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Yaghoubi M, Cressman S, Edwards L, Shechter S, Doyle-Waters MM, Keown P, Sapir-Pichhadze R, Bryan S. A Systematic Review of Kidney Transplantation Decision Modelling Studies. APPLIED HEALTH ECONOMICS AND HEALTH POLICY 2023; 21:39-51. [PMID: 35945483 DOI: 10.1007/s40258-022-00744-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/19/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Genome-based precision medicine strategies promise to minimize premature graft loss after renal transplantation, through precision approaches to immune compatibility matching between kidney donors and recipients. The potential adoption of this technology calls for important changes to clinical management processes and allocation policy. Such potential policy change decisions may be supported by decision models from health economics, comparative effectiveness research and operations management. OBJECTIVE We used a systematic approach to identify and extract information about models published in the kidney transplantation literature and provide an overview of the status of our collective model-based knowledge about the kidney transplant process. METHODS Database searches were conducted in MEDLINE, Embase, Web of Science and other sources, for reviews and primary studies. We reviewed all English-language papers that presented a model that could be a tool to support decision making in kidney transplantation. Data were extracted on the clinical context and modelling methods used. RESULTS A total of 144 studies were included, most of which focused on a single component of the transplantation process, such as immunosuppressive therapy or donor-recipient matching and organ allocation policies. Pre- and post-transplant processes have rarely been modelled together. CONCLUSION A whole-disease modelling approach is preferred to inform precision medicine policy, given its potential upstream implementation in the treatment pathway. This requires consideration of pre- and post-transplant natural history, risk factors for allograft dysfunction and failure, and other post-transplant outcomes. Our call is for greater collaboration across disciplines and whole-disease modelling approaches to more accurately simulate complex policy decisions about the integration of precision medicine tools in kidney transplantation.
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Affiliation(s)
- Mohsen Yaghoubi
- Department of Pharmacy Practice, Mercer University College of Pharmacy, Atlanta, USA
| | - Sonya Cressman
- Faculty of Health Sciences, Simon Fraser University, School of Population and Public Health, University of British Columbia, Vancouver, Canada
| | - Louisa Edwards
- School of Population and Public Health, University of British Columbia, Vancouver, V6T 1Z3, Canada
| | - Steven Shechter
- Sauder School of Business, University of British Columbia, Vancouver, Canada
| | - Mary M Doyle-Waters
- Centre for Clinical Epidemiology and Evaluation, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, Canada
| | - Paul Keown
- Department of Medicine, Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
| | | | - Stirling Bryan
- School of Population and Public Health, University of British Columbia, Vancouver, V6T 1Z3, Canada.
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Blasioli E, Mansouri B, Tamvada SS, Hassini E. Vaccine Allocation and Distribution: A Review with a Focus on Quantitative Methodologies and Application to Equity, Hesitancy, and COVID-19 Pandemic. OPERATIONS RESEARCH FORUM 2023; 4:27. [PMCID: PMC10028329 DOI: 10.1007/s43069-023-00194-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
This review focuses on vaccine distribution and allocation in the context of the current COVID-19 pandemic. The implications discussed are in the areas of equity in vaccine distribution and allocation (at a national level as well as worldwide), vaccine hesitancy, game-theoretic modeling to guide decision-making and policy-making at a governmental level, distribution and allocation barriers (in particular in low-income countries), and operations research (OR) mathematical models to plan and execute vaccine distribution and allocation. To conduct this review, we adopt a novel methodology that consists of three phases. The first phase deploys a bibliometric analysis; the second phase concentrates on a network analysis; and the last phase proposes a refined literature review based on the results obtained by the previous two phases. The quantitative techniques utilized to conduct the first two phases allow describing the evolution of the research in this area and its potential ramifications in future. In conclusion, we underscore the significance of operations research (OR)/management science (MS) research in addressing numerous challenges and trade-offs connected to the current pandemic and its strategic impact in future research.
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Affiliation(s)
- Emanuele Blasioli
- grid.25073.330000 0004 1936 8227DeGroote School of Business, McMaster University, Hamilton, Canada
| | - Bahareh Mansouri
- grid.412362.00000 0004 1936 8219Sobey School of Business, Saint Mary’s University, Halifax, Canada
| | - Srinivas Subramanya Tamvada
- grid.29857.310000 0001 2097 4281Department of Industrial and Manufacturing Engineering, Pennsylvania State University, State College, PA, USA, PennsyIvania, USA
| | - Elkafi Hassini
- grid.25073.330000 0004 1936 8227DeGroote School of Business, McMaster University, Hamilton, Canada
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3
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Mekov E, Ilieva V. Machine learning in lung transplantation: Where are we? Presse Med 2022; 51:104140. [PMID: 36252820 DOI: 10.1016/j.lpm.2022.104140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 09/29/2022] [Indexed: 11/06/2022] Open
Abstract
Lung transplantation has been accepted as a viable treatment for end-stage respiratory failure. While regression models continue to be a standard approach for attempting to predict patients' outcomes after lung transplantation, more sophisticated supervised machine learning (ML) techniques are being developed and show encouraging results. Transplant clinicians could utilize ML as a decision-support tool in a variety of situations (e.g. waiting list mortality, donor selection, immunosuppression, rejection prediction). Although for some topics ML is at an advanced stage of research (i.e. imaging and pathology) there are certain topics in lung transplantation that needs to be aware of the benefits it could provide.
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Affiliation(s)
- Evgeni Mekov
- Department of Occupational Diseases, Faculty of Medicine, Medical University - Sofia, Sofia, Bulgaria
| | - Viktoria Ilieva
- Department of Anesthesiology and Intensive Care, Faculty of Medicine, Medical University - Sofia, Sofia, Bulgaria.
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Connor KL, O'Sullivan ED, Marson LP, Wigmore SJ, Harrison EM. The Future Role of Machine Learning in Clinical Transplantation. Transplantation 2021; 105:723-735. [PMID: 32826798 DOI: 10.1097/tp.0000000000003424] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The use of artificial intelligence and machine learning (ML) has revolutionized our daily lives and will soon be instrumental in healthcare delivery. The rise of ML is due to multiple factors: increasing access to massive datasets, exponential increases in processing power, and key algorithmic developments that allow ML models to tackle increasingly challenging questions. Progressively more transplantation research is exploring the potential utility of ML models throughout the patient journey, although this has not yet widely transitioned into the clinical domain. In this review, we explore common approaches used in ML in solid organ clinical transplantation and consider opportunities for ML to help clinicians and patients. We discuss ways in which ML can aid leverage of large complex datasets, generate cutting-edge prediction models, perform clinical image analysis, discover novel markers in molecular data, and fuse datasets to generate novel insights in modern transplantation practice. We focus on key areas in transplantation in which ML is driving progress, explore the future potential roles of ML, and discuss the challenges and limitations of these powerful tools.
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Affiliation(s)
- Katie L Connor
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom.,Edinburgh Transplant Unit, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.,Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Eoin D O'Sullivan
- Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Lorna P Marson
- Edinburgh Transplant Unit, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.,Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Stephen J Wigmore
- Edinburgh Transplant Unit, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.,Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Ewen M Harrison
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
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A multi-stage stochastic programming approach to epidemic resource allocation with equity considerations. Health Care Manag Sci 2021; 24:597-622. [PMID: 33970390 PMCID: PMC8107811 DOI: 10.1007/s10729-021-09559-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 02/19/2021] [Indexed: 01/16/2023]
Abstract
Existing compartmental models in epidemiology are limited in terms of optimizing the resource allocation to control an epidemic outbreak under disease growth uncertainty. In this study, we address this core limitation by presenting a multi-stage stochastic programming compartmental model, which integrates the uncertain disease progression and resource allocation to control an infectious disease outbreak. The proposed multi-stage stochastic program involves various disease growth scenarios and optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals. We define two new equity metrics, namely infection and capacity equity, and explicitly consider equity for allocating treatment funds and facilities over multiple time stages. We also study the multi-stage value of the stochastic solution (VSS), which demonstrates the superiority of the proposed stochastic programming model over its deterministic counterpart. We apply the proposed formulation to control the Ebola Virus Disease (EVD) in Guinea, Sierra Leone, and Liberia of West Africa to determine the optimal and fair resource-allocation strategies. Our model balances the proportion of infections over all regions, even without including the infection equity or prevalence equity constraints. Model results also show that allocating treatment resources proportional to population is sub-optimal, and enforcing such a resource allocation policy might adversely impact the total number of infections and deaths, and thus resulting in a high cost that we have to pay for the fairness. Our multi-stage stochastic epidemic-logistics model is practical and can be adapted to control other infectious diseases in meta-populations and dynamically evolving situations.
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Karami F, Kernodle AB, Ishaque T, Segev DL, Gentry SE. Allocating kidneys in optimized heterogeneous circles. Am J Transplant 2021; 21:1179-1185. [PMID: 32808468 DOI: 10.1111/ajt.16274] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Revised: 07/19/2020] [Accepted: 08/12/2020] [Indexed: 01/25/2023]
Abstract
Recently, the Organ Procurement and Transplant Network approved a plan to allocate kidneys within 250-nm circles around donor hospitals. These homogeneous circles might not substantially reduce geographic differences in transplant rates because deceased donor kidney supply and demand differ across the country. Using Scientific Registry of Transplant Recipients data from 2016-2019, we used an integer program to design unique, heterogeneous circles with sizes between 100 and 500 nm that reduced supply/demand ratio variation across transplant centers. We weighted demand according to wait time because candidates who have waited longer have higher priority. We compared supply/demand ratios and average travel distance of kidneys, using heterogeneous circles and 250 and 500-nm fixed-distance homogeneous circles. We found that 40% of circles could be 250 nm or smaller, while reducing supply/demand ratio variation more than homogeneous circles. Supply/demand ratios across centers for heterogeneous circles ranged from 0.06 to 0.13 kidneys per wait-year, compared to 0.04 to 0.47 and 0.05 to 0.15 kidneys per wait-year for 250-nm and 500-nm homogeneous circles, respectively. The average travel distance for kidneys using heterogeneous, and 250-nm and 500-nm fixed-distance circles was 173 nm, 134 nm, and 269 nm, respectively. Heterogeneous circles reduce geographic disparity compared to homogeneous circles, while maintaining reasonable travel distances.
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Affiliation(s)
- Fatemeh Karami
- Industrial Engineering Department, University of Louisville, Louisville, Kentucky, USA.,Department of Surgery, Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Amber B Kernodle
- Department of Surgery, Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Tanveen Ishaque
- Department of Surgery, Johns Hopkins Hospital, Baltimore, 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.,Scientific Registry of Transplant Recipients, Minneapolis, Minnesota, USA
| | - Sommer E Gentry
- Department of Mathematics, United States Naval Academy, Annapolis, Maryland, USA
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Shoaib M, Prabhakar U, Mahlawat S, Ramamohan V. A discrete-event simulation model of the kidney transplantation system in Rajasthan, India. Health Syst (Basingstoke) 2020; 11:30-47. [DOI: 10.1080/20476965.2020.1848355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
Affiliation(s)
- Mohd Shoaib
- Department of Mechanical Engineering, Indian Institute of Technology Delhi Hauz Khas, New Delhi, India
| | - Utkarsh Prabhakar
- Department of Mechanical Engineering, Indian Institute of Technology Delhi Hauz Khas, New Delhi, India
| | - Sumit Mahlawat
- Department of Mechanical Engineering, Indian Institute of Technology Delhi Hauz Khas, New Delhi, India
| | - Varun Ramamohan
- Department of Mechanical Engineering, Indian Institute of Technology Delhi Hauz Khas, New Delhi, India
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Zhou S, Massie AB, Luo X, Ruck JM, Chow EK, Bowring MG, Bae S, Segev DL, Gentry SE. Geographic disparity in kidney transplantation under KAS. Am J Transplant 2018; 18:1415-1423. [PMID: 29232040 PMCID: PMC5992006 DOI: 10.1111/ajt.14622] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2017] [Revised: 11/21/2017] [Accepted: 12/01/2017] [Indexed: 01/25/2023]
Abstract
The Kidney Allocation System fundamentally altered kidney allocation, causing a substantial increase in regional and national sharing that we hypothesized might impact geographic disparities. We measured geographic disparity in deceased donor kidney transplant (DDKT) rate under KAS (6/1/2015-12/1/2016), and compared that with pre-KAS (6/1/2013-12/3/2014). We modeled DSA-level DDKT rates with multilevel Poisson regression, adjusting for allocation factors under KAS. Using the model we calculated a novel, improved metric of geographic disparity: the median incidence rate ratio (MIRR) of transplant rate, a measure of DSA-level variation that accounts for patient casemix and is robust to outlier values. Under KAS, MIRR was 1.75 1.811.86 for adults, meaning that similar candidates across different DSAs have a median 1.81-fold difference in DDKT rate. The impact of geography was greater than the impact of factors emphasized by KAS: having an EPTS score ≤20% was associated with a 1.40-fold increase (IRR = 1.35 1.401.45 , P < .01) and a three-year dialysis vintage was associated with a 1.57-fold increase (IRR = 1.56 1.571.59 , P < .001) in transplant rate. For pediatric candidates, MIRR was even more pronounced, at 1.66 1.922.27 . There was no change in geographic disparities with KAS (P = .3). Despite extensive changes to kidney allocation under KAS, geography remains a primary determinant of access to DDKT.
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Affiliation(s)
- Sheng Zhou
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Allan B. Massie
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD,Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD
| | - Xun Luo
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Jessica M. Ruck
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Eric K.H. Chow
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Mary G. Bowring
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Sunjae Bae
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD,Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD
| | - Dorry L. Segev
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD,Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD
| | - Sommer E. Gentry
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD,US Naval Academy, Annapolis, MD
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