: 22680 DCD: 276 |
- |
- |
- |
DBD: 85.7 DCD: 82.8 |
- |
- |
Czerwinski 2016(37) |
Poland |
Registry |
Both |
- |
- |
DBD & DCD |
|
|
15009 |
95.0 |
91.0 |
87.0 |
88.0 |
82.0 |
74.0 |
Kyllonen 2000(38) |
Finland |
R.cohort |
Both |
62.8 |
38.8 |
DBD |
- |
- |
1407 |
95.9 |
91.9 |
86.0 |
91.3 |
84.1 |
76.8 |
Laging 2012(39) |
Netherlands |
R.cohort |
Both |
55.0 |
45.7±16.1 |
DBD & DCD |
- |
- |
513 |
- |
- |
- |
- |
- |
- |
Lionaki 2014(40) |
Greece |
R.cohort |
Both |
64.4 |
52.9±15.7 |
DBD & DCD |
ECD: 42.5 SCD: 57.5 |
|
334 |
- |
- |
ECD: 93.7 SCD: 96.4 |
- |
- |
- |
McDonald 2002(41) |
Australia |
Registry |
Both |
63.0 |
43.8 |
DBD & DCD |
- |
- |
2362 |
- |
- |
- |
91.0 |
85.0 |
78.0 |
Moers 2009(40) |
Netherlands |
Registry |
Both |
45.0 |
67 |
DBD & DCD |
- |
- |
1011 |
- |
- |
- |
- |
- |
- |
Mok 2012(42) |
Singapore |
R.cohort |
Both |
63.3 |
44.7±11.7 |
DBD & DCD |
ECD: 72.6 SCD: 14.2 |
- |
332 |
96.7 |
94.8 |
91.5 |
89.8 |
85.9 |
80.8 |
Mukherjee 2018(43) |
India |
R.cohort |
Both |
65.2 |
43.9±17.0 |
DBD & DCD |
- |
- |
92 |
98.9 |
83.3 |
70.8 |
98.9 |
91.6 |
77.1 |
Nagaraja 2012(44) |
United Kingdom |
R.cohort |
Both |
- |
DBD: 52 DCD: 46 |
DBD & DCD |
- |
- |
DBD: 52 DCD: 46 |
DBD: 95.0 DCD: 96.0 |
- |
- |
DBD: 88.0 DCD: 96.0 |
- |
- |
Newstead 1992(44) |
England |
R.cohort |
Both |
- |
- |
DBD & DCD |
- |
- |
397 |
- |
- |
- |
87.0 |
79.0 |
- |
Noppakun 2015(45) |
Thailand |
Registry |
Both |
73.1 |
34.7±13.3 |
DBD |
- |
- |
1745 |
96.3 |
- |
93.1 |
95.6 |
- |
88.4 |
OKelly 2001(46) |
Ireland |
R.cohort |
Both |
58.8 |
34.7 |
DBD & DCD |
- |
- |
436 |
95.0 |
90.7 |
85.7 |
85.2 |
77.6 |
67.4 |
Pessione 2003(47) |
France |
R.cohort |
Both |
65.7 |
41±13.7 |
DBD & DCD |
- |
- |
7209 |
- |
- |
- |
91.1 |
85.6 |
- |
Salmela 2001(48) |
Finland |
R.cohort |
Both |
- |
- |
DBD & DCD |
- |
- |
1008 |
96.3 |
- |
- |
92.9 |
- |
- |
Saunders 1984(49) |
USA |
R.cohort |
both |
68.2 |
32.1±15.4 |
DBD & DCD |
- |
- |
104 |
- |
- |
- |
91.5 |
85.6 |
- |
Savoye 2007(50) |
France |
Registry |
both |
58.7 |
54.8±14.9 |
DBD & DCD |
ECD |
- |
2845 |
96.2 |
88.9 |
82.8 |
88.1 |
80.8 |
73.2 |
Sener 2009(51) |
England |
R.cohort |
both |
- |
55 |
DBD & DCD |
- |
- |
468 |
- |
- |
- |
86.5 |
77.4 |
65.2 |
Stratta 2016(52) |
USA |
R.cohort |
both |
ECD: 58.5 SCD: 61.6 |
ECD: 61.1±6.4 SCD: 37.0±13.8 |
DBD & DCD |
ECD & SCD |
ECD: 28.1±6.1 SCD: 26.7±7.8 |
ECD: 354 SCD: 589 |
ECD: 93.7 SCD: 96.3 |
- |
- |
ECD: 83.3 SCD: 91.7 |
- |
- |
Kandus 2016(53) |
Slovenia |
R.cohort |
both |
- |
- |
DBD & DCD |
ECD |
- |
793 |
98.1 |
- |
93.8 |
94.3 |
- |
87.5 |
Jacobi 2014(54) |
Germany |
R.cohort |
both |
- |
- |
DBD & DCD |
ECD & SCD |
- |
ECD: 174 SCD: 208 |
96.8 |
- |
- |
90.4 |
- |
- |
Heylen 2017(55) |
Belgium |
Registry |
both |
DBD: 52.0 DCD: 60.0 |
DBD: 54 DCD: 51 |
DBD & DCD |
- |
- |
DBD: 17006 DCD: 1059 |
- |
- |
- |
- |
- |
DBD: 74.5 DCD: 76.4 |
Heilman 2015(46, 56) |
USA |
R.cohort |
both |
ECD: 74.0 SCD: 78.0 |
ECD: 56.6±9.1 SCD: 32.3±13.2 |
DBD |
ECD & SCD |
- |
ECD: 23 SCD: 139 |
- |
- |
- |
ECD: 95.6 SCD: 97.1 |
- |
- |
Hamed 2015(57) |
UK |
Registry |
both |
- |
- |
DBD & DCD |
ECD & SCD |
- |
DBD: 366 DCD: 435 |
- |
- |
- |
DBD: 89.8 DCD: 86.4 |
- |
- |
Gondos 2013(58) |
Germany (Europe, white American, African American, Hispanic American) |
Registry |
both |
- |
- |
DBD & DCD |
- |
- |
Europe: 23530 white American: 15772 African American: 11148 Hispanic American: 5338 |
- |
- |
- |
Europe: 90.6 white American: 91.1 African American: 88.7 Hispanic American: 91.5 |
- |
Europe: 56.6 white American: 45.7 African American: 33.7:48.2 Hispanic American |
Fujita 2014(59) |
Japan |
R.cohort |
both |
63.4 |
45.7±16.7 |
DBD & DCD |
- |
- |
350 |
- |
- |
- |
97.0 |
- |
85.0 |
Ferreira 2017(58) |
Portugal |
R.cohort |
both |
67.3 |
46.3±16.5 |
DBD & DCD |
ECD & SCD |
- |
ECD: 150 SCD: 467 |
95.8 |
91.3 |
88.2 |
93.9 |
90.5 |
85.5 |
Ergün 2019(60) |
Netherlands |
R.cohort |
both |
- |
54.0 |
DBD |
ECD & SCD |
24.5 |
ECD: 696 SCD: 1173 |
- |
- |
- |
90.9 |
- |
82.0 |
Emiroglu 2005(12) |
Turkey |
R.cohort |
both |
- |
45.0 |
DBD & DCD |
- |
- |
58 |
94.8 |
- |
- |
92.6 |
- |
- |
Aceto 2019(61) |
Italy |
R.cohort |
both |
62.0 |
46.7±17.0 |
DBD & DCD |
- |
- |
87 |
95.4 |
- |
- |
96.5 |
- |
- |
ALI 2015(62) |
Saudi Arabia |
R.cohort |
both |
87.7 |
35.7±12.3 |
DBD & DCD |
- |
- |
284 |
98.8 |
- |
94.8 |
94.1 |
- |
85.8 |
Butala 2013(63) |
United States |
Registry |
both |
59 |
- |
DBD & DCD |
- |
- |
73714 |
94.7 |
- |
80.0 |
94.0 |
- |
78.0 |
Chen 2013(64) |
China |
R.cohort |
both |
76.0 |
26.7±11.9 |
DCD |
- |
20.5±3.1 |
71 |
100 |
- |
- |
95.7 |
92.4 |
- |
Farrugia 2013(65) |
England |
R.cohort |
both |
- |
- |
DBD & DCD |
- |
- |
19688 |
96.2 |
- |
- |
- |
- |
- |
Harada 2009(66) |
Brazil |
R.cohort |
both |
57.5 |
42.2 |
DBD & DCD |
ECD & SCD |
- |
ECD: 135 SCD: 641 |
ECD: 71.1 SCD: 80.2 |
- |
- |
ECD: 82.2 SCD: 88.2 |
- |
- |
Hassanian 2009(5) |
Canada |
R.cohort |
both |
- |
- |
DBD & DCD |
ECD & SCD |
- |
335 |
86.3 |
- |
7.4 |
- |
- |
- |
Hwang 2014(67) |
Korea |
R.cohort |
both |
ECD: 67.7 SCD: 65.2 |
ECD: 56.8±6.3 SCD: 37.5±13.5 |
DBD & DCD |
ECD & SCD |
ECD: 23.1±3.1 SCD: 22.7±3.4 |
ECD: 31 SCD: 164 |
ECD: 96.8 SCD: 98.1 |
|
ECD: 89.3 SCD: 89.0 |
ECD: 93.5 SCD: 93.8 |
- |
ECD: 76.3 SCD: 89.2 |
Kang 2018(68) |
Korea |
R.cohort |
both |
45.0 |
37.2 |
DBD & DCD |
- |
- |
127 |
98.4 |
96.8 |
95.9 |
96.8 |
91.2 |
84.7 |
Kim 2014(69) |
Korea |
R.cohort |
both |
ECD: 12.6 SCD: 24.1 |
ECD: 62.5±10.5 SCD: 42.7±11.7 |
DBD & DCD |
ECD & SCD |
- |
ECD: 14 SCD: 31 |
ECD: 92.9 SCD: 87.1 |
- |
- |
ECD: 92.8 SCD: 87.1 |
- |
- |
Lee 2010(27) |
RKorea.cohort |
both |
57.4 |
31.3 |
DBD & DCD |
- |
- |
216 |
- |
- |
- |
- |
- |
- |
|
Lewandowska 2016(70) |
Poland |
R.cohort |
both |
- |
42.1±14.5 |
DBD |
- |
- |
8206 |
- |
- |
87.5 |
- |
- |
74.4 |
Papalia 2010(71) |
Italy |
R.cohort |
both |
- |
- |
- |
- |
24.4±2.6 |
194 |
- |
- |
- |
- |
- |
- |
Papachristou 2014(72) |
Greece |
R.cohort |
both |
ECD: 54.7 SCD: 63.6 |
ECD: 56.0 SCD: 27.0 |
DBD & DCD |
ECD & SCD |
ECD: 27.3±4.3 SCD: 25.1±6.4 |
ECD: 53 SCD: 162 |
ECD: 93.6 SCD: 96.8 |
ECD: 93.6, SCD: 93.9 |
ECD: 85.3 SCD: 92.2 |
ECD: 88.4 SCD: 90.7 |
ECD: 81.8 SCD: 88.5 |
- |
Noseworthy 2004(73) |
Canada |
R.cohort |
both |
- |
- |
DBD & DCD |
- |
- |
- |
- |
- |
- |
- |
- |
- |
Nakamura 2018(74) |
Japan |
R.cohort |
both |
- |
DBD: 37.3±19.8 CDC: 49.8±13.0 |
DBD & DCD |
ECD & SCD |
- |
DBD: 9 DCD: 19 |
- |
- |
- |
DBD: 100 DCD: 100 |
DBD: 100 DCD: 94.7 |
DBD: 100 DCD: 82.1 |
Molina 2015(75, 76) |
Spain |
R.cohort |
both |
81.0 |
45.0±16.0 |
DBD & DCD |
- |
- |
59 |
97.0 |
- |
- |
92.0 |
- |
|
Malek-Hosseini 2006(75) |
Iran |
R.cohort |
both |
67.3 |
33.6±12.5 |
DBD & DCD |
- |
- |
97 |
93.0 |
90.5 |
- |
- |
- |
- |
Tasaki 2014(77) |
Japan |
R.cohort |
both |
- |
45.7 |
DBD & DCD |
- |
- |
56 |
98.2 |
- |
96.2 |
89.0 |
- |
80.3 |
Sagban 2014(78) |
Germany |
R.cohort |
both |
- |
52.0±16.2 |
DBD & DCD |
- |
- |
1134 |
- |
- |
- |
- |
- |
85.3 |
Sirivongs 2004(79) |
Thailand |
R.cohort |
both |
76.4 |
30.18±11.4 |
DBD & DCD |
- |
- |
89 |
- |
- |
- |
90.6 |
- |
83.0 |
Tomita 2017(80) |
Japan |
R.cohort |
both |
ECD: 62.0, SCD: 55.3 |
ECD: 60.4±8.2 SCD: 38.8±15.0 |
DBD & DCD |
ECD & SCD |
- |
ECD: 50 SCD: 47 |
100.0 |
- |
92.9 |
100.0 |
- |
93.5 |
WOO 1998(81) |
United Kingdom |
R.cohort |
both |
- |
- |
DBD & DCD |
- |
- |
589 |
95.0 |
- |
82.0 |
84.0 |
- |
68.0 |
Zhang 2018(26) |
China |
R.cohort |
both |
- |
- |
DBD & DCD |
- |
- |
524 |
- |
- |
- |
94.9 |
91.3 |
91.3 |
Zhu 2018(82) |
China |
Registry |
both |
DBD: 59.5, DCD: 72.1 |
DBD: 40.3±16.7 DCD: 42.2±15.8 |
DBD & DCD |
ECD & SCD |
- |
DBD: 415 DCD: 133 |
DBD: 98.9 DCD: 98.4 |
- |
- |
DBD: 98.4 DCD: 92.4 |
- |
- |
Ko 2018(83) |
Korea |
R.cohort |
both |
ECD: 68.9, SCD: 67.0 |
ECD: 58.2±5.4 SCD: 41.7±10.9 |
DBD & DCD |
ECD & SCD |
- |
ECD: 90 SCD: 200 |
|
|
|
|
|
|
Nakamura 2017(84) |
Japan |
Registry |
both |
- |
49.6±15.0 |
DCD |
- |
- |
589 |
95.5 |
92.9 |
90.9 |
85.6 |
80.4 |
75.1 |
Boer 2002(85) |
Belgium |
R.cohort |
both |
- |
- |
DBD & DCD |
- |
- |
16 |
93.0 |
- |
- |
44.0 |
- |
- |
Davidson 2019(86) |
South Africa |
R.cohort |
both |
DBD: 86.0, DCD: 67.0 |
DBD: 31.0 DCD: 34.0 |
DBD & DCD |
- |
- |
DBD: 123 DCD: 7 |
89.1 |
- |
84.5 |
88.8 |
- |
78.6 |
Gallinat 2016(87) |
Germany |
Registry |
both |
73.0 |
51.0 |
DBD & DCD |
- |
- |
78 |
96.0 |
93.0 |
89.0 |
92.0 |
89.0 |
89.0 |
Lai 2014(88) |
China |
R.cohort |
both |
- |
- |
DBD & DCD |
- |
- |
166 |
84.8 |
82.1 |
77.1 |
93.6 |
89.4 |
83.6 |
McLarena 1999(89) |
UK |
R.cohort |
both |
62.0 |
35 |
DBD & DCD |
- |
- |
681 |
- |
- |
- |
84.7 |
77.5 |
70.0 |
Nemati 2014(90) |
Iran |
R.cohort |
both |
- |
- |
DBD & DCD |
- |
- |
103 |
95.1 |
95.4 |
- |
97.0 |
67.4 |
- |
Rezapour 2017(91) |
Iran |
R.cohort |
both |
67.0 |
- |
DBD & DCD |
- |
- |
120 |
- |
- |
- |
90.8 |
- |
- |
Savdie 1982(92) |
Australia |
R.cohort |
both |
- |
- |
DBD & DCD |
- |
- |
404 |
79.0 |
- |
60.9 |
63.0 |
|
45.9 |
Seo 2015(93) |
Korea |
R.cohort |
both |
65.6 |
40.9±14.5 |
DBD & DCD |
- |
22.7±1.3 |
191 |
98.1 |
96.3 |
96.3 |
97.3 |
92.2 |
90.6 |
Dalinkevciene 2010(94) |
Lithuania |
R.cohort |
both |
- |
- |
DBD & DCD |
- |
- |
159 |
97.0 |
9.0 |
94.0 |
85.0 |
82.0 |
71.0 |
Molmenti 2016(95) |
Greece |
Registry |
both |
60.6 |
50.1±13.0 |
DBD & DCD |
- |
- |
61167 |
- |
- |
- |
- |
- |
- |
Simforoosh 2011(96) |
Iran |
R.cohort |
both |
60.8 |
28.9 |
DBD & DCD |
- |
- |
138 |
96.0 |
- |
- |
93.0 |
- |
- |
Yoshida 2002(97) |
Japan |
R.cohort |
both |
- |
40.2 |
DCD |
|
|
107 |
85.7 |
- |
77.0 |
- |
- |
- |
Vergoulas 2008(98) |
Greece |
R.cohort |
both |
- |
51.5±16.9 |
DBD & DCD |
- |
- |
29 |
100.0 |
- |
100.0 |
100.0 |
- |
93.7 |
DBD: donor after brain death, DCD: donor after cardiac death, ECD: expanded-criteria donor, SCD: Standard-criteria donor, SS: sample size, R.cohort: a retrospective cohort
Description of Included Articles
In this meta-analysis, the data from 249369 patients from 29 countries were analyzed. The largest sample size was from Gondos et al., (58).in Germany in 2013. They used the registry data to report the graft survival of 55778 patients of different ethnicities, such as European, African, African- American, and Hispanic. The smallest sample size belonged to Nakamura et al., 's (84) study in Japan, which examined 16 kidney transplants' survival from cadaver donors. Thirteen studies were registry-based, (37, 41, 50, 55, 57, 58, 63, 74, 82, 87, 95, 99, 100) and other studies had retrospective designs. Table 2 shows the most important causes of death in deceased donors were reported by twenty-six articles. Trauma and cerebrovascular were the most common causes of death.
Graft and Patient Survival Rate
The 1-year graft survival rate was 90% (95% CI: 89% - 92%). Also, 2-, 3-, 5- and 10-year graft survival rate were ranged from 80% (95% CI: 90% -70%) to 52% (95% CI: 48%-60%), respectively (Figure 2). The 1-year patient survival was 95% (95% CI: 94% -96%) and 3-, 5- and 10-years patient survival rate were ranged from 83% (95% CI :91% -74%) to 73% (95% CI: 57% -89%), respectively (Figure 3). Regardless of Savdia et al., (103) 10-year patient survival increases to 84%, but there were no significant changes in short-term patient and graft survival. The 1-, 5- and 15-year graft survival rate in transplant recipients from DBD donor were 92% (95%CI: 88%- 96%), 79% (95%CI: 76%-83%) and 65% (95%CI: 56%-73%) respectively (Figure 4). The 1- and 5-year patient survival rate of transplant recipients from DBD were 94% (95% CI: 97% -91%) and 90% (95% CI: 85% -95%) respectively (Figure 5). The 1-, 3- and 5-year graft survival rate in ECD recipients were 88% (95% CI: 85% -91%), 78% (95% CI: 70%-87%) and 68% (95% CI: 61), respectively, (Figure 6-A) and 1- and the 3-year graft survival rate were 93% (95% CI: 91% -94%) and 80% (95% CI: 78% -83%) respectively, according to SCDs (Figure 6-B). Also 1-year patient survival rate in SCD kidneys was 96% (95% CI: 94% 97%) and 3- and 5- years survival rate were 86% (95% CI: 79%-92%) and 87% (95% CI: 79% -95), respectively. (Figure 7, A). The 1-and 5-year patient survival in ECDs were 96% (95% CI: 93% -98%) and 92% (95% CI: 86% -98%), respectively (Figure 7, B).
Hazard Ratio
Given that most of the studies have found the Hazard Ratio (HR) of graft rejection and patient death attributable to the recipient factors, few studies have been performed to determine the HR of risk factors associated with deceased donors. Therefore, only three factors including the age of donors, ECD kidney, and male sex were included for analysis. HR age of deceased donors was 1.01(95% CI: 0.99 to 1.04) for graft rejection and patient death. ECD kidney was a risk factor for graft rejection (HR:1.14, 95% CI: 1.00, 1.27). Male sex was a significant protective factor in patient survival. According to the results shown in graph 13, the risk of death in men was HR: 0.86 (95% CI: 75-97). However, the male sex has no effect on the survival of the kidney transplant. (HR=0.95, 95% CI:0.83, 1.06).
Figure 2. 1-,2-,3-,5- and 10-year graft survival from a deceased donor
Table 2. Cause of donor death of the studies included in this meta-analysis
Author (Year) |
Intracranial Bleeding |
Trauma |
Cerebrovascular |
Judicial Death |
Traffic Accident |
Anoxia |
Suicide |
Brain Hemorrhage |
Ischemic Brain Injury |
Cardiovascular Disease |
Respiratory Disease |
Brain Tumor |
Other |
Ali 2015(62) |
- |
15.7 |
32.6 |
- |
34.1 |
- |
- |
- |
- |
- |
- |
- |
17.6 |
Augliene 2017(32) |
- |
35.6 |
63.3 |
- |
- |
- |
- |
- |
- |
- |
- |
- |
1.1 |
Boer 2002(85) |
- |
55.3 |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
43.0 |
Butala 2013(63) |
- |
40.0 |
41.0 |
- |
- |
15.0 |
- |
- |
- |
- |
- |
- |
3.0 |
Centellas-Perez 2019(34) |
- |
16.4 |
65.6 |
- |
- |
13.1 |
- |
- |
- |
- |
- |
1.1 |
3.8 |
Chen 2013(64) |
- |
52.0 |
36.0 |
- |
- |
12.0 |
- |
- |
- |
- |
- |
- |
- |
Ferreira 2017(101) |
- |
15.5 |
31.4 |
- |
- |
- |
- |
- |
- |
- |
- |
- |
53.1 |
Fujita 2014(59) |
- |
36.0 |
55.4 |
- |
- |
- |
6.5 |
- |
- |
- |
- |
- |
2.1 |
Heylen 2017(55) |
- |
23.1 |
68.4 |
- |
- |
5.9 |
- |
- |
- |
- |
- |
- |
2.4 |
Hwang 2014(67) |
|
30.7 |
56.9 |
- |
- |
- |
7.1 |
- |
- |
- |
- |
- |
4.6 |
Kim 2014(96) |
- |
- |
53.3 |
- |
- |
- |
- |
- |
- |
- |
- |
- |
46.7 |
Ko 2018(83) |
- |
26.3 |
57.5 |
- |
- |
- |
3.8 |
- |
- |
- |
- |
- |
12.5 |
Kute 2014(31) |
59.4 |
21.6 |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
19.0 |
Kyllonen 2000(38) |
60.0 |
31.6 |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
8.4 |
Lionaki 2014(40) |
- |
- |
56.7 |
- |
- |
- |
- |
- |
- |
- |
- |
- |
43.3 |
Moers 2009(99) |
- |
14.0 |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
86.0 |
Mok 2012(42) |
- |
16.3 |
67.8 |
13.3 |
|
- |
- |
- |
- |
|
|
|
2.7 |
Molina 2015(76) |
- |
- |
39.0 |
- |
- |
- |
- |
- |
- |
- |
- |
- |
61.0 |
Nakamura 2017(84) |
- |
16.4 |
54.3 |
- |
- |
5.9 |
- |
- |
- |
1.9 |
2.6 |
1.6 |
17.1 |
Noppakun 2015(100) |
- |
- |
11.2 |
- |
49.7 |
- |
- |
- |
- |
- |
- |
- |
13.9 |
Papachristou 2014(72) |
- |
37.2 |
- |
- |
- |
- |
- |
48.4 |
7.4 |
0.9 |
- |
- |
6.1 |
Pessione 2003(47) |
- |
- |
44.5 |
- |
- |
- |
- |
- |
- |
- |
- |
- |
55.5 |
Savoye 2007(50) |
- |
- |
62.5 |
- |
- |
- |
- |
- |
- |
- |
- |
- |
37.5 |
Tasaki 2014(77) |
- |
- |
48.7 |
- |
- |
- |
- |
- |
- |
- |
- |
- |
51.3 |
Tomita 2017(80) |
- |
- |
55.6 |
- |
- |
- |
- |
- |
- |
|
|
|
44.4 |
Seo 2015(93) |
- |
31.4 |
56.0 |
- |
- |
- |
7.3 |
- |
- |
- |
- |
- |
4.7 |
Figure 3: 1- year graft and patient survival year in DCD recipients
Figure 4: 1, 5, and 15-year graft survival in DBD recipients
Figure 5: 1 and 5-year patient survival in DBD recipients
Figure 6: A, 1, 3, and 5-year graft survival in ECD recipients. B, 1 and 3-year graft survival in SCD recipients
Figure 7: A, 1, and 3 -year patient survival in SCD recipients. Figure 7: B, 1 and 5 -year patient survival in ECD recipient
Discussion
This systematic review and meta-analysis for the first time used all registry-based and retrospective studies estimated the graft survival and patient survival, who have received kidneys from a deceased donor. Currently, the waiting list of patients requiring kidney transplantation is higher than transplant donors. Therefore; on the one hand, the mechanism of kidney donation from deceased donors has been developed in different countries, on the other hand, concerns about the benefits of kidney transplantation from the deceased increased. Our findings showed that one-year-old kidney transplant survival and patient survivals were 90% and 95%, respectively. According to Figure 1, in Boer et al., (85) in 2002, the lowest 1-year graft survival rate (56%) and Savdie et al., (92) in 1982 lowest patient survival rate (79%) were reported, and in Mukherjee et al., (43) in 2018, the highest one-year-old graft survival was reported from 92 deceased donor transplantation in India. Also, in Kang et al., (68) and Taski et al., (74) in 2018 and 2014, the highest one-year-old patient survival was reported. So far, a no-meta-analysis study has been performed to quantify kidney transplant and patient survival from deceased donors, and thus the comparison of our results is limited. In Noordzij et al., (102) report, between 2005 and 2009, the 1-year kidney transplant and patient survival rate from deceased donors in 12 European countries were 90.4% and 95.9%, respectively. Also, five-year kidney transplant survival and patient survival between 2002 and 2006 were 77.2% and 86.7% respectively. According to our findings, One-year graft survival has more variability compared to one-year patient survival. This issue is also observed in long-term graft survival. It is important to consider the potential factors that might generate these differences and the wide range of the reported one-year graft survival in the included study:
1: Immunosuppressive Regime
Recent advances in immunosuppressive protocols significantly reduced the rate of acute rejection from 60% to 35% (103). With the use of Imuran and Prednisolone since 1960, the 1-year survival rate of living donors has increased from 75% to 90%. In 1995, Cellcept entered into the treatment protocol. This immunosuppressive drug, increased long-term kidney transplant survival by 15% to 20% with reduced acute rejection (104, 105). Therefore, the presence of articles from the 1980s and 1990s will increase the variability of short- and long-term graft and patient survival.
2:Deceased Donor Expanded Criteria
Although some of the studies have reported kidney transplant survival by ECD or SCD, pooled reporting of graft survival in many other studies can be a source of heterogeneity in survival reports. Our findings showed that the one-year survival rate of kidney transplantation from SCD was three percent greater than ECDs. This difference in 5-year patient survival increased to five percent. A systematic review study conducted by Pascual et al., which examined transplant outcomes from ECDs, reported no difference in graft survival and patient survival compared to SCDs in single-center observational studies. In contrast, multicenter and registry-based studies found poorer one-year to 15-year graft and patient survival in ECD recipients. Shortage of organ transplantation from living-related and living-unrelated donors led to the development of standards for cadaveric transplant and consequently increased kidney transplant resources. In all the articles included in this meta-analysis that determined kidney transplant survival and patient survival based on ECD and SCD, ECD donors were DDs older than 60 years and DDs 50 to 59 years old who met two of the following criteria: (1) history of hypertension, (2)cerebrovascular accident as a cause of brain death, and (3)final pre-procurement serum creatinine (SCr) level >1.5 mg/dL. Also, a kidney that has a relative risk of rejection of more than 1.7 compared to the age group of 10 to 39 without hypertension and high creatine is considered an ECD kidney. One of our inclusion criteria was the HR report for ECD compared to SCD. As shown in graph 12, only four studies, using Cox regression, measured the HR of graft loss for ECD. Our findings showed that HR graft rejection for ECD donors was 14% higher than the standard group. OPTN/SRTR 2017 Annual Data Report (106) shows deceased donation dramatically increased, and this increase in the age group of 18 to 34 years is more than in other groups. Their report shows despite the deceased donor increase, 18% have been discarded due to older age and diabetes. In summary, although ECD is associated with an increased risk of graft rejection in comparison with SCDs, the 5-year increase in life expectancy in ECDs compared to dialysis patients remains the ECD kidney as a better choice for ESRD treatment (106).
DBD vs DCD
Although in 2009, only 10% of kidney transplants in the USA were DCD; this index increased to 15.8% in 2011 and about 20% in 2017 (107, 108). Despite the development of the kidney transplant policy for the deceased donor, one of the limitations of our study was the lack of reporting of graft- and patient survival rates based on the type of donor in many articles included in the final analysis. We showed that the one-year graft survival rate in DCD recipients was 5% lower than in DBD recipients, whereas no differences were observed in the one-year patient survival rate. Prolonged warm ischemia time, higher risk of ischemia-reperfusion injury, (109-111), and poor quality vessels and/or endothelial activation (57, 112). the most important causes of lower graft survival in DCD recipients are presented.
In one of the largest cohort studies in the UK, after adjusting the age of recipient and donor as well as cold ischemic time, none of the HLA mismatch level, number of HLA mismatches, HLA-DR mismatches, Machine perfusion, and warm ischemic time were not effective in graft rejection from DCD.
According to the findings of this cohort, only the aging of recipients and donors was the influential factor in DCD graft rejection. (113). Although the incidence of DFG after DCD has been reported as 27% to 73% in different studies, (113, 114) with respect to the effective factors such as donor’s and recipient’s age, cold ischemic time, and HLA-matching, kidney transplants from DCD have acceptable results compared to dialysis.
Conclusions
The findings of the first comprehensive meta-analysis of graft survival and patient survival of the deceased donor, using all single-center, multicenter, and registry-based studies showed that overall short-term and long-term graft and patient survival are desirable. Our findings confirm that ECD recipients have a poorer graft survival chance than SCDs and despite the shorter one-year survival in DCDs, the short-term patient survival is similar to DBDs. We also concluded that men had better survival than women but did not differ in graft survival.
Authors' contributions
All authors contributed equally.
Acknowledgments
We would like to thank the Research Center in Emergency and Disaster Health, University of Social Welfare and Rehabilitation Sciences.
Conflict of interest
All authors declare that there is no conflict of interest.
Funding
There is no funding to report.
Ethical statements
Not applicable.
Data availability
All data and information will be provided on request.
Abbreviations:
DBD Donation after brain death
DCD Donation after circulatory death
DD Deceased donor
ECD Expanded-criteria donors
ESRD End-stage renal disease
HR Hazard ratio
SCD Standard-criteria donors