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Ge J, Kim WR, Kwong AJ. Common definitions and variables are needed for the United States to join the conversation on acute-on-chronic liver failure. Am J Transplant 2024:S1600-6135(24)00423-4. [PMID: 38977243 DOI: 10.1016/j.ajt.2024.06.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 06/19/2024] [Accepted: 06/28/2024] [Indexed: 07/10/2024]
Abstract
Acute-on-chronic liver failure (ACLF) is a variably defined syndrome characterized by acute decompensation of cirrhosis with organ failures. At least 13 different definitions and diagnostic criteria for ACLF have been proposed, and there is increasing recognition that patients with ACLF may face disadvantages in the current United States liver allocation system. There is a need, therefore, for more standardized data collection and consensus to improve study design and outcome assessment in ACLF. In this article, we discuss the current landscape of transplantation for patients with ACLF, strategies to optimize organ utility, and data opportunities based on emerging technologies to facilitate improved data collection.
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Affiliation(s)
- Jin Ge
- Division of Gastroenterology and Hepatology, Department of Medicine, University of California - San Francisco, San Francisco, California, USA
| | - W Ray Kim
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Allison J Kwong
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.
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2
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Akshat S, Gentry SE, Raghavan S. Heterogeneous donor circles for fair liver transplant allocation. Health Care Manag Sci 2024; 27:20-45. [PMID: 35854169 PMCID: PMC10896798 DOI: 10.1007/s10729-022-09602-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 05/25/2022] [Indexed: 11/04/2022]
Abstract
The United States (U.S.) Department of Health and Human Services is interested in increasing geographical equity in access to liver transplant. The geographical disparity in the U.S. is fundamentally an outcome of variation in the organ supply to patient demand (s/d) ratios across the country (which cannot be treated as a single unit due to its size). To design a fairer system, we develop a nonlinear integer programming model that allocates the organ supply in order to maximize the minimum s/d ratios across all transplant centers. We design circular donation regions that are able to address the issues raised in legal challenges to earlier organ distribution frameworks. This allows us to reformulate our model as a set-partitioning problem. Our policy can be viewed as a heterogeneous donor circle policy, where the integer program optimizes the radius of the circle around each donation location. Compared to the current policy, which has fixed radius circles around donation locations, the heterogeneous donor circle policy greatly improves both the worst s/d ratio and the range between the maximum and minimum s/d ratios. We found that with the fixed radius policy of 500 nautical miles (NM), the s/d ratio ranges from 0.37 to 0.84 at transplant centers, while with the heterogeneous circle policy capped at a maximum radius of 500 NM, the s/d ratio ranges from 0.55 to 0.60, closely matching the national s/d ratio average of 0.5983. Our model matches the supply and demand in a more equitable fashion than existing policies and has a significant potential to improve the liver transplantation landscape.
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Affiliation(s)
- Shubham Akshat
- The Robert H. Smith School of Business, University of Maryland, College Park, MD, 20742, USA
| | - Sommer E Gentry
- Department of Surgery and Department of Population Health, Grossman School of Medicine, New York University, New York, NY, 10016, USA
| | - S Raghavan
- The Robert H. Smith School of Business and Institute for Systems Research, University of Maryland, College Park, MD, 20742, USA.
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3
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Li C, Jiang X, Zhang K. A transformer-based deep learning approach for fairly predicting post-liver transplant risk factors. J Biomed Inform 2024; 149:104545. [PMID: 37992791 DOI: 10.1016/j.jbi.2023.104545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/11/2023] [Accepted: 11/13/2023] [Indexed: 11/24/2023]
Abstract
Liver transplantation is a life-saving procedure for patients with end-stage liver disease. There are two main challenges in liver transplant: finding the best matching patient for a donor and ensuring transplant equity among different subpopulations. The current MELD scoring system evaluates a patient's mortality risk if not receiving an organ within 90 days. However, the donor-patient matching should also consider post-transplant risk factors, such as cardiovascular disease, chronic rejection, etc., which are all common complications after transplant. Accurate prediction of these risk scores remains a significant challenge. In this study, we used predictive models to solve the above challenges. Specifically, we proposed a deep learning model to predict multiple risk factors after a liver transplant. By formulating it as a multi-task learning problem, the proposed deep neural network was trained to simultaneously predict the five post-transplant risks and achieve equal good performance by exploiting task-balancing techniques. We also proposed a novel fairness-achieving algorithm to ensure prediction fairness across different subpopulations. We used electronic health records of 160,360 liver transplant patients, including demographic information, clinical variables, and laboratory values, collected from the liver transplant records of the United States from 1987 to 2018. The model's performance was evaluated using various performance metrics such as AUROC and AUPRC. Our experiment results highlighted the success of our multi-task model in achieving task balance while maintaining accuracy. The model significantly reduced the task discrepancy by 39 %. Further application of the fairness-achieving algorithm substantially reduced fairness disparity among all sensitive attributes (gender, age group, and race/ethnicity) in each risk factor. It underlined the potency of integrating fairness considerations into the task-balancing framework, ensuring robust and fair predictions across multiple tasks and diverse demographic groups.
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Affiliation(s)
- Can Li
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xiaoqian Jiang
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Kai Zhang
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
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4
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Mankowski MA, Wood NL, Segev DL, Gentry SE. Removing geographic boundaries from liver allocation: A method for designing continuous distribution scores. Clin Transplant 2023; 37:e15017. [PMID: 37204074 PMCID: PMC10657628 DOI: 10.1111/ctr.15017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 03/31/2023] [Accepted: 04/30/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND The Organ Procurement and Transplantation Network (OPTN) is eliminating geographic boundaries in liver allocation, in favor of continuous distribution. Continuous distribution allocates organs via a composite allocation score (CAS): a weighted sum of attributes like medical urgency, candidate biology, and placement efficiency. The opportunity this change represents, to include new variables and features for prioritizing candidates, will require lengthy and contentious discussions to establish community consensus. Continuous distribution could instead be implemented rapidly by computationally translating the allocation priorities for pediatric, status 1, and O/B blood type liver candidates that are presently implemented via geographic boundaries into points and weights in a CAS. METHODS Using simulation with optimization, we designed a CAS that is minimally disruptive to existing prioritizations, and that eliminates geographic boundaries and minimizes waitlist deaths without harming vulnerable populations. RESULTS Compared with Acuity Circles (AC) in a 3-year simulation, our optimized CAS decreased deaths from 7771.2 to 7678.8 while decreasing average (272.66 NM vs. 264.30 NM) and median (201.14 NM vs. 186.49 NM) travel distances. Our CAS increased travel only for high MELD and status 1 candidates (423.24 NM vs. 298.74 NM), and reduced travel for other candidates (198.98 NM vs. 250.09 NM); overall travel burden decreased. CONCLUSION Our CAS reduced waitlist deaths by sending livers for high-MELD and status 1 candidates farther, while keeping livers for lower MELD candidates nearby. This advanced computational method can be applied again after wider discussions of adding new priorities conclude; our method designs score weightings to achieve any specified feasible allocation outcomes.
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Affiliation(s)
- Michal A Mankowski
- Department of Surgery, NYU Grossman School of Medicine, NYU Langone Health, New York, New York, USA
| | - Nicholas L Wood
- Chronic Disease Research Group, Hennepin Healthcare Research Institute, Minneapolis, Minnesota, USA
| | - Dorry L Segev
- Department of Surgery, NYU Grossman School of Medicine, NYU Langone Health, New York, New York, USA
- Department of Population Health, NYU Grossman School of Medicine, NYU Langone Health, New York, New York, USA
| | - Sommer E Gentry
- Department of Surgery, NYU Grossman School of Medicine, NYU Langone Health, New York, New York, USA
- Department of Population Health, NYU Grossman School of Medicine, NYU Langone Health, New York, New York, USA
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5
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Wood NL, VanDerwerken DN, Segev DL, Gentry SE. Logistical burden of offers and allocation inefficiency in circle-based liver allocation. Liver Transpl 2023; 29:26-33. [PMID: 35696252 DOI: 10.1002/lt.26527] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/13/2022] [Accepted: 06/08/2022] [Indexed: 01/14/2023]
Abstract
Recent changes to liver allocation replaced donor service areas with circles as the geographic unit of allocation. Circle-based allocation might increase the number of transplantation centers and candidates required to place a liver, thereby increasing the logistical burden of making and responding to offers on organ procurement organizations and transplantation centers. Circle-based allocation might also increase distribution time and cold ischemia time (CIT), particularly in densely populated areas of the country, thereby decreasing allocation efficiency. Using Scientific Registry of Transplant Recipient data from 2019 to 2021, we evaluated the number of transplantation centers and candidates required to place livers in the precircles and postcircles eras, nationally and by donor region. Compared with the precircles era, livers were offered to more candidates (5 vs. 9; p < 0.001) and centers (3 vs. 5; p < 0.001) before being accepted; more centers were involved in the match run by offer number 50 (9 vs. 14; p < 0.001); CIT increased by 0.2 h (5.9 h vs. 6.1 h; p < 0.001); and distribution time increased by 2.0 h (30.6 h vs. 32.6 h; p < 0.001). Increased burden varied geographically by donor region; livers recovered in Region 9 were offered to many more candidates (4 vs. 12; p < 0.001) and centers (3 vs. 8; p < 0.001) before being accepted, resulting in the largest increase in CIT (5.4 h vs. 6.0 h; p < 0.001). Circle-based allocation is associated with increased logistical burdens that are geographically heterogeneous. Continuous distribution systems will have to be carefully designed to avoid exacerbating this problem.
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Affiliation(s)
- Nicholas L Wood
- Department of Mathematics , United States Naval Academy , Annapolis , Maryland , USA
| | | | - Dorry L Segev
- Department of Surgery , Johns Hopkins Hospital , Baltimore , Maryland , USA.,Department of Epidemiology , Johns Hopkins School of Public Health , Baltimore , Maryland , USA
| | - Sommer E Gentry
- Department of Mathematics , United States Naval Academy , Annapolis , Maryland , USA
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6
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Asrani SK, Saracino G, Wall A, Trotter JF, Testa G, Hernaez R, Sharma P, Kwong A, Banerjee S, McKenna G. Assessment of donor quality and risk of graft failure after liver transplantation: The ID 2 EAL score. Am J Transplant 2022; 22:2921-2930. [PMID: 36053559 DOI: 10.1111/ajt.17191] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 07/19/2022] [Accepted: 08/30/2022] [Indexed: 01/25/2023]
Abstract
Accurate assessment of donor quality at the time of organ offer for liver transplantation candidates may be inadequately captured by the donor risk index (DRI). We sought to develop and validate a novel objective and simple model to assess donor risk using donor level variables available at the time of organ offer. We utilized national data from candidates undergoing primary LT (2013-2019) and assessed the prediction of graft failure 1 year after LT. The final components were donor Insulin-dependent diabetes mellitus, Donor type (DCD or DBD), cause of Death = CVA, serum creatinine, Age, height, and weight (length). The ID2 EAL score had better discrimination than DRI using bootstrap corrected concordant index over time, especially in the current era. We explored donor-recipient matching. Relative risk of graft failure ranged from 1.15 to 3.5 based on relevant donor-recipient matching by the ID2 EAL score. As an example, for certain recipients, a young DCD donor offer was preferable to an older DBD with relevant comorbidities. The ID2 EAL score may serve as an important tool for patient discussion about donor risk and decisions regarding offer acceptance. In addition, the score may be preferable to succinctly capture donor risk in future organ allocation that considers continuous distribution (www.iddealscore.com).
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Affiliation(s)
- Sumeet K Asrani
- Baylor University Medical Center, Baylor Scott and White Heath, Dallas, Texas, USA
| | - Giovanna Saracino
- Baylor University Medical Center, Baylor Scott and White Heath, Dallas, Texas, USA
| | - Anji Wall
- Baylor University Medical Center, Baylor Scott and White Heath, Dallas, Texas, USA
| | - James F Trotter
- Baylor University Medical Center, Baylor Scott and White Heath, Dallas, Texas, USA
| | - Giuliano Testa
- Baylor University Medical Center, Baylor Scott and White Heath, Dallas, Texas, USA
| | | | | | - Allison Kwong
- Stanford University, Division of Gastroenterology and Hepatology, Stanford, California, USA
| | - Srikanta Banerjee
- School of Health Sciences, Walden University, Minneapolis, Minnesota, USA
| | - Gregory McKenna
- Baylor University Medical Center, Baylor Scott and White Heath, Dallas, Texas, USA
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7
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Hassan A, Sharma P. CAQ Corner: Evolution of liver allocation policy. Liver Transpl 2022; 28:1785-1795. [PMID: 35531883 DOI: 10.1002/lt.26497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/18/2022] [Accepted: 04/29/2022] [Indexed: 01/13/2023]
Affiliation(s)
- Ammar Hassan
- Division of Gastroenterology, University of Michigan Health West, Grand Rapids, Michigan, USA.,Division of Gastroenterology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Pratima Sharma
- Division of Gastroenterology, University of Michigan Health West, Grand Rapids, Michigan, USA
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8
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9
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Wey A, Noreen S, Gentry S, Cafarella M, Trotter J, Salkowski N, Segev D, Israni A, Kasiske B, Hirose R, Snyder J. The Effect of Acuity Circles on Deceased Donor Transplant and Offer Rates Across Model for End-Stage Liver Disease Scores and Exception Statuses. Liver Transpl 2022; 28:363-375. [PMID: 34482614 DOI: 10.1002/lt.26286] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 08/25/2021] [Accepted: 08/29/2021] [Indexed: 01/05/2023]
Abstract
Acuity circles (AC), the new liver allocation system, was implemented on February 4, 2020. Difference-in-differences analyses estimated the effect of AC on adjusted deceased donor transplant and offer rates across Pediatric End-Stage Liver Disease (PELD) and Model for End-Stage Liver Disease (MELD) categories and types of exception statuses. The offer rates were the number of first offers, top 5 offers, and top 10 offers on the match run per person-year. Each analysis adjusted for candidate characteristics and only used active candidate time on the waiting list. The before-AC period was February 4, 2019, to February 3, 2020, and the after-AC period was February 4, 2020, to February 3, 2021. Candidates with PELD/MELD scores 29 to 32 and PELD/MELD scores 33 to 36 had higher transplant rates than candidates with PELD/MELD scores 15 to 28 after AC compared with before AC (transplant rate ratios: PELD/MELD scores 29-32, 2.34 3.324.71 ; PELD/MELD scores 33-36, 1.70 2.513.71 ). Candidates with PELD/MELD scores 29 or higher had higher offer rates than candidates with PELD/MELD scores 15 to 28, and candidates with PELD/MELD scores 29 to 32 had the largest difference (offer rate ratios [ORR]: first offers, 2.77 3.955.63 ; top 5 offers, 3.90 4.394.95 ; top 10 offers, 4.85 5.305.80 ). Candidates with exceptions had lower offer rates than candidates without exceptions for offers in the top 5 (ORR: hepatocellular carcinoma [HCC], 0.68 0.770.88 ; non-HCC, 0.73 0.810.89 ) and top 10 (ORR: HCC, 0.59 0.650.71 ; non-HCC, 0.69 0.750.81 ). Recipients with PELD/MELD scores 15 to 28 and an HCC exception received a larger proportion of donation after circulatory death (DCD) donors after AC than before AC, although the differences in the liver donor risk index were comparatively small. Thus, candidates with PELD/MELD scores 29 to 34 and no exceptions had better access to transplant after AC, and donor quality did not notably change beyond the proportion of DCD donors.
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Affiliation(s)
- Andrew Wey
- Scientific Registry of Transplant Recipients, Hennepin Healthcare Research Institute, Minneapolis, MN
| | - Samantha Noreen
- Organ Procurement and Transplantation Network, United Network for Organ Sharing, Richmond, VA
| | - Sommer Gentry
- Scientific Registry of Transplant Recipients, Hennepin Healthcare Research Institute, Minneapolis, MN.,Department of Mathematics, United States Naval Academy, Annapolis, MD
| | - Matt Cafarella
- Organ Procurement and Transplantation Network, United Network for Organ Sharing, Richmond, VA
| | - James Trotter
- Division of Transplant Hepatology, Baylor University Medical Center, Dallas, TX
| | - Nicholas Salkowski
- Scientific Registry of Transplant Recipients, Hennepin Healthcare Research Institute, Minneapolis, MN
| | - Dorry Segev
- Scientific Registry of Transplant Recipients, Hennepin Healthcare Research Institute, Minneapolis, MN.,Department of Surgery, Johns Hopkins University, Baltimore, MD
| | - Ajay Israni
- Scientific Registry of Transplant Recipients, Hennepin Healthcare Research Institute, Minneapolis, MN.,Department of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN.,Department of Medicine, Hennepin Healthcare, Minneapolis, MN
| | - Bertram Kasiske
- Scientific Registry of Transplant Recipients, Hennepin Healthcare Research Institute, Minneapolis, MN.,Department of Medicine, Hennepin Healthcare, Minneapolis, MN
| | - Ryutaro Hirose
- Scientific Registry of Transplant Recipients, Hennepin Healthcare Research Institute, Minneapolis, MN.,Department of Surgery, University of California, San Francisco, San Francisco, CA
| | - Jon Snyder
- Scientific Registry of Transplant Recipients, Hennepin Healthcare Research Institute, Minneapolis, MN.,Department of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN
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10
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Stewart D. Moving Toward Continuous Organ Distribution. CURRENT TRANSPLANTATION REPORTS 2021. [DOI: 10.1007/s40472-021-00352-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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11
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Khorsandi SE, Hardgrave HJ, Osborn T, Klutts G, Nigh J, Spencer-Cole RT, Kakos CD, Anastasiou I, Mavros MN, Giorgakis E. Artificial Intelligence in Liver Transplantation. Transplant Proc 2021; 53:2939-2944. [PMID: 34740449 DOI: 10.1016/j.transproceed.2021.09.045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 09/30/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Advancements based on artificial intelligence have emerged in all areas of medicine. Many decisions in organ transplantation can now potentially be addressed in a more precise manner with the aid of artificial intelligence. METHOD/RESULTS All elements of liver transplantation consist of a set of input variables and a set of output variables. Artificial intelligence identifies relationships between the input variables; that is, how they select the data groups to train patterns and how they can predict the potential outcomes of the output variables. The most widely used classifiers to address the different aspects of liver transplantation are artificial neural networks, decision tree classifiers, random forest, and naïve Bayes classification models. Artificial intelligence applications are being evaluated in liver transplantation, especially in organ allocation, donor-recipient matching, survival prediction analysis, and transplant oncology. CONCLUSION In the years to come, deep learning-based models will be used by liver transplant experts to support their decisions, especially in areas where securing equitability in the transplant process needs to be optimized.
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Affiliation(s)
- Shirin Elizabeth Khorsandi
- Institute of Liver Studies, King's College Hospital, Denmark Hill, London, UK; Institute of Hepatology, Foundation for Liver Research, Denmark Hill, London, UK; Faculty of Life Sciences & Medicine, King's College London, Strand, London, UK
| | - Hailey J Hardgrave
- College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Tamara Osborn
- Department of Surgery, University of Arkansas for Medical Sciences Medical Center, Little Rock, Arkansas
| | - Garrett Klutts
- Department of Surgery, University of Arkansas for Medical Sciences Medical Center, Little Rock, Arkansas
| | - Joe Nigh
- Department of Surgery, University of Arkansas for Medical Sciences Medical Center, Little Rock, Arkansas
| | | | - Christos D Kakos
- Surgery Working Group, Society of Junior Doctors, Athens, Greece
| | - Ioannis Anastasiou
- Department of Medicine, University of Arkansas for Medical Sciences Medical Center, Little Rock, Arkansas
| | - Michail N Mavros
- Department of Surgery, University of Arkansas for Medical Sciences Medical Center, Little Rock, Arkansas; Surgical Oncology, University of Arkansas for Medical Sciences Winthrop P. Rockefeller Cancer Institute, Little Rock, Arkansas
| | - Emmanouil Giorgakis
- Department of Surgery, University of Arkansas for Medical Sciences Medical Center, Little Rock, Arkansas; Surgical Oncology, University of Arkansas for Medical Sciences Winthrop P. Rockefeller Cancer Institute, Little Rock, Arkansas.
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12
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Polyak A, Kuo A, Sundaram V. Evolution of liver transplant organ allocation policy: Current limitations and future directions. World J Hepatol 2021; 13:830-839. [PMID: 34552690 PMCID: PMC8422916 DOI: 10.4254/wjh.v13.i8.830] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 06/22/2021] [Accepted: 07/22/2021] [Indexed: 02/06/2023] Open
Abstract
Since the adoption of the model for end-stage liver disease (MELD) score for organ allocation in 2002, numerous changes to the system of liver allocation and distribution have been made with the goal of decreasing waitlist mortality and minimizing geographic variability in median MELD score at time of transplant without worsening post-transplant outcomes. These changes include the creation and adoption of the MELD-Na score for allocation, Regional Share 15, Regional Share for Status 1, Regional Share 35/National Share 15, and, most recently, the Acuity Circles Distribution Model. However, geographic differences in median MELD at time of transplant remain as well as limits to the MELD score for allocation, as etiology of liver disease and need for transplant changes. Acute-on-chronic liver failure (ACLF) is a subset of liver failure where prevalence is rising and has been shown to have an increased mortality rate and need for transplantation that is under-demonstrated by the MELD score. This underscores the limitations of the MELD score and raises the question of whether MELD is the most accurate, objective allocation system. Alternatives to the MELD score have been proposed and studied, however MELD score remains as the current system used for allocation. This review highlights policy changes since the adoption of the MELD score, addresses limitations of the MELD score, reviews proposed alternatives to MELD, and examines the specific implications of these changes and alternatives for ACLF.
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Affiliation(s)
- Alexander Polyak
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
| | - Alexander Kuo
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
| | - Vinay Sundaram
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
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13
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Strauss AT, Boyarsky BJ, Garonzik-Wang JM, Werbel W, Durand CM, Avery RK, Jackson KR, Kernodle AB, Baker T, Snyder J, Segev DL, Massie AB. Liver transplantation in the United States during the COVID-19 pandemic: National and center-level responses. Am J Transplant 2021; 21:1838-1847. [PMID: 33107180 PMCID: PMC9800484 DOI: 10.1111/ajt.16373] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 10/02/2020] [Accepted: 10/15/2020] [Indexed: 01/25/2023]
Abstract
COVID-19 has profoundly affected the American health care system; its effect on the liver transplant (LT) waitlist based on COVID-19 incidence has not been characterized. Using SRTR data, we compared observed LT waitlist registrations, waitlist mortality, deceased donor LTs (DDLT), and living donor LTs (LDLT) 3/15/2020-8/31/2020 to expected values based on historical trends 1/2016-1/2020, stratified by statewide COVID-19 incidence. Overall, from 3/15 to 4/30, new listings were 11% fewer than expected (IRR = 0.84 0.890.93 ), LDLTs were 49% fewer (IRR = 0.37 0.510.72 ), and DDLTs were 9% fewer (IRR = 0.85 0.910.97 ). In May, new listings were 21% fewer (IRR = 0.74 0.790.84 ), LDLTs were 42% fewer (IRR = 0.39 0.580.85 ) and DDLTs were 13% more (IRR = 1.07 1.151.23 ). Centers in states with the highest incidence 3/15-4/30 had 59% more waitlist deaths (IRR = 1.09 1.592.32 ) and 34% fewer DDLTs (IRR = 0.50 0.660.86 ). By August, waitlist outcomes were occurring at expected rates, except for DDLT (13% more across all incidences). While the early COVID-affected states endured major transplant practice changes, later in the pandemic the newly COVID-affected areas were not impacted to the same extent. These results speak to the adaptability of the transplant community in addressing the pandemic and applying new knowledge to patient care.
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Affiliation(s)
- Alexandra T. Strauss
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Brian J. Boyarsky
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - William Werbel
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Christine M. Durand
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Robin K. Avery
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kyle R. Jackson
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Amber B. Kernodle
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Talia Baker
- Department of Surgery, University of Chicago Medicine, Chicago, Illinois, USA
| | - Jon Snyder
- Scientific Registry of Transplant Recipients, Minneapolis, Minnesota, USA
| | - Dorry L. Segev
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, Maryland, USA
- Scientific Registry of Transplant Recipients, Minneapolis, Minnesota, USA
| | - Allan B. Massie
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, Maryland, USA
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