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1
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Christodoulidis G, Tsagkidou K, Bartzi D, Prisacariu IA, Agko ES, Koumarelas KE, Zacharoulis D. Sarcopenia and frailty: An in-depth analysis of the pathophysiology and effect on liver transplant candidates. World J Hepatol 2025; 17:106182. [DOI: 10.4254/wjh.v17.i5.106182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2025] [Revised: 04/09/2025] [Accepted: 05/07/2025] [Indexed: 05/27/2025] Open
Abstract
Cirrhosis represents the end stage of chronic liver disease, significantly reducing life expectancy as it progresses from a compensated to a decompensated state, leading to serious complications. Recent improvements in medical treatment have created a shift in cirrhosis management. Various causes, including hepatitis viruses, alcohol consumption, and fatty liver disease, contribute to cirrhosis and are closely linked to liver cancer. The disease develops through hepatocyte necrosis and regeneration, resulting in fibrosis and sinusoidal capillarization, leading to portal hypertension and complications such as ascites, hepatic encephalopathy, and organ dysfunction. Cirrhosis also holds an increased risk of hepatocellular carcinoma. Diagnosing cirrhosis involves assessing fibrosis scores through blood tests and measuring liver stiffness through elastography. Liver transplantation is the definitive treatment for end-stage liver disease and acute liver failure.
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Affiliation(s)
| | - Kyriaki Tsagkidou
- Department of Gastroenterology, University Hospital of Larisa, Larisa 41100, Thessalia, Greece
| | - Dimitra Bartzi
- Department of Oncology, The 251 Airforce General Hospital, Athens 11525, Greece
| | - Ioana A Prisacariu
- Department of Rehabilitation, Luzerner Kantonsspital Wolhusen, Lucerne 6110, Luzern, Switzerland
| | - Eirini S Agko
- Department of Intensive Care Unit, Asklepios Paulinen Clinic Wiesbaden, Wiesbaden 65197, Germany
| | - Konstantinos E Koumarelas
- Department of General and Orthopaedic Surgery, Luzerner Kantonsspital Wolhusen, Lucerne 6110, Luzern, Switzerland
| | - Dimitrios Zacharoulis
- Department of General Surgery, University of Thessaly, Larisa 41110, Thessalia, Greece
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2
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Karageorgos FF, Karakasi KE, Kofinas A, Antoniadis N, Katsanos G, Tsoulfas G. Evolving Transplant Oncology: Evolving Criteria for Better Decision-Making. Diagnostics (Basel) 2025; 15:820. [PMID: 40218170 PMCID: PMC11988714 DOI: 10.3390/diagnostics15070820] [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: 12/20/2024] [Revised: 03/10/2025] [Accepted: 03/21/2025] [Indexed: 04/14/2025] Open
Abstract
Transplant oncology integrates a wide variety of fields, such as surgery, oncology, and transplant medicine, intending to increase the range of studies and treatments for hepatobiliary cancers and other liver-related malignant lesions. Liver transplantation (LT) has proven to be an effective treatment for hepatocellular carcinoma. While the Milan criteria are still the gold standard, several new, more inclusive criteria have been proposed, and hepatocellular carcinoma has become a major indication for liver transplantation. The continuous evolution of diagnostic technologies supported this with higher image quality and more accurate staging. This review describes the current applications of transplant oncology in hepatocellular carcinoma, cholangiocarcinoma, neuroendocrine tumors, and liver metastatic disease from colorectal cancer and discusses the path that led to the development of transplant oncology as an organized approach to managing gastrointestinal malignancies through transplantation. More importantly, the significance of a multidisciplinary approach and criteria in the selection of suitable candidates are discussed. In addition, newer aspects of transplant oncology, such as immunotherapy, circulating tumor DNA (ctDNA), novel surgical techniques, and the utilization of artificial intelligence, are presented. Finally, the opportunities and challenges involved in the field's future, as well as the evolution of the criteria used over the years and insightful thoughts for the future of the criteria, are discussed.
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Affiliation(s)
| | | | | | | | | | - Georgios Tsoulfas
- Department of Transplantation Surgery, Center for Research and Innovation in Solid Organ Transplantation, Aristotle University School of Medicine, 54642 Thessaloniki, Greece; (F.F.K.)
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3
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Pruinelli L, Balakrishnan K, Ma S, Li Z, Wall A, Lai JC, Schold JD, Pruett T, Simon G. Transforming liver transplant allocation with artificial intelligence and machine learning: a systematic review. BMC Med Inform Decis Mak 2025; 25:98. [PMID: 39994720 PMCID: PMC11852809 DOI: 10.1186/s12911-025-02890-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2024] [Accepted: 01/22/2025] [Indexed: 02/26/2025] Open
Abstract
BACKGROUND The principles of urgency, utility, and benefit are fundamental concepts guiding the ethical and practical decision-making process for organ allocation; however, LT allocation still follows an urgency model. AIM To identify and analyze data elements used in Machine Learning (ML) and Artificial Intelligence (AI) methods, data sources, and their focus on urgency, utility, or benefit in LT. METHODS A comprehensive search across Ovid Medline and Scopus was conducted for studies published from 2002 to June 2023. Inclusion criteria targeted quantitative studies using ML/AI for candidates, donors, or recipients. Two reviewers assessed eligibility and extracted data, following PRISMA guidelines. RESULTS A total of 20 papers were included, synthesizing results into five major categories. Eight studies were led by a Spanish team, focusing on donor-recipient matching and proposing machine learning models to predict post- LT survival. Other international studies addressed organ supply-demand issues and developed predictive models to optimize LT outcomes. The studies highlight the potential of ML/AI to enhance LT allocation and outcomes. Despite advancements, limitations included the lack of robust transplant-related benefit models and improvements in urgency models compared to MELD. DISCUSSION This review highlighted the potential of AI and ML to enhance liver transplant allocation and outcomes. Significant advancements were noted, but limitations such as the need for better urgency models and the absence of a transplant-related benefit model remain. Most studies emphasized utility, focusing on survival outcomes. Future research should address the interpretability and generalizability of these models to improve organ allocation and post-LT survival predictions.
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Affiliation(s)
- Lisiane Pruinelli
- Department of Family, Community and Health Systems Science, University of Florida, Gainesville, Florida, US.
- Department of Surgery, University of Florida, Gainesville, Florida, US.
| | - Kiruthika Balakrishnan
- Department of Family, Community and Health Systems Science, University of Florida, Gainesville, Florida, US
| | - Sisi Ma
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
- Division of General Internal Medicine, University of Minnesota, Minneapolis, Minnesota, USA
| | - Zhigang Li
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Anji Wall
- Baylor University Medical Center in Dallas, Dallas, Texas, USA
| | - Jennifer C Lai
- Department of Medicine, University of California, San Francisco, California, USA
| | - Jesse D Schold
- Departments of Surgery and Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Timothy Pruett
- Department of Surgery, University of Minnesota, Minneapolis, Minnesota, US
| | - Gyorgy Simon
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
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4
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Ge F, Li HY, Zhang M, Arif M, Alam T. TCellPredX: A Novel Approach for Accurate Prediction of Hepatitis C Virus Linear T Cell Epitopes. ACS OMEGA 2024; 9:51494-51507. [PMID: 39758636 PMCID: PMC11696426 DOI: 10.1021/acsomega.4c08715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2024] [Revised: 11/29/2024] [Accepted: 12/04/2024] [Indexed: 01/07/2025]
Abstract
Hepatitis C Virus (HCV) is a bloodborne RNA virus that leads to severe liver diseases, and currently, no effective prophylactic biologics are available to prevent its transmission. The prevention of HCV is closely related to the major histocompatibility complex (MHC). Linear antigenic peptides of HCV, known as T cell epitopes (TCEs), are crucial in the presentation process by MHC molecules to T cells, playing a key role in immune responses. Therefore, the rapid and accurate identification of these TCE-HCVs is essential for advancing vaccine development. Herein, we propose TCellPredX, a novel integrated predictor for TCE-HCV identification. TCellPredX leverages five distinct feature encoding schemes, including local and global sequence encodings, composition-transition-distribution descriptors, physicochemical properties, and embeddings from two protein language models, which are processed through 12 machine learning algorithms. Our results indicate that feature fusion significantly enhances predictive accuracy. Moreover, the maximal relevance minimal redundancy feature selection method is particularly effective in isolating informative features, ensuring the model's use of the most informative data. Additionally, ensemble models, especially when combined with an averaged voting strategy, demonstrate superior stability and accuracy compared to individual classifiers, effectively reducing noise and enhancing model robustness. TCellPredX achieves notable accuracies of 0.900 and 0.897 in 10-fold cross-validation and independent test, respectively. Furthermore, TCellPredX's high accuracy is validated on experimentally verified peptide sequences documented for their potential benefits in vaccine development. Overall, TCellPredX can offer a robust tool for the precise identification of TCE-HCV, potentially serving as a cornerstone for future epitope research and advancing HCV vaccines development.
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Affiliation(s)
- Fang Ge
- State
Key Laboratory of Organic Electronics and Information Displays, Institute of Advanced Materials (IAM), Nanjing University
of Posts and Telecommunications, 6 Wenyuan Road, Nanjing 210023, China
| | - Hao-Yang Li
- School
of Computer, Jiangsu University of Science
and Technology, 666 Changhui Road, Zhenjiang 212100, China
| | - Ming Zhang
- School
of Computer, Jiangsu University of Science
and Technology, 666 Changhui Road, Zhenjiang 212100, China
| | - Muhammad Arif
- College
of Science and Engineering, Hamad Bin Khalifa
University, Doha 34110, Qatar
| | - Tanvir Alam
- College
of Science and Engineering, Hamad Bin Khalifa
University, Doha 34110, Qatar
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5
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Zhuo E, Yang W, Wang Y, Tang Y, Wang W, Zhou L, Chen Y, Li P, Chen B, Gao W, Liu W. Global trends in machine learning applied to clinical research in liver cancer: Bibliometric and visualization analysis (2001-2024). Medicine (Baltimore) 2024; 103:e40790. [PMID: 39654222 PMCID: PMC11631000 DOI: 10.1097/md.0000000000040790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2024] [Revised: 11/09/2024] [Accepted: 11/14/2024] [Indexed: 12/12/2024] Open
Abstract
This study explores the intersection of liver cancer and machine learning through bibliometric analysis. The aim is to identify highly cited papers in the field and examine the current research landscape, highlighting emerging trends and key areas of focus in liver cancer and machine learning. By analyzing citation patterns, this study sheds light on the evolving role of machine learning in liver cancer research and its potential for future advancements.
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Affiliation(s)
- Enba Zhuo
- Department of Anesthesiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wenzhi Yang
- First Clinical College, Anhui Medical University, Hefei, China
| | - Yafen Wang
- Department of Radiation Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yanchao Tang
- Department of Anesthesiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wanrong Wang
- First Clinical College, Anhui Medical University, Hefei, China
| | - Lingyan Zhou
- First Clinical College, Anhui Medical University, Hefei, China
| | - Yanjun Chen
- First Clinical College, Anhui Medical University, Hefei, China
| | - Pengman Li
- Department of Anesthesiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Bangjie Chen
- Department of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Weimin Gao
- First Clinical College, Anhui Medical University, Hefei, China
| | - Wang Liu
- Department of General Surgery, Sanya Central Hospital (The Third People’s Hospital of Hainan Province), Sanya, China
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6
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Balakrishnan K, Olson S, Simon G, Pruinelli L. Machine learning for post-liver transplant survival: Bridging the gap for long-term outcomes through temporal variation features. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 257:108442. [PMID: 39368442 DOI: 10.1016/j.cmpb.2024.108442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2024] [Revised: 09/16/2024] [Accepted: 09/24/2024] [Indexed: 10/07/2024]
Abstract
BACKGROUND The long-term survival of liver transplant (LT) recipients is essential for optimizing organ allocation and estimating mortality outcomes. While models like the Model-for-End-Stage-Liver-Disease (MELD) predict 90-day mortality on the waiting list, they do not predict post-LT survival accurately. There is a need for predictive models that can forecast post-LT survival beyond the immediate period after transplantation. METHOD This study introduces new temporal variation features for predicting post-LT survival during the waiting list period. Cox Proportional-Hazards regression (CoxPH), Random Survival Forest (RSF), and Extreme Gradient Boosting (XGB) models are utilized, along with patient demographics and waiting list duration. Data from 716 LT patients from the University of Minnesota CTSI (2011-2021) are used to develop, evaluate, and compare post-LT survival prediction models. RESULTS The temporal variation features, particularly when combined with the RSF model, proved most effective in predicting post-LT survival, with a C-index of 0.71 and an IBS of 0.151. This outperformed the predictive capability of the most recent MELD score, which had a C-index of <0.51 in the same cohort. CONCLUSIONS Incorporating temporal variation features with the RSF model enhances long-term post-LT survival predictions. These insights can assist clinicians and patients in making more informed decisions about organ allocation and understanding the utility of LT, ultimately leading to improved patient outcomes.
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Affiliation(s)
- Kiruthika Balakrishnan
- Department of Family, Community and Health Systems Science, University of Florida, Gainesville, FL, USA
| | - Sawyer Olson
- School of Statistics, University of Minnesota, MN, USA
| | - Gyorgy Simon
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
| | - Lisiane Pruinelli
- Department of Family, Community and Health Systems Science, University of Florida, Gainesville, FL, USA; Department of Surgery, University of Florida, Gainesville, FL, USA.
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7
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Rabindranath M, Naghibzadeh M, Zhao X, Holdsworth S, Brudno M, Sidhu A, Bhat M. Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold? Transplantation 2024; 108:1700-1708. [PMID: 39042768 DOI: 10.1097/tp.0000000000004876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.
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Affiliation(s)
- Madhumitha Rabindranath
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
| | - Maryam Naghibzadeh
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Xun Zhao
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Sandra Holdsworth
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Michael Brudno
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Aman Sidhu
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Mamatha Bhat
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
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8
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Gulla A, Jakiunaite I, Juchneviciute I, Dzemyda G. A narrative review: predicting liver transplant graft survival using artificial intelligence modeling. FRONTIERS IN TRANSPLANTATION 2024; 3:1378378. [PMID: 38993758 PMCID: PMC11235265 DOI: 10.3389/frtra.2024.1378378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 04/22/2024] [Indexed: 07/13/2024]
Abstract
Liver transplantation is the only treatment for patients with liver failure. As demand for liver transplantation grows, it remains a challenge to predict the short- and long-term survival of the liver graft. Recently, artificial intelligence models have been used to evaluate the short- and long-term survival of the liver transplant. To make the models more accurate, suitable liver transplantation characteristics must be used as input to train them. In this narrative review, we reviewed studies concerning liver transplantations published in the PubMed, Web of Science, and Cochrane databases between 2017 and 2022. We picked out 17 studies using our selection criteria and analyzed them, evaluating which medical characteristics were used as input for creation of artificial intelligence models. In eight studies, models estimating only short-term liver graft survival were created, while in five of the studies, models for the prediction of only long-term liver graft survival were built. In four of the studies, artificial intelligence algorithms evaluating both the short- and long-term liver graft survival were created. Medical characteristics that were used as input in reviewed studies and had the biggest impact on the accuracy of the model were the recipient's age, recipient's body mass index, creatinine levels in the recipient's serum, recipient's international normalized ratio, diabetes mellitus, and recipient's model of end-stage liver disease score. To conclude, in order to define important liver transplantation characteristics that could be used as an input for artificial intelligence algorithms when predicting liver graft survival, more models need to be created and analyzed, in order to fully support the results of this review.
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Affiliation(s)
- Aiste Gulla
- Faculty of Medicine, Institute of Clinical Medicine, Vilnius University, Vilnius, Lithuania
| | | | - Ivona Juchneviciute
- Faculty of Mathematics and Informatics, Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania
| | - Gintautas Dzemyda
- Faculty of Mathematics and Informatics, Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania
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Chongo G, Soldera J. Use of machine learning models for the prognostication of liver transplantation: A systematic review. World J Transplant 2024; 14:88891. [PMID: 38576762 PMCID: PMC10989468 DOI: 10.5500/wjt.v14.i1.88891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/08/2023] [Accepted: 12/11/2023] [Indexed: 03/15/2024] Open
Abstract
BACKGROUND Liver transplantation (LT) is a life-saving intervention for patients with end-stage liver disease. However, the equitable allocation of scarce donor organs remains a formidable challenge. Prognostic tools are pivotal in identifying the most suitable transplant candidates. Traditionally, scoring systems like the model for end-stage liver disease have been instrumental in this process. Nevertheless, the landscape of prognostication is undergoing a transformation with the integration of machine learning (ML) and artificial intelligence models. AIM To assess the utility of ML models in prognostication for LT, comparing their per formance and reliability to established traditional scoring systems. METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, we conducted a thorough and standardized literature search using the PubMed/MEDLINE database. Our search imposed no restrictions on publication year, age, or gender. Exclusion criteria encompassed non-English stu dies, review articles, case reports, conference papers, studies with missing data, or those exhibiting evident methodological flaws. RESULTS Our search yielded a total of 64 articles, with 23 meeting the inclusion criteria. Among the selected studies, 60.8% originated from the United States and China combined. Only one pediatric study met the criteria. Notably, 91% of the studies were published within the past five years. ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values (ranging from 0.6 to 1) across all studies, surpassing the performance of traditional scoring systems. Random forest exhibited superior predictive capa bilities for 90-d mortality following LT, sepsis, and acute kidney injury (AKI). In contrast, gradient boosting excelled in predicting the risk of graft-versus-host disease, pneumonia, and AKI. CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT, marking a significant evolution in the field of prognostication.
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Affiliation(s)
- Gidion Chongo
- Department of Gastroenterology, University of South Wales, Cardiff CF37 1DL, United Kingdom
| | - Jonathan Soldera
- Department of Gastroenterology, University of South Wales, Cardiff CF37 1DL, United Kingdom
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10
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Thongprayoon C, Miao J, Jadlowiec C, Mao SA, Mao M, Leeaphorn N, Kaewput W, Pattharanitima P, Valencia OAG, Tangpanithandee S, Krisanapan P, Suppadungsuk S, Nissaisorakarn P, Cooper M, Cheungpasitporn W. Distinct clinical profiles and post-transplant outcomes among kidney transplant recipients with lower education levels: uncovering patterns through machine learning clustering. Ren Fail 2023; 45:2292163. [PMID: 38087474 PMCID: PMC11001364 DOI: 10.1080/0886022x.2023.2292163] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 12/03/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Educational attainment significantly influences post-transplant outcomes in kidney transplant patients. However, research on specific attributes of lower-educated subgroups remains underexplored. This study utilized unsupervised machine learning to segment kidney transplant recipients based on education, further analyzing the relationship between these segments and post-transplant results. METHODS Using the OPTN/UNOS 2017-2019 data, consensus clustering was applied to 20,474 kidney transplant recipients, all below a college/university educational threshold. The analysis concentrated on recipient, donor, and transplant features, aiming to discern pivotal attributes for each cluster and compare post-transplant results. RESULTS Four distinct clusters emerged. Cluster 1 comprised younger, non-diabetic, first-time recipients from non-hypertensive younger donors. Cluster 2 predominantly included white patients receiving their first-time kidney transplant either preemptively or within three years, mainly from living donors. Cluster 3 included younger re-transplant recipients, marked by elevated PRA, fewer HLA mismatches. In contrast, Cluster 4 captured older, diabetic patients transplanted after prolonged dialysis duration, primarily from lower-grade donors. Interestingly, Cluster 2 showcased the most favorable post-transplant outcomes. Conversely, Clusters 1, 3, and 4 revealed heightened risks for graft failure and mortality in comparison. CONCLUSIONS Through unsupervised machine learning, this study proficiently categorized kidney recipients with lesser education into four distinct clusters. Notably, the standout performance of Cluster 2 provides invaluable insights, underscoring the necessity for adept risk assessment and tailored transplant strategies, potentially elevating care standards for this patient cohort.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Shennen A. Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL, USA
| | - Michael Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL, USA
| | - Napat Leeaphorn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL, USA
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok, Thailand
| | | | - Oscar A. Garcia Valencia
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
- Chakri Naruebodindra Medical Institute, Ramathibodi Hospital, Mahidol University, Samut Prakan, Thailand
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
- Department of Internal Medicine, Thammasat University, Pathum Thani, Thailand
| | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
- Chakri Naruebodindra Medical Institute, Ramathibodi Hospital, Mahidol University, Samut Prakan, Thailand
| | - Pitchaphon Nissaisorakarn
- Department of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Matthew Cooper
- Division of Transplant Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
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11
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Drezga-Kleiminger M, Demaree-Cotton J, Koplin J, Savulescu J, Wilkinson D. Should AI allocate livers for transplant? Public attitudes and ethical considerations. BMC Med Ethics 2023; 24:102. [PMID: 38012660 PMCID: PMC10683249 DOI: 10.1186/s12910-023-00983-0] [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: 09/04/2023] [Accepted: 11/14/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Allocation of scarce organs for transplantation is ethically challenging. Artificial intelligence (AI) has been proposed to assist in liver allocation, however the ethics of this remains unexplored and the view of the public unknown. The aim of this paper was to assess public attitudes on whether AI should be used in liver allocation and how it should be implemented. METHODS We first introduce some potential ethical issues concerning AI in liver allocation, before analysing a pilot survey including online responses from 172 UK laypeople, recruited through Prolific Academic. FINDINGS Most participants found AI in liver allocation acceptable (69.2%) and would not be less likely to donate their organs if AI was used in allocation (72.7%). Respondents thought AI was more likely to be consistent and less biased compared to humans, although were concerned about the "dehumanisation of healthcare" and whether AI could consider important nuances in allocation decisions. Participants valued accuracy, impartiality, and consistency in a decision-maker, more than interpretability and empathy. Respondents were split on whether AI should be trained on previous decisions or programmed with specific objectives. Whether allocation decisions were made by transplant committee or AI, participants valued consideration of urgency, survival likelihood, life years gained, age, future medication compliance, quality of life, future alcohol use and past alcohol use. On the other hand, the majority thought the following factors were not relevant to prioritisation: past crime, future crime, future societal contribution, social disadvantage, and gender. CONCLUSIONS There are good reasons to use AI in liver allocation, and our sample of participants appeared to support its use. If confirmed, this support would give democratic legitimacy to the use of AI in this context and reduce the risk that donation rates could be affected negatively. Our findings on specific ethical concerns also identify potential expectations and reservations laypeople have regarding AI in this area, which can inform how AI in liver allocation could be best implemented.
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Affiliation(s)
- Max Drezga-Kleiminger
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
- Oxford Uehiro Centre for Practical Ethics, Faculty of Philosophy, University of Oxford, Oxford, OX1 2JD, UK
| | - Joanna Demaree-Cotton
- Oxford Uehiro Centre for Practical Ethics, Faculty of Philosophy, University of Oxford, Oxford, OX1 2JD, UK
| | - Julian Koplin
- Monash Bioethics Centre, Monash University, Melbourne, Australia
| | - Julian Savulescu
- Oxford Uehiro Centre for Practical Ethics, Faculty of Philosophy, University of Oxford, Oxford, OX1 2JD, UK
- Murdoch Children's Research Institute, Melbourne, Australia
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Dominic Wilkinson
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
- Oxford Uehiro Centre for Practical Ethics, Faculty of Philosophy, University of Oxford, Oxford, OX1 2JD, UK.
- Murdoch Children's Research Institute, Melbourne, Australia.
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- John Radcliffe Hospital, Oxford, UK.
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12
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Skurzak S, Bonini A, Cerchiara P, Laici C, De Gasperi A, Prosperi M, Perego M, Guffanti EA, Chierego G, Azan G, Balagna R, Siniscalchi A, Monti G, Tosi M, Esposito C, Cerutti E, Finazzi S. A simple machine learning-derived rule to promote ERAS pathways in Liver Transplantation. JOURNAL OF LIVER TRANSPLANTATION 2023; 12:100179. [DOI: 10.1016/j.liver.2023.100179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2025] Open
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13
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Yu YC, Zhang W, O'Gara D, Li JS, Chang SH. A moment kernel machine for clinical data mining to inform medical decision making. Sci Rep 2023; 13:10459. [PMID: 37380721 DOI: 10.1038/s41598-023-36752-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 06/09/2023] [Indexed: 06/30/2023] Open
Abstract
Machine learning-aided medical decision making presents three major challenges: achieving model parsimony, ensuring credible predictions, and providing real-time recommendations with high computational efficiency. In this paper, we formulate medical decision making as a classification problem and develop a moment kernel machine (MKM) to tackle these challenges. The main idea of our approach is to treat the clinical data of each patient as a probability distribution and leverage moment representations of these distributions to build the MKM, which transforms the high-dimensional clinical data to low-dimensional representations while retaining essential information. We then apply this machine to various pre-surgical clinical datasets to predict surgical outcomes and inform medical decision making, which requires significantly less computational power and time for classification while yielding favorable performance compared to existing methods. Moreover, we utilize synthetic datasets to demonstrate that the developed moment-based data mining framework is robust to noise and missing data, and achieves model parsimony giving an efficient way to generate satisfactory predictions to aid personalized medical decision making.
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Affiliation(s)
- Yao-Chi Yu
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Wei Zhang
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - David O'Gara
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Jr-Shin Li
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA.
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, 63130, USA.
- Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, MO, 63130, USA.
| | - Su-Hsin Chang
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA.
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14
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Bhat M, Rabindranath M, Chara BS, Simonetto DA. Artificial intelligence, machine learning, and deep learning in liver transplantation. J Hepatol 2023; 78:1216-1233. [PMID: 37208107 DOI: 10.1016/j.jhep.2023.01.006] [Citation(s) in RCA: 71] [Impact Index Per Article: 35.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 01/11/2023] [Accepted: 01/16/2023] [Indexed: 05/21/2023]
Abstract
Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and omics data in the development of an appropriate treatment plan. Current methods to collate clinical information are susceptible to some degree of subjectivity; thus, clinical decision-making in LT could benefit from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to reduce waitlist mortality and improve post-transplant outcomes. In the post-LT setting, AI could help guide the management of LT recipients, particularly by predicting patient and graft survival, along with identifying risk factors for disease recurrence and other associated complications. Although AI shows promise in medicine, there are limitations to its clinical deployment which include dataset imbalances for model training, data privacy issues, and a lack of available research practices to benchmark model performance in the real world. Overall, AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.
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Affiliation(s)
- Mamatha Bhat
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Division of Gastroenterology & Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada.
| | - Madhumitha Rabindranath
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Beatriz Sordi Chara
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Douglas A Simonetto
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
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15
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Thongprayoon C, Miao J, Jadlowiec CC, Mao SA, Mao MA, Vaitla P, Leeaphorn N, Kaewput W, Pattharanitima P, Tangpanithandee S, Krisanapan P, Nissaisorakarn P, Cooper M, Cheungpasitporn W. Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:977. [PMID: 37241209 PMCID: PMC10223300 DOI: 10.3390/medicina59050977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 05/10/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023]
Abstract
Background and Objectives: The aim of our study was to categorize very highly sensitized kidney transplant recipients with pre-transplant panel reactive antibody (PRA) ≥ 98% using an unsupervised machine learning approach as clinical outcomes for this population are inferior, despite receiving increased allocation priority. Identifying subgroups with higher risks for inferior outcomes is essential to guide individualized management strategies for these vulnerable recipients. Materials and Methods: To achieve this, we analyzed the Organ Procurement and Transplantation Network (OPTN)/United Network for Organ Sharing (UNOS) database from 2010 to 2019 and performed consensus cluster analysis based on the recipient-, donor-, and transplant-related characteristics in 7458 kidney transplant patients with pre-transplant PRA ≥ 98%. The key characteristics of each cluster were identified by calculating the standardized mean difference. The post-transplant outcomes were compared between the assigned clusters. Results: We identified two distinct clusters and compared the post-transplant outcomes among the assigned clusters of very highly sensitized kidney transplant patients. Cluster 1 patients were younger (median age 45 years), male predominant, and more likely to have previously undergone a kidney transplant, but had less diabetic kidney disease. Cluster 2 recipients were older (median 54 years), female predominant, and more likely to be undergoing a first-time transplant. While patient survival was comparable between the two clusters, cluster 1 had lower death-censored graft survival and higher acute rejection compared to cluster 2. Conclusions: The unsupervised machine learning approach categorized very highly sensitized kidney transplant patients into two clinically distinct clusters with differing post-transplant outcomes. A better understanding of these clinically distinct subgroups may assist the transplant community in developing individualized care strategies and improving the outcomes for very highly sensitized kidney transplant patients.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (J.M.); (S.T.); (P.K.)
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (J.M.); (S.T.); (P.K.)
| | | | - Shennen A. Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Michael A. Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Pradeep Vaitla
- Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Napat Leeaphorn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand
| | - Pattharawin Pattharanitima
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (J.M.); (S.T.); (P.K.)
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (J.M.); (S.T.); (P.K.)
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Pitchaphon Nissaisorakarn
- Department of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | | | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (J.M.); (S.T.); (P.K.)
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16
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Thongprayoon C, Vaitla P, Jadlowiec CC, Mao SA, Mao MA, Acharya PC, Leeaphorn N, Kaewput W, Pattharanitima P, Tangpanithandee S, Krisanapan P, Nissaisorakarn P, Cooper M, Cheungpasitporn W. Differences between kidney retransplant recipients as identified by machine learning consensus clustering. Clin Transplant 2023; 37:e14943. [PMID: 36799718 DOI: 10.1111/ctr.14943] [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: 05/28/2022] [Revised: 08/13/2022] [Accepted: 02/11/2023] [Indexed: 02/18/2023]
Abstract
BACKGROUND Our study aimed to characterize kidney retransplant recipients using an unsupervised machine-learning approach. METHODS We performed consensus cluster analysis based on the recipient-, donor-, and transplant-related characteristics in 17 443 kidney retransplant recipients in the OPTN/UNOS database from 2010 to 2019. We identified each cluster's key characteristics using the standardized mean difference of >.3. We compared the posttransplant outcomes, including death-censored graft failure and patient death among the assigned clusters RESULTS: Consensus cluster analysis identified three distinct clusters of kidney retransplant recipients. Cluster 1 recipients were predominantly white and were less sensitized. They were most likely to receive a living donor kidney transplant and more likely to be preemptive (30%) or need ≤1 year of dialysis (32%). In contrast, cluster 2 recipients were the most sensitized (median PRA 95%). They were more likely to have been on dialysis >1 year, and receive a nationally allocated, low HLA mismatch, standard KDPI deceased donor kidney. Recipients in cluster 3 were more likely to be minorities (37% Black; 15% Hispanic). They were moderately sensitized with a median PRA of 87% and were also most likely to have been on dialysis >1 year. They received locally allocated high HLA mismatch kidneys from standard KDPI deceased donors. Thymoglobulin was the most commonly used induction agent for all three clusters. Cluster 1 had the most favorable patient and graft survival, while cluster 3 had the worst patient and graft survival. CONCLUSION The use of an unsupervised machine learning approach characterized kidney retransplant recipients into three clinically distinct clusters with differing posttransplant outcomes. Recipients with moderate allosensitization, such as those represented in cluster 3, are perhaps more disadvantaged in the kidney retransplantation process. Potential opportunities for improvement specific to these re-transplant recipients include working to improve opportunities to improve access to living donor kidney transplantation, living donor paired exchange and identifying strategies for better HLA matching.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Pradeep Vaitla
- Division of Nephrology, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | | | - Shennen A Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, Florida, USA
| | - Michael A Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, Florida, USA
| | - Prakrati C Acharya
- Division of Nephrology, Texas Tech Health Sciences Center El Paso, El Paso, Texas, USA
| | - Napat Leeaphorn
- Renal Transplant Program, University of Missouri-Kansas City School of Medicine/Saint Luke's Health System, Kansas City, Missouri, USA
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok, Thailand
| | | | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani, Thailand
| | - Pitchaphon Nissaisorakarn
- Department of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Matthew Cooper
- Department of Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
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17
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Thongprayoon C, Vaitla P, Jadlowiec CC, Leeaphorn N, Mao SA, Mao MA, Qureshi F, Kaewput W, Qureshi F, Tangpanithandee S, Krisanapan P, Pattharanitima P, Acharya PC, Nissaisorakarn P, Cooper M, Cheungpasitporn W. Distinct Phenotypes of Non-Citizen Kidney Transplant Recipients in the United States by Machine Learning Consensus Clustering. MEDICINES (BASEL, SWITZERLAND) 2023; 10:25. [PMID: 37103780 PMCID: PMC10144541 DOI: 10.3390/medicines10040025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 03/24/2023] [Indexed: 04/28/2023]
Abstract
Better understanding of the different phenotypes/subgroups of non-U.S. citizen kidney transplant recipients may help the transplant community to identify strategies that improve outcomes among non-U.S. citizen kidney transplant recipients. This study aimed to cluster non-U.S. citizen kidney transplant recipients using an unsupervised machine learning approach; Methods: We conducted a consensus cluster analysis based on recipient-, donor-, and transplant- related characteristics in non-U.S. citizen kidney transplant recipients in the United States from 2010 to 2019 in the OPTN/UNOS database using recipient, donor, and transplant-related characteristics. Each cluster's key characteristics were identified using the standardized mean difference. Post-transplant outcomes were compared among the clusters; Results: Consensus cluster analysis was performed in 11,300 non-U.S. citizen kidney transplant recipients and identified two distinct clusters best representing clinical characteristics. Cluster 1 patients were notable for young age, preemptive kidney transplant or dialysis duration of less than 1 year, working income, private insurance, non-hypertensive donors, and Hispanic living donors with a low number of HLA mismatch. In contrast, cluster 2 patients were characterized by non-ECD deceased donors with KDPI <85%. Consequently, cluster 1 patients had reduced cold ischemia time, lower proportion of machine-perfused kidneys, and lower incidence of delayed graft function after kidney transplant. Cluster 2 had higher 5-year death-censored graft failure (5.2% vs. 9.8%; p < 0.001), patient death (3.4% vs. 11.4%; p < 0.001), but similar one-year acute rejection (4.7% vs. 4.9%; p = 0.63), compared to cluster 1; Conclusions: Machine learning clustering approach successfully identified two clusters among non-U.S. citizen kidney transplant recipients with distinct phenotypes that were associated with different outcomes, including allograft loss and patient survival. These findings underscore the need for individualized care for non-U.S. citizen kidney transplant recipients.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA (S.T.); (P.K.)
| | - Pradeep Vaitla
- Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | | | - Napat Leeaphorn
- Renal Transplant Program, University of Missouri-Kansas City School of Medicine/Saint Luke’s Health System, Kansas City, MO 64108, USA
| | - Shennen A. Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Michael A. Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Fahad Qureshi
- School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64108, USA
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA (S.T.); (P.K.)
| | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA (S.T.); (P.K.)
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA (S.T.); (P.K.)
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand
| | - Pattharawin Pattharanitima
- Division of Nephrology, Department of Internal Medicine, Faculty of Medicine Thammasat University, Pathum Thani 12120, Thailand
| | - Prakrati C. Acharya
- Division of Nephrology, Texas Tech Health Sciences Center El Paso, El Paso, TX 79905, USA
| | - Pitchaphon Nissaisorakarn
- Department of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Matthew Cooper
- Medstar Georgetown Transplant Institute, Georgetown University School of Medicine, Washington, DC 21042, USA
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA (S.T.); (P.K.)
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18
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Thongprayoon C, Jadlowiec CC, Mao SA, Mao MA, Leeaphorn N, Kaewput W, Pattharanitima P, Nissaisorakarn P, Cooper M, Cheungpasitporn W. Distinct phenotypes of kidney transplant recipients aged 80 years or older in the USA by machine learning consensus clustering. BMJ SURGERY, INTERVENTIONS, & HEALTH TECHNOLOGIES 2023; 5:e000137. [PMID: 36843871 PMCID: PMC9944353 DOI: 10.1136/bmjsit-2022-000137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 02/05/2023] [Indexed: 02/22/2023] Open
Abstract
OBJECTIVES This study aimed to identify distinct clusters of very elderly kidney transplant recipients aged ≥80 and assess clinical outcomes among these unique clusters. DESIGN Cohort study with machine learning (ML) consensus clustering approach. SETTING AND PARTICIPANTS All very elderly (age ≥80 at time of transplant) kidney transplant recipients in the Organ Procurement and Transplantation Network/United Network for Organ Sharing database database from 2010 to 2019. MAIN OUTCOME MEASURES Distinct clusters of very elderly kidney transplant recipients and their post-transplant outcomes including death-censored graft failure, overall mortality and acute allograft rejection among the assigned clusters. RESULTS Consensus cluster analysis was performed in 419 very elderly kidney transplant and identified three distinct clusters that best represented the clinical characteristics of very elderly kidney transplant recipients. Recipients in cluster 1 received standard Kidney Donor Profile Index (KDPI) non-extended criteria donor (ECD) kidneys from deceased donors. Recipients in cluster 2 received kidneys from older, hypertensive ECD deceased donors with a KDPI score ≥85%. Kidneys for cluster 2 patients had longer cold ischaemia time and the highest use of machine perfusion. Recipients in clusters 1 and 2 were more likely to be on dialysis at the time of transplant (88.3%, 89.4%). Recipients in cluster 3 were more likely to be preemptive (39%) or had a dialysis duration less than 1 year (24%). These recipients received living donor kidney transplants. Cluster 3 had the most favourable post-transplant outcomes. Compared with cluster 3, cluster 1 had comparable survival but higher death-censored graft failure, while cluster 2 had lower patient survival, higher death-censored graft failure and more acute rejection. CONCLUSIONS Our study used an unsupervised ML approach to cluster very elderly kidney transplant recipients into three clinically unique clusters with distinct post-transplant outcomes. These findings from an ML clustering approach provide additional understanding towards individualised medicine and opportunities to improve care for very elderly kidney transplant recipients.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Shennen A Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, Florida, USA
| | - Michael A Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, Florida, USA
| | - Napat Leeaphorn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, Florida, USA
- Renal Transplant Program, Saint Luke's Health System, Kansas City, Missouri, USA
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok, Thailand
| | | | | | - Matthew Cooper
- Division of Transplant, Department of Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
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Ivanics T, So D, Claasen MPAW, Wallace D, Patel MS, Gravely A, Choi WJ, Shwaartz C, Walker K, Erdman L, Sapisochin G. Machine learning-based mortality prediction models using national liver transplantation registries are feasible but have limited utility across countries. Am J Transplant 2023; 23:64-71. [PMID: 36695623 DOI: 10.1016/j.ajt.2022.12.002] [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: 06/13/2022] [Revised: 10/04/2022] [Accepted: 10/14/2022] [Indexed: 01/13/2023]
Abstract
Many countries curate national registries of liver transplant (LT) data. These registries are often used to generate predictive models; however, potential performance and transferability of these models remain unclear. We used data from 3 national registries and developed machine learning algorithm (MLA)-based models to predict 90-day post-LT mortality within and across countries. Predictive performance and external validity of each model were assessed. Prospectively collected data of adult patients (aged ≥18 years) who underwent primary LTs between January 2008 and December 2018 from the Canadian Organ Replacement Registry (Canada), National Health Service Blood and Transplantation (United Kingdom), and United Network for Organ Sharing (United States) were used to develop MLA models to predict 90-day post-LT mortality. Models were developed using each registry individually (based on variables inherent to the individual databases) and using all 3 registries combined (variables in common between the registries [harmonized]). The model performance was evaluated using area under the receiver operating characteristic (AUROC) curve. The number of patients included was as follows: Canada, n = 1214; the United Kingdom, n = 5287; and the United States, n = 59,558. The best performing MLA-based model was ridge regression across both individual registries and harmonized data sets. Model performance diminished from individualized to the harmonized registries, especially in Canada (individualized ridge: AUROC, 0.74; range, 0.73-0.74; harmonized: AUROC, 0.68; range, 0.50-0.73) and US (individualized ridge: AUROC, 0.71; range, 0.70-0.71; harmonized: AUROC, 0.66; range, 0.66-0.66) data sets. External model performance across countries was poor overall. MLA-based models yield a fair discriminatory potential when used within individual databases. However, the external validity of these models is poor when applied across countries. Standardization of registry-based variables could facilitate the added value of MLA-based models in informing decision making in future LTs.
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Affiliation(s)
- Tommy Ivanics
- Multi-Organ Transplant Program, University Health Network Toronto, Ontario, Canada; Department of Surgery, Henry Ford Hospital, Detroit, Michigan, USA; Department of Surgical Sciences, Akademiska Sjukhuset, Uppsala University, Uppsala, Sweden
| | - Delvin So
- The Centre of Computational Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Marco P A W Claasen
- Multi-Organ Transplant Program, University Health Network Toronto, Ontario, Canada; Department of Surgery, division of HPB & Transplant Surgery, Erasmus MC Transplant Institute, University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - David Wallace
- Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine and Institute of Liver Studies, King's College Hospital NHS Foundation Trust, London, UK
| | - Madhukar S Patel
- Division of Surgical Transplantation, Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Annabel Gravely
- Multi-Organ Transplant Program, University Health Network Toronto, Ontario, Canada
| | - Woo Jin Choi
- Multi-Organ Transplant Program, University Health Network Toronto, Ontario, Canada
| | - Chaya Shwaartz
- Multi-Organ Transplant Program, University Health Network Toronto, Ontario, Canada
| | - Kate Walker
- Department of Nephrology and Transplantation, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Lauren Erdman
- The Centre of Computational Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Gonzalo Sapisochin
- Multi-Organ Transplant Program, University Health Network Toronto, Ontario, Canada.
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Rueda J, Rodríguez JD, Jounou IP, Hortal-Carmona J, Ausín T, Rodríguez-Arias D. "Just" accuracy? Procedural fairness demands explainability in AI-based medical resource allocations. AI & SOCIETY 2022:1-12. [PMID: 36573157 PMCID: PMC9769482 DOI: 10.1007/s00146-022-01614-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022]
Abstract
The increasing application of artificial intelligence (AI) to healthcare raises both hope and ethical concerns. Some advanced machine learning methods provide accurate clinical predictions at the expense of a significant lack of explainability. Alex John London has defended that accuracy is a more important value than explainability in AI medicine. In this article, we locate the trade-off between accurate performance and explainable algorithms in the context of distributive justice. We acknowledge that accuracy is cardinal from outcome-oriented justice because it helps to maximize patients' benefits and optimizes limited resources. However, we claim that the opaqueness of the algorithmic black box and its absence of explainability threatens core commitments of procedural fairness such as accountability, avoidance of bias, and transparency. To illustrate this, we discuss liver transplantation as a case of critical medical resources in which the lack of explainability in AI-based allocation algorithms is procedurally unfair. Finally, we provide a number of ethical recommendations for when considering the use of unexplainable algorithms in the distribution of health-related resources.
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Affiliation(s)
- Jon Rueda
- Department of Philosophy 1, University of Granada, Granada, Spain
- FiloLab Scientific Unit of Excellence, University of Granada, Granada, Spain
| | | | | | | | - Txetxu Ausín
- Institute of Philosophy, Spanish National Research Council, Madrid, Spain
| | - David Rodríguez-Arias
- Department of Philosophy 1, University of Granada, Granada, Spain
- FiloLab Scientific Unit of Excellence, University of Granada, Granada, Spain
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21
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Tangpanithandee S, Thongprayoon C, Jadlowiec CC, Mao SA, Mao MA, Vaitla P, Leeaphorn N, Kaewput W, Pattharanitima P, Krisanapan P, Nissaisorakarn P, Cooper M, Cheungpasitporn W. Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:1831. [PMID: 36557033 PMCID: PMC9783488 DOI: 10.3390/medicina58121831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/03/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022]
Abstract
Background and Objectives: Our study aimed to cluster dual kidney transplant recipients using an unsupervised machine learning approach to characterize donors and recipients better and to compare the survival outcomes across these various clusters. Materials and Methods: We performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in 2821 dual kidney transplant recipients from 2010 to 2019 in the OPTN/UNOS database. We determined the important characteristics of each assigned cluster and compared the post-transplant outcomes between clusters. Results: Two clinically distinct clusters were identified by consensus cluster analysis. Cluster 1 patients was characterized by younger patients (mean recipient age 49 ± 13 years) who received dual kidney transplant from pediatric (mean donor age 3 ± 8 years) non-expanded criteria deceased donor (100% non-ECD). In contrast, Cluster 2 patients were characterized by older patients (mean recipient age 63 ± 9 years) who received dual kidney transplant from adult (mean donor age 59 ± 11 years) donor with high kidney donor profile index (KDPI) score (59% had KDPI ≥ 85). Cluster 1 had higher patient survival (98.0% vs. 94.6% at 1 year, and 92.1% vs. 76.3% at 5 years), and lower acute rejection (4.2% vs. 6.1% within 1 year), when compared to cluster 2. Death-censored graft survival was comparable between two groups (93.5% vs. 94.9% at 1 year, and 89.2% vs. 84.8% at 5 years). Conclusions: In summary, DKT in the United States remains uncommon. Two clusters, based on specific recipient and donor characteristics, were identified through an unsupervised machine learning approach. Despite varying differences in donor and recipient age between the two clusters, death-censored graft survival was excellent and comparable. Broader utilization of DKT from high KDPI kidneys and pediatric en bloc kidneys should be encouraged to better address the ongoing organ shortage.
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Affiliation(s)
- Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Shennen A. Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Michael A. Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Pradeep Vaitla
- Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Napat Leeaphorn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand
| | | | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Division of Nephrology, Department of Internal Medicine, Thammasat University, Bangkok 12120, Thailand
| | - Pitchaphon Nissaisorakarn
- Department of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Matthew Cooper
- Medstar Georgetown Transplant Institute, Georgetown University School of Medicine, Washington, DC 21042, USA
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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22
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Jadlowiec CC, Thongprayoon C, Leeaphorn N, Kaewput W, Pattharanitima P, Cooper M, Cheungpasitporn W. Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Kidney Transplant Recipients With DGF and Associated Outcomes. Transpl Int 2022; 35:10810. [PMID: 36568137 PMCID: PMC9773391 DOI: 10.3389/ti.2022.10810] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 11/14/2022] [Indexed: 12/14/2022]
Abstract
Data and transplant community opinion on delayed graft function (DGF), and its impact on outcomes, remains varied. An unsupervised machine learning consensus clustering approach was applied to categorize the clinical phenotypes of kidney transplant (KT) recipients with DGF using OPTN/UNOS data. DGF was observed in 20.9% (n = 17,073) of KT and most kidneys had a KDPI score <85%. Four distinct clusters were identified. Cluster 1 recipients were young, high PRA re-transplants. Cluster 2 recipients were older diabetics and more likely to receive higher KDPI kidneys. Cluster 3 recipients were young, black, and non-diabetic; they received lower KDPI kidneys. Cluster 4 recipients were middle-aged, had diabetes or hypertension and received well-matched standard KDPI kidneys. By cluster, one-year patient survival was 95.7%, 92.5%, 97.2% and 94.3% (p < 0.001); one-year graft survival was 89.7%, 87.1%, 91.6%, and 88.7% (p < 0.001). There were no differences between clusters after accounting for death-censored graft loss (p = 0.08). Clinically meaningful differences in recipient characteristics were noted between clusters, however, after accounting for death and return to dialysis, there were no differences in death-censored graft loss. Greater emphasis on recipient comorbidities as contributors to DGF and outcomes may help improve utilization of DGF at-risk kidneys.
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Affiliation(s)
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, United States
| | - Napat Leeaphorn
- Renal Transplant Program, University of Missouri-Kansas City School of Medicine, Saint Luke’s Health System, Kansas City, MO, United States
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok, Thailand
| | | | - Matthew Cooper
- Medstar Georgetown Transplant Institute, Georgetown University, Washington, DC, United States
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, United States
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23
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Zhang X, Gavaldà R, Baixeries J. Interpretable prediction of mortality in liver transplant recipients based on machine learning. Comput Biol Med 2022; 151:106188. [PMID: 36306583 DOI: 10.1016/j.compbiomed.2022.106188] [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: 01/08/2022] [Revised: 09/24/2022] [Accepted: 10/08/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Accurate prediction of the mortality of post-liver transplantation is an important but challenging task. It relates to optimizing organ allocation and estimating the risk of possible dysfunction. Existing risk scoring models, such as the Balance of Risk (BAR) score and the Survival Outcomes Following Liver Transplantation (SOFT) score, do not predict the mortality of post-liver transplantation with sufficient accuracy. In this study, we evaluate the performance of machine learning models and establish an explainable machine learning model for predicting mortality in liver transplant recipients. METHOD The optimal feature set for the prediction of the mortality was selected by a wrapper method based on binary particle swarm optimization (BPSO). With the selected optimal feature set, seven machine learning models were applied to predict mortality over different time windows. The best-performing model was used to predict mortality through a comprehensive comparison and evaluation. An interpretable approach based on machine learning and SHapley Additive exPlanations (SHAP) is used to explicitly explain the model's decision and make new discoveries. RESULTS With regard to predictive power, our results demonstrated that the feature set selected by BPSO outperformed both the feature set in the existing risk score model (BAR score, SOFT score) and the feature set processed by principal component analysis (PCA). The best-performing model, extreme gradient boosting (XGBoost), was found to improve the Area Under a Curve (AUC) values for mortality prediction by 6.7%, 11.6%, and 17.4% at 3 months, 3 years, and 10 years, respectively, compared to the SOFT score. The main predictors of mortality and their impact were discussed for different age groups and different follow-up periods. CONCLUSIONS Our analysis demonstrates that XGBoost can be an ideal method to assess the mortality risk in liver transplantation. In combination with the SHAP approach, the proposed framework provides a more intuitive and comprehensive interpretation of the predictive model, thereby allowing the clinician to better understand the decision-making process of the model and the impact of factors associated with mortality risk in liver transplantation.
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Affiliation(s)
- Xiao Zhang
- Department of Computer Science, Universitat Politècnica de Catalunya, Barcelona, 08034, Spain.
| | | | - Jaume Baixeries
- Department of Computer Science, Universitat Politècnica de Catalunya, Barcelona, 08034, Spain
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24
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Gotlieb N, Azhie A, Sharma D, Spann A, Suo NJ, Tran J, Orchanian-Cheff A, Wang B, Goldenberg A, Chassé M, Cardinal H, Cohen JP, Lodi A, Dieude M, Bhat M. The promise of machine learning applications in solid organ transplantation. NPJ Digit Med 2022; 5:89. [PMID: 35817953 PMCID: PMC9273640 DOI: 10.1038/s41746-022-00637-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 06/24/2022] [Indexed: 11/16/2022] Open
Abstract
Solid-organ transplantation is a life-saving treatment for end-stage organ disease in highly selected patients. Alongside the tremendous progress in the last several decades, new challenges have emerged. The growing disparity between organ demand and supply requires optimal patient/donor selection and matching. Improvements in long-term graft and patient survival require data-driven diagnosis and management of post-transplant complications. The growing abundance of clinical, genetic, radiologic, and metabolic data in transplantation has led to increasing interest in applying machine-learning (ML) tools that can uncover hidden patterns in large datasets. ML algorithms have been applied in predictive modeling of waitlist mortality, donor–recipient matching, survival prediction, post-transplant complications diagnosis, and prediction, aiming to optimize immunosuppression and management. In this review, we provide insight into the various applications of ML in transplant medicine, why these were used to evaluate a specific clinical question, and the potential of ML to transform the care of transplant recipients. 36 articles were selected after a comprehensive search of the following databases: Ovid MEDLINE; Ovid MEDLINE Epub Ahead of Print and In-Process & Other Non-Indexed Citations; Ovid Embase; Cochrane Database of Systematic Reviews (Ovid); and Cochrane Central Register of Controlled Trials (Ovid). In summary, these studies showed that ML techniques hold great potential to improve the outcome of transplant recipients. Future work is required to improve the interpretability of these algorithms, ensure generalizability through larger-scale external validation, and establishment of infrastructure to permit clinical integration.
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Affiliation(s)
- Neta Gotlieb
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.,Department of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Amirhossein Azhie
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Divya Sharma
- Department of Gastroenterology, Toronto General Hospital Research Institute, Toronto, ON, Canada
| | - Ashley Spann
- Division of Gastroenterology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nan-Ji Suo
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Jason Tran
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Ani Orchanian-Cheff
- Library and Information Services, University Health Network, Toronto, ON, Canada
| | - Bo Wang
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Anna Goldenberg
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Michael Chassé
- Department of Medicine (Critical Care), University of Montreal Hospital, Montréal, QC, Canada.,Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada
| | - Heloise Cardinal
- Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada.,Centre hospitalier de l'Université de Montréal Research Center, Université de Montréal, Montréal, QC, Canada
| | - Joseph Paul Cohen
- Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada.,Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA.,Mila, Quebec Artificial Intelligence Institute, Montréal, QC, Canada
| | - Andrea Lodi
- Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada.,Canada Excellence Research Chair, Polytechnique Montréal, Montréal, QC, Canada
| | - Melanie Dieude
- Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada.,Centre hospitalier de l'Université de Montréal Research Center, Université de Montréal, Montréal, QC, Canada.,Department Microbiology, Infectiology and Immunology, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada.,Héma-Québec, Montréal, QC, Canada
| | - Mamatha Bhat
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada. .,Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada. .,Division of Gastroenterology and Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada.
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25
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Zahn J, Germond A, Lundgren AY, Cicerone MT. Discriminating cell line specific features of antibiotic-resistant strains of Escherichia coli from Raman spectra via machine learning analysis. JOURNAL OF BIOPHOTONICS 2022; 15:e202100274. [PMID: 35238159 PMCID: PMC9262779 DOI: 10.1002/jbio.202100274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 02/02/2022] [Accepted: 02/28/2022] [Indexed: 06/14/2023]
Abstract
While Raman spectroscopy can provide label-free discrimination between highly similar biological species, the discrimination is often marginal, and optimal use of spectral information is imperative. Here, we compare two machine learning models, an artificial neural network and a support vector machine, for discriminating between Raman spectra of 11 bacterial mutants of Escherichia coli MDS42. While we find that both models discriminate the 11 bacterial strains with similarly high accuracy, sensitivity and specificity, it is clear that the models form different class boundaries. By extracting strain-specific (and function-specific) spectral features utilized by the models, we find that both models utilize a small subset of high intensity peaks while separate subsets of lower intensity peaks are utilized by only one method or the other. This analysis highlights the need for methods to use the complete spectral information more effectively, beginning with a better understanding of the distinct information gained from each model.
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Affiliation(s)
- Jessica Zahn
- Department of Chemistry and Biochemistry, Georgia Institute of Technology, 950 Atlantic Drive, Atlanta, GA 30332, USA
| | - Arno Germond
- INRAE, UR 370 Qualité des Produits Animaux (QuaPA) Équipe Imagerie & Transferts (IT), 63122 Saint-Gènes-Champanelle, France
| | - Alice Y Lundgren
- Department of Mathematics, Brigham Young University, 275 TMCB, Provo, UT 84602, USA
| | - Marcus T Cicerone
- Department of Chemistry and Biochemistry, Georgia Institute of Technology, 950 Atlantic Drive, Atlanta, GA 30332, USA
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26
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Nagai S, Nallabasannagari AR, Moonka D, Reddiboina M, Yeddula S, Kitajima T, Francis I, Abouljoud M. Use of neural network models to predict liver transplantation waitlist mortality. Liver Transpl 2022; 28:1133-1143. [PMID: 35224855 DOI: 10.1002/lt.26442] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 01/21/2022] [Accepted: 02/12/2022] [Indexed: 02/07/2023]
Abstract
Current liver transplantation (LT) organ allocation relies on Model for End-Stage Liver Disease-sodium scores to predict mortality in patients awaiting LT. This study aims to develop neural network (NN) models that more accurately predict LT waitlist mortality. The study evaluates patients listed for LT between February 27, 2002, and June 30, 2021, using the Organ Procurement and Transplantation Network/United Network for Organ Sharing registry. We excluded patients listed with Model for End-Stage Liver Disease (MELD) exception scores and those listed for multiorgan transplant, except for liver-kidney transplant. A subset of data from the waiting list was used to create a mortality prediction model at 90 days after listing with 105,140 patients. A total of 28 variables were selected for model creation. The data were split using random sampling into training, validation, and test data sets in a 60:20:20 ratio. The performance of the model was assessed using area under the receiver operating curve (AUC-ROC) and area under the precision-recall curve (AUC-PR). AUC-ROC for 90-day mortality was 0.936 (95% confidence interval [CI], 0.934-0.937), and AUC-PR was 0.758 (95% CI, 0.754-0.762). The NN 90-day mortality model outperformed MELD-based models for both AUC-ROC and AUC-PR. The 90-day mortality model specifically identified more waitlist deaths with a higher recall (sensitivity) of 0.807 (95% CI, 0.803-0.811) versus 0.413 (95% CI, 0.409-0.418; p < 0.001). The performance metrics were compared by breaking the test data set into multiple patient subsets by ethnicity, gender, region, age, diagnosis group, and year of listing. The NN 90-day mortality model outperformed MELD-based models across all subsets in predicting mortality. In conclusion, organ allocation based on NN modeling has the potential to decrease waitlist mortality and lead to more equitable allocation systems in LT.
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Affiliation(s)
- Shunji Nagai
- Division of Transplant and Hepatobiliary Surgery, Henry Ford Hospital, Detroit, Michigan, USA.,Henry Ford Transplant Institute, Detroit, Michigan, USA
| | | | - Dilip Moonka
- Henry Ford Transplant Institute, Detroit, Michigan, USA.,Division of Gastroenterology and Hepatology, Henry Ford Hospital, Detroit, Michigan, USA
| | | | | | - Toshihiro Kitajima
- Division of Transplant and Hepatobiliary Surgery, Henry Ford Hospital, Detroit, Michigan, USA.,Henry Ford Transplant Institute, Detroit, Michigan, USA
| | - Iman Francis
- Henry Ford Transplant Institute, Detroit, Michigan, USA
| | - Marwan Abouljoud
- Division of Transplant and Hepatobiliary Surgery, Henry Ford Hospital, Detroit, Michigan, USA.,Henry Ford Transplant Institute, Detroit, Michigan, USA
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27
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Thongprayoon C, Mao SA, Jadlowiec CC, Mao MA, Leeaphorn N, Kaewput W, Vaitla P, Pattharanitima P, Tangpanithandee S, Krisanapan P, Qureshi F, Nissaisorakarn P, Cooper M, Cheungpasitporn W. Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States. J Clin Med 2022; 11:3288. [PMID: 35743357 PMCID: PMC9224965 DOI: 10.3390/jcm11123288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/28/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022] Open
Abstract
Background: This study aimed to better characterize morbidly obese kidney transplant recipients, their clinical characteristics, and outcomes by using an unsupervised machine learning approach. Methods: Consensus cluster analysis was applied to OPTN/UNOS data from 2010 to 2019 based on recipient, donor, and transplant characteristics in kidney transplant recipients with a pre-transplant BMI ≥ 40 kg/m2. Key cluster characteristics were identified using the standardized mean difference. Post-transplant outcomes, including death-censored graft failure, patient death, and acute allograft rejection, were compared among the clusters. Results: Consensus clustering analysis identified 3204 kidney transplant recipients with a BMI ≥ 40 kg/m2. In this cohort, five clinically distinct clusters were identified. Cluster 1 recipients were predominantly white and non-sensitized, had a short dialysis time or were preemptive, and were more likely to receive living donor kidney transplants. Cluster 2 recipients were older and diabetic. They were likely to have been on dialysis >3 years and receive a standard KDPI deceased donor kidney. Cluster 3 recipients were young, black, and had kidney disease secondary to hypertension or glomerular disease. Cluster 3 recipients had >3 years of dialysis and received non-ECD, young, deceased donor kidney transplants with a KDPI < 85%. Cluster 4 recipients were diabetic with variable dialysis duration who either received non-ECD standard KDPI kidneys or living donor kidney transplants. Cluster 5 recipients were young retransplants that were sensitized. One-year patient survival in clusters 1, 2, 3, 4, and 5 was 98.0%, 94.4%, 98.5%, 98.7%, and 97%, and one-year death-censored graft survival was 98.1%, 93.0%, 96.1%, 98.8%, and 93.0%, respectively. Cluster 2 had the worst one-year patient survival. Clusters 2 and 5 had the worst one-year death-censored graft survival. Conclusions: With the application of unsupervised machine learning, variable post-transplant outcomes are observed among morbidly obese kidney transplant recipients. Recipients with earlier access to transplant and living donation show superior outcomes. Unexpectedly, reduced graft survival in cluster 3 recipients perhaps underscores socioeconomic access to post-transplant support and minorities being disadvantaged in access to preemptive and living donor transplants. Despite obesity-related concerns, one-year patient and graft survival were favorable in all clusters, and obesity itself should be reconsidered as a hard barrier to kidney transplantation.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55902, USA; (C.T.); (S.T.); (P.K.); (F.Q.)
| | - Shennen A. Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL 32224, USA;
| | | | - Michael A. Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Napat Leeaphorn
- Renal Transplant Program, University of Missouri-Kansas City School of Medicine/Saint Luke’s Health System, Kansas City, MO 64108, USA;
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand
| | - Pradeep Vaitla
- Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA;
| | - Pattharawin Pattharanitima
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55902, USA; (C.T.); (S.T.); (P.K.); (F.Q.)
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55902, USA; (C.T.); (S.T.); (P.K.); (F.Q.)
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55902, USA; (C.T.); (S.T.); (P.K.); (F.Q.)
| | - Pitchaphon Nissaisorakarn
- Department of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA;
| | - Matthew Cooper
- Medstar Georgetown Transplant Institute, Georgetown University School of Medicine, Washington, DC 20007, USA;
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55902, USA; (C.T.); (S.T.); (P.K.); (F.Q.)
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28
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Tran J, Sharma D, Gotlieb N, Xu W, Bhat M. Application of machine learning in liver transplantation: a review. Hepatol Int 2022; 16:495-508. [PMID: 35020154 DOI: 10.1007/s12072-021-10291-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 12/15/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Machine learning (ML) has been increasingly applied in the health-care and liver transplant setting. The demand for liver transplantation continues to expand on an international scale, and with advanced aging and complex comorbidities, many challenges throughout the transplantation decision-making process must be better addressed. There exist massive datasets with hidden, non-linear relationships between demographic, clinical, laboratory, genetic, and imaging parameters that conventional methods fail to capitalize on when reviewing their predictive potential. Pre-transplant challenges include addressing efficacies of liver segmentation, hepatic steatosis assessment, and graft allocation. Post-transplant applications include predicting patient survival, graft rejection and failure, and post-operative morbidity risk. AIM In this review, we describe a comprehensive summary of ML applications in liver transplantation including the clinical context and how to overcome challenges for clinical implementation. METHODS Twenty-nine articles were identified from Ovid MEDLINE, MEDLINE Epub Ahead of Print and In-Process and Other Non-Indexed Citations, Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials. CONCLUSION ML is vastly interrogated in liver transplantation with promising applications in pre- and post-transplant settings. Although challenges exist including site-specific training requirements, the demand for more multi-center studies, and optimization hurdles for clinical interpretability, the powerful potential of ML merits further exploration to enhance patient care.
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Affiliation(s)
- Jason Tran
- Department of Medicine, University of Ottawa, Ottawa, Canada
| | - Divya Sharma
- Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Biostatistics, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Neta Gotlieb
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Wei Xu
- Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Biostatistics, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Mamatha Bhat
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
- Division of Gastroenterology, Department of Medicine, University of Toronto, 585 University Avenue, Toronto, ON, M5G 2N2, Canada.
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29
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Thongprayoon C, Jadlowiec CC, Kaewput W, Vaitla P, Mao SA, Mao MA, Leeaphorn N, Qureshi F, Pattharanitima P, Qureshi F, Acharya PC, Nissaisorakarn P, Cooper M, Cheungpasitporn W. Distinct Phenotypes of Kidney Transplant Recipients in the United States with Limited Functional Status as Identified through Machine Learning Consensus Clustering. J Pers Med 2022; 12:859. [PMID: 35743647 PMCID: PMC9225038 DOI: 10.3390/jpm12060859] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 05/22/2022] [Accepted: 05/23/2022] [Indexed: 01/27/2023] Open
Abstract
Background: There have been concerns regarding increased perioperative mortality, length of hospital stay, and rates of graft loss in kidney transplant recipients with functional limitations. The application of machine learning consensus clustering approach may provide a novel understanding of unique phenotypes of functionally limited kidney transplant recipients with distinct outcomes in order to identify strategies to improve outcomes. Methods: Consensus cluster analysis was performed based on recipient-, donor-, and transplant-related characteristics in 3205 functionally limited kidney transplant recipients (Karnofsky Performance Scale (KPS) < 40% at transplant) in the OPTN/UNOS database from 2010 to 2019. Each cluster’s key characteristics were identified using the standardized mean difference. Posttransplant outcomes, including death-censored graft failure, patient death, and acute allograft rejection were compared among the clusters Results: Consensus cluster analysis identified two distinct clusters that best represented the clinical characteristics of kidney transplant recipients with limited functional status prior to transplant. Cluster 1 patients were older in age and were more likely to receive deceased donor kidney transplant with a higher number of HLA mismatches. In contrast, cluster 2 patients were younger, had shorter dialysis duration, were more likely to be retransplants, and were more likely to receive living donor kidney transplants from HLA mismatched donors. As such, cluster 2 recipients had a higher PRA, less cold ischemia time, and lower proportion of machine-perfused kidneys. Despite having a low KPS, 5-year patient survival was 79.1 and 83.9% for clusters 1 and 2; 5-year death-censored graft survival was 86.9 and 91.9%. Cluster 1 had lower death-censored graft survival and patient survival but higher acute rejection, compared to cluster 2. Conclusion: Our study used an unsupervised machine learning approach to characterize kidney transplant recipients with limited functional status into two clinically distinct clusters with differing posttransplant outcomes.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA;
| | | | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand
| | - Pradeep Vaitla
- Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA;
| | - Shennen A. Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Michael A. Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Napat Leeaphorn
- Renal Transplant Program, University of Missouri-Kansas City School of Medicine/Saint Luke’s Health System, Kansas City, MO 64108, USA;
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA;
| | - Pattharawin Pattharanitima
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Fahad Qureshi
- School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64108, USA;
| | - Prakrati C. Acharya
- Division of Nephrology, Texas Tech Health Sciences Center El Paso, El Paso, TX 79905, USA;
| | - Pitchaphon Nissaisorakarn
- Department of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA;
| | - Matthew Cooper
- Medstar Georgetown Transplant Institute, Georgetown University School of Medicine, Washington, DC 21042, USA;
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA;
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Thongprayoon C, Vaitla P, Jadlowiec CC, Leeaphorn N, Mao SA, Mao MA, Pattharanitima P, Bruminhent J, Khoury NJ, Garovic VD, Cooper M, Cheungpasitporn W. Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Black Kidney Transplant Recipients and Associated Outcomes. JAMA Surg 2022; 157:e221286. [PMID: 35507356 PMCID: PMC9069346 DOI: 10.1001/jamasurg.2022.1286] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Importance Among kidney transplant recipients, Black patients continue to have worse graft function and reduced patient and graft survival. Better understanding of different phenotypes and subgroups of Black kidney transplant recipients may help the transplant community to identify individualized strategies to improve outcomes among these vulnerable groups. Objective To cluster Black kidney transplant recipients in the US using an unsupervised machine learning approach. Design, Setting, and Participants This cohort study performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in Black kidney transplant recipients in the US from January 1, 2015, to December 31, 2019, in the Organ Procurement and Transplantation Network/United Network for Organ Sharing database. Each cluster's key characteristics were identified using the standardized mean difference, and subsequently the posttransplant outcomes were compared among the clusters. Data were analyzed from June 9 to July 17, 2021. Exposure Machine learning consensus clustering approach. Main Outcomes and Measures Death-censored graft failure, patient death within 3 years after kidney transplant, and allograft rejection within 1 year after kidney transplant. Results Consensus cluster analysis was performed for 22 687 Black kidney transplant recipients (mean [SD] age, 51.4 [12.6] years; 13 635 men [60%]), and 4 distinct clusters that best represented their clinical characteristics were identified. Cluster 1 was characterized by highly sensitized recipients of deceased donor kidney retransplants; cluster 2, by recipients of living donor kidney transplants with no or short prior dialysis; cluster 3, by young recipients with hypertension and without diabetes who received young deceased donor transplants with low kidney donor profile index scores; and cluster 4, by older recipients with diabetes who received kidneys from older donors with high kidney donor profile index scores and extended criteria donors. Cluster 2 had the most favorable outcomes in terms of death-censored graft failure, patient death, and allograft rejection. Compared with cluster 2, all other clusters had a higher risk of death-censored graft failure and death. Higher risk for rejection was found in clusters 1 and 3, but not cluster 4. Conclusions and Relevance In this cohort study using an unsupervised machine learning approach, the identification of clinically distinct clusters among Black kidney transplant recipients underscores the need for individualized care strategies to improve outcomes among vulnerable patient groups.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota
| | - Pradeep Vaitla
- Division of Nephrology, University of Mississippi Medical Center, Jackson
| | | | - Napat Leeaphorn
- Renal Transplant Program, University of Missouri-Kansas City School of Medicine, Saint Luke's Health System
| | - Shennen A Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, Florida
| | - Michael A Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, Florida
| | | | - Jackrapong Bruminhent
- Ramathibodi Excellence Center for Organ Transplantation, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.,Division of Infectious Diseases, Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Nadeen J Khoury
- Department of Nephrology, Department of Medicine, Henry Ford Hospital, Detroit, Michigan
| | - Vesna D Garovic
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota
| | | | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota
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31
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Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14:765-793. [PMID: 35582107 PMCID: PMC9048537 DOI: 10.4251/wjgo.v14.i4.765] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/24/2021] [Accepted: 03/25/2022] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.
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Affiliation(s)
- Chrysanthos D Christou
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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32
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Miller RJH, Sabovčik F, Cauwenberghs N, Vens C, Khush KK, Heidenreich PA, Haddad F, Kuznetsova T. Temporal Shift and Predictive Performance of Machine Learning for Heart Transplant Outcomes. J Heart Lung Transplant 2022; 41:928-936. [DOI: 10.1016/j.healun.2022.03.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 02/25/2022] [Accepted: 03/23/2022] [Indexed: 11/27/2022] Open
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Henn J, Buness A, Schmid M, Kalff JC, Matthaei H. Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review. Langenbecks Arch Surg 2022; 407:51-61. [PMID: 34716472 PMCID: PMC8847247 DOI: 10.1007/s00423-021-02348-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 10/03/2021] [Indexed: 12/16/2022]
Abstract
PURPOSE An indication for surgical therapy includes balancing benefits against risk, which remains a key task in all surgical disciplines. Decisions are oftentimes based on clinical experience while guidelines lack evidence-based background. Various medical fields capitalized the application of machine learning (ML), and preliminary research suggests promising implications in surgeons' workflow. Hence, we evaluated ML's contemporary and possible future role in clinical decision-making (CDM) focusing on abdominal surgery. METHODS Using the PICO framework, relevant keywords and research questions were identified. Following the PRISMA guidelines, a systemic search strategy in the PubMed database was conducted. Results were filtered by distinct criteria and selected articles were manually full text reviewed. RESULTS Literature review revealed 4,396 articles, of which 47 matched the search criteria. The mean number of patients included was 55,843. A total of eight distinct ML techniques were evaluated whereas AUROC was applied by most authors for comparing ML predictions vs. conventional CDM routines. Most authors (N = 30/47, 63.8%) stated ML's superiority in the prediction of benefits and risks of surgery. The identification of highly relevant parameters to be integrated into algorithms allowing a more precise prognosis was emphasized as the main advantage of ML in CDM. CONCLUSIONS A potential value of ML for surgical decision-making was demonstrated in several scientific articles. However, the low number of publications with only few collaborative studies between surgeons and computer scientists underpins the early phase of this highly promising field. Interdisciplinary research initiatives combining existing clinical datasets and emerging techniques of data processing may likely improve CDM in abdominal surgery in the future.
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Affiliation(s)
- Jonas Henn
- Department of General, Visceral, Thoracic and Vascular Surgery, University of Bonn, Bonn, Germany
| | - Andreas Buness
- Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Bonn, Germany
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany
| | - Matthias Schmid
- Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Bonn, Germany
| | - Jörg C Kalff
- Department of General, Visceral, Thoracic and Vascular Surgery, University of Bonn, Bonn, Germany
| | - Hanno Matthaei
- Department of General, Visceral, Thoracic and Vascular Surgery, University of Bonn, Bonn, Germany.
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34
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Ferrarese A, Sartori G, Orrù G, Frigo AC, Pelizzaro F, Burra P, Senzolo M. Machine learning in liver transplantation: a tool for some unsolved questions? Transpl Int 2021; 34:398-411. [PMID: 33428298 DOI: 10.1111/tri.13818] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 08/24/2020] [Accepted: 01/08/2021] [Indexed: 12/13/2022]
Abstract
Machine learning has recently been proposed as a useful tool in many fields of Medicine, with the aim of increasing diagnostic and prognostic accuracy. Models based on machine learning have been introduced in the setting of solid organ transplantation too, where prognosis depends on a complex, multidimensional and nonlinear relationship between variables pertaining to the donor, the recipient and the surgical procedure. In the setting of liver transplantation, machine learning models have been developed to predict pretransplant survival in patients with cirrhosis, to assess the best donor-to-recipient match during allocation processes, and to foresee postoperative complications and outcomes. This is a narrative review on the role of machine learning in the field of liver transplantation, highlighting strengths and pitfalls, and future perspectives.
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Affiliation(s)
- Alberto Ferrarese
- Multivisceral Transplant Unit, Department of Surgery, Oncology and Gastroenterology, Padua University Hospital, Padua, Italy
| | - Giuseppe Sartori
- Forensic Neuropsychology and Forensic Neuroscience, PhD Program in Mind Brain and Computer Science, Department of General Psychology, Padua University, Padua, Italy
| | - Graziella Orrù
- Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Pisa, Italy
| | - Anna Chiara Frigo
- Department of Cardiac-Thoracic-Vascular Sciences and Public Health, Biostatistics, Epidemiology and Public Health Unit, University of Padua, Padova, Veneto, Italy
| | - Filippo Pelizzaro
- Multivisceral Transplant Unit, Department of Surgery, Oncology and Gastroenterology, Padua University Hospital, Padua, Italy
| | - Patrizia Burra
- Multivisceral Transplant Unit, Department of Surgery, Oncology and Gastroenterology, Padua University Hospital, Padua, Italy
| | - Marco Senzolo
- Multivisceral Transplant Unit, Department of Surgery, Oncology and Gastroenterology, Padua University Hospital, Padua, Italy
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Thrift WJ, Ronaghi S, Samad M, Wei H, Nguyen DG, Cabuslay AS, Groome CE, Santiago PJ, Baldi P, Hochbaum AI, Ragan R. Deep Learning Analysis of Vibrational Spectra of Bacterial Lysate for Rapid Antimicrobial Susceptibility Testing. ACS NANO 2020; 14:15336-15348. [PMID: 33095005 DOI: 10.1021/acsnano.0c05693] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Rapid antimicrobial susceptibility testing (AST) is an integral tool to mitigate the unnecessary use of powerful and broad-spectrum antibiotics that leads to the proliferation of multi-drug-resistant bacteria. Using a sensor platform composed of surface-enhanced Raman scattering (SERS) sensors with control of nanogap chemistry and machine learning algorithms for analysis of complex spectral data, bacteria metabolic profiles post antibiotic exposure are correlated with susceptibility. Deep neural network models are able to discriminate the responses of Escherichia coli and Pseudomonas aeruginosa to antibiotics from untreated cells in SERS data in 10 min after antibiotic exposure with greater than 99% accuracy. Deep learning analysis is also able to differentiate responses from untreated cells with antibiotic dosages up to 10-fold lower than the minimum inhibitory concentration observed in conventional growth assays. In addition, analysis of SERS data using a generative model, a variational autoencoder, identifies spectral features in the P. aeruginosa lysate data associated with antibiotic efficacy. From this insight, a combinatorial dataset of metabolites is selected to extend the latent space of the variational autoencoder. This culture-free dataset dramatically improves classification accuracy to select effective antibiotic treatment in 30 min. Unsupervised Bayesian Gaussian mixture analysis achieves 99.3% accuracy in discriminating between susceptible versus resistant to antibiotic cultures in SERS using the extended latent space. Discriminative and generative models rapidly provide high classification accuracy with small sets of labeled data, which enormously reduces the amount of time needed to validate phenotypic AST with conventional growth assays. Thus, this work outlines a promising approach toward practical rapid AST.
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Affiliation(s)
- William John Thrift
- Department of Materials Science and Engineering, University of California, Irvine, California 92697, United States
| | - Sasha Ronaghi
- Sage Hill School, Newport Coast, California 92657, United States
| | - Muntaha Samad
- Department of Computer Science, University of California, Irvine, California 92697, United States
| | - Hong Wei
- Department of Materials Science and Engineering, University of California, Irvine, California 92697, United States
| | - Dean Gia Nguyen
- Department of Chemical and Biomolecular Engineering, University of California, Irvine, California 92697, United States
| | | | - Chloe E Groome
- Department of Materials Science and Engineering, University of California, Irvine, California 92697, United States
| | - Peter Joseph Santiago
- Department of Materials Science and Engineering, University of California, Irvine, California 92697, United States
| | - Pierre Baldi
- Department of Computer Science, University of California, Irvine, California 92697, United States
| | - Allon I Hochbaum
- Department of Materials Science and Engineering, University of California, Irvine, California 92697, United States
- Department of Chemistry, University of California, Irvine, California 92617, United States
- Department of Chemical and Biomolecular Engineering, University of California, Irvine, California 92697, United States
- Department of Molecular Biology and Biochemistry, University of California, Irvine, California 92697, United States
| | - Regina Ragan
- Department of Materials Science and Engineering, University of California, Irvine, California 92697, United States
- Department of Chemical and Biomolecular Engineering, University of California, Irvine, California 92697, United States
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36
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Sun L, Marsh JN, Matlock MK, Chen L, Gaut JP, Brunt EM, Swamidass SJ, Liu TC. Deep learning quantification of percent steatosis in donor liver biopsy frozen sections. EBioMedicine 2020; 60:103029. [PMID: 32980688 PMCID: PMC7522765 DOI: 10.1016/j.ebiom.2020.103029] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 09/09/2020] [Accepted: 09/10/2020] [Indexed: 12/15/2022] Open
Abstract
Background Pathologist evaluation of donor liver biopsies provides information for accepting or discarding potential donor livers. Due to the urgent nature of the decision process, this is regularly performed using frozen sectioning at the time of biopsy. The percent steatosis in a donor liver biopsy correlates with transplant outcome, however there is significant inter- and intra-observer variability in quantifying steatosis, compounded by frozen section artifact. We hypothesized that a deep learning model could identify and quantify steatosis in donor liver biopsies. Methods We developed a deep learning convolutional neural network that generates a steatosis probability map from an input whole slide image (WSI) of a hematoxylin and eosin-stained frozen section, and subsequently calculates the percent steatosis. Ninety-six WSI of frozen donor liver sections from our transplant pathology service were annotated for steatosis and used to train (n = 30 WSI) and test (n = 66 WSI) the deep learning model. Findings The model had good correlation and agreement with the annotation in both the training set (r of 0.88, intraclass correlation coefficient [ICC] of 0.88) and novel input test sets (r = 0.85 and ICC=0.85). These measurements were superior to the estimates of the on-service pathologist at the time of initial evaluation (r = 0.52 and ICC=0.52 for the training set, and r = 0.74 and ICC=0.72 for the test set). Interpretation Use of this deep learning algorithm could be incorporated into routine pathology workflows for fast, accurate, and reproducible donor liver evaluation. Funding Mid-America Transplant Society
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Affiliation(s)
- Lulu Sun
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States
| | - Jon N Marsh
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States; Institue for Informatics (I(2)), Washington University School of Medicine, St. Louis, MO, United States
| | - Matthew K Matlock
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States
| | - Ling Chen
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
| | - Joseph P Gaut
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States
| | - Elizabeth M Brunt
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States
| | - S Joshua Swamidass
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States; Institue for Informatics (I(2)), Washington University School of Medicine, St. Louis, MO, United States.
| | - Ta-Chiang Liu
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States; Lead contact.
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