Advancements in Predictive Tools for Primary Graft Dysfunction in Liver Transplantation: A Comprehensive Review
Abstract
:Highlights
- Primary graft dysfunction (PGD) involves early allograft dysfunction (EAD) and more severe primary nonfunction (PNF), both stemming from ischemia–reperfusion injury (IRI).
- Accurate and early diagnosis of PGD is crucial to the retransplantation decision-making process and the implementation of future mitigation strategies.
- Novel tools for predicting PNF utilize serum markers, marker-derived models, tissue biopsy analysis, metabolomics, evaluations of organ perfusion and liver metabolism, and assessments of graft perfusate during machine perfusion.
- The current implementation of extended criteria donors (ECD) exacerbates the limitations of the binary EAD criteria and has prompted a refreshed approach to the graft dysfunction assessment protocol.
- Recently, new serum parameters have been associated with EAD occurrence, and their incorporation into graft dysfunction evaluations may improve accuracy.
Abstract
1. Introduction
2. Ischemia–Reperfusion Injury (IRI) on the Cellular Level
3. Prediction of Primary Nonfunction
3.1. Serum Lactate Concentration
3.2. Scoring Models and Alternative Serum Markers
3.3. Assessment during Machine Perfusion
3.4. Role of the Liver Biopsy
3.5. Measurement of Graft Metabolism and Perfusion
4. Assessment of Early Allograft Dysfunction
Year | Study [Ref.] | Size | Outcome | Incidence | Model | c-Statistic |
---|---|---|---|---|---|---|
2013 | Wagener et al. [54] | 572 | EGF | 7% | MELD | 0.81 |
2014 | Angelico et al. [56] | 2864 | EGF | 7% | BAR | 0.57 |
D-MELD | 0.58 | |||||
SOFT | 0.59 | |||||
DReAM | 0.66 | |||||
2015 | Pareja et al. [57] | 829 | EGF | 10% | MEAF | no data |
2017 | Benko et al. [55] | 116 | EGF | 10% | MELD | 0.84 |
2017 | Jochmans et al. [58] | 660 | EGF | 12% | MEAF | 0.73 |
EAD | 0.64 | |||||
2018 | Agopian et al. [60] | 2008 | EGF | 11% | L-GrAFT | 0.83 |
MEAF | 0.70 | |||||
EAD | 0.68 | |||||
2019 | Diaz-Nieto et al. [64] | 1194 | Graft failure within 30 days | 9% | Diaz-Nieto score | no data |
2020 | Richards et al. [59] | 183 | Graft failure within 28 days | 8% | MEAF | 0.74 |
2020 | Avolio et al. [62] | 1609 | EGF | 7% | EASE | 0.87 |
DRI | no data | |||||
EAD | 0.72 | |||||
D-MELD | 0.70 | |||||
ET-DRI | 0.63 | |||||
MEAF | no data | |||||
L-GrAFT | 0.85 | |||||
538 | 8% | EASE | 0.78 | |||
DRI | 0.57 | |||||
EAD | 0.63 | |||||
D-MELD | 0.72 | |||||
ET-DRI | 0.58 | |||||
MEAF | 0.73 | |||||
L-GrAFT | 0.71 | |||||
2021 | Lai et al. [66] | 1262 | EGF | 15% | CCI | 0.94 |
MELD | 0.60 | |||||
D-MELD | 0.60 | |||||
BAR | 0.60 | |||||
EAD | 0.58 | |||||
520 | 3% | CCI | 0.77 | |||
MELD | 0.57 | |||||
D-MELD | 0.57 | |||||
BAR | 0.56 | |||||
EAD | 0.47 | |||||
2021 | Agopian et al. [61] | 3201 | EGF | 7% | L-GrAFT | 0.78 |
MEAF | 0.72 | |||||
EAD | 0.68 | |||||
171 | 4% | L-GrAFT | 0.81 | |||
MEAF | 0.57 | |||||
EAD | 0.64 | |||||
2021 | Rhu et al. [63] | 1153 | Graft failure within 2 months | 7% | ABC Model | 0.73 |
MEAF | 0.69 | |||||
EAD | 0.66 | |||||
359 | 13% | ABC Model | 0.74 | |||
MEAF | 0.71 | |||||
EAD | 0.66 | |||||
2022 | Manzia et al. [65] | 331 | EGF | 16% | mEAD | 0.74 |
EAD | 0.64 | |||||
123 | 5% | mEAD | 0.68 | |||
EAD | 0.52 | |||||
2023 | Moosburner [67] | 906 | EGF | no data | DRI | 0.50 |
ET-DRI | 0.54 | |||||
D-MELD | 0.59 | |||||
MEAF | 0.72 | |||||
L-GrAFT | 0.80 | |||||
EASE | 0.80 | |||||
ABC Model | 0.68 | |||||
EAD | 0.69 | |||||
BAR | 0.60 | |||||
2023 | Nie et al. [22] | 720 | EGF | 9% | MEAF | 0.80 |
King-PNF score | 0.87 | |||||
BAR-Lac | 0.76 |
Alternative Assessment of Allograft Dysfunction
Year | Study [Ref.] | Size | Outcome | Incidence | Markers |
---|---|---|---|---|---|
2013 | Hong et al. [90] | 304 | EAD | 16% | ↑ phosphorus |
2015 | Nedel et al. [94] | 21 | EAD | 10% | ↓ TAFI |
2016 | Karakhanova et al. [77] | 41 | EAD | 49% | ↓ IFNɣ, ↑ IL-10, ↑ CXCL10 |
2016 | Chae et al. [91] | 104 | EAD | 30% | ↑ BNP |
2016 | Ceglarek et al. [84] | 40 | MEAF ≥ 6.1 | no data | ↓ SIE%/↓ CHE% |
2017 | Yang et al. [87] | 231 | EAD | 17% | ↓ sTC |
2018 | Chae et al. [76] | 226 | EAD | 12% | ↑ IL-6, ↑ IL-17 |
2018 | Faitot et al. [73] | 274 | EAD | 29% | ↑ IL-6 |
2018 | Pollara et al. [88] | 65 | EAD | no data | ↑ mtDNA |
2019 | Gorgen et al. [93] | 227 | EAD | 27% | ↓ factor V |
2018 | Tsai et al. [85] | 51 | EAD | 24% | ↓ cholesterol oleate, ↓ LysoPC, ↑ PC |
2019 | Kwon et al. [81] | 1960 | EAD | 11% | ↑ NLR |
2019 | Thomsen et al. [82] | 27 | EAD | 59% | ↑ sCD163 |
2019 | Park et al. [83] | 588 | EAD | 14% | ↑ CRP/ALB |
2020 | Faria et al. [79] | 22 | EAD | 50% | ↑ HMGB1, ↑ nucleosome |
2020 | Nunez et al. [68] | 99 | EAD | no data | ↑ IL-33, ↑ C3a, C5a |
2021 | Tsai et al. [86] | 74 | EAD | 30% | ↑ betaine, ↓ LysoPC, ↓ PC, ↑ palmitic acid |
2021 | Yoshino et al. [89] | 21 | EAD | 33% | ↑ cmtDNA |
2021 | Barbier et al. [70] | 40 | EAD | no data | ↑ IL-33 |
2022 | Zhang et al. [92] | 150 | EAD | 35% | ↑ Mb |
5. Conclusions and Recommendations
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Year | Study [Ref.] | Size | Outcome | Incidence | Model | c-Statistic |
---|---|---|---|---|---|---|
2017 | Al-Freah et al. [21] | 1125 | PNF | 3.70% | King-PNF score | 0.91 |
UK EGD Criteria | 0.67 | |||||
US PNF Criteria | 0.78 | |||||
2023 | Nie et al. [22] | 720 | PNF | 3.90% | MEAF | 0.87 |
King-PNF score | 0.89 | |||||
BAR-Lac | 0.83 | |||||
EAD | 39% | MEAF | 0.84 | |||
King-PNF score | 0.81 | |||||
BAR-Lac | 0.63 | |||||
early graft failure | 9.30% | MEAF | 0.80 | |||
King-PNF score | 0.87 | |||||
BAR-Lac | 0.76 |
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Gierej, P.; Radziszewski, M.; Figiel, W.; Grąt, M. Advancements in Predictive Tools for Primary Graft Dysfunction in Liver Transplantation: A Comprehensive Review. J. Clin. Med. 2024, 13, 3762. https://doi.org/10.3390/jcm13133762
Gierej P, Radziszewski M, Figiel W, Grąt M. Advancements in Predictive Tools for Primary Graft Dysfunction in Liver Transplantation: A Comprehensive Review. Journal of Clinical Medicine. 2024; 13(13):3762. https://doi.org/10.3390/jcm13133762
Chicago/Turabian StyleGierej, Piotr, Marcin Radziszewski, Wojciech Figiel, and Michał Grąt. 2024. "Advancements in Predictive Tools for Primary Graft Dysfunction in Liver Transplantation: A Comprehensive Review" Journal of Clinical Medicine 13, no. 13: 3762. https://doi.org/10.3390/jcm13133762
APA StyleGierej, P., Radziszewski, M., Figiel, W., & Grąt, M. (2024). Advancements in Predictive Tools for Primary Graft Dysfunction in Liver Transplantation: A Comprehensive Review. Journal of Clinical Medicine, 13(13), 3762. https://doi.org/10.3390/jcm13133762