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Al Moussawy M, Lakkis ZS, Ansari ZA, Cherukuri AR, Abou-Daya KI. The transformative potential of artificial intelligence in solid organ transplantation. FRONTIERS IN TRANSPLANTATION 2024; 3:1361491. [PMID: 38993779 PMCID: PMC11235281 DOI: 10.3389/frtra.2024.1361491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 03/01/2024] [Indexed: 07/13/2024]
Abstract
Solid organ transplantation confronts numerous challenges ranging from donor organ shortage to post-transplant complications. Here, we provide an overview of the latest attempts to address some of these challenges using artificial intelligence (AI). We delve into the application of machine learning in pretransplant evaluation, predicting transplant rejection, and post-operative patient outcomes. By providing a comprehensive overview of AI's current impact, this review aims to inform clinicians, researchers, and policy-makers about the transformative power of AI in enhancing solid organ transplantation and facilitating personalized medicine in transplant care.
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Affiliation(s)
- Mouhamad Al Moussawy
- Department of Surgery, Thomas E. Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, United States
| | - Zoe S Lakkis
- Health Sciences Research Training Program, University of Pittsburgh, Pittsburgh, PA, United States
| | - Zuhayr A Ansari
- Health Sciences Research Training Program, University of Pittsburgh, Pittsburgh, PA, United States
| | - Aravind R Cherukuri
- Department of Surgery, Thomas E. Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, United States
| | - Khodor I Abou-Daya
- Department of Surgery, Thomas E. Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, United States
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Badrouchi S, Bacha MM, Ahmed A, Ben Abdallah T, Abderrahim E. Predicting long-term outcomes of kidney transplantation in the era of artificial intelligence. Sci Rep 2023; 13:21273. [PMID: 38042904 PMCID: PMC10693633 DOI: 10.1038/s41598-023-48645-w] [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: 01/16/2023] [Accepted: 11/29/2023] [Indexed: 12/04/2023] Open
Abstract
The ability to accurately predict long-term kidney transplant survival can assist nephrologists in making therapeutic decisions. However, predicting kidney transplantation (KT) outcomes is challenging due to the complexity of the factors involved. Artificial intelligence (AI) has become an increasingly important tool in the prediction of medical outcomes. Our goal was to utilize both conventional and AI-based methods to predict long-term kidney transplant survival. Our study included 407 KTs divided into two groups (group A: with a graft lifespan greater than 5 years and group B: with poor graft survival). We first performed a traditional statistical analysis and then developed predictive models using machine learning (ML) techniques. Donors in group A were significantly younger. The use of Mycophenolate Mofetil (MMF) was the only immunosuppressive drug that was significantly associated with improved graft survival. The average estimated glomerular filtration rate (eGFR) in the 3rd month post-KT was significantly higher in group A. The number of hospital readmissions during the 1st year post-KT was a predictor of graft survival. In terms of early post-transplant complications, delayed graft function (DGF), acute kidney injury (AKI), and acute rejection (AR) were significantly associated with poor graft survival. Among the 35 AI models developed, the best model had an AUC of 89.7% (Se: 91.9%; Sp: 87.5%). It was based on ten variables selected by an ML algorithm, with the most important being hypertension and a history of red-blood-cell transfusion. The use of AI provided us with a robust model enabling fast and precise prediction of 5-year graft survival using early and easily collectible variables. Our model can be used as a decision-support tool to early detect graft status.
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Affiliation(s)
- Samarra Badrouchi
- Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia.
- Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, Tunisia.
- Laboratory of Kidney Transplantation Immunology and Immunopathology (LR03SP01), Charles Nicolle Hospital, Tunis, Tunisia.
| | - Mohamed Mongi Bacha
- Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia
- Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, Tunisia
- Laboratory of Kidney Transplantation Immunology and Immunopathology (LR03SP01), Charles Nicolle Hospital, Tunis, Tunisia
| | - Abdulaziz Ahmed
- Department of Health Services Administration, School of Health Professions, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Taieb Ben Abdallah
- Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia
- Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, Tunisia
- Laboratory of Kidney Transplantation Immunology and Immunopathology (LR03SP01), Charles Nicolle Hospital, Tunis, Tunisia
| | - Ezzedine Abderrahim
- Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia
- Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, Tunisia
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Oomen L, de Jong H, Bouts AHM, Keijzer-Veen MG, Cornelissen EAM, de Wall LL, Feitz WFJ, Bootsma-Robroeks CMHHT. A pre-transplantation risk assessment tool for graft survival in Dutch pediatric kidney recipients. Clin Kidney J 2023; 16:1122-1131. [PMID: 37398686 PMCID: PMC10310505 DOI: 10.1093/ckj/sfad057] [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: 10/31/2022] [Indexed: 07/04/2023] Open
Abstract
Background A prediction model for graft survival including donor and recipient characteristics could help clinical decision-making and optimize outcomes. The aim of this study was to develop a risk assessment tool for graft survival based on essential pre-transplantation parameters. Methods The data originated from the national Dutch registry (NOTR; Nederlandse OrgaanTransplantatie Registratie). A multivariable binary logistic model was used to predict graft survival, corrected for the transplantation era and time after transplantation. Subsequently, a prediction score was calculated from the β-coefficients. For internal validation, derivation (80%) and validation (20%) cohorts were defined. Model performance was assessed with the area under the curve (AUC) of the receiver operating characteristics curve, Hosmer-Lemeshow test and calibration plots. Results In total, 1428 transplantations were performed. Ten-year graft survival was 42% for transplantations before 1990, which has improved to the current value of 92%. Over time, significantly more living and pre-emptive transplantations have been performed and overall donor age has increased (P < .05).The prediction model included 71 829 observations of 554 transplantations between 1990 and 2021. Other variables incorporated in the model were recipient age, re-transplantation, number of human leucocyte antigen (HLA) mismatches and cause of kidney failure. The predictive capacity of this model had AUCs of 0.89, 0.79, 0.76 and 0.74 after 1, 5, 10 and 20 years, respectively (P < .01). Calibration plots showed an excellent fit. Conclusions This pediatric pre-transplantation risk assessment tool exhibits good performance for predicting graft survival within the Dutch pediatric population. This model might support decision-making regarding donor selection to optimize graft outcomes. Trial registration ClinicalTrials.gov Identifier: NCT05388955.
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Affiliation(s)
| | - Huib de Jong
- Department of Pediatric Nephrology, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Antonia H M Bouts
- Department of Pediatric Nephrology, Amsterdam University Medical Center, Emma Children's Hospital, Amsterdam, The Netherlands
| | - Mandy G Keijzer-Veen
- Department of Pediatric Nephrology, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Elisabeth A M Cornelissen
- Department of Pediatric Nephrology, Radboudumc Amalia Children's Hospital, Nijmegen, The Netherlands
| | - Liesbeth L de Wall
- Department of Urology, Division of Pediatric Urology, Radboudumc Amalia Children's Hospital, Nijmegen, The Netherlands
| | - Wout F J Feitz
- Department of Urology, Division of Pediatric Urology, Radboudumc Amalia Children's Hospital, Nijmegen, The Netherlands
| | - Charlotte M H H T Bootsma-Robroeks
- Department of Pediatric Nephrology, Radboudumc Amalia Children's Hospital, Nijmegen, The Netherlands
- University of Groningen, University Medical Center Groningen, Department of Pediatrics, Pediatric Nephrology, Beatrix Children's Hospital, Groningen, The Netherlands
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Truchot A, Raynaud M, Kamar N, Naesens M, Legendre C, Delahousse M, Thaunat O, Buchler M, Crespo M, Linhares K, Orandi BJ, Akalin E, Pujol GS, Silva HT, Gupta G, Segev DL, Jouven X, Bentall AJ, Stegall MD, Lefaucheur C, Aubert O, Loupy A. Machine learning does not outperform traditional statistical modelling for kidney allograft failure prediction. Kidney Int 2023; 103:936-948. [PMID: 36572246 DOI: 10.1016/j.kint.2022.12.011] [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: 06/14/2022] [Revised: 11/04/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022]
Abstract
Machine learning (ML) models have recently shown potential for predicting kidney allograft outcomes. However, their ability to outperform traditional approaches remains poorly investigated. Therefore, using large cohorts of kidney transplant recipients from 14 centers worldwide, we developed ML-based prediction models for kidney allograft survival and compared their prediction performances to those achieved by a validated Cox-Based Prognostication System (CBPS). In a French derivation cohort of 4000 patients, candidate determinants of allograft failure including donor, recipient and transplant-related parameters were used as predictors to develop tree-based models (RSF, RSF-ERT, CIF), Support Vector Machine models (LK-SVM, AK-SVM) and a gradient boosting model (XGBoost). Models were externally validated with cohorts of 2214 patients from Europe, 1537 from North America, and 671 from South America. Among these 8422 kidney transplant recipients, 1081 (12.84%) lost their grafts after a median post-transplant follow-up time of 6.25 years (Inter Quartile Range 4.33-8.73). At seven years post-risk evaluation, the ML models achieved a C-index of 0.788 (95% bootstrap percentile confidence interval 0.736-0.833), 0.779 (0.724-0.825), 0.786 (0.735-0.832), 0.527 (0.456-0.602), 0.704 (0.648-0.759) and 0.767 (0.711-0.815) for RSF, RSF-ERT, CIF, LK-SVM, AK-SVM and XGBoost respectively, compared with 0.808 (0.792-0.829) for the CBPS. In validation cohorts, ML models' discrimination performances were in a similar range of those of the CBPS. Calibrations of the ML models were similar or less accurate than those of the CBPS. Thus, when using a transparent methodological pipeline in validated international cohorts, ML models, despite overall good performances, do not outperform a traditional CBPS in predicting kidney allograft failure. Hence, our current study supports the continued use of traditional statistical approaches for kidney graft prognostication.
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Affiliation(s)
- Agathe Truchot
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France
| | - Marc Raynaud
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France
| | - Nassim Kamar
- Université Paul Sabatier, INSERM, Department of Nephrology and Organ Transplantation, CHU Rangueil and Purpan, Toulouse, France
| | - Maarten Naesens
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium
| | - Christophe Legendre
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Kidney Transplant Department, Necker Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Michel Delahousse
- Department of Transplantation, Nephrology and Clinical Immunology, Foch Hospital, Suresnes, France
| | - Olivier Thaunat
- Department of Transplantation, Nephrology and Clinical Immunology, Hospices Civils de Lyon, Lyon, France
| | - Matthias Buchler
- Nephrology and Immunology Department, Bretonneau Hospital, Tours, France
| | - Marta Crespo
- Department of Nephrology, Hospital del Mar Barcelona, Barcelona, Spain
| | - Kamilla Linhares
- Hospital do Rim, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Babak J Orandi
- University of Alabama at Birmingham Heersink School of Medicine, Birmingham, Alabama, USA
| | - Enver Akalin
- Renal Division, Montefiore Medical Centre, Kidney Transplantation Program, Albert Einstein College of Medicine, New York, New York, USA
| | - Gervacio Soler Pujol
- Unidad de Trasplante Renopancreas, Centro de Educacion Medica e Investigaciones Clinicas Buenos Aires, Buenos Aires, Argentina
| | - Helio Tedesco Silva
- Hospital do Rim, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Gaurav Gupta
- Division of Nephrology, Department of Internal Medicine, Virginia Commonwealth University School of Medicine, Richmond, Virginia, USA
| | - Dorry L Segev
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Xavier Jouven
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Cardiology Department, European Georges Pompidou Hospital, Paris, France
| | - Andrew J Bentall
- William J von Liebig Centre for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark D Stegall
- William J von Liebig Centre for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota, USA
| | - Carmen Lefaucheur
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Kidney Transplant Department, Saint-Louis Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Olivier Aubert
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Kidney Transplant Department, Necker Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Alexandre Loupy
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Kidney Transplant Department, Necker Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France.
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Yaghoubi M, Cressman S, Edwards L, Shechter S, Doyle-Waters MM, Keown P, Sapir-Pichhadze R, Bryan S. A Systematic Review of Kidney Transplantation Decision Modelling Studies. APPLIED HEALTH ECONOMICS AND HEALTH POLICY 2023; 21:39-51. [PMID: 35945483 DOI: 10.1007/s40258-022-00744-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/19/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Genome-based precision medicine strategies promise to minimize premature graft loss after renal transplantation, through precision approaches to immune compatibility matching between kidney donors and recipients. The potential adoption of this technology calls for important changes to clinical management processes and allocation policy. Such potential policy change decisions may be supported by decision models from health economics, comparative effectiveness research and operations management. OBJECTIVE We used a systematic approach to identify and extract information about models published in the kidney transplantation literature and provide an overview of the status of our collective model-based knowledge about the kidney transplant process. METHODS Database searches were conducted in MEDLINE, Embase, Web of Science and other sources, for reviews and primary studies. We reviewed all English-language papers that presented a model that could be a tool to support decision making in kidney transplantation. Data were extracted on the clinical context and modelling methods used. RESULTS A total of 144 studies were included, most of which focused on a single component of the transplantation process, such as immunosuppressive therapy or donor-recipient matching and organ allocation policies. Pre- and post-transplant processes have rarely been modelled together. CONCLUSION A whole-disease modelling approach is preferred to inform precision medicine policy, given its potential upstream implementation in the treatment pathway. This requires consideration of pre- and post-transplant natural history, risk factors for allograft dysfunction and failure, and other post-transplant outcomes. Our call is for greater collaboration across disciplines and whole-disease modelling approaches to more accurately simulate complex policy decisions about the integration of precision medicine tools in kidney transplantation.
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Affiliation(s)
- Mohsen Yaghoubi
- Department of Pharmacy Practice, Mercer University College of Pharmacy, Atlanta, USA
| | - Sonya Cressman
- Faculty of Health Sciences, Simon Fraser University, School of Population and Public Health, University of British Columbia, Vancouver, Canada
| | - Louisa Edwards
- School of Population and Public Health, University of British Columbia, Vancouver, V6T 1Z3, Canada
| | - Steven Shechter
- Sauder School of Business, University of British Columbia, Vancouver, Canada
| | - Mary M Doyle-Waters
- Centre for Clinical Epidemiology and Evaluation, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, Canada
| | - Paul Keown
- Department of Medicine, Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
| | | | - Stirling Bryan
- School of Population and Public Health, University of British Columbia, Vancouver, V6T 1Z3, Canada.
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Aslani N, Galehdar N, Garavand A. A systematic review of data mining applications in kidney transplantation. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2023.101165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
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Badrouchi S, Bacha MM, Hedri H, Ben Abdallah T, Abderrahim E. Toward generalizing the use of artificial intelligence in nephrology and kidney transplantation. J Nephrol 2022; 36:1087-1100. [PMID: 36547773 PMCID: PMC9773693 DOI: 10.1007/s40620-022-01529-0] [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: 05/28/2022] [Accepted: 11/20/2022] [Indexed: 12/24/2022]
Abstract
With its robust ability to integrate and learn from large sets of clinical data, artificial intelligence (AI) can now play a role in diagnosis, clinical decision making, and personalized medicine. It is probably the natural progression of traditional statistical techniques. Currently, there are many unmet needs in nephrology and, more particularly, in the kidney transplantation (KT) field. The complexity and increase in the amount of data, and the multitude of nephrology registries worldwide have enabled the explosive use of AI within the field. Nephrologists in many countries are already at the center of experiments and advances in this cutting-edge technology and our aim is to generalize the use of AI among nephrologists worldwide. In this paper, we provide an overview of AI from a medical perspective. We cover the core concepts of AI relevant to the practicing nephrologist in a consistent and simple way to help them get started, and we discuss the technical challenges. Finally, we focus on the KT field: the unmet needs and the potential role that AI can play to fill these gaps, then we summarize the published KT-related studies, including predictive factors used in each study, which will allow researchers to quickly focus on the most relevant issues.
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Affiliation(s)
- Samarra Badrouchi
- Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia ,Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, Tunisia
| | - Mohamed Mongi Bacha
- Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia ,Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, Tunisia ,Laboratory of Kidney Transplantation Immunology and Immunopathology (LR03SP01), Charles Nicolle Hospital, Tunis, Tunisia
| | - Hafedh Hedri
- Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia ,Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, Tunisia
| | - Taieb Ben Abdallah
- Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia ,Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, Tunisia ,Laboratory of Kidney Transplantation Immunology and Immunopathology (LR03SP01), Charles Nicolle Hospital, Tunis, Tunisia
| | - Ezzedine Abderrahim
- Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia ,Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, Tunisia
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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: 1.0] [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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Magherini R, Mussi E, Volpe Y, Furferi R, Buonamici F, Servi M. Machine Learning for Renal Pathologies: An Updated Survey. SENSORS 2022; 22:s22134989. [PMID: 35808481 PMCID: PMC9269842 DOI: 10.3390/s22134989] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 06/22/2022] [Accepted: 06/29/2022] [Indexed: 12/04/2022]
Abstract
Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.
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Giron-Luque F, Baez-Suarez Y, Garcia-Lopez A, Patino-Jaramillo N. Safety and Intraoperative Results in Live Kidney Donors with Vascular Multiplicity After Hand-Assisted Laparoscopy Living Donor Nephrectomy. Res Rep Urol 2022; 14:23-31. [PMID: 35118016 PMCID: PMC8801362 DOI: 10.2147/rru.s341028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 01/03/2022] [Indexed: 11/23/2022] Open
Abstract
Background and Purpose Vascular multiplicity is the most frequent anatomic variation in kidney donors. Despite concerns about risks, these allografts are increasingly used to overcome the shortage of kidney donors. The safety and clinical outcomes in living kidney donors were evaluated with vascular multiplicity after hand-assisted laparoscopic living donor nephrectomy (HALDN). Patients and Methods Data from all living kidney donors who underwent HALDN from 2008 to 2021 was retrospectively reviewed. Patients were divided into two groups as single (SRV) and multiple renal vessels (MRV), and a comparative analysis was done. The primary outcomes include operating room time (ORT), days of hospital stay, estimated blood loss, complications, conversion, and re-operations. Results MRV were present in 166 out of 612 donors (27.1%). Among those, 10 (1.6%) donors had simultaneous multiple arteries and veins. Additionally, the prevalence of artery and vein multiplicity was 21.8% (n = 134) and 3.5% (n = 22), respectively. Warm ischemia time was significantly different among the two groups but not clinically important. The number of conversions to open technique, the mean ORT, the median blood loss, and days of hospital stay were similar between the SRV and MRV groups, without significant differences. According to the modified Clavien-classification system, no differences were found in the complication rates between the two groups (p = 0.29). Complication rates were 3.3% and 3.6% for the SRV and MRV groups, respectively. Conclusion HALDN is a procedure with safe intraoperative results, even with vascular multiplicity. The presence of multiple renal arteries or veins has no negative impact on the outcome of the donor after living donor nephrectomy.
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Affiliation(s)
| | | | - Andrea Garcia-Lopez
- Research Department, Colombiana de Trasplantes, Bogotá, Colombia
- Correspondence: Andrea Garcia-Lopez Research Department, Colombiana de Trasplantes, Av Carrera 30, No. 47A-74, Bogotá, ColombiaTel +57 300 502 4618 Email
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Taherkhani N, Sepehri MM, Khasha R, Shafaghi S. Determining the Level of Importance of Variables in Predicting Kidney Transplant Survival Based on a Novel Ranking Method. Transplantation 2021; 105:2307-2315. [PMID: 33534528 DOI: 10.1097/tp.0000000000003623] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Kidney transplantation is the best alternative treatment for end-stage renal disease. To optimal use of donated kidneys, graft predicted survival can be used as a factor to allocate kidneys. The performance of prediction techniques is highly dependent on the correct selection of predictors. Hence, the main objective of this research is to propose a novel method for ranking the effective variables for predicting the kidney transplant survival. METHODS Five classification models were used to classify kidney recipients in long- and short-term survival classes. Synthetic minority oversampling and random undersampling were used to overcome the imbalanced class problem. In dealing with missing values, 2 approaches were used (eliminating and imputing them). All variables were categorized into 4 levels. The ranking was evaluated using the sensitivity analysis approach. RESULTS Thirty-four of the 41 variables were identified as important variables, of which, 5 variables were categorized in very important level ("Recipient creatinine at discharge," "Recipient dialysis time," "Donor history of diabetes," "Donor kidney biopsy," and "Donor cause of death"), 17 variables in important level, and 12 variables in the low important level. CONCLUSIONS In this study, we identify new variables that have not been addressed in any of the previous studies (eg, AGE_DIF and MATCH_GEN). On the other hand, in kidney allocation systems, 2 main criteria are considered: equity and utility. One of the utility subcriteria is the graft survival. Our study findings can be used in the design of systems to predict the graft survival.
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Affiliation(s)
- Nasrin Taherkhani
- Faculty Member of Computer Engineering, Payam-e-Noor University, Saveh, Iran
| | - Mohammad Mehdi Sepehri
- Department of Healthcare Systems Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
| | - Roghaye Khasha
- Center of Excellence in Healthcare Systems Engineering, Tarbiat Modares University, Tehran, Iran
| | - Shadi Shafaghi
- Lung Transplantation Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
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12
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Naqvi SAA, Tennankore K, Vinson A, Roy PC, Abidi SSR. Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study. J Med Internet Res 2021; 23:e26843. [PMID: 34448704 PMCID: PMC8433864 DOI: 10.2196/26843] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 03/10/2021] [Accepted: 05/06/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Kidney transplantation is the optimal treatment for patients with end-stage renal disease. Short- and long-term kidney graft survival is influenced by a number of donor and recipient factors. Predicting the success of kidney transplantation is important for optimizing kidney allocation. OBJECTIVE The aim of this study was to predict the risk of kidney graft failure across three temporal cohorts (within 1 year, within 5 years, and after 5 years following a transplant) based on donor and recipient characteristics. We analyzed a large data set comprising over 50,000 kidney transplants covering an approximate 20-year period. METHODS We applied machine learning-based classification algorithms to develop prediction models for the risk of graft failure for three different temporal cohorts. Deep learning-based autoencoders were applied for data dimensionality reduction, which improved the prediction performance. The influence of features on graft survival for each cohort was studied by investigating a new nonoverlapping patient stratification approach. RESULTS Our models predicted graft survival with area under the curve scores of 82% within 1 year, 69% within 5 years, and 81% within 17 years. The feature importance analysis elucidated the varying influence of clinical features on graft survival across the three different temporal cohorts. CONCLUSIONS In this study, we applied machine learning to develop risk prediction models for graft failure that demonstrated a high level of prediction performance. Acknowledging that these models performed better than those reported in the literature for existing risk prediction tools, future studies will focus on how best to incorporate these prediction models into clinical care algorithms to optimize the long-term health of kidney recipients.
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Affiliation(s)
| | | | - Amanda Vinson
- Division of Nephrology, Dalhousie University, Halifax, NS, Canada
| | - Patrice C Roy
- Department of Computer Science, Dalhousie University, Halifax, NS, Canada
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13
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Korytowska N, Wyczałkowska-Tomasik A, Pączek L, Giebułtowicz J. Evaluation of Salivary Indoxyl Sulfate with Proteinuria for Predicting Graft Deterioration in Kidney Transplant Recipients. Toxins (Basel) 2021; 13:571. [PMID: 34437442 PMCID: PMC8402605 DOI: 10.3390/toxins13080571] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 08/11/2021] [Accepted: 08/13/2021] [Indexed: 12/16/2022] Open
Abstract
Acute kidney injury (AKI) is a significant risk factor for developing chronic kidney disease and progression to end-stage renal disease in elderly patients. AKI is also a relatively common complication after kidney transplantation (KTx) associated with graft failure. Since the lifespan of a transplanted kidney is limited, the risk of the loss/deterioration of graft function (DoGF) should be estimated to apply the preventive treatment. The collection of saliva and urine is more convenient than collecting blood and can be performed at home. The study aimed to verify whether non-invasive biomarkers, determined in saliva and urine, may be useful in the prediction of DoGF in kidney transplant recipients (KTRs) (n = 92). Salivary and serum toxins (p-cresol sulfate, pCS; indoxyl sulfate, IS) concentrations were determined using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Urinary proteins, hemoglobin, and glucose were measured using a semi-quantitative strip test. Salivary IS (odds ratio (OR) = 1.19), and proteinuria (OR = 3.69) were demonstrated as independent factors for the prediction of DoGF. Satisfactory discriminatory power (area under the receiver operating characteristic curve (AUC) = 0.71 ± 0.07) and calibration of the model were obtained. The model showed that categories of the increasing probability of the risk of DoGF are associated with the decreased risk of graft survival. The non-invasive diagnostic biomarkers are a useful screening tool to identify high-risk patients for DoGF.
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Affiliation(s)
- Natalia Korytowska
- Department of Bioanalysis and Drugs Analysis, Faculty of Pharmacy, Medical University of Warsaw, 1 Banacha, 02-097 Warsaw, Poland;
| | - Aleksandra Wyczałkowska-Tomasik
- Department of Immunology, Transplantology, and Internal Diseases, Medical University of Warsaw, 59 Nowogrodzka, 02-006 Warsaw, Poland; (A.W.-T.); (L.P.)
| | - Leszek Pączek
- Department of Immunology, Transplantology, and Internal Diseases, Medical University of Warsaw, 59 Nowogrodzka, 02-006 Warsaw, Poland; (A.W.-T.); (L.P.)
| | - Joanna Giebułtowicz
- Department of Bioanalysis and Drugs Analysis, Faculty of Pharmacy, Medical University of Warsaw, 1 Banacha, 02-097 Warsaw, Poland;
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14
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Connor KL, O'Sullivan ED, Marson LP, Wigmore SJ, Harrison EM. The Future Role of Machine Learning in Clinical Transplantation. Transplantation 2021; 105:723-735. [PMID: 32826798 DOI: 10.1097/tp.0000000000003424] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The use of artificial intelligence and machine learning (ML) has revolutionized our daily lives and will soon be instrumental in healthcare delivery. The rise of ML is due to multiple factors: increasing access to massive datasets, exponential increases in processing power, and key algorithmic developments that allow ML models to tackle increasingly challenging questions. Progressively more transplantation research is exploring the potential utility of ML models throughout the patient journey, although this has not yet widely transitioned into the clinical domain. In this review, we explore common approaches used in ML in solid organ clinical transplantation and consider opportunities for ML to help clinicians and patients. We discuss ways in which ML can aid leverage of large complex datasets, generate cutting-edge prediction models, perform clinical image analysis, discover novel markers in molecular data, and fuse datasets to generate novel insights in modern transplantation practice. We focus on key areas in transplantation in which ML is driving progress, explore the future potential roles of ML, and discuss the challenges and limitations of these powerful tools.
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Affiliation(s)
- Katie L Connor
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom.,Edinburgh Transplant Unit, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.,Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Eoin D O'Sullivan
- Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Lorna P Marson
- Edinburgh Transplant Unit, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.,Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Stephen J Wigmore
- Edinburgh Transplant Unit, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.,Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Ewen M Harrison
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
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15
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Senanayake S, Kularatna S, Healy H, Graves N, Baboolal K, Sypek MP, Barnett A. Development and validation of a risk index to predict kidney graft survival: the kidney transplant risk index. BMC Med Res Methodol 2021; 21:127. [PMID: 34154541 PMCID: PMC8215818 DOI: 10.1186/s12874-021-01319-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 05/17/2021] [Indexed: 12/23/2022] Open
Abstract
Background Kidney graft failure risk prediction models assist evidence-based medical decision-making in clinical practice. Our objective was to develop and validate statistical and machine learning predictive models to predict death-censored graft failure following deceased donor kidney transplant, using time-to-event (survival) data in a large national dataset from Australia. Methods Data included donor and recipient characteristics (n = 98) of 7,365 deceased donor transplants from January 1st, 2007 to December 31st, 2017 conducted in Australia. Seven variable selection methods were used to identify the most important independent variables included in the model. Predictive models were developed using: survival tree, random survival forest, survival support vector machine and Cox proportional regression. The models were trained using 70% of the data and validated using the rest of the data (30%). The model with best discriminatory power, assessed using concordance index (C-index) was chosen as the best model. Results Two models, developed using cox regression and random survival forest, had the highest C-index (0.67) in discriminating death-censored graft failure. The best fitting Cox model used seven independent variables and showed moderate level of prediction accuracy (calibration). Conclusion This index displays sufficient robustness to be used in pre-transplant decision making and may perform better than currently available tools. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-021-01319-5.
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Affiliation(s)
- Sameera Senanayake
- Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health & Social Work, Queensland University of Technology (QUT), Brisbane, QLD, Australia. .,Australian Centre for Health Services Innovation, Queensland University of Technology, 60 Musk Ave, QLD, 4059, Kelvin Grove, Australia.
| | - Sanjeewa Kularatna
- Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health & Social Work, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - Helen Healy
- Royal Brisbane and Women's Hospital, Brisbane, Australia.,School of Medicine, University of Queensland, Brisbane, Australia
| | | | - Keshwar Baboolal
- Royal Brisbane and Women's Hospital, Brisbane, Australia.,School of Medicine, University of Queensland, Brisbane, Australia
| | - Matthew P Sypek
- Australia and New Zealand Dialysis and Transplant (ANZDATA) Registry, Adelaide, South Australia, Australia
| | - Adrian Barnett
- Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health & Social Work, Queensland University of Technology (QUT), Brisbane, QLD, Australia
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16
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Toward Advancing Long-Term Outcomes of Kidney Transplantation with Artificial Intelligence. TRANSPLANTOLOGY 2021. [DOI: 10.3390/transplantology2020012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
After decades of pioneering advances and improvements, kidney transplantation is now the renal replacement therapy of choice for most patients with end-stage kidney disease (ESKD). Despite this success, the high risk of premature death and frequent occurrence of graft failure remain important clinical and research challenges. The current burst of studies and other innovative initiatives using artificial intelligence (AI) for a wide range of analytical and practical applications in biomedical areas seems to correlate with the same trend observed in publications in the kidney transplantation field, and points toward the potential of such novel approaches to address the aforementioned aim of improving long-term outcomes of kidney transplant recipients (KTR). However, at the same time, this trend underscores now more than ever the old methodological challenges and potential threats that the research and clinical community needs to be aware of and actively look after with regard to AI-driven evidence. The purpose of this narrative mini-review is to explore challenges for obtaining applicable and adequate kidney transplant data for analyses using AI techniques to develop prediction models, and to propose next steps in the field. We make a call to act toward establishing the strong collaborations needed to bring innovative synergies further augmented by AI, which have the potential to impact the long-term care of KTR. We encourage researchers and clinicians to submit their invaluable research, including original clinical and imaging studies, database studies from registries, meta-analyses, and AI research in the kidney transplantation field.
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17
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Bae S, Massie AB, Caffo BS, Jackson KR, Segev DL. Machine learning to predict transplant outcomes: helpful or hype? A national cohort study. Transpl Int 2020; 33:1472-1480. [PMID: 32996170 PMCID: PMC8269970 DOI: 10.1111/tri.13695] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 11/30/2019] [Accepted: 06/29/2020] [Indexed: 12/13/2022]
Abstract
An increasing number of studies claim machine learning (ML) predicts transplant outcomes more accurately. However, these claims were possibly confounded by other factors, namely, supplying new variables to ML models. To better understand the prospects of ML in transplantation, we compared ML to conventional regression in a "common" analytic task: predicting kidney transplant outcomes using national registry data. We studied 133 431 adult deceased-donor kidney transplant recipients between 2005 and 2017. Transplant centers were randomly divided into 70% training set (190 centers/97 787 recipients) and 30% validation set (82 centers/35 644 recipients). Using the training set, we performed regression and ML procedures [gradient boosting (GB) and random forests (RF)] to predict delayed graft function, one-year acute rejection, death-censored graft failure C, all-cause graft failure, and death. Their performances were compared on the validation set using -statistics. In predicting rejection, regression (C = 0.601 0.6110.621 ) actually outperformed GB (C = 0.581 0.5910.601 ) and RF (C = 0.569 0.5790.589 ). For all other outcomes, the C-statistics were nearly identical across methods (delayed graft function, 0.717-0.723; death-censored graft failure, 0.637-0.642; all-cause graft failure, 0.633-0.635; and death, 0.705-0.708). Given its shortcomings in model interpretability and hypothesis testing, ML is advantageous only when it clearly outperforms conventional regression; in the case of transplant outcomes prediction, ML seems more hype than helpful.
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Affiliation(s)
- Sunjae Bae
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Allan B Massie
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Kyle R Jackson
- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Dorry L Segev
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
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18
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Machine learning for predicting long-term kidney allograft survival: a scoping review. Ir J Med Sci 2020; 190:807-817. [PMID: 32761550 DOI: 10.1007/s11845-020-02332-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 07/26/2020] [Indexed: 12/24/2022]
Abstract
Supervised machine learning (ML) is a class of algorithms that "learn" from existing input-output pairs, which is gaining popularity in pattern recognition for classification and prediction problems. In this scoping review, we examined the use of supervised ML algorithms for the prediction of long-term allograft survival in kidney transplant recipients. Data sources included PubMed, the Cumulative Index to Nursing and Allied Health Literature, and the Institute for Electrical and Electronics Engineers (IEEE) Xplore libraries from inception to November 2019. We screened titles and abstracts and potentially eligible full-text reports to select studies and subsequently abstracted the data. Eleven studies were identified. Decision trees were the most commonly used method (n = 8), followed by artificial neural networks (ANN) (n = 4) and Bayesian belief networks (n = 2). The area under receiver operating curve (AUC) was the most common measure of discrimination (n = 7), followed by sensitivity (n = 5) and specificity (n = 4). Model calibration examining the reliability in risk prediction was performed using either the Pearson r or the Hosmer-Lemeshow test in four studies. One study showed that logistic regression had comparable performance to ANN, while another study demonstrated that ANN performed better in terms of sensitivity, specificity, and accuracy, as compared with a Cox proportional hazards model. We synthesized the evidence related to the comparison of ML techniques with traditional statistical approaches for prediction of long-term allograft survival in patients with a kidney transplant. The methodological and reporting quality of included studies was poor. Our study also demonstrated mixed results in terms of the predictive potential of the models.
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19
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Burlacu A, Iftene A, Jugrin D, Popa IV, Lupu PM, Vlad C, Covic A. Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review. BIOMED RESEARCH INTERNATIONAL 2020; 2020:9867872. [PMID: 32596403 PMCID: PMC7303737 DOI: 10.1155/2020/9867872] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 05/15/2020] [Accepted: 05/25/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND The purpose of this review is to depict current research and impact of artificial intelligence/machine learning (AI/ML) algorithms on dialysis and kidney transplantation. Published studies were presented from two points of view: What medical aspects were covered? What AI/ML algorithms have been used? METHODS We searched four electronic databases or studies that used AI/ML in hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation (KT). Sixty-nine studies were split into three categories: AI/ML and HD, PD, and KT, respectively. We identified 43 trials in the first group, 8 in the second, and 18 in the third. Then, studies were classified according to the type of algorithm. RESULTS AI and HD trials covered: (a) dialysis service management, (b) dialysis procedure, (c) anemia management, (d) hormonal/dietary issues, and (e) arteriovenous fistula assessment. PD studies were divided into (a) peritoneal technique issues, (b) infections, and (c) cardiovascular event prediction. AI in transplantation studies were allocated into (a) management systems (ML used as pretransplant organ-matching tools), (b) predicting graft rejection, (c) tacrolimus therapy modulation, and (d) dietary issues. CONCLUSIONS Although guidelines are reluctant to recommend AI implementation in daily practice, there is plenty of evidence that AI/ML algorithms can predict better than nephrologists: volumes, Kt/V, and hypotension or cardiovascular events during dialysis. Altogether, these trials report a robust impact of AI/ML on quality of life and survival in G5D/T patients. In the coming years, one would probably witness the emergence of AI/ML devices that facilitate the management of dialysis patients, thus increasing the quality of life and survival.
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Affiliation(s)
- Alexandru Burlacu
- Department of Interventional Cardiology-Cardiovascular Diseases Institute, Iasi, Romania
- “Grigore T. Popa” University of Medicine, Iasi, Romania
| | - Adrian Iftene
- Faculty of Computer Science, “Alexandru Ioan Cuza” University of Iasi, Romania
| | - Daniel Jugrin
- Center for Studies and Interreligious and Intercultural Dialogue, University of Bucharest, Romania
| | - Iolanda Valentina Popa
- “Grigore T. Popa” University of Medicine, Iasi, Romania
- Institute of Gastroenterology and Hepatology, Iasi, Romania
| | | | - Cristiana Vlad
- “Grigore T. Popa” University of Medicine, Iasi, Romania
- Department of Internal Medicine-Nephrology, Iasi, Romania
| | - Adrian Covic
- “Grigore T. Popa” University of Medicine, Iasi, Romania
- Nephrology Clinic, Dialysis and Renal Transplant Center-‘C.I. Parhon' University Hospital, Iasi, Romania
- The Academy of Romanian Scientists (AOSR), Romania
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20
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Sealfon RSG, Mariani LH, Kretzler M, Troyanskaya OG. Machine learning, the kidney, and genotype-phenotype analysis. Kidney Int 2020; 97:1141-1149. [PMID: 32359808 PMCID: PMC8048707 DOI: 10.1016/j.kint.2020.02.028] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 01/13/2020] [Accepted: 02/06/2020] [Indexed: 01/23/2023]
Abstract
With biomedical research transitioning into data-rich science, machine learning provides a powerful toolkit for extracting knowledge from large-scale biological data sets. The increasing availability of comprehensive kidney omics compendia (transcriptomics, proteomics, metabolomics, and genome sequencing), as well as other data modalities such as electronic health records, digital nephropathology repositories, and radiology renal images, makes machine learning approaches increasingly essential for analyzing human kidney data sets. Here, we discuss how machine learning approaches can be applied to the study of kidney disease, with a particular focus on how they can be used for understanding the relationship between genotype and phenotype.
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Affiliation(s)
- Rachel S G Sealfon
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York, USA
| | - Laura H Mariani
- Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA
| | - Matthias Kretzler
- Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA.
| | - Olga G Troyanskaya
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA; Department of Computer Science, Princeton University, Princeton, New Jersey, USA.
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21
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Burlacu A, Iftene A, Busoiu E, Cogean D, Covic A. Challenging the supremacy of evidence-based medicine through artificial intelligence: the time has come for a change of paradigms. Nephrol Dial Transplant 2019; 35:191-194. [PMID: 31697377 DOI: 10.1093/ndt/gfz203] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Accepted: 09/02/2019] [Indexed: 12/14/2022] Open
Affiliation(s)
- Alexandru Burlacu
- Department of Interventional Cardiology, Cardiovascular Diseases Institute, 'Grigore T. Popa' University of Medicine, Iasi, Romania
| | - Adrian Iftene
- Faculty of Computer Science, 'Alexandru Ioan Cuza' University of Iasi, Iasi, Romania
| | - Eugen Busoiu
- Artificial Intelligence Community, Iasi, Romania
| | - Dragos Cogean
- Software Development Gemini CAD Systems, Iasi, Romania
| | - Adrian Covic
- Nephrology Clinic, Dialysis and Renal Transplant Center, 'C.I. Parhon' University Hospital, 'Grigore T. Popa' University of Medicine, Iasi, Romania
- The Academy of Romanian Scientists (AOSR)
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22
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Foroutan F, Friesen EL, Clark KE, Motaghi S, Zyla R, Lee Y, Kamran R, Ali E, De Snoo M, Orchanian-Cheff A, Ribic C, Treleaven DJ, Guyatt G, Meade MO. Risk Factors for 1-Year Graft Loss After Kidney Transplantation: Systematic Review and Meta-Analysis. Clin J Am Soc Nephrol 2019; 14:1642-1650. [PMID: 31540931 PMCID: PMC6832056 DOI: 10.2215/cjn.05560519] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 08/05/2019] [Indexed: 01/15/2023]
Abstract
BACKGROUND AND OBJECTIVES With expansion of the pool of kidney grafts, through the use of higher-risk donors, and increased attention to donor management strategies, the 1-year graft survival rate is subject to change. It is, therefore, useful to elucidate 1-year graft survival rates by dissecting the characteristics of the low-risk and high-risk kidney transplant cases. The objective of our study was to evaluate factors purported to influence the risk of 1-year graft loss in kidney transplant recipients. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS We searched bibliographic databases from 2000 to 2017 and included observational studies that measured the association between donor, recipient, the transplant operation, or early postoperative complications, and 1-year death-censored graft loss. RESULTS We identified 35 eligible primary studies, with 20 risk factors amenable to meta-analysis. Six factors were associated with graft loss, with moderate to high degree of certainty: donor age (hazard ratio [HR], 1.11 per 10-year increase; 95% confidence interval [95% CI], 1.04 to 1.18), extended criteria donors (HR, 1.35; 95% CI, 1.28 to 1.42), deceased donors (HR, 1.54; 95% CI, 1.32 to 1.82), number of HLA mismatches (HR, 1.08 per one mismatch increase; 95% CI, 1.07 to 1.09), recipient age (HR, 1.17 per 10-year increase; 95% CI, 1.09 to 1.25), and delayed graft function (HR, 1.89; 95% CI, 1.46 to 2.47) as risk factors for 1-year graft loss. Pooled analyses also excluded, with a high degree of certainty, any associations of cold ischemia time, recipient race, pretransplant body mass index, diabetes, and hypertension with 1-year graft loss. CONCLUSIONS Recipient age, donor age, standard versus extended criteria donor, living versus deceased donor, HLA mismatch, and delayed graft function all predicted 1-year graft survival. The effect of each risk factor is small.
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Affiliation(s)
- Farid Foroutan
- Ted Rogers Centre for Heart Research, Multi-Organ Transplant Program, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact and
| | - Erik Loewen Friesen
- Library and Information Services, University Health Network, Toronto, Ontario, Canada
| | - Kathryn Elizabeth Clark
- Ted Rogers Centre for Heart Research, Multi-Organ Transplant Program, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
| | | | - Roman Zyla
- Library and Information Services, University Health Network, Toronto, Ontario, Canada
| | - Yung Lee
- Department of Health Research Methods, Evidence, and Impact and
| | - Rakhshan Kamran
- Department of Health Research Methods, Evidence, and Impact and
| | - Emir Ali
- Department of Health Research Methods, Evidence, and Impact and
| | - Mitch De Snoo
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada; and
| | | | - Christine Ribic
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada; and
| | - Darin J. Treleaven
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada; and
| | - Gordon Guyatt
- Department of Health Research Methods, Evidence, and Impact and
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23
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A new proposed feature selection method to predict kidney transplantation outcome. HEALTH AND TECHNOLOGY 2019. [DOI: 10.1007/s12553-019-00369-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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24
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Senanayake S, Barnett A, Graves N, Healy H, Baboolal K, Kularatna S. Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study. F1000Res 2019; 8:1810. [PMID: 32419922 PMCID: PMC7199287 DOI: 10.12688/f1000research.20661.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/24/2019] [Indexed: 03/29/2024] Open
Abstract
Background: A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients. Several kidney graft outcome prediction models, developed using machine learning methods, are available in the literature. However, most of those models used small datasets and none of the machine learning-based prediction models available in the medical literature modelled time-to-event (survival) information, but instead used the binary outcome of failure or not. The objective of this study is to develop two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using time-to-event data in a large national dataset from Australia. Methods: The dataset provided by the Australia and New Zealand Dialysis and Transplant Registry will be used for the analysis. This retrospective dataset contains the cohort of patients who underwent a kidney transplant in Australia from January 1 st, 2007, to December 31 st, 2017. This included 3,758 live donor transplants and 7,365 deceased donor transplants. Three machine learning methods (survival tree, random survival forest and survival support vector machine) and one traditional regression method, Cox proportional regression, will be used to develop the two predictive models. The best predictive model will be selected based on the model's performance. Discussion: This protocol describes the development of two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using a large national dataset from Australia. Furthermore, these two models will be the most comprehensive kidney graft failure predictive models that have used survival data to model using machine learning techniques. Thus, these models are expected to provide valuable insight into the complex interactions between graft failure and donor and recipient characteristics.
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Affiliation(s)
- Sameera Senanayake
- Australian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
| | - Adrian Barnett
- Australian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
| | - Nicholas Graves
- Australian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
| | - Helen Healy
- Royal Brisbane Hospital for Women, Brisbane, QLD, 4001, Australia
- School of Medicine, University of Queensland, Brisbane, QLD, 4001, Australia
| | - Keshwar Baboolal
- Royal Brisbane Hospital for Women, Brisbane, QLD, 4001, Australia
- School of Medicine, University of Queensland, Brisbane, QLD, 4001, Australia
| | - Sanjeewa Kularatna
- Australian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
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Senanayake S, Barnett A, Graves N, Healy H, Baboolal K, Kularatna S. Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study. F1000Res 2019; 8:1810. [PMID: 32419922 PMCID: PMC7199287 DOI: 10.12688/f1000research.20661.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/04/2020] [Indexed: 02/03/2023] Open
Abstract
Background: A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients. Several kidney graft outcome prediction models, developed using machine learning methods, are available in the literature. However, most of those models used small datasets and none of the machine learning-based prediction models available in the medical literature modelled time-to-event (survival) information, but instead used the binary outcome of failure or not. The objective of this study is to develop two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using time-to-event data in a large national dataset from Australia. Methods: The dataset provided by the Australia and New Zealand Dialysis and Transplant Registry will be used for the analysis. This retrospective dataset contains the cohort of patients who underwent a kidney transplant in Australia from January 1 st, 2007, to December 31 st, 2017. This included 3,758 live donor transplants and 7,365 deceased donor transplants. Three machine learning methods (survival tree, random survival forest and survival support vector machine) and one traditional regression method, Cox proportional regression, will be used to develop the two predictive models (for live donor and deceased donor transplants). The best predictive model will be selected based on the model's performance. Discussion: This protocol describes the development of two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using a large national dataset from Australia. Furthermore, these two models will be the most comprehensive kidney graft failure predictive models that have used survival data to model using machine learning techniques. Thus, these models are expected to provide valuable insight into the complex interactions between graft failure and donor and recipient characteristics.
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Affiliation(s)
- Sameera Senanayake
- Australian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
| | - Adrian Barnett
- Australian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
| | - Nicholas Graves
- Australian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
| | - Helen Healy
- Royal Brisbane Hospital for Women, Brisbane, QLD, 4001, Australia
- School of Medicine, University of Queensland, Brisbane, QLD, 4001, Australia
| | - Keshwar Baboolal
- Royal Brisbane Hospital for Women, Brisbane, QLD, 4001, Australia
- School of Medicine, University of Queensland, Brisbane, QLD, 4001, Australia
| | - Sanjeewa Kularatna
- Australian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
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Atallah DM, Badawy M, El-Sayed A. Intelligent feature selection with modified K-nearest neighbor for kidney transplantation prediction. SN APPLIED SCIENCES 2019. [DOI: 10.1007/s42452-019-1329-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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Taherkhani N, Sepehri MM, Shafaghi S, Khatibi T. Identification and weighting of kidney allocation criteria: a novel multi-expert fuzzy method. BMC Med Inform Decis Mak 2019; 19:182. [PMID: 31492132 PMCID: PMC6729045 DOI: 10.1186/s12911-019-0892-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Accepted: 08/09/2019] [Indexed: 02/05/2023] Open
Abstract
Background Kidney allocation is a multi-criteria and complex decision-making problem, which should also consider ethical issues in addition to the medical aspects. Leading countries in this field use a point scoring system to allocate kidneys. Hence, the purpose of this study is to identify and weight the kidney allocation criteria considering the balance between utility and equity. Methods To do this, a new fuzzy hybrid approach is proposed, which consists of two steps: In the first step, Fuzzy Delphi Method (FDM) is used to identify the effective criteria in the kidney allocation algorithm. In the second step, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) is employed to determine the weight of the criteria. Results The results showed that the highest weight belongs to “Medical emergency” criterion and the lowest weight to “5 HLA mismatches”, which is similar to Euro-transplant kidney allocation system (ETKAS). The developed method is evaluated in two steps. First, the proposed model is implemented using a real case study from the Iranian Kidney Allocation System. It was shown that the proposed model has the potential to improve allocation outcome. Second, the proposed model’s superiority to the current model is approved by the experts using the results display in the profile matrix. Finally, sensitivity analysis is performed to check the robustness of the proposed model. Conclusions This paper contributes to the kidney allocation literature by doing the following: (a) developing a comprehensive framework for identification and weightings of criteria for kidney allocation, (b) using, for the first time, the IF-AHP technique to consider hesitancy of decision makers and uncertainty in organ allocation, and (c) proposing an appropriate framework for the countries that intend to improve or modify their organ allocation system. Electronic supplementary material The online version of this article (10.1186/s12911-019-0892-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nasrin Taherkhani
- Group of Information Technology, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, 1411713116, Iran
| | - Mohammad Mehdi Sepehri
- Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, 1411713116, Iran.
| | - Shadi Shafaghi
- Lung Transplantation Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLDD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Toktam Khatibi
- Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, 1411713116, Iran
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Senanayake S, White N, Graves N, Healy H, Baboolal K, Kularatna S. Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models. Int J Med Inform 2019; 130:103957. [PMID: 31472443 DOI: 10.1016/j.ijmedinf.2019.103957] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 07/15/2019] [Accepted: 08/21/2019] [Indexed: 01/11/2023]
Abstract
INTRODUCTION Machine learning has been increasingly used to develop predictive models to diagnose different disease conditions. The heterogeneity of the kidney transplant population makes predicting graft outcomes extremely challenging. Several kidney graft outcome prediction models have been developed using machine learning, and are available in the literature. However, a systematic review of machine learning based prediction methods applied to kidney transplant has not been done to date. The main aim of our study was to perform an in-depth systematic analysis of different machine learning methods used to predict graft outcomes among kidney transplant patients, and assess their usefulness as an aid to decision-making. METHODS A systemic review of machine learning methods used to predict graft outcomes among kidney transplant patients was carried out using a search of the Medline, the Cumulative Index to Nursing and Allied Health Literature, EMBASE, PsycINFO and Cochrane databases. RESULTS A total of 295 articles were identified and extracted. Of these, 18 met the inclusion criteria. Most of the studies were published in the United States after 2010. The population size used to develop the models varied from 80 to 92,844, and the number of features in the models ranged from 6 to 71. The most common machine learning methods used were artificial neural networks, decision trees and Bayesian belief networks. Most of the machine learning based predictive models predicted graft failure with high sensitivity and specificity. Only one machine learning based prediction model had modelled time-to-event (survival) information. Seven studies compared the predictive performance of machine learning models with traditional regression methods and the performance of machine learning methods was found to be mixed, when compared with traditional regression methods. CONCLUSION There was a wide variation in the size of the study population and the input variables used. However, the prediction accuracy provided mixed results when machine learning and traditional predictive methods are compared. Based on reported gains in predictive performance, machine learning has the potential to improve kidney transplant outcome prediction and aid medical decision making.
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Affiliation(s)
- Sameera Senanayake
- Australian Centre for Health Service Innovation, Queensland University of Technology, Australia.
| | - Nicole White
- Australian Centre for Health Service Innovation, Queensland University of Technology, Australia
| | - Nicholas Graves
- Australian Centre for Health Service Innovation, Queensland University of Technology, Australia
| | - Helen Healy
- Royal Brisbane Hospital for Women, Brisbane, Australia; School of Medicine, University of Queensland, Australia
| | - Keshwar Baboolal
- Royal Brisbane Hospital for Women, Brisbane, Australia; School of Medicine, University of Queensland, Australia
| | - Sanjeewa Kularatna
- Australian Centre for Health Service Innovation, Queensland University of Technology, Australia
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A Prognostic Tool for Individualized Prediction of Graft Failure Risk within Ten Years after Kidney Transplantation. J Transplant 2019; 2019:7245142. [PMID: 31093367 PMCID: PMC6476124 DOI: 10.1155/2019/7245142] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 03/25/2019] [Indexed: 12/04/2022] Open
Abstract
Identification of patients at risk of kidney graft loss relies on early individual prediction of graft failure. Data from 616 kidney transplant recipients with a follow-up of at least one year were retrospectively studied. A joint latent class model investigating the impact of serum creatinine (Scr) time-trajectories and onset of de novo donor-specific anti-HLA antibody (dnDSA) on graft survival was developed. The capacity of the model to calculate individual predicted probabilities of graft failure over time was evaluated in 80 independent patients. The model classified the patients in three latent classes with significantly different Scr time profiles and different graft survivals. Donor age contributed to explaining latent class membership. In addition to the SCr classes, the other variables retained in the survival model were proteinuria measured one-year after transplantation (HR=2.4, p=0.01), pretransplant non-donor-specific antibodies (HR=3.3, p<0.001), and dnDSA in patient who experienced acute rejection (HR=15.9, p=0.02). In the validation dataset, individual predictions of graft failure risk provided good predictive performances (sensitivity, specificity, and overall accuracy of graft failure prediction at ten years were 77.7%, 95.8%, and 85%, resp.) for the 60 patients who had not developed dnDSA. For patients with dnDSA individual risk of graft failure was not predicted with a so good performance.
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Mark E, Goldsman D, Gurbaxani B, Keskinocak P, Sokol J. Using machine learning and an ensemble of methods to predict kidney transplant survival. PLoS One 2019; 14:e0209068. [PMID: 30625130 PMCID: PMC6326487 DOI: 10.1371/journal.pone.0209068] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2018] [Accepted: 11/29/2018] [Indexed: 01/08/2023] Open
Abstract
We used an ensemble of statistical methods to build a model that predicts kidney transplant survival and identifies important predictive variables. The proposed model achieved better performance, measured by Harrell’s concordance index, than the Estimated Post Transplant Survival model used in the kidney allocation system in the U.S., and other models published recently in the literature. The model has a five-year concordance index of 0.724 (in comparison, the concordance index is 0.697 for the Estimated Post Transplant Survival model, the state of the art currently in use). It combines predictions from random survival forests with a Cox proportional hazards model. The rankings of importance for the model’s variables differ by transplant recipient age. Better survival predictions could eventually lead to more efficient allocation of kidneys and improve patient outcomes.
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Affiliation(s)
- Ethan Mark
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- * E-mail:
| | - David Goldsman
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Brian Gurbaxani
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Pinar Keskinocak
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Joel Sokol
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
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Abstract
BACKGROUND Most current scoring tools to predict allograft and patient survival upon kidney transplantion are based on variables collected posttransplantation. We developed a novel score to predict posttransplant outcomes using pretransplant information including routine laboratory data available before or at the time of transplantation. METHODS Linking the 5-year patient data of a large dialysis organization to the Scientific Registry of Transplant Recipients, we identified 15 125 hemodialysis patients who underwent first deceased transplantion. Prediction models were developed using Cox models for (a) mortality, (b) allograft loss (death censored), and (c) combined death or transplant failure. The cohort was randomly divided into a two thirds set (Nd = 10 083) for model development and a one third set (Nv = 5042) for validation. Model predictive discrimination was assessed using the index of concordance, or C statistic, which accounts for censoring in time-to-event models (a-c). We used the bootstrap method to assess model overfitting and calibration using the development dataset. RESULTS Patients were 50 ± 13 years of age and included 39% women, 15% African Americans, and 36% persons with diabetes. For prediction of posttransplant mortality and graft loss, 10 predictors were used (recipients' age, cause and length of end-stage renal disease, hemoglobin, albumin, selected comorbidities, race and type of insurance as well as donor age, diabetes status, extended criterion donor kidney, and number of HLA mismatches). The new model (www.TransplantScore.com) showed the overall best discrimination (C-statistics, 0.70; 95% confidence interval [95% CI], 0.67-0.73 for mortality; 0.63; 95% CI, 0.60-0.66 for graft failure; 0.63; 95% CI, 0.61-0.66 for combined outcome). CONCLUSIONS The new prediction tool, using data available before the time of transplantation, predicts relevant clinical outcomes and may perform better to predict patients' graft survival than currently used tools.
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Prémaud A, Filloux M, Gatault P, Thierry A, Büchler M, Munteanu E, Marquet P, Essig M, Rousseau A. An adjustable predictive score of graft survival in kidney transplant patients and the levels of risk linked to de novo donor-specific anti-HLA antibodies. PLoS One 2017; 12:e0180236. [PMID: 28671951 PMCID: PMC5495333 DOI: 10.1371/journal.pone.0180236] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Accepted: 06/12/2017] [Indexed: 11/25/2022] Open
Abstract
Most predictive models and scores of graft survival in renal transplantation include factors known before transplant or at the end of the first year. They cannot be updated thereafter, even in patients developing donor-specific anti-HLA antibodies and acute rejection.We developed a conditional and adjustable score for prediction of graft failure (AdGFS) up to 10 years post-transplantation in 664 kidney transplant patients. AdGFS was externally validated and calibrated in 896 kidney transplant patients.The final model included five baseline factors (pretransplant non donor-specific anti-HLA antibodies, donor age, serum creatinine measured at 1 year, longitudinal serum creatinine clusters during the first year, proteinuria measured at 1 year), and two predictors updated over time (de novo donor-specific anti-HLA antibodies and first acute rejection). AdGFS was able to stratify patients into four risk-groups, at different post-transplantation times. It showed good discrimination (time-dependent ROC curve at ten years: 0.83 (CI95% 0.76–0.89).
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Affiliation(s)
- Aurélie Prémaud
- INSERM, U850, Limoges, France
- Univ. Limoges, UMR_S850, Limoges, France
- * E-mail:
| | - Matthieu Filloux
- CHU Limoges, Service d’immunologie et immunogénétique, Limoges, France
- CNRS, CRIBL, UMR 7276, Limoges, France
| | - Philippe Gatault
- CHU Tours, Service Néphrologie–et Immunologie Clinique, Tours, France
| | - Antoine Thierry
- CHU Poitiers, Service de Néphrologie-Hémodialyse-Transplantation rénale, Poitiers, France
| | - Matthias Büchler
- CHU Tours, Service Néphrologie–et Immunologie Clinique, Tours, France
| | - Eliza Munteanu
- CHU Limoges, Service de néphrologie, dialyse-transplantations, Limoges, France
| | - Pierre Marquet
- INSERM, U850, Limoges, France
- Univ. Limoges, UMR_S850, Limoges, France
- CHU Limoges, Service de pharmacologie, toxicologie et pharmacovigilance, Limoges, France
| | - Marie Essig
- INSERM, U850, Limoges, France
- Univ. Limoges, UMR_S850, Limoges, France
- CHU Limoges, Service de néphrologie, dialyse-transplantations, Limoges, France
| | - Annick Rousseau
- INSERM, U850, Limoges, France
- Univ. Limoges, UMR_S850, Limoges, France
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Kaboré R, Haller MC, Harambat J, Heinze G, Leffondré K. Risk prediction models for graft failure in kidney transplantation: a systematic review. Nephrol Dial Transplant 2017; 32:ii68-ii76. [DOI: 10.1093/ndt/gfw405] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Accepted: 10/03/2016] [Indexed: 01/01/2023] Open
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Decruyenaere A, Decruyenaere P, Peeters P, Vermassen F, Dhaene T, Couckuyt I. Prediction of delayed graft function after kidney transplantation: comparison between logistic regression and machine learning methods. BMC Med Inform Decis Mak 2015; 15:83. [PMID: 26466993 PMCID: PMC4607098 DOI: 10.1186/s12911-015-0206-y] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2015] [Accepted: 09/30/2015] [Indexed: 01/05/2023] Open
Abstract
Background Predictive models for delayed graft function (DGF) after kidney transplantation are usually developed using logistic regression. We want to evaluate the value of machine learning methods in the prediction of DGF. Methods 497 kidney transplantations from deceased donors at the Ghent University Hospital between 2005 and 2011 are included. A feature elimination procedure is applied to determine the optimal number of features, resulting in 20 selected parameters (24 parameters after conversion to indicator parameters) out of 55 retrospectively collected parameters. Subsequently, 9 distinct types of predictive models are fitted using the reduced data set: logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs; using linear, radial basis function and polynomial kernels), decision tree (DT), random forest (RF), and stochastic gradient boosting (SGB). Performance of the models is assessed by computing sensitivity, positive predictive values and area under the receiver operating characteristic curve (AUROC) after 10-fold stratified cross-validation. AUROCs of the models are pairwise compared using Wilcoxon signed-rank test. Results The observed incidence of DGF is 12.5 %. DT is not able to discriminate between recipients with and without DGF (AUROC of 52.5 %) and is inferior to the other methods. SGB, RF and polynomial SVM are mainly able to identify recipients without DGF (AUROC of 77.2, 73.9 and 79.8 %, respectively) and only outperform DT. LDA, QDA, radial SVM and LR also have the ability to identify recipients with DGF, resulting in higher discriminative capacity (AUROC of 82.2, 79.6, 83.3 and 81.7 %, respectively), which outperforms DT and RF. Linear SVM has the highest discriminative capacity (AUROC of 84.3 %), outperforming each method, except for radial SVM, polynomial SVM and LDA. However, it is the only method superior to LR. Conclusions The discriminative capacities of LDA, linear SVM, radial SVM and LR are the only ones above 80 %. None of the pairwise AUROC comparisons between these models is statistically significant, except linear SVM outperforming LR. Additionally, the sensitivity of linear SVM to identify recipients with DGF is amongst the three highest of all models. Due to both reasons, the authors believe that linear SVM is most appropriate to predict DGF.
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Affiliation(s)
| | | | - Patrick Peeters
- Department of Nephrology, Ghent University Hospital, Ghent, Belgium
| | - Frank Vermassen
- Department of Thoracic and Vascular Surgery, Ghent University Hospital, Ghent, Belgium
| | - Tom Dhaene
- Department of Information Technology (INTEC), Ghent University - iMinds, Ghent, Belgium
| | - Ivo Couckuyt
- Department of Information Technology (INTEC), Ghent University - iMinds, Ghent, Belgium
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Cashion AK, Hathaway DK, Stanfill A, Thomas F, Ziebarth JD, Cui Y, Cowan PA, Eason J. Pre-transplant predictors of one yr weight gain after kidney transplantation. Clin Transplant 2015; 28:1271-8. [PMID: 25159302 DOI: 10.1111/ctr.12456] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/19/2014] [Indexed: 11/27/2022]
Abstract
Clinically useful predictors of weight gain could be used to reduce the epidemic of post-kidney transplant obesity and resulting co-morbidities. The purpose of this study was to identify predictors of weight gain at 12 months following kidney transplant in a cohort of 96 recipients. Demographic, clinical, and environmental data were obtained at transplant and 12 months. Descriptive, correlational, and Bayesian network analysis were used to identify predictors. For the 52 (55.9%) recipients who gained weight, the average amount gained was 9.18 ± 6.59 kg. From the 15 baseline factors that met inclusion criteria, Bayesian network modeling identified four baseline predictors for weight gain: younger age, higher carbohydrate consumption, higher trunk fat percentage, and higher perception of mental health quality of life. Three are modifiable through either pre- or immediate post-transplant clinical intervention programs.
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Affiliation(s)
- A K Cashion
- National Institutes of Health/National Institute of Nursing Research, Bethesda, MD, USA; The University of Tennessee Health Science Center, Memphis, TN, USA
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Kidney Donor Risk Index (KDRI) Fails to Predict Kidney Allograft Survival in HIV (+) Recipients. Transplantation 2014; 98:436-42. [DOI: 10.1097/tp.0000000000000073] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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