Prognostic role of the Donor Risk Index, the Eurotransplant Donor Risk Index, and the Balance of Risk score on graft loss after liver transplantation
2021; Springer Science+Business Media; Volume: 34; Issue: 5 Linguagem: Inglês
10.1111/tri.13861
ISSN1432-2277
AutoresVladimir J. Lozanovski, Pascal Probst, Alireza Arefidoust, Ali Ramouz, Ehsan Aminizadeh, Mohammadsadegh Nikdad, Elias Khajeh, Omid Ghamarnejad, Saeed Shafiei, Sadeq Ali‐Hasan‐Al‐Saegh, Svenja Seide, Eva Kalkum, Arash Nickkholgh, Zoltán Czigány, Georg Lurje, Markus Mieth, Arianeb Mehrabi,
Tópico(s)Renal Transplantation Outcomes and Treatments
ResumoTransplant InternationalVolume 34, Issue 5 p. 778-800 Meta-AnalysisOpen Access Prognostic role of the Donor Risk Index, the Eurotransplant Donor Risk Index, and the Balance of Risk score on graft loss after liver transplantation Vladimir J. Lozanovski, orcid.org/0000-0002-7332-3973 Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, Germany Liver Cancer Center Heidelberg (LCCH), University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorPascal Probst, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, Germany The Study Center of the German Surgical Society (SDGC), University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorAlireza Arefidoust, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorAli Ramouz, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorEhsan Aminizadeh, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorMohammadsadegh Nikdad, orcid.org/0000-0001-5789-8171 Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorElias Khajeh, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorOmid Ghamarnejad, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorSaeed Shafiei, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorSadeq Ali-Hasan-Al-Saegh, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorSvenja E. Seide, Institute of Medical Biometry and Informatics (IMBI), University of Heidelberg, Heidelberg, GermanySearch for more papers by this authorEva Kalkum, The Study Center of the German Surgical Society (SDGC), University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorArash Nickkholgh, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorZoltan Czigany, Department of Surgery and Transplantation, University Hospital RWTH Aachen, Aachen, GermanySearch for more papers by this authorGeorg Lurje, Department of Surgery, Charité –Universitätsmedizin Berlin, Berlin, GermanySearch for more papers by this authorMarkus Mieth, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorArianeb Mehrabi, Corresponding Author arianeb.mehrabi@med.uni-heidelberg.de orcid.org/0000-0001-6163-1525 Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, Germany Liver Cancer Center Heidelberg (LCCH), University Hospital Heidelberg, Heidelberg, Germany Correspondence Arianeb Mehrabi FICS, FEBS, FACS, Head of Division for Abdominal Transplantation, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany. Tel.: +49 6221 56 6205; fax: +49 6221 56 33934; e-mail: arianeb.mehrabi@med.uni-heidelberg.deSearch for more papers by this author Vladimir J. Lozanovski, orcid.org/0000-0002-7332-3973 Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, Germany Liver Cancer Center Heidelberg (LCCH), University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorPascal Probst, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, Germany The Study Center of the German Surgical Society (SDGC), University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorAlireza Arefidoust, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorAli Ramouz, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorEhsan Aminizadeh, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorMohammadsadegh Nikdad, orcid.org/0000-0001-5789-8171 Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorElias Khajeh, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorOmid Ghamarnejad, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorSaeed Shafiei, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorSadeq Ali-Hasan-Al-Saegh, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorSvenja E. Seide, Institute of Medical Biometry and Informatics (IMBI), University of Heidelberg, Heidelberg, GermanySearch for more papers by this authorEva Kalkum, The Study Center of the German Surgical Society (SDGC), University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorArash Nickkholgh, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorZoltan Czigany, Department of Surgery and Transplantation, University Hospital RWTH Aachen, Aachen, GermanySearch for more papers by this authorGeorg Lurje, Department of Surgery, Charité –Universitätsmedizin Berlin, Berlin, GermanySearch for more papers by this authorMarkus Mieth, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, GermanySearch for more papers by this authorArianeb Mehrabi, Corresponding Author arianeb.mehrabi@med.uni-heidelberg.de orcid.org/0000-0001-6163-1525 Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Heidelberg, Germany Liver Cancer Center Heidelberg (LCCH), University Hospital Heidelberg, Heidelberg, Germany Correspondence Arianeb Mehrabi FICS, FEBS, FACS, Head of Division for Abdominal Transplantation, Department of General, Visceral and Transplant Surgery, University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany. Tel.: +49 6221 56 6205; fax: +49 6221 56 33934; e-mail: arianeb.mehrabi@med.uni-heidelberg.deSearch for more papers by this author First published: 16 March 2021 https://doi.org/10.1111/tri.13861Citations: 1AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Summary This study aimed to identify cutoff values for donor risk index (DRI), Eurotransplant (ET)-DRI, and balance of risk (BAR) scores that predict the risk of liver graft loss. MEDLINE and Web of Science databases were searched systematically and unrestrictedly. Graft loss odds ratios and 95% confidence intervals were assessed by meta-analyses using Mantel–Haenszel tests with a random-effects model. Cutoff values for predicting graft loss at 3 months, 1 year, and 3 years were analyzed for each of the scores. Measures of calibration and discrimination used in studies validating the DRI and the ET-DRI were summarized. DRI ≥ 1.4 (six studies, n = 35 580 patients) and ET-DRI ≥ 1.4 (four studies, n = 11 666 patients) were associated with the highest risk of graft loss at all time points. BAR > 18 was associated with the highest risk of 3-month and 1-year graft loss (n = 6499 patients). A DRI cutoff of 1.8 and an ET-DRI cutoff of 1.7 were estimated using a summary receiver operator characteristic curve, but the sensitivity and specificity of these cutoff values were low. A DRI and ET-DRI score ≥ 1.4 and a BAR score > 18 have a negative influence on graft survival, but these cutoff values are not well suited for predicting graft loss. Introduction Liver transplantation is the standard treatment for patients with advanced liver disease and prolongs the recipients’ life expectancy. Improved outcome after transplantation has increased the number of recipients on the waiting lists and transplant centers, but has also raised the issue of fair and adequate organ allocation [1, 2]. Because of the dire need for liver grafts, strict donor criteria have been relaxed in recent years [3]. Model of end-stage liver disease (MELD) score-based allocation has reduced mortality on the waiting lists, but has increased the one-year mortality following transplantation [4]. According to OPTN, 20% of patients with a chronic liver disease and high MELD score either drop out from the waiting list because of disease progression or die waiting for transplantation [5]. In Eurotransplant (ET), up to 30% of patients drop out from the waiting list because of death or because their condition deteriorates [6]. Therefore, donor–recipient matching has become crucial in achieving reasonable outcomes after transplantation, especially when allocating extended donor criteria (EDC) organs to sicker recipients [2, 7]. The donor risk index (DRI) is a scoring system that was found to significantly influence outcomes after liver transplantation in a large cohort of 20 023 deceased donor transplants from the Scientific Registry of Transplant Recipients database [8]. The DRI was validated within the ET network, but because of differences in donor age, cause of death, donation after cardiac death, split liver donation, and organ allocation, the DRI values were different between the Organ Procurement and Transplantation Network (OPTN) and the ET region. To accommodate these differences, a scoring system tailored to the ET region (ET-DRI) was implemented [9, 10]. The balance of risk (BAR) scoring system is a simple model that was calculated based on 37 255 patients in the United Network for Organ Sharing (UNOS) database [11]. The BAR score identified six donor and recipient factors that best predicted the outcome of liver transplantation. These predictors were found to be superior to the model for end-stage liver disease (MELD) score, the D-MELD (donor age multiplied by recipient MELD) score, and the DRI at predicting transplant outcome [11]. The DRI, ET-DRI, and BAR scores are continuous scoring systems that include donor, graft, and recipient parameters available at the time of organ allocation. As such, they allow information about graft-associated risk to be shared during the allocation procedure. These scores all use just a few covariates, which makes them more applicable than other more complex scoring systems. However, different cutoff values have been suggested for the DRI, ET-DRI, and BAR scores, and no consensus has been reached. This systematic review and meta-analysis aimed to evaluate at which cutoff values the DRI, ET-DRI, and BAR scores would predict an increased risk for graft failure after liver transplantation. Methods The study was conducted according to a predefined protocol, which is available upon request, and adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [12]. Literature search MEDLINE and Web of Science databases were searched systematically and without any restrictions on date of publication as previously reported [13]. Studies comparing the effect of different DRI, ET-DRI, and BAR cutoff values on graft loss published until December 2020 were identified. Citations of relevant articles were also screened for additional eligible studies. The search terms used for the DRI and the ET-DRI were (“Index” OR “DRI”) AND “Transplant*” AND (“Liver” OR “Hepatic”) AND "Donor". The search terms used for the BAR score were “Transplant*” AND (“Liver” OR “Hepatic”) AND "Donor" AND (“Balance of Risk” OR “BAR” OR “Retransplantation” OR “Life support” OR “Recipient Age” OR “Cold Ischemia” OR “Cold Ischaemia” OR “Donor Age”). Terminology and definitions The DRI considers donor age, cause of death, race, donation after cardiac death (DCD), split liver graft, donor’s height, organ location (local, regional, or national), and cold ischemia time [8]. The ET-DRI considers donor age, cause of death, donation after cardiac death, split liver graft, organ location (regional or national), cold ischemia time, rescue allocation, and gamma-glutamyltransferase levels [9]. The BAR score considers recipient MELD score, recipient age, donor age, retransplantation, cold ischemia time, and recipient’s life support dependence at the time of allocation [11]. Eligibility criteria The Population, Intervention, Comparison, Outcome, Time and Study design (PICOTS) strategy was used to select studies with the following inclusion criteria: Population: patients with end-stage liver disease undergoing primary liver transplantation. Intervention: patients transplanted with grafts from donors with higher DRI/ET-DRI/BAR score. Comparator: patients transplanted with grafts from donors with lower DRI/ET-DRI/BAR score. Outcomes: postoperative graft loss. Time: predictive ability of the DRI, ET-DRI, and BAR scores at three months, one year, and three years after liver transplantation. Study design: any study design (cross-sectional, case–control, and cohort studies) except study protocols, narrative or systematic reviews, common overviews, letters, case reports, experimental studies, and conference abstracts [14]. Studies not meeting these inclusion criteria and studies that did not report the outcomes of interest were excluded. Articles were carefully reviewed to exclude overlapping reports and duplicate publications. Studies that assessed the same patient collective more than once without providing additional information were excluded and only the study with the largest patient collective was included. Studies in languages other than English and German were also excluded. Two reviewers screened article titles and abstracts according to the inclusion and exclusion criteria, and the resulting full-text articles were further assessed for eligibility based on the inclusion criteria. A third reviewer resolved any discrepancies. Study data were extracted using the CHARMS checklist (checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies) [15]. Outcomes Differences in graft loss rates following liver transplantation from donors with different DRI, ET-DRI, and BAR score cutoff values were assessed. Based on previously reported cutoff values, the main outcome of the meta-analysis was to identify the DRI, ET-DRI, and BAR scores that predicted the best possible 3-month, 1-year, and 3-year graft survival. Graft loss was a combined endpoint, defined as the time from liver transplantation to either patient’s death or retransplantation (whichever came first). Quality assessment and assessment of bias Risk of bias and study applicability were evaluated using the prediction model risk of bias assessment tool (PROBAST). The risk of bias was considered high, and the evidence quality was considered low if the study did not address the issues in each domain. Studies with the lowest risk of bias were considered to have highest quality evidence. The risk of bias, the study methodology, and the relevance of the findings to the research question (applicability) were rated “high,” “low,” or “unclear” based on a predefined questionnaire and scoring guide [16]. Statistical analysis Review Manager (RevMan, version 5.3.5, The Cochrane Collaboration, The Nordic Cochrane Center, Copenhagen, Denmark) was used to conduct the meta-analyses. R (a language and environment for statistical computing, R Core Team, 2020, R Foundation for Statistical Computing, Vienna, Austria; https://www.R-project.org/) was used to evaluate the discrimination of the evaluated scores and to perform the SROC analysis. Dichotomous data were presented as odds ratios (OR) with 95% confidence intervals (CI). Pairwise meta-analyses were performed using the Mantel–Haenszel random-effects model to account for between-trial heterogeneity [17, 18]. The statistical heterogeneity between included studies was evaluated using the I2. Values of I2 between 50% and 75%, heterogeneity were regarded as moderate, while I2 values > 75% were regarded as considerable. To evaluate score discrimination, the area under the receiver operating curve (AUC) value was used. Pooled AUC values were estimated for the DRI, ET-DRI, and BAR scores for each endpoint at different time points. Measures of calibration, such as sensitivity and specificity along with the reported cutoffs, were extracted. To estimate an optimal cutoff, summary receiver operator characteristics curve (SROC) analyses were performed [19]. A P value < 0.05 was considered significant in all analyses. Results Study selection and selection criteria The literature search yielded 5492 potentially eligible articles. After excluding duplicates and screening titles and abstracts, the full texts of 106 articles were further assessed for eligibility. Of these, 57 articles were excluded because they presented no quantitative data about the endpoints of interest (n = 17), because the patients did not meet the inclusion criteria (n = 13), or because they did not evaluate the DRI, the ET-DRI, or the BAR score (n = 27). This left 49 studies that were included in the qualitative analysis (Fig. 1). Only studies that clearly defined cutoff values for DRI, ET-DRI, and BAR scores, evaluated the impact of these cutoffs on graft survival, provided enough data on donor numbers and survival, and did not analyze overlapping collectives were eligible for analysis. Nine studies fulfilled these criteria and were included in the quantitative analysis. Six studies were included in the meta-analysis of the DRI [3, 7-9, 20, 21], four studies were included in the meta-analysis of the ET-DRI [9, 20-22], and two studies were included in the meta-analysis of the BAR score [23, 24]. Figure 1Open in figure viewerPowerPoint PRISMA flowchart. Studies and patients All included studies were retrospective cohort analyses conducted in Europe, Asia, Africa, South America, Canada, and the United States between 2006 and 2020 [3, 7-11, 24-53]. A total of 35 580 liver transplant patients were included in the DRI meta-analysis, and 11 666 liver transplant patients were included in the ET-DRI meta-analysis (Tables 1 and 2). A total of 6499 liver transplant patients were included in the meta-analysis of BAR scores (Table 3). The follow-up ranged from 1 month to 240 months. Table 1. Study characteristics of included trials that analyzed DRI-associated graft loss. First author and year Study period Nr. of patients Age (median or mean ± SD) Gender (male/female) Race Study groups Outcome Follow-up Feng, 2006** According to Feng et al. [8] Donor risk index = exp[(0.154 if 40 ≤ age < 50) + (0.274 if 50 ≤ age < 60) + (0.424 if 60 ≤ age < 70) + (0.501 if 70 ≤ age) + (0.079 if COD = anoxia) + (0.145 if COD = CVA) + (0.184 if COD = other) + (0.176 if race = African American) + (0.126 if race = other) + (0.411 if DCD) + (0.422 if partial/split) + (0.066 ((170–height)/10)) + (0.105 if regional share) + (0.244 if national share) + (0.010 × cold time)]. [8] 01.1998–12.2002 20 023 0–17: 2394 18–39: 7852 40–49: 3752 50–59: 3273 60–69: 1896 70 + 85: 6 11 911/8112 Caucasian: 17 143 African American: 2337 Other: 523 0.0 < DRI ≤ 1.0, 1.0 < DRI ≤ 1.1, 1.1 < DRI ≤ 1.2, 1.2 < DRI ≤ 1.3, 1.3 < DRI ≤ 1.4, 1.4 < DRI ≤ 1.5, 1.5 < DRI ≤ 1.6, 1.6 < DRI ≤ 1.8, 1.8 < DRI ≤ 2.0, 2.0 < DRI Graft survival 3 years Maluf, 2006** According to Feng et al. [8] Donor risk index = exp[(0.154 if 40 ≤ age < 50) + (0.274 if 50 ≤ age < 60) + (0.424 if 60 ≤ age < 70) + (0.501 if 70 ≤ age) + (0.079 if COD = anoxia) + (0.145 if COD = CVA) + (0.184 if COD = other) + (0.176 if race = African American) + (0.126 if race = other) + (0.411 if DCD) + (0.422 if partial/split) + (0.066 ((170–height)/10)) + (0.105 if regional share) + (0.244 if national share) + (0.010 × cold time)]. [37] 06.2002–06.2005 12 056 NA NA NA EDC vs. no-EDC graft recipients (DRI = 1.7 used to define EDC grafts) Graft survival 1 year Volk, 2008** According to Feng et al. [8] Donor risk index = exp[(0.154 if 40 ≤ age < 50) + (0.274 if 50 ≤ age < 60) + (0.424 if 60 ≤ age < 70) + (0.501 if 70 ≤ age) + (0.079 if COD = anoxia) + (0.145 if COD = CVA) + (0.184 if COD = other) + (0.176 if race = African American) + (0.126 if race = other) + (0.411 if DCD) + (0.422 if partial/split) + (0.066 ((170–height)/10)) + (0.105 if regional share) + (0.244 if national share) + (0.010 × cold time)]. [52] 01.1997–08.2007 47 985 NA NA NA pre-MELD vs. post-MELD era Patient survival 5 years Avolio, 2008** According to Feng et al. [8] Donor risk index = exp[(0.154 if 40 ≤ age < 50) + (0.274 if 50 ≤ age < 60) + (0.424 if 60 ≤ age < 70) + (0.501 if 70 ≤ age) + (0.079 if COD = anoxia) + (0.145 if COD = CVA) + (0.184 if COD = other) + (0.176 if race = African American) + (0.126 if race = other) + (0.411 if DCD) + (0.422 if partial/split) + (0.066 ((170–height)/10)) + (0.105 if regional share) + (0.244 if national share) + (0.010 × cold time)]. [27] NA 223 NA NA NA low-risk DRI (DRI < 1.7), high-risk DRI (DRI ≥ 1.7) Graft survival 5 years Boin. 2008** According to Feng et al. [8] Donor risk index = exp[(0.154 if 40 ≤ age < 50) + (0.274 if 50 ≤ age < 60) + (0.424 if 60 ≤ age < 70) + (0.501 if 70 ≤ age) + (0.079 if COD = anoxia) + (0.145 if COD = CVA) + (0.184 if COD = other) + (0.176 if race = African American) + (0.126 if race = other) + (0.411 if DCD) + (0.422 if partial/split) + (0.066 ((170–height)/10)) + (0.105 if regional share) + (0.244 if national share) + (0.010 × cold time)]. [30] 01.1994–12.2006 232 NA NA NA HCV- and HCV + graft recipients Patient survival of HCV + recipients transplanted with grafts from donors older or younger than 50 years. ** According to Feng et al. [8] Donor risk index = exp[(0.154 if 40 ≤ age < 50) + (0.274 if 50 ≤ age < 60) + (0.424 if 60 ≤ age < 70) + (0.501 if 70 ≤ age) + (0.079 if COD = anoxia) + (0.145 if COD = CVA) + (0.184 if COD = other) + (0.176 if race = African American) + (0.126 if race = other) + (0.411 if DCD) + (0.422 if partial/split) + (0.066 ((170–height)/10)) + (0.105 if regional share) + (0.244 if national share) + (0.010 × cold time)]. DRI = 1.7 used as cutoff Patient survival 5 years Schaubel, 2008** According to Feng et al. [8] Donor risk index = exp[(0.154 if 40 ≤ age < 50) + (0.274 if 50 ≤ age < 60) + (0.424 if 60 ≤ age < 70) + (0.501 if 70 ≤ age) + (0.079 if COD = anoxia) + (0.145 if COD = CVA) + (0.184 if COD = other) + (0.176 if race = African American) + (0.126 if race = other) + (0.411 if DCD) + (0.422 if partial/split) + (0.066 ((170–height)/10)) + (0.105 if regional share) + (0.244 if national share) + (0.010 × cold time)]. [44] 09.2001–07.2005 26 165 NA NA NA low-risk DRI: 0 < DRI ≤ 1.075, medium risk DRI: 1.075 < DRI ≤ 1.65, high-risk DRI: DRI > 1.65 Patient survival 3 years Bonney, 2009** According to Feng et al. [8] Donor risk index = exp[(0.154 if 40 ≤ age < 50) + (0.274 if 50 ≤ age < 60) + (0.424 if 60 ≤ age < 70) + (0.501 if 70 ≤ age) + (0.079 if COD = anoxia) + (0.145 if COD = CVA) + (0.184 if COD = other) + (0.176 if race = African American) + (0.126 if race = other) + (0.411 if DCD) + (0.422 if partial/split) + (0.066 ((170–height)/10)) + (0.105 if regional share) + (0.244 if national share) + (0.010 × cold time)]. [31] 01.1995–12.2005 1090 50 ± 4.61 NA Caucasian: 1035 African American: 11 Other: 44 low-risk MELD < 15, intermediate MELD = 15–30; high-risk MELD > 30 low-risk DRI: DRI < 1.8, high-risk DRI: DRI ≥ 1.8 Graft survival, patient survival 1–125 months Maluf, 2009** According to Feng et al. [8] Donor risk index = exp[(0.154 if 40 ≤ age < 50) + (0.274 if 50 ≤ age < 60) + (0.424 if 60 ≤ age < 70) + (0.501 if 70 ≤ age) + (0.079 if COD = anoxia) + (0.145 if COD = CVA) + (0.184 if COD = other) + (0.176 if race = African American) + (0.126 if race = other) + (0.411 if DCD) + (0.422 if partial/split) + (0.066 ((170–height)/10)) + (0.105 if regional share) + (0.244 if national share) + (0.010 × cold time)]. [38] 01.2000–06.2006 16 678 NA 9940/6738 Caucasian: 12 178 African American: 2138 Hispanic: 1859 Asian: 198 Other: 523 HCV- and HCV + graft recipients Effect of MELD score on HCV-DRI interaction Graft survival 5 years Vitale, 2009** According to Feng et al. [8] Donor risk index = exp[(0.154 if 40 ≤ age < 50) + (0.274 if 50 ≤ age < 60) + (0.424 if 60 ≤ age < 70) + (0.501 if 70 ≤ age) + (0.079 if COD = anoxia) + (0.145 if COD = CVA) + (0.184 if COD = other) + (0.176 if race = African American) + (0.126 if race = other) + (0.411 if DCD) + (0.422 if partial/split) + (0.066 ((170–height)/10)) + (0.105 if regional share) + (0.244 if national share) + (0.010 × cold time)]. [51] 12.2006–03.2008 74 53.25 ± 18.18 NA NA NA PGD 1 year Palmiero, 2010** According to Feng et al. [8] Donor risk index = exp[(0.154 if 40 ≤ age < 50) + (0.274 if 50 ≤ age < 60) + (0.424 if 60 ≤ age < 70) + (0.501 if 70 ≤ age) + (0.079 if COD = anoxia) + (0.145 if COD = CVA) + (0.184 if COD = other) + (0.176 if race = African American) + (0.126 if race = other) + (0.411 if DCD) + (0.422 if partial/split) + (0.066 ((170–height)/10)) + (0.105 if regional share) + (0.244 if national share) + (0.010 × cold time)]. [41] 07.2004–06.2006 1786 38.4 998/788 NA pre- and post-MELD era low-risk DRI: DRI < 1.7 high-risk DRI: DRI ≥ 1.7 Patient survival 6 years Ghinolfi, 2011** According to Feng et al. [8] Donor risk index = exp[(0.154 if 40 ≤ age < 50) + (0.274 if 50 ≤ age < 60) + (0.424 if 60 ≤ age < 70) + (0.501 if 70 ≤ age) + (0.079 if COD = anoxia) + (0.145 if COD = CVA) + (0.184 if COD = other) + (0.176 if race = African American) + (0.126 if race = other) + (0.411 if DCD) + (0.422 if partial/split) + (0.066 ((170–height)/10)) + (0.105 if regional share) + (0.244 if national share) + (0.010 × cold time)]. [34] 08.2006–11.2007 148 51.2 ± 20.3 80/68 NA temporary portocaval shunt (TPCS) and (no-TPCS). low-risk DRI: DRI < 1.8 high-risk DRI: DRI ≥ 1.8 Graft survival 2.5 years Ozhathil, 2011** According to Feng et al. [8] Donor risk index = exp[(0.154 if 40 ≤ age < 50) + (0.274 if 50 ≤ age < 60) + (0.424 if 60 ≤ age < 70) + (0.501 if 70 ≤ age) + (0.079 if COD = anoxia) + (0.145 if COD = CVA) + (0.184 if COD = other) + (0.176 if race = African American) + (0.126 if race = other) + (0.411 if DCD) + (0.422 if partial/split) + (0.066 ((170–height)/10)) + (0.105 if regional share) + (0.244 if national share) + (0.010 × cold time)]. [55] 01.2002–12.2008 31 576 NA 18 798/12 778 Caucasian: 21 928 African American: 4798 Other: 4850 low-risk DRI: DRI ≤ 1.63 moderate risk DRI: 1.64 < DRI ≤ 1.90 high-risk DRI: 1.90 < DRI ≤ 2.26 very high-risk DRI: DRI > 2.26 Graft survival 6 years Dutkowski, 2011** According to Feng et al. [8] Donor risk index = exp[(0.154 if 40 ≤ age < 50) + (0.274 if 50 ≤ age < 60) + (0.424 if 60 ≤ age < 70) + (0.501 if 70 ≤ age) + (0.079 if COD = anoxia) + (0.145 if COD = CVA) + (0.184 if COD = other) + (0.176 if race = African American) + (0.126 if race = other) + (0.411 if DCD) + (0.422 if partial/split) + (0.066 ((170–height)/10)) + (0.105 if regional share) + (0.244 if national share) +
Referência(s)