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Liu X, Shen J, Yan H, Hu J, Liao G, Liu D, Zhou S, Zhang J, Liao J, Guo Z, Li Y, Yang S, Li S, Chen H, Guo Y, Li M, Fan L, Li L, Luo P, Zhao M, Liu Y. Posttransplant complications: molecular mechanisms and therapeutic interventions. MedComm (Beijing) 2024; 5:e669. [PMID: 39224537 PMCID: PMC11366828 DOI: 10.1002/mco2.669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 07/02/2024] [Accepted: 07/08/2024] [Indexed: 09/04/2024] Open
Abstract
Posttransplantation complications pose a major challenge to the long-term survival and quality of life of organ transplant recipients. These complications encompass immune-mediated complications, infectious complications, metabolic complications, and malignancies, with each type influenced by various risk factors and pathological mechanisms. The molecular mechanisms underlying posttransplantation complications involve a complex interplay of immunological, metabolic, and oncogenic processes, including innate and adaptive immune activation, immunosuppressant side effects, and viral reactivation. Here, we provide a comprehensive overview of the clinical features, risk factors, and molecular mechanisms of major posttransplantation complications. We systematically summarize the current understanding of the immunological basis of allograft rejection and graft-versus-host disease, the metabolic dysregulation associated with immunosuppressive agents, and the role of oncogenic viruses in posttransplantation malignancies. Furthermore, we discuss potential prevention and intervention strategies based on these mechanistic insights, highlighting the importance of optimizing immunosuppressive regimens, enhancing infection prophylaxis, and implementing targeted therapies. We also emphasize the need for future research to develop individualized complication control strategies under the guidance of precision medicine, ultimately improving the prognosis and quality of life of transplant recipients.
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Affiliation(s)
- Xiaoyou Liu
- Department of Organ transplantationThe First Affiliated Hospital, Guangzhou Medical UniversityGuangzhouChina
| | - Junyi Shen
- Department of OncologyZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Hongyan Yan
- Department of Organ transplantationThe First Affiliated Hospital, Guangzhou Medical UniversityGuangzhouChina
| | - Jianmin Hu
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Guorong Liao
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Ding Liu
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Song Zhou
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Jie Zhang
- Department of Organ transplantationThe First Affiliated Hospital, Guangzhou Medical UniversityGuangzhouChina
| | - Jun Liao
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Zefeng Guo
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Yuzhu Li
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Siqiang Yang
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Shichao Li
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Hua Chen
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Ying Guo
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Min Li
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Lipei Fan
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Liuyang Li
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Peng Luo
- Department of OncologyZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Ming Zhao
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Yongguang Liu
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
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Calleja R, Durán M, Ayllón MD, Ciria R, Briceño J. Machine learning in liver surgery: Benefits and pitfalls. World J Clin Cases 2024; 12:2134-2137. [PMID: 38680268 PMCID: PMC11045503 DOI: 10.12998/wjcc.v12.i12.2134] [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: 12/20/2023] [Revised: 02/08/2024] [Accepted: 03/29/2024] [Indexed: 04/16/2024] Open
Abstract
The application of machine learning (ML) algorithms in various fields of hepatology is an issue of interest. However, we must be cautious with the results. In this letter, based on a published ML prediction model for acute kidney injury after liver surgery, we discuss some limitations of ML models and how they may be addressed in the future. Although the future faces significant challenges, it also holds a great potential.
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Affiliation(s)
- Rafael Calleja
- Hepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba, Córdoba 14004, Spain
| | - Manuel Durán
- Hepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba, Córdoba 14004, Spain
| | - María Dolores Ayllón
- Hepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba, Córdoba 14004, Spain
| | - Ruben Ciria
- Hepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba, Córdoba 14004, Spain
| | - Javier Briceño
- Hepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba, Córdoba 14004, Spain
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Michelson AP, Oh I, Gupta A, Puri V, Kreisel D, Gelman AE, Nava R, Witt CA, Byers DE, Halverson L, Vazquez-Guillamet R, Payne PRO, Hachem RR. Developing machine learning models to predict primary graft dysfunction after lung transplantation. Am J Transplant 2024; 24:458-467. [PMID: 37468109 DOI: 10.1016/j.ajt.2023.07.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 06/21/2023] [Accepted: 07/04/2023] [Indexed: 07/21/2023]
Abstract
Primary graft dysfunction (PGD) is the leading cause of morbidity and mortality in the first 30 days after lung transplantation. Risk factors for the development of PGD include donor and recipient characteristics, but how multiple variables interact to impact the development of PGD and how clinicians should consider these in making decisions about donor acceptance remain unclear. This was a single-center retrospective cohort study to develop and evaluate machine learning pipelines to predict the development of PGD grade 3 within the first 72 hours of transplantation using donor and recipient variables that are known at the time of donor offer acceptance. Among 576 bilateral lung recipients, 173 (30%) developed PGD grade 3. The cohort underwent a 75% to 25% train-test split, and lasso regression was used to identify 11 variables for model development. A K-nearest neighbor's model showing the best calibration and performance with relatively small confidence intervals was selected as the final predictive model with an area under the receiver operating characteristics curve of 0.65. Machine learning models can predict the risk for development of PGD grade 3 based on data available at the time of donor offer acceptance. This may improve donor-recipient matching and donor utilization in the future.
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Affiliation(s)
- Andrew P Michelson
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA; Institute for Informatics, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Inez Oh
- Institute for Informatics, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Aditi Gupta
- Institute for Informatics, Washington University School of Medicine, Saint Louis, Missouri, USA; Division of Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Varun Puri
- Division of Cardiothoracic Surgery, Department of Surgery, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Daniel Kreisel
- Division of Cardiothoracic Surgery, Department of Surgery, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Andrew E Gelman
- Division of Cardiothoracic Surgery, Department of Surgery, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Ruben Nava
- Division of Cardiothoracic Surgery, Department of Surgery, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Chad A Witt
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Derek E Byers
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Laura Halverson
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Rodrigo Vazquez-Guillamet
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Philip R O Payne
- Institute for Informatics, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Ramsey R Hachem
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA.
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Mumtaz H, Saqib M, Jabeen S, Muneeb M, Mughal W, Sohail H, Safdar M, Mehmood Q, Khan MA, Ismail SM. Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review. Front Med (Lausanne) 2023; 10:1227168. [PMID: 37849490 PMCID: PMC10577305 DOI: 10.3389/fmed.2023.1227168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 09/20/2023] [Indexed: 10/19/2023] Open
Abstract
The core idea behind precision medicine is to pinpoint the subpopulations that differ from one another in terms of disease risk, drug responsiveness, and treatment outcomes due to differences in biology and other traits. Biomarkers are found through genomic sequencing. Multi-dimensional clinical and biological data are created using these biomarkers. Better analytic methods are needed for these multidimensional data, which can be accomplished by using artificial intelligence (AI). An updated review of 80 latest original publications is presented on four main fronts-preventive medicine, medication development, treatment outcomes, and diagnostic medicine-All these studies effectively illustrated the significance of AI in precision medicine. Artificial intelligence (AI) has revolutionized precision medicine by swiftly analyzing vast amounts of data to provide tailored treatments and predictive diagnostics. Through machine learning algorithms and high-resolution imaging, AI assists in precise diagnoses and early disease detection. AI's ability to decode complex biological factors aids in identifying novel therapeutic targets, allowing personalized interventions and optimizing treatment outcomes. Furthermore, AI accelerates drug discovery by navigating chemical structures and predicting drug-target interactions, expediting the development of life-saving medications. With its unrivaled capacity to comprehend and interpret data, AI stands as an invaluable tool in the pursuit of enhanced patient care and improved health outcomes. It's evident that AI can open a new horizon for precision medicine by translating complex data into actionable information. To get better results in this regard and to fully exploit the great potential of AI, further research is required on this pressing subject.
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Affiliation(s)
| | | | | | - Muhammad Muneeb
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Wajiha Mughal
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Hassan Sohail
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Myra Safdar
- Armed Forces Institute of Cardiology and National Institute of Heart Diseases (AFIC-NIHD), Rawalpindi, Pakistan
| | - Qasim Mehmood
- Department of Medicine, King Edward Medical University, Lahore, Pakistan
| | - Muhammad Ahsan Khan
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
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Bambha K, Kim NJ, Sturdevant M, Perkins JD, Kling C, Bakthavatsalam R, Healey P, Dick A, Reyes JD, Biggins SW. Maximizing utility of nondirected living liver donor grafts using machine learning. Front Immunol 2023; 14:1194338. [PMID: 37457719 PMCID: PMC10344453 DOI: 10.3389/fimmu.2023.1194338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 06/13/2023] [Indexed: 07/18/2023] Open
Abstract
Objective There is an unmet need for optimizing hepatic allograft allocation from nondirected living liver donors (ND-LLD). Materials and method Using OPTN living donor liver transplant (LDLT) data (1/1/2000-12/31/2019), we identified 6328 LDLTs (4621 right, 644 left, 1063 left-lateral grafts). Random forest survival models were constructed to predict 10-year graft survival for each of the 3 graft types. Results Donor-to-recipient body surface area ratio was an important predictor in all 3 models. Other predictors in all 3 models were: malignant diagnosis, medical location at LDLT (inpatient/ICU), and moderate ascites. Biliary atresia was important in left and left-lateral graft models. Re-transplant was important in right graft models. C-index for 10-year graft survival predictions for the 3 models were: 0.70 (left-lateral); 0.63 (left); 0.61 (right). Similar C-indices were found for 1-, 3-, and 5-year graft survivals. Comparison of model predictions to actual 10-year graft survivals demonstrated that the predicted upper quartile survival group in each model had significantly better actual 10-year graft survival compared to the lower quartiles (p<0.005). Conclusion When applied in clinical context, our models assist with the identification and stratification of potential recipients for hepatic grafts from ND-LLD based on predicted graft survivals, while accounting for complex donor-recipient interactions. These analyses highlight the unmet need for granular data collection and machine learning modeling to identify potential recipients who have the best predicted transplant outcomes with ND-LLD grafts.
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Affiliation(s)
- Kiran Bambha
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Washington, Seattle, WA, United States
- Center for Liver Investigation Fostering discovery (C-LIFE), University of Washington, Seattle, WA, United States
- Clinical and Bio-Analytics Transplant Laboratory (C-BATL), University of Washington, Seattle, WA, United States
| | - Nicole J. Kim
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Washington, Seattle, WA, United States
- Center for Liver Investigation Fostering discovery (C-LIFE), University of Washington, Seattle, WA, United States
| | - Mark Sturdevant
- Clinical and Bio-Analytics Transplant Laboratory (C-BATL), University of Washington, Seattle, WA, United States
- Division of Transplant Surgery, Department of Surgery, University of Washington, Seattle, WA, United States
| | - James D. Perkins
- Clinical and Bio-Analytics Transplant Laboratory (C-BATL), University of Washington, Seattle, WA, United States
- Division of Transplant Surgery, Department of Surgery, University of Washington, Seattle, WA, United States
| | - Catherine Kling
- Clinical and Bio-Analytics Transplant Laboratory (C-BATL), University of Washington, Seattle, WA, United States
- Division of Transplant Surgery, Department of Surgery, University of Washington, Seattle, WA, United States
| | - Ramasamy Bakthavatsalam
- Clinical and Bio-Analytics Transplant Laboratory (C-BATL), University of Washington, Seattle, WA, United States
- Division of Transplant Surgery, Department of Surgery, University of Washington, Seattle, WA, United States
| | - Patrick Healey
- Clinical and Bio-Analytics Transplant Laboratory (C-BATL), University of Washington, Seattle, WA, United States
- Pediatric Transplant Surgery Division, Department of Surgery, Seattle Children’s Hospital, Seattle, WA, United States
| | - Andre Dick
- Clinical and Bio-Analytics Transplant Laboratory (C-BATL), University of Washington, Seattle, WA, United States
- Pediatric Transplant Surgery Division, Department of Surgery, Seattle Children’s Hospital, Seattle, WA, United States
| | - Jorge D. Reyes
- Clinical and Bio-Analytics Transplant Laboratory (C-BATL), University of Washington, Seattle, WA, United States
- Division of Transplant Surgery, Department of Surgery, University of Washington, Seattle, WA, United States
- Pediatric Transplant Surgery Division, Department of Surgery, Seattle Children’s Hospital, Seattle, WA, United States
| | - Scott W. Biggins
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Washington, Seattle, WA, United States
- Center for Liver Investigation Fostering discovery (C-LIFE), University of Washington, Seattle, WA, United States
- Clinical and Bio-Analytics Transplant Laboratory (C-BATL), University of Washington, Seattle, WA, United States
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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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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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Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor-Recipient Matching? MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58121743. [PMID: 36556945 PMCID: PMC9783019 DOI: 10.3390/medicina58121743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/16/2022] [Accepted: 11/25/2022] [Indexed: 11/30/2022]
Abstract
Liver transplantation outcomes have improved in recent years. However, with the emergence of expanded donor criteria, tools to better assist donor-recipient matching have become necessary. Most of the currently proposed scores based on conventional biostatistics are not good classifiers of a problem that is considered "unbalanced." In recent years, the implementation of artificial intelligence in medicine has experienced exponential growth. Deep learning, a branch of artificial intelligence, may be the answer to this classification problem. The ability to handle a large number of variables with speed, objectivity, and multi-objective analysis is one of its advantages. Artificial neural networks and random forests have been the most widely used deep classifiers in this field. This review aims to give a brief overview of D-R matching and its evolution in recent years and how artificial intelligence may be able to provide a solution.
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Carlson SF, Kamalia MA, Zimmerman MT, Urrutia RA, Joyce DL. The current and future role of artificial intelligence in optimizing donor organ utilization and recipient outcomes in heart transplantation. HEART, VESSELS AND TRANSPLANTATION 2022. [DOI: 10.24969/hvt.2022.350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Heart failure (HF) is a leading cause of morbidity and mortality in the United States. While medical management and mechanical circulatory support have undergone significant advancement in recent years, orthotopic heart transplantation (OHT) remains the most definitive therapy for refractory HF. OHT has seen steady improvement in patient survival and quality of life (QoL) since its inception, with one-year mortality now under 8%. However, a significant number of HF patients are unable to receive OHT due to scarcity of donor hearts. The United Network for Organ Sharing has recently revised its organ allocation criteria in an effort to provide more equitable access to OHT. Despite these changes, there are many potential donor hearts that are inevitably rejected. Arbitrary regulations from the centers for Medicare and Medicaid services and fear of repercussions if one-year mortality falls below established values has led to a current state of excessive risk aversion for which organs are accepted for OHT. Furthermore, non-standardized utilization of extended criteria donors and donation after circulatory death, exacerbate the organ shortage. Data-driven systems can improve donor-recipient matching, better predict patient QoL post-OHT, and decrease needless organ waste through more uniform application of acceptance criteria. Thus, we propose a data-driven future for OHT and a move to patient-centric and holistic transplantation care processes.
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10
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Briceño J, Calleja R, Hervás C. Artificial intelligence and liver transplantation: Looking for the best donor-recipient pairing. Hepatobiliary Pancreat Dis Int 2022; 21:347-353. [PMID: 35321836 DOI: 10.1016/j.hbpd.2022.03.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 02/28/2022] [Indexed: 02/07/2023]
Abstract
Decision-making based on artificial intelligence (AI) methodology is increasingly present in all areas of modern medicine. In recent years, models based on deep-learning have begun to be used in organ transplantation. Taking into account the huge number of factors and variables involved in donor-recipient (D-R) matching, AI models may be well suited to improve organ allocation. AI-based models should provide two solutions: complement decision-making with current metrics based on logistic regression and improve their predictability. Hundreds of classifiers could be used to address this problem. However, not all of them are really useful for D-R pairing. Basically, in the decision to assign a given donor to a candidate in waiting list, a multitude of variables are handled, including donor, recipient, logistic and perioperative variables. Of these last two, some of them can be inferred indirectly from the team's previous experience. Two groups of AI models have been used in the D-R matching: artificial neural networks (ANN) and random forest (RF). The former mimics the functional architecture of neurons, with input layers and output layers. The algorithms can be uni- or multi-objective. In general, ANNs can be used with large databases, where their generalizability is improved. However, they are models that are very sensitive to the quality of the databases and, in essence, they are black-box models in which all variables are important. Unfortunately, these models do not allow to know safely the weight of each variable. On the other hand, RF builds decision trees and works well with small cohorts. In addition, they can select top variables as with logistic regression. However, they are not useful with large databases, due to the extreme number of decision trees that they would generate, making them impractical. Both ANN and RF allow a successful donor allocation in over 80% of D-R pairing, a number much higher than that obtained with the best statistical metrics such as model for end-stage liver disease, balance of risk score, and survival outcomes following liver transplantation scores. Many barriers need to be overcome before these deep-learning-based models can be included for D-R matching. The main one of them is the resistance of the clinicians to leave their own decision to autonomous computational models.
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Affiliation(s)
- Javier Briceño
- Unit of Liver Transplantation, Department of General Surgery, Hospital Universitario Reina Sofía, Córdoba, Spain; Maimónides Institute of Biomedical Research of Córdoba (IMIBIC), Córdoba, Spain.
| | - Rafael Calleja
- Unit of Liver Transplantation, Department of General Surgery, Hospital Universitario Reina Sofía, Córdoba, Spain; Maimónides Institute of Biomedical Research of Córdoba (IMIBIC), Córdoba, Spain
| | - César Hervás
- Maimónides Institute of Biomedical Research of Córdoba (IMIBIC), Córdoba, Spain; Department of Computer Sciences and Numerical Analysis, Universidad de Córdoba, Córdoba, Spain
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Mucenic M, de Mello Brandão AB, Marroni CA. Artificial intelligence and human liver allocation: Potential benefits and ethical implications. Artif Intell Gastroenterol 2022; 3:21-27. [DOI: 10.35712/aig.v3.i1.21] [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: 12/24/2021] [Revised: 02/13/2022] [Accepted: 02/23/2022] [Indexed: 02/06/2023] Open
Abstract
Since its implementation almost two decades ago, the urgency allocation policy has improved the survival of patients on the waiting list for liver transplantation worldwide. The Model for End-Stage Liver Disease score is widely used to predict waiting list mortality. Due to some limitations related to its use, there is an active investigation to develop other prognostic scores. Liver allocation (LA) entails complex decision-making, and grafts are occasionally not directed to the recipients who are more likely to survive. Prognostic scores have, thus far, failed to predict post-operatory survival. Furthermore, the increasing use of marginal donors is associated with worse outcomes. Adequate donor-recipient pairing could help avoid retransplantation or futile procedures and reduce postoperative complications, mortality, hospitalization time, and costs. Artificial intelligence has applications in several medical fields. Machine learning algorithms (MLAs) use large amounts of data to detect unforeseen patterns and complex interactions between variables. Artificial neural networks and decision trees were the most common forms of MLA tested on LA. Some researchers have shown them to be superior for predicting waiting list mortality and graft failure than conventional statistical methods. These promising techniques are increasingly being considered for implementation.
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Affiliation(s)
- Marcos Mucenic
- Liver Transplant Adult Group, Irmandade da Santa Casa de Misericórdia de Porto Alegre, Porto Alegre 90020-090, RS, Brazil
| | | | - Claudio Augusto Marroni
- Hepatology, Universidade Federal de Ciencias da Saude de Porto Alegre, Porto Alegre 90050-170, RS, Brazil
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Spoletini G, Ferri F, Mauro A, Mennini G, Bianco G, Cardinale V, Agnes S, Rossi M, Avolio AW, Lai Q. CONUT Score Predicts Early Morbidity After Liver Transplantation: A Collaborative Study. Front Nutr 2022; 8:793885. [PMID: 35071299 PMCID: PMC8777109 DOI: 10.3389/fnut.2021.793885] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/06/2021] [Indexed: 12/11/2022] Open
Abstract
Introduction: Liver transplantation (LT) is burdened by the risk of post-operative morbidity. Identifying patients at higher risk of developing complications can help allocate resources in the perioperative phase. Controlling Nutritional Status (CONUT) score, based on lymphocyte count, serum albumin, and cholesterol levels, has been applied to various surgical specialties, proving reliable in predicting complications and prognosis. Our study aims to investigate the role of the CONUT score in predicting the development of early complications (within 90 days) after LT. Methods: This is a retrospective analysis of 209 patients with a calculable CONUT score within 2 months before LT. The ability of the CONUT score to predict severe complications, defined as a Comprehensive Complication Index (CCI) ≥42.1, was examined. Inverse Probability Treatment Weighting was used to balance the study population against potential confounders. Results: Patients with a CCI ≥42.1 had higher CONUT score values (median: 7 vs. 5, P-value < 0.0001). The CONUT score showed a good diagnostic ability regarding post-LT morbidity, with an AUC = 0.72 (95.0%CI = 0.64–0.79; P-value < 0.0001). The CONUT score was the only independent risk factor identified for a complicated post-LT course, with an odds ratio = 1.39 (P-value < 0.0001). The 90-day survival rate was 98.8% and 87.5% for patients with a CONUT score <8 and ≥8, respectively. Conclusions: Pre-operative CONUT score is a helpful tool to identify patients at increased post-LT morbidity risk. Further refinements in the score composition, specific to the LT population, could be obtained with prospective studies.
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Affiliation(s)
- Gabriele Spoletini
- General Surgery and Liver Transplantation, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Flaminia Ferri
- General Surgery and Organ Transplantation Unit, Sapienza University of Rome, Rome, Italy
| | - Alberto Mauro
- General Surgery and Liver Transplantation, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Gianluca Mennini
- General Surgery and Organ Transplantation Unit, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Bianco
- General Surgery and Liver Transplantation, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Vincenzo Cardinale
- General Surgery and Organ Transplantation Unit, Sapienza University of Rome, Rome, Italy
| | - Salvatore Agnes
- General Surgery and Liver Transplantation, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Massimo Rossi
- General Surgery and Organ Transplantation Unit, Sapienza University of Rome, Rome, Italy
| | - Alfonso Wolfango Avolio
- General Surgery and Liver Transplantation, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Quirino Lai
- General Surgery and Organ Transplantation Unit, Sapienza University of Rome, Rome, Italy
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