*Result*: A Global Review of Organ Allocation Simulation Models.
Title:
A Global Review of Organ Allocation Simulation Models.
Authors:
Cremers R; Econometric Institute, Erasmus University, Rotterdam, the Netherlands., Stewart D; Department of Surgery, NYU Grossman School of Medicine, New York, NY., Massie AB; Department of Surgery, NYU Grossman School of Medicine, New York, NY.; Department of Population Health, NYU Grossman School of Medicine, New York, NY., Segev DL; Department of Surgery, NYU Grossman School of Medicine, New York, NY.; Department of Population Health, NYU Grossman School of Medicine, New York, NY., Gentry SE; Department of Surgery, NYU Grossman School of Medicine, New York, NY.; Department of Population Health, NYU Grossman School of Medicine, New York, NY., Mankowski MA; Department of Surgery, NYU Grossman School of Medicine, New York, NY.
Source:
Transplantation [Transplantation] 2026 Mar 01; Vol. 110 (3), pp. e573-e582. Date of Electronic Publication: 2026 Feb 04.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 0132144 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1534-6080 (Electronic) Linking ISSN: 00411337 NLM ISO Abbreviation: Transplantation Subsets: MEDLINE
Imprint Name(s):
Publication: Hagerstown, MD : Lippincott Williams & Wilkins
Original Publication: Baltimore, Williams & Wilkins.
Original Publication: Baltimore, Williams & Wilkins.
MeSH Terms:
References:
Metropolis N, Ulam S. The Monte Carlo method. J Am Stat Assoc. 1949;44:335–341.
Tocher KD, Owen DG. The automatic programming of simulations. J Simul. 2008;2:143–152.
Banks J, Carson JS, Nelson BL, Nicol DM. Discrete-Event System Simulation. 4th ed. Prentice-Hall; 2005.
Ridge JC, Jones SK, Nielsen MS, et al. Capacity planning for intensive care units. Eur J Oper Res. 1998;105:346–355.
Dumas MB. Simulation modeling for hospital bed planning. Simulation. 1984;43:69–78.
Ruth RJ, Wyszewianski L, Herline G. Kidney transplantation: a simulation model for examining demand and supply. Manage Sci. 1985;31:515–526.
Pritsker AAB, Martin DL, Reust JS, et al. Organ transplantation policy evaluation. In: Proceedings of the 27th Conference on Winter Simulation. Arlington, VA; 1995:1314–1323.
Taranto SE, Harper AM, Edwards EB, et al. Developing a national allocation model for cadaveric kidneys. In: 2000 Winter Simulation Conference Proceedings (Cat. No. 00CH37165). Vol. 2. Orlando, FL; 2000:1971–1977.
Scientific Registry of Transplant Recipients. Kidney-pancreas simulated allocation model. Available at https://www.srtr.org/media/1295/kpsam-2015-user-guide.pdf . Accessed September 21, 2024.
Scientific Registry of Transplant Recipients. Liver simulated allocation model. Available at https://www.srtr.org/media/1361/LSAM-2019-User-Guide.pdf . Accessed September 21, 2024.
Scientific Registry of Transplant Recipients. Thoracic simulated allocation model. Available at https://www.srtr.org/media/1294/tsam-2015-user-guide.pdf . Accessed September 21, 2024.
Scientific Registry of Transplant Recipients. Background and methodology in simulation analysis: OASim. Available at https://www.srtr.org/media/1837/srtr-simulation-methodology.pdf . Accessed September 21, 2024.
Organ Procurement and Transplantation Network/United Network for Organ Sharing. Continuous distribution of kidneys and pancreata. Available at https://hrsa.unos.org/media/npdlps1k/public-comment_kidney_cd-update_summer-24.pdf. Accessed September 21, 2024.
Scientific Registry of Transplant Recipients. SRC analytical methods subcommittee meeting minutes. Available at https://www.srtr.org/media/1547/src-ams-minutes_20220121.pdf . Accessed September 21, 2024.
de Ferrante H, de Rosner-Van Rosmalen M, Smeulders B, et al. A discrete event simulator for policy evaluation in liver allocation in Eurotransplant. ArXiv. Preprint posted online April 22, 2025. doi:10.48550/arXiv.2410.10840. (PMID: 10.48550/arXiv.2410.10840)
Beck JR, Pauker SG. The Markov process in medical prognosis. Med Decis Making. 1983;3:419–458.
Alagoz O, Bryce CL, Shechter S, et al. Incorporating biological natural history in simulation models: empirical estimates of the progression of end-stage liver disease. Med Decis Making. 2003;25:620–632.
Medved D, Nugues P, Nilsson J. Simulating the outcome of heart allocation policies using deep neural networks. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honolulu, HI; 2018:6141–6144.
Tran J, Divya S, Gotlieb N, et al. Application of machine learning in liver transplantation: a review. Hepatol Int. 2022;16:495–508.
Mulvihill MS, Lee HJ, Weber J, et al. Variability in donor organ offer acceptance and lung transplantation survival. J Heart Lung Transplant. 2020;39:353–362.
Kim SP, Gupta D, Israni AK, et al. Accept/decline decision module for the liver simulated allocation model. Health Care Manag Sci. 2015;18:35–57.
Health Resources and Services Administration. OPTN policies. Available at https://optn.transplant.hrsa.gov/media/eavh5bf3/optn_policies.pdf . Accessed November 21, 2024.
Stegall MD, Stock PG, Andreoni K, et al. Why do we have the kidney allocation system we have today? A history of the 2014 kidney allocation system. Hum Immunol. 2017;78:4–8.
Gharibi Z, Hahsler M. A simulation-based optimization model to study the impact of multiple-region listing and information sharing on kidney transplant outcomes. Int J Environ Res Public Health. 2021;18:873.
Mankowski MA, Wood NL, Segev DL, et al. Removing geographic boundaries from liver allocation: a method for designing continuous distribution scores. Clin Transplant. 2023;37:e15017.
Papalexopoulos TP, Bertsimas D, Cohen IG, et al. Ethics-by-design: efficient, fair and inclusive resource allocation using machine learning. J Law Biosci. 2022;9:lsac012.
Organ Procurement and Transplantation Network. Continuous distribution—lung. Available at https://optn.transplant.hrsa.gov/policies-bylaws/a-closer-look/continuous-distribution/continuous-distribution-lung/ . Accessed February 19, 2025.
Siebers PO, Macal CM, Garnett J, et al. Discrete-event simulation is dead, long live agent-based simulation! J Simul. 2010;4:204–210.
Rose J, Gunsalus PR, Lehr CJ, et al. A modular simulation framework for organ allocation. J Heart Lung Transplant. 2024;43:1326–1335.
Rosendale J, Vece G, Lindblad K, et al. Developing and verifying an artificial twin of the organ procurement and transplant process: a systems approach. J Simul. 2024;18:65–87.
Johnson JG, Firth J, Bird SM, et al. The effect of altering eligibility criteria for entry onto a kidney transplant waiting list. Nephrol Dial Transplant. 2001;16:816–823.
de Meester J, Bogers M, de Winter H, et al. Which ABO-matching rule should be the decisive factor in the choice between a highly urgent and an elective patient? Transpl Int. 2002;15:431–435.
Gentry S, Chow E, Massie A, et al. Gerrymandering for justice: redistricting US liver allocation. Interfaces. 2015;45:462–480.
Davis A, Mehrotra S, Friedewald J, et al. Characteristics of a simulation model of the national kidney transplantation system. In: 2013 Winter Simulations Conference (WSC). Washington D.C.; 2013:2320–2329.
Kilambi V, Barah M, Formica RN, et al. Evaluation of opening offers early for deceased donor kidneys at risk of nonutilization. Clin J Am Soc Nephrol. 2024;19:233–240.
Shechter SM, Bryce CL, Alagoz O, et al. A clinically based discrete-event simulation of end-stage liver disease and the organ allocation process. Med Decis Making. 2005;25:199–209.
Iyer AK, Zenarosa GL, Schaefer AJ, et al. A biologically based discrete-event simulation model of liver transplantation in the United States for pediatric and adult patients. In: Proceedings of the 2011 Winter Simulation Conference (WSC). Phoenix, AZ; 2011:1275–1282.
Bryce CL, Chang CCH, Ren Y, et al. Using time-varying models to estimate post-transplant survival in pediatric liver transplant recipients. PLoS One. 2018;13:e0198132.
Feng W, Kong N, Wan H. A simulation study of cadaveric liver allocation with a single-score patient prioritization formula. J Simul. 2013;7:109–125.
Sandikçi B, Tunç S, Tanriover B. A new simulation model for kidney transplantation in the United States. In: 2019 Winter Simulation Conference (WSC). National Harbor, MD; 2019:1079–1090.
Kilambi V, Bui K, Mehrotra SL. An open-source simulation software platform for community research and development for liver allocation policies. Transplantation. 2018;102:e47–e48.
Kilambi V, Bui K, Mehrotra S. LivSim manual: a simulator for modeling liver allocation policies in US Organ Procurement and Transplantation Network System. Available at https://github.com/LivSim2017/LivSim-Codes/blob/master/LivSim_Manual.pdf . Accessed September 23, 2024.
Hasankhani F, Khademi A. Efficient and fair heart allocation policies for transplantation. MDM Policy Pract. 2017;2:2381468317709475.
Niemann M, Lachmann N, Geneugelijk K, et al. Computational Eurotransplant kidney allocation simulations demonstrate the feasibility and benefit of T-cell epitope matching. PLoS Comput Biol. 2021;17:e1009248.
de Ferrante HC, Goya RL, Smeulders BML, et al. The ETKidney simulator: a discrete event simulator to assess the impact of alternative kidney allocation rules in Eurotransplant. ArXiv. Preprint posted online February 20, 2025. doi:10.48550/arXiv.2502.15001. (PMID: 10.48550/arXiv.2502.15001)
Zhang Y, Hu A, Lin Y, et al. simKAP: simulation framework for the kidney allocation process with decision making model. Sci Rep. 2023;13:16367.
Jacquelinet C, Audry B, Golbreich C, et al. Changing kidney allocation policy in France: the value of simulation. In: AMIA Annual Symposium Proceedings. Vol. 2006. Washington D.C.; 2006:374.
Shoaib M, Prabhakar U, Mahlawat S, et al. A discrete-event simulation model of the kidney transplantation system in Rajasthan, India. Health Syst. 2022;11:30–47.
Sjule HM, Vinter CN, Dueland S, et al. The spillover effects of extending liver transplantation to patients with Colorectal Liver Metastases: a discrete event simulation analysis. Med Decis Making. 2024;44:529–542.
Crowe S, Vasilakis C, Gallivan S, et al. Informing the management of pediatric heart transplant waiting lists: complementary use of simulation and analytical modeling. In: 2015 Winter Simulation Conference (WSC). 2015:1654–1665.
McKenney C, Torabi J, Todd R, et al. Wasted potential: decoding the trifecta of donor kidney shortage, underutilization, and rising discard rates. Transplantology. 2024;5:51–64.
Nguyen SN, Schiazza A, Richmond ME, et al. Trends in pediatric donor heart discard rates and the potential use of unallocated hearts for allogeneic valve transplantation. JTCVS Open. 2023;15:374–381.
Torabi J, Todd R, van Leeuwen LL, et al. A decade of liver transplantation in the United States: drivers of discard and underutilization. Transplant Direct. 2024;10:e1605.
Eden J, Da Silva RXS, Cortes-Cerisuelo M, et al. Utilization of livers donated after circulatory death for transplantation—an international comparison. J Hepatol. 2023;78:1007–1016.
Scientific Registry of Transplant Recipients. Analysis report: update. Available at https://optn.transplant.hrsa.gov/media/2985/ki2019_01_analysisreport.pdf . Accessed February 20, 2025.
Scientific Registry of Transplant Recipients. KIPA2022_01 allocation simulation analysis report: 4 continuous distribution policy scenarios. Available at https://optn.transplant.hrsa.gov/media/uveoty1q/srtr_kipacd2022_01_analysisreport_20221020.pdf . Accessed February 20, 2025.
Scientific Registry of Transplant Recipients. KIPA2024_01 simulation analysis report. Available at https://optn.transplant.hrsa.gov/media/baldrgot/ki2024_01_request_analysis_report.pdf . Accessed February 20, 2025.
van den Hout WB, Smits JM, Deng MC, et al.; Comparative Outcome and Clinical Profiles in Transplantation study group. The heart-allocation simulation model: a tool for comparison of transplantation allocation policies. Transplantation. 2003;76:1492–1497.
Wood NL, Mogul DB, Perito ER, et al. Liver simulated allocation model does not effectively predict organ offer decisions for pediatric liver transplant candidates. Am J Transplant. 2021;21:3157–3162.
Perito ER, Mogul DB, VanDerwerken D, et al. The impact of increased allocation priority for children awaiting liver transplant: a liver simulated allocation model (LSAM) analysis. J Pediatr Gastroenterol Nutr. 2019;68:472–479.
Goldberg DS, Levine M, Karp S, et al. Share 35 changes in center-level liver acceptance practices. Liver Transpl. 2017;23:604–613.
Goel A, Kim WR, Pyke J, et al. Liver simulated allocation modeling: were the predictions accurate for share 35? Transplantation. 2018;102:769–774.
Lehr CJ, Skeans M, Valapour M. Validating thoracic simulated allocation model predictions for impact of broader geographic sharing of donor lungs on transplant waitlist outcomes. J Heart Lung Transplant. 2020;39:433–440.
Gustafson S, Israni A, Pyke J, et al. Projection versus reality: KPSAM and KAS. Am J Transplant. 2016;16:625–626.
Bernards S, Lee E, Leung N, et al. Liver simulated allocation model (LSAM) of a height-based policy change to improve sex disparity in liver transplantation (LT). Am J Transplant. 2021;21:776–776.
Gebel HM, Kasiske BL, Gustafson SK, et al. Allocating deceased donor kidneys to candidates with high panel–reactive antibodies. Clin J Am Soc Nephrol. 2016;11:505–511.
Kilambi V, Mehrotra S. Improving liver allocation using optimized neighborhoods. Transplantation. 2017;101:350–359.
Tocher KD, Owen DG. The automatic programming of simulations. J Simul. 2008;2:143–152.
Banks J, Carson JS, Nelson BL, Nicol DM. Discrete-Event System Simulation. 4th ed. Prentice-Hall; 2005.
Ridge JC, Jones SK, Nielsen MS, et al. Capacity planning for intensive care units. Eur J Oper Res. 1998;105:346–355.
Dumas MB. Simulation modeling for hospital bed planning. Simulation. 1984;43:69–78.
Ruth RJ, Wyszewianski L, Herline G. Kidney transplantation: a simulation model for examining demand and supply. Manage Sci. 1985;31:515–526.
Pritsker AAB, Martin DL, Reust JS, et al. Organ transplantation policy evaluation. In: Proceedings of the 27th Conference on Winter Simulation. Arlington, VA; 1995:1314–1323.
Taranto SE, Harper AM, Edwards EB, et al. Developing a national allocation model for cadaveric kidneys. In: 2000 Winter Simulation Conference Proceedings (Cat. No. 00CH37165). Vol. 2. Orlando, FL; 2000:1971–1977.
Scientific Registry of Transplant Recipients. Kidney-pancreas simulated allocation model. Available at https://www.srtr.org/media/1295/kpsam-2015-user-guide.pdf . Accessed September 21, 2024.
Scientific Registry of Transplant Recipients. Liver simulated allocation model. Available at https://www.srtr.org/media/1361/LSAM-2019-User-Guide.pdf . Accessed September 21, 2024.
Scientific Registry of Transplant Recipients. Thoracic simulated allocation model. Available at https://www.srtr.org/media/1294/tsam-2015-user-guide.pdf . Accessed September 21, 2024.
Scientific Registry of Transplant Recipients. Background and methodology in simulation analysis: OASim. Available at https://www.srtr.org/media/1837/srtr-simulation-methodology.pdf . Accessed September 21, 2024.
Organ Procurement and Transplantation Network/United Network for Organ Sharing. Continuous distribution of kidneys and pancreata. Available at https://hrsa.unos.org/media/npdlps1k/public-comment_kidney_cd-update_summer-24.pdf. Accessed September 21, 2024.
Scientific Registry of Transplant Recipients. SRC analytical methods subcommittee meeting minutes. Available at https://www.srtr.org/media/1547/src-ams-minutes_20220121.pdf . Accessed September 21, 2024.
de Ferrante H, de Rosner-Van Rosmalen M, Smeulders B, et al. A discrete event simulator for policy evaluation in liver allocation in Eurotransplant. ArXiv. Preprint posted online April 22, 2025. doi:10.48550/arXiv.2410.10840. (PMID: 10.48550/arXiv.2410.10840)
Beck JR, Pauker SG. The Markov process in medical prognosis. Med Decis Making. 1983;3:419–458.
Alagoz O, Bryce CL, Shechter S, et al. Incorporating biological natural history in simulation models: empirical estimates of the progression of end-stage liver disease. Med Decis Making. 2003;25:620–632.
Medved D, Nugues P, Nilsson J. Simulating the outcome of heart allocation policies using deep neural networks. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honolulu, HI; 2018:6141–6144.
Tran J, Divya S, Gotlieb N, et al. Application of machine learning in liver transplantation: a review. Hepatol Int. 2022;16:495–508.
Mulvihill MS, Lee HJ, Weber J, et al. Variability in donor organ offer acceptance and lung transplantation survival. J Heart Lung Transplant. 2020;39:353–362.
Kim SP, Gupta D, Israni AK, et al. Accept/decline decision module for the liver simulated allocation model. Health Care Manag Sci. 2015;18:35–57.
Health Resources and Services Administration. OPTN policies. Available at https://optn.transplant.hrsa.gov/media/eavh5bf3/optn_policies.pdf . Accessed November 21, 2024.
Stegall MD, Stock PG, Andreoni K, et al. Why do we have the kidney allocation system we have today? A history of the 2014 kidney allocation system. Hum Immunol. 2017;78:4–8.
Gharibi Z, Hahsler M. A simulation-based optimization model to study the impact of multiple-region listing and information sharing on kidney transplant outcomes. Int J Environ Res Public Health. 2021;18:873.
Mankowski MA, Wood NL, Segev DL, et al. Removing geographic boundaries from liver allocation: a method for designing continuous distribution scores. Clin Transplant. 2023;37:e15017.
Papalexopoulos TP, Bertsimas D, Cohen IG, et al. Ethics-by-design: efficient, fair and inclusive resource allocation using machine learning. J Law Biosci. 2022;9:lsac012.
Organ Procurement and Transplantation Network. Continuous distribution—lung. Available at https://optn.transplant.hrsa.gov/policies-bylaws/a-closer-look/continuous-distribution/continuous-distribution-lung/ . Accessed February 19, 2025.
Siebers PO, Macal CM, Garnett J, et al. Discrete-event simulation is dead, long live agent-based simulation! J Simul. 2010;4:204–210.
Rose J, Gunsalus PR, Lehr CJ, et al. A modular simulation framework for organ allocation. J Heart Lung Transplant. 2024;43:1326–1335.
Rosendale J, Vece G, Lindblad K, et al. Developing and verifying an artificial twin of the organ procurement and transplant process: a systems approach. J Simul. 2024;18:65–87.
Johnson JG, Firth J, Bird SM, et al. The effect of altering eligibility criteria for entry onto a kidney transplant waiting list. Nephrol Dial Transplant. 2001;16:816–823.
de Meester J, Bogers M, de Winter H, et al. Which ABO-matching rule should be the decisive factor in the choice between a highly urgent and an elective patient? Transpl Int. 2002;15:431–435.
Gentry S, Chow E, Massie A, et al. Gerrymandering for justice: redistricting US liver allocation. Interfaces. 2015;45:462–480.
Davis A, Mehrotra S, Friedewald J, et al. Characteristics of a simulation model of the national kidney transplantation system. In: 2013 Winter Simulations Conference (WSC). Washington D.C.; 2013:2320–2329.
Kilambi V, Barah M, Formica RN, et al. Evaluation of opening offers early for deceased donor kidneys at risk of nonutilization. Clin J Am Soc Nephrol. 2024;19:233–240.
Shechter SM, Bryce CL, Alagoz O, et al. A clinically based discrete-event simulation of end-stage liver disease and the organ allocation process. Med Decis Making. 2005;25:199–209.
Iyer AK, Zenarosa GL, Schaefer AJ, et al. A biologically based discrete-event simulation model of liver transplantation in the United States for pediatric and adult patients. In: Proceedings of the 2011 Winter Simulation Conference (WSC). Phoenix, AZ; 2011:1275–1282.
Bryce CL, Chang CCH, Ren Y, et al. Using time-varying models to estimate post-transplant survival in pediatric liver transplant recipients. PLoS One. 2018;13:e0198132.
Feng W, Kong N, Wan H. A simulation study of cadaveric liver allocation with a single-score patient prioritization formula. J Simul. 2013;7:109–125.
Sandikçi B, Tunç S, Tanriover B. A new simulation model for kidney transplantation in the United States. In: 2019 Winter Simulation Conference (WSC). National Harbor, MD; 2019:1079–1090.
Kilambi V, Bui K, Mehrotra SL. An open-source simulation software platform for community research and development for liver allocation policies. Transplantation. 2018;102:e47–e48.
Kilambi V, Bui K, Mehrotra S. LivSim manual: a simulator for modeling liver allocation policies in US Organ Procurement and Transplantation Network System. Available at https://github.com/LivSim2017/LivSim-Codes/blob/master/LivSim_Manual.pdf . Accessed September 23, 2024.
Hasankhani F, Khademi A. Efficient and fair heart allocation policies for transplantation. MDM Policy Pract. 2017;2:2381468317709475.
Niemann M, Lachmann N, Geneugelijk K, et al. Computational Eurotransplant kidney allocation simulations demonstrate the feasibility and benefit of T-cell epitope matching. PLoS Comput Biol. 2021;17:e1009248.
de Ferrante HC, Goya RL, Smeulders BML, et al. The ETKidney simulator: a discrete event simulator to assess the impact of alternative kidney allocation rules in Eurotransplant. ArXiv. Preprint posted online February 20, 2025. doi:10.48550/arXiv.2502.15001. (PMID: 10.48550/arXiv.2502.15001)
Zhang Y, Hu A, Lin Y, et al. simKAP: simulation framework for the kidney allocation process with decision making model. Sci Rep. 2023;13:16367.
Jacquelinet C, Audry B, Golbreich C, et al. Changing kidney allocation policy in France: the value of simulation. In: AMIA Annual Symposium Proceedings. Vol. 2006. Washington D.C.; 2006:374.
Shoaib M, Prabhakar U, Mahlawat S, et al. A discrete-event simulation model of the kidney transplantation system in Rajasthan, India. Health Syst. 2022;11:30–47.
Sjule HM, Vinter CN, Dueland S, et al. The spillover effects of extending liver transplantation to patients with Colorectal Liver Metastases: a discrete event simulation analysis. Med Decis Making. 2024;44:529–542.
Crowe S, Vasilakis C, Gallivan S, et al. Informing the management of pediatric heart transplant waiting lists: complementary use of simulation and analytical modeling. In: 2015 Winter Simulation Conference (WSC). 2015:1654–1665.
McKenney C, Torabi J, Todd R, et al. Wasted potential: decoding the trifecta of donor kidney shortage, underutilization, and rising discard rates. Transplantology. 2024;5:51–64.
Nguyen SN, Schiazza A, Richmond ME, et al. Trends in pediatric donor heart discard rates and the potential use of unallocated hearts for allogeneic valve transplantation. JTCVS Open. 2023;15:374–381.
Torabi J, Todd R, van Leeuwen LL, et al. A decade of liver transplantation in the United States: drivers of discard and underutilization. Transplant Direct. 2024;10:e1605.
Eden J, Da Silva RXS, Cortes-Cerisuelo M, et al. Utilization of livers donated after circulatory death for transplantation—an international comparison. J Hepatol. 2023;78:1007–1016.
Scientific Registry of Transplant Recipients. Analysis report: update. Available at https://optn.transplant.hrsa.gov/media/2985/ki2019_01_analysisreport.pdf . Accessed February 20, 2025.
Scientific Registry of Transplant Recipients. KIPA2022_01 allocation simulation analysis report: 4 continuous distribution policy scenarios. Available at https://optn.transplant.hrsa.gov/media/uveoty1q/srtr_kipacd2022_01_analysisreport_20221020.pdf . Accessed February 20, 2025.
Scientific Registry of Transplant Recipients. KIPA2024_01 simulation analysis report. Available at https://optn.transplant.hrsa.gov/media/baldrgot/ki2024_01_request_analysis_report.pdf . Accessed February 20, 2025.
van den Hout WB, Smits JM, Deng MC, et al.; Comparative Outcome and Clinical Profiles in Transplantation study group. The heart-allocation simulation model: a tool for comparison of transplantation allocation policies. Transplantation. 2003;76:1492–1497.
Wood NL, Mogul DB, Perito ER, et al. Liver simulated allocation model does not effectively predict organ offer decisions for pediatric liver transplant candidates. Am J Transplant. 2021;21:3157–3162.
Perito ER, Mogul DB, VanDerwerken D, et al. The impact of increased allocation priority for children awaiting liver transplant: a liver simulated allocation model (LSAM) analysis. J Pediatr Gastroenterol Nutr. 2019;68:472–479.
Goldberg DS, Levine M, Karp S, et al. Share 35 changes in center-level liver acceptance practices. Liver Transpl. 2017;23:604–613.
Goel A, Kim WR, Pyke J, et al. Liver simulated allocation modeling: were the predictions accurate for share 35? Transplantation. 2018;102:769–774.
Lehr CJ, Skeans M, Valapour M. Validating thoracic simulated allocation model predictions for impact of broader geographic sharing of donor lungs on transplant waitlist outcomes. J Heart Lung Transplant. 2020;39:433–440.
Gustafson S, Israni A, Pyke J, et al. Projection versus reality: KPSAM and KAS. Am J Transplant. 2016;16:625–626.
Bernards S, Lee E, Leung N, et al. Liver simulated allocation model (LSAM) of a height-based policy change to improve sex disparity in liver transplantation (LT). Am J Transplant. 2021;21:776–776.
Gebel HM, Kasiske BL, Gustafson SK, et al. Allocating deceased donor kidneys to candidates with high panel–reactive antibodies. Clin J Am Soc Nephrol. 2016;11:505–511.
Kilambi V, Mehrotra S. Improving liver allocation using optimized neighborhoods. Transplantation. 2017;101:350–359.
Entry Date(s):
Date Created: 20260204 Date Completed: 20260217 Latest Revision: 20260217
Update Code:
20260218
DOI:
10.1097/TP.0000000000005595
PMID:
41634911
Database:
MEDLINE
*Further Information*
*Summary: Since their early development in the 1980s, Simulated Allocation Models (SAMs) have helped policymakers forecast the impact of proposed allocation policy changes on patient outcomes before implementation. In the United States, models like the Kidney-Pancreas Simulated Allocation Model, Liver Simulated Allocation Model, and Thoracic Simulated Allocation Model have been instrumental in shaping organ allocation policies. Analogous models have emerged globally, including the ETKidney and Eurotransplant Liver Allocation System simulators for the Eurotransplant region, to address country and region-specific allocation challenges. This review categorizes and compares SAMs based on their core assumptions, data, and modeling approaches. We highlight challenges in model validation, the use of synthetic data, and model transparency. While simplifying assumptions are often necessary because of limited data, their influence on results should be clearly communicated to ensure policymakers can interpret model predictions accurately. Furthermore, model validation using both retrospective and prospective data is essential to assess performance under evolving policies. Greater transparency through open-source models, detailed reporting of assumptions, and validation efforts can enhance collaboration, reproducibility, and confidence in transplant research. By providing a global perspective on SAMs, this review aims to inform future research and policy development, promoting evidence-based policy development in organ transplantation.
(Copyright © 2026 Wolters Kluwer Health, Inc. All rights reserved.)*
*The authors declare no conflicts of interest.*