1
|
Liu XY, Feng RT, Feng WX, Jiang WW, Chen JA, Zhong GL, Chen CW, Li ZJ, Zeng JD, Liu D, Zhou S, Hu JM, Liao GR, Liao J, Guo ZF, Li YZ, Yang SQ, Li SC, Chen H, Guo Y, Li M, Fan LP, Yan HY, Chen JR, Li LY, Liu YG. An integrated machine learning model enhances delayed graft function prediction in pediatric renal transplantation from deceased donors. BMC Med 2024; 22:407. [PMID: 39304842 DOI: 10.1186/s12916-024-03624-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Accepted: 09/10/2024] [Indexed: 09/22/2024] Open
Abstract
BACKGROUND Kidney transplantation is the optimal renal replacement therapy for children with end-stage renal disease; however, delayed graft function (DGF), a common post-operative complication, may negatively impact the long-term outcomes of both the graft and the pediatric recipient. However, there is limited research on DGF in pediatric kidney transplant recipients. This study aims to develop a predictive model for the risk of DGF occurrence after pediatric kidney transplantation by integrating donor and recipient characteristics and utilizing machine learning algorithms, ultimately providing guidance for clinical decision-making. METHODS This single-center retrospective cohort study includes all recipients under 18 years of age who underwent single-donor kidney transplantation at our hospital between 2016 and 2023, along with their corresponding donors. Demographic, clinical, and laboratory examination data were collected from both donors and recipients. Univariate logistic regression models and differential analysis were employed to identify features associated with DGF. Subsequently, a risk score for predicting DGF occurrence (DGF-RS) was constructed based on machine learning combinations. Model performance was evaluated using the receiver operating characteristic curves, decision curve analysis (DCA), and other methods. RESULTS The study included a total of 140 pediatric kidney transplant recipients, among whom 37 (26.4%) developed DGF. Univariate analysis revealed that high-density lipoprotein cholesterol (HDLC), donor after circulatory death (DCD), warm ischemia time (WIT), cold ischemia time (CIT), gender match, and donor creatinine were significantly associated with DGF (P < 0.05). Based on these six features, the random forest model (mtry = 5, 75%p) exhibited the best predictive performance among 97 machine learning models, with the area under the curve values reaching 0.983, 1, and 0.905 for the entire cohort, training set, and validation set, respectively. This model significantly outperformed single indicators. The DCA curve confirmed the clinical utility of this model. CONCLUSIONS In this study, we developed a machine learning-based predictive model for DGF following pediatric kidney transplantation, termed DGF-RS, which integrates both donor and recipient characteristics. The model demonstrated excellent predictive accuracy and provides essential guidance for clinical decision-making. These findings contribute to our understanding of the pathogenesis of DGF.
Collapse
Affiliation(s)
- Xiao-You Liu
- Department of Organ Transplantation, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510163, China
| | - Run-Tao Feng
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Wen-Xiang Feng
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Wei-Wei Jiang
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Jian-An Chen
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Guang-Li Zhong
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Chao-Wei Chen
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Zi-Jian Li
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Jia-Dong Zeng
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Ding Liu
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Song Zhou
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Jian-Min Hu
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Guo-Rong Liao
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Jun Liao
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Ze-Feng Guo
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Yu-Zhu Li
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Si-Qiang Yang
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Shi-Chao Li
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Hua Chen
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Ying Guo
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Min Li
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Li-Pei Fan
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Hong-Yan Yan
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Jian-Rong Chen
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Liu-Yang Li
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Yong-Guang Liu
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
| |
Collapse
|
2
|
Kotowska K, Wojciuk B, Sieńko J, Bogacz A, Stukan I, Drożdżal S, Czerny B, Tejchman K, Trybek G, Machaliński B, Kotowski M. The Role of Vitamin D Metabolism Genes and Their Genomic Background in Shaping Cyclosporine A Dosage Parameters after Kidney Transplantation. J Clin Med 2024; 13:4966. [PMID: 39201108 PMCID: PMC11355102 DOI: 10.3390/jcm13164966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 08/12/2024] [Accepted: 08/19/2024] [Indexed: 09/02/2024] Open
Abstract
Background: Kidney transplantation is followed by immunosuppressive therapy involving calcineurin inhibitors (CNIs) such as cyclosporin A. However, long-term high CNIs doses can lead to vitamin D deficiency, and genetic variations influencing vitamin D levels can indirectly impact the necessary CNIs dosage. This study investigates the impact of genetic variations of vitamin D binding protein (DBP) rs2282679 and CYP2R1 hydroxylase rs10741657 polymorphisms on the cyclosporin A dosage in kidney transplant recipients. Additional polymorphisims of genes that are predicted to influence the pharmacogenetic profile were included. Methods: Gene polymorphisms in 177 kidney transplant recipients were analyzed using data mining techniques, including the Random Forest algorithm and Classification and Regression Trees (C&RT). The relationship between the concentration/dose (C/D) ratio of cyclosporin A and genetic profiles was assessed to determine the predictive value of DBP rs2282679 and CYP2R1 rs10741657 polymorphisms. Results: Polymorphic variants of the DBP (rs2282679) demonstrated a strong predictive value for the cyclosporin A C/D ratio in post-kidney transplantation patients. By contrast, the CYP2R1 polymorphism (rs10741657) did not show predictive significance. Additionally, the immune response genes rs231775 CTLA4 and rs1800896 IL10 were identified as predictors of cyclosporin A response, though these did not result in statistically significant differences. Conclusions:DBP rs2282679 polymorphisms can significantly predict the cyclosporin A C/D ratio, potentially enhancing the accuracy of CNI dosing. This can help identify patient groups at risk of vitamin D deficiency, ultimately improving the management of kidney transplant recipients. Understanding these genetic influences allows for more personalized and effective treatment strategies, contributing to better long-term outcomes for patients.
Collapse
Affiliation(s)
- Katarzyna Kotowska
- Clinic of Maxillofacial Surgery, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
| | - Bartosz Wojciuk
- Department of Immunological Diagnostics, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
| | - Jerzy Sieńko
- Institute of Physical Culture Sciences, University of Szczecin, 70-453 Szczecin, Poland
| | - Anna Bogacz
- Department of Personalized Medicine and Cell Therapy, Regional Blood Center, 60-354 Poznan, Poland
| | - Iga Stukan
- Department of General Pathology, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
| | - Sylwester Drożdżal
- Department of Nephrology, Transplantology and Internal Medicine, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
| | - Bogusław Czerny
- Department of General Pharmacology and Pharmacoeconomics, Pomeranian Medical University in Szczecin, 71-210 Szczecin, Poland
| | - Karol Tejchman
- Department of General Surgery and Transplantation, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
| | - Grzegorz Trybek
- Department of Interdisciplinary Dentistry, Pomeranian Medical University, 70-204 Szczecin, Poland
| | - Bogusław Machaliński
- Department of General Pathology, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
| | - Maciej Kotowski
- Department of General Surgery and Transplantation, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
| |
Collapse
|
3
|
Simeri A, Pezzi G, Arena R, Papalia G, Szili-Torok T, Greco R, Veltri P, Greco G, Pezzi V, Provenzano M, Zaza G. Artificial intelligence in chronic kidney diseases: methodology and potential applications. Int Urol Nephrol 2024:10.1007/s11255-024-04165-8. [PMID: 39052168 DOI: 10.1007/s11255-024-04165-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2024] [Accepted: 07/17/2024] [Indexed: 07/27/2024]
Abstract
Chronic kidney disease (CKD) represents a significant global health challenge, characterized by kidney damage and decreased function. Its prevalence has steadily increased, necessitating a comprehensive understanding of its epidemiology, risk factors, and management strategies. While traditional prognostic markers such as estimated glomerular filtration rate (eGFR) and albuminuria provide valuable insights, they may not fully capture the complexity of CKD progression and associated cardiovascular (CV) risks.This paper reviews the current state of renal and CV risk prediction in CKD, highlighting the limitations of traditional models and the potential for integrating artificial intelligence (AI) techniques. AI, particularly machine learning (ML) and deep learning (DL), offers a promising avenue for enhancing risk prediction by analyzing vast and diverse patient data, including genetic markers, biomarkers, and imaging. By identifying intricate patterns and relationships within datasets, AI algorithms can generate more comprehensive risk profiles, enabling personalized and nuanced risk assessments.Despite its potential, the integration of AI into clinical practice faces challenges such as the opacity of some algorithms and concerns regarding data quality, privacy, and bias. Efforts towards explainable AI (XAI) and rigorous data governance are essential to ensure transparency, interpretability, and trustworthiness in AI-driven predictions.
Collapse
Affiliation(s)
- Andrea Simeri
- Department of Mathematics and Computer Science, University of Calabria, 87036, Rende, CS, Italy
| | - Giuseppe Pezzi
- Department of Medical and Surgical Sciences, University of Catanzaro, 88100, Catanzaro, Italy
| | - Roberta Arena
- Nephrology, Dialysis and Renal Transplant Unit, Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Rende - Hospital 'SS. Annunziata', Cosenza, Italy
| | - Giuliana Papalia
- Nephrology, Dialysis and Renal Transplant Unit, Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Rende - Hospital 'SS. Annunziata', Cosenza, Italy
| | - Tamas Szili-Torok
- Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen, Groningen, the Netherlands
| | - Rosita Greco
- Nephrology, Dialysis and Renal Transplant Unit, Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Rende - Hospital 'SS. Annunziata', Cosenza, Italy
| | - Pierangelo Veltri
- Department of Computer Science, Modeling, Electronics and Systems Engineering, University of Calabria, 87036, Rende, CS, Italy
| | - Gianluigi Greco
- Department of Mathematics and Computer Science, University of Calabria, 87036, Rende, CS, Italy
| | - Vincenzo Pezzi
- Nephrology, Dialysis and Renal Transplant Unit, Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Rende - Hospital 'SS. Annunziata', Cosenza, Italy
| | - Michele Provenzano
- Nephrology, Dialysis and Renal Transplant Unit, Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Rende - Hospital 'SS. Annunziata', Cosenza, Italy.
| | - Gianluigi Zaza
- Nephrology, Dialysis and Renal Transplant Unit, Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Rende - Hospital 'SS. Annunziata', Cosenza, Italy
| |
Collapse
|
4
|
Assis de Souza A, Stubbs AP, Hesselink DA, Baan CC, Boer K. Cherry on Top or Real Need? A Review of Explainable Machine Learning in Kidney Transplantation. Transplantation 2024:00007890-990000000-00768. [PMID: 38773859 DOI: 10.1097/tp.0000000000005063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2024]
Abstract
Research on solid organ transplantation has taken advantage of the substantial acquisition of medical data and the use of artificial intelligence (AI) and machine learning (ML) to answer diagnostic, prognostic, and therapeutic questions for many years. Nevertheless, despite the question of whether AI models add value to traditional modeling approaches, such as regression models, their "black box" nature is one of the factors that have hindered the translation from research to clinical practice. Several techniques that make such models understandable to humans were developed with the promise of increasing transparency in the support of medical decision-making. These techniques should help AI to close the gap between theory and practice by yielding trust in the model by doctors and patients, allowing model auditing, and facilitating compliance with emergent AI regulations. But is this also happening in the field of kidney transplantation? This review reports the use and explanation of "black box" models to diagnose and predict kidney allograft rejection, delayed graft function, graft failure, and other related outcomes after kidney transplantation. In particular, we emphasize the discussion on the need (or not) to explain ML models for biological discovery and clinical implementation in kidney transplantation. We also discuss promising future research paths for these computational tools.
Collapse
Affiliation(s)
- Alvaro Assis de Souza
- Department of Internal Medicine, Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Andrew P Stubbs
- Department of Pathology and Clinical Bioinformatics, Erasmus MC Stubbs Group, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Dennis A Hesselink
- Department of Internal Medicine, Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Carla C Baan
- Department of Internal Medicine, Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Karin Boer
- Department of Internal Medicine, Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
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.
Collapse
|
9
|
Shahmoradi L, Borhani A, Langarizadeh M, Pourmand G, Fard ZA, Rezayi S. Predicting the survival of kidney transplantation: design and evaluation of a smartphone-based application. BMC Nephrol 2022; 23:219. [PMID: 35729490 PMCID: PMC9210621 DOI: 10.1186/s12882-022-02841-4] [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: 10/24/2021] [Accepted: 06/10/2022] [Indexed: 11/10/2022] Open
Abstract
Background Prediction of graft survival for Kidney Transplantation (KT) is considered a risky task due to the scarcity of donating organs and the use of health care resources. The present study aimed to design and evaluate a smartphone-based application to predict the survival of KT in patients with End-Stage Renal Disease (ESRD). Method Based on the initial review, a researcher-made questionnaire was developed to assess the information needs of the application through urologists and nephrologists. By using information obtained from the questionnaire, a checklist was prepared, and the information of 513 patients with kidney failure was collected from their records at Sina Urological Research Center. Then, three data mining algorithms were applied to them. The smartphone-based application for the prediction of kidney transplant survival was designed, and a standard usability assessment questionnaire was used to evaluate the designed application. Results Three information elements related to the required data in different sections of demographic information, sixteen information elements related to patient clinical information, and four critical capabilities were determined for the design of the smartphone-based application. C5.0 algorithm with the highest accuracy (87.21%) was modeled as the application inference engine. The application was developed based on the PhoneGap framework. According to the participants’ scores (urologists and nephrologists) regarding the usability evaluation of the application, it can be concluded that both groups participating in the study could use the program, and they rated the application at a "good" level. Conclusion Since the overall performance or usability of the smartphone-based app was evaluated at a reasonable level, it can be used with certainty to predict kidney transplant survival. Supplementary Information The online version contains supplementary material available at 10.1186/s12882-022-02841-4.
Collapse
Affiliation(s)
- Leila Shahmoradi
- Department of Health Information Management and Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Alireza Borhani
- Department of Health Information Management and Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Mostafa Langarizadeh
- Department of Health Information Management and Medical Informatics, School of Health Management and Information Science, Iran University of Medical Sciences, Tehran, Iran
| | - Gholamreza Pourmand
- Urology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Ziba Aghsaei Fard
- Urology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Sorayya Rezayi
- Department of Health Information Management and Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
| |
Collapse
|
10
|
The role of uropathogenic Escherichia coli adhesive molecules in inflammatory response- comparative study on immunocompetent hosts and kidney recipients. PLoS One 2022; 17:e0268243. [PMID: 35604916 PMCID: PMC9126363 DOI: 10.1371/journal.pone.0268243] [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: 09/14/2021] [Accepted: 04/25/2022] [Indexed: 11/19/2022] Open
Abstract
Background Urinary tract infections (UTI) represent one of the most common contagious diseases in humans. Uropathogenic Escherichia coli (UPEC) strains are recognized as the most frequent causative agent, and these express a range of virulence factors including the adhesins. Immune response to UPEC under immunosuppression has not been fully understood yet. Interleukin 1β (IL1β), 6 (IL6) and 17 (IL17) represent clinically relevant markers of inflammation. Aim The study aimed to investigate the interplay between UPEC genotype and hosts’ immune status in shaping local inflammatory response in the course of an UTI episode. The respective numbers of: 18 kidney recipients with UPEC UTI, 28 immunocompetent hosts with UPEC UTI and 29 healthy controls were involved. Urine IL1β, IL6, and IL17/creatinine ratios in relation to fimH, csgA, papC, tosA, and flu genes presence in UPEC isolated from the urine samples were analyzed. Apart from traditional statistics, also machine learning algorithms were applied. Results The urine levels of IL1β and IL 6 were similar in kidney recipients and the immunocompetent hosts. IL1β levels were higher in both kidney recipients and immunocompetent hosts than in controls, while IL6 levels were higher only in immunocompetent hosts than in controls. In the machine learning classification model, high urine IL17 levels were significantly more prevalent in controls, while low IL17 levels in urines infected with Ag43-positive UPEC strains, regardless of the host’s immune status. In the traditional statistical analysis, IL17 levels appeared significantly higher in urine samples from kidney recipients infected with Ag43–negative UPEC strains. Conclusions In the UTI- affected patients, the combination of the immune status of an individual and Ag43 status of the UPEC strain determined urine IL17 level in the analyzed group. However, IL17 levels above median were overall more prevalent in controls.
Collapse
|
11
|
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.
Collapse
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
| |
Collapse
|
12
|
Seyahi N, Ozcan SG. Artificial intelligence and kidney transplantation. World J Transplant 2021; 11:277-289. [PMID: 34316452 PMCID: PMC8290997 DOI: 10.5500/wjt.v11.i7.277] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 05/17/2021] [Accepted: 06/04/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence and its primary subfield, machine learning, have started to gain widespread use in medicine, including the field of kidney transplantation. We made a review of the literature that used artificial intelligence techniques in kidney transplantation. We located six main areas of kidney transplantation that artificial intelligence studies are focused on: Radiological evaluation of the allograft, pathological evaluation including molecular evaluation of the tissue, prediction of graft survival, optimizing the dose of immunosuppression, diagnosis of rejection, and prediction of early graft function. Machine learning techniques provide increased automation leading to faster evaluation and standardization, and show better performance compared to traditional statistical analysis. Artificial intelligence leads to improved computer-aided diagnostics and quantifiable personalized predictions that will improve personalized patient care.
Collapse
Affiliation(s)
- Nurhan Seyahi
- Department of Nephrology, Istanbul University-Cerrahpaşa, Cerrahpaşa Medical Faculty, Istanbul 34098, Fatih, Turkey
| | - Seyda Gul Ozcan
- Department of Internal Medicine, Istanbul University-Cerrahpaşa, Cerrahpaşa Medical Faculty, Istanbul 34098, Fatih, Turkey
| |
Collapse
|
13
|
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.
Collapse
|
14
|
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: 21] [Impact Index Per Article: 5.3] [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.
Collapse
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
| |
Collapse
|
15
|
Shah M, Naik N, Somani BK, Hameed BMZ. Artificial intelligence (AI) in urology-Current use and future directions: An iTRUE study. Turk J Urol 2020; 46:S27-S39. [PMID: 32479253 PMCID: PMC7731952 DOI: 10.5152/tud.2020.20117] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 04/12/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Artificial intelligence (AI) is used in various urological conditions such as urolithiasis, pediatric urology, urogynecology, benign prostate hyperplasia (BPH), renal transplant, and uro-oncology. The various models of AI and its application in urology subspecialties are reviewed and discussed. MATERIAL AND METHODS Search strategy was adapted to identify and review the literature pertaining to the application of AI in urology using the keywords "urology," "artificial intelligence," "machine learning," "deep learning," "artificial neural networks," "computer vision," and "natural language processing" were included and categorized. Review articles, editorial comments, and non-urologic studies were excluded. RESULTS The article reviewed 47 articles that reported characteristics and implementation of AI in urological cancer. In all cases with benign conditions, artificial intelligence was used to predict outcomes of the surgical procedure. In urolithiasis, it was used to predict stone composition, whereas in pediatric urology and BPH, it was applied to predict the severity of condition. In cases with malignant conditions, it was applied to predict the treatment response, survival, prognosis, and recurrence on the basis of the genomic and biomarker studies. These results were also found to be statistically better than routine approaches. Application of radiomics in classification and nuclear grading of renal masses, cystoscopic diagnosis of bladder cancers, predicting Gleason score, and magnetic resonance imaging with computer-assisted diagnosis for prostate cancers are few applications of AI that have been studied extensively. CONCLUSIONS In the near future, we will see a shift in the clinical paradigm as AI applications will find their place in the guidelines and revolutionize the decision-making process.
Collapse
Affiliation(s)
- Milap Shah
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
| | - Nithesh Naik
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
- Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Bhaskar K. Somani
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
- Department of Urological Surgery, University Hospital Southampton NHS Trust, Southampton, UK
| | - BM Zeeshan Hameed
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
- KMC Innovation Centre, Manipal Academy of Higher Education, Manipal, Karnataka, India
| |
Collapse
|
16
|
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.
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
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.
Collapse
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.
| |
Collapse
|
19
|
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.
Collapse
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
| |
Collapse
|
20
|
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.
Collapse
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
| |
Collapse
|
21
|
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.
Collapse
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
| |
Collapse
|
22
|
Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2017.01.012] [Citation(s) in RCA: 151] [Impact Index Per Article: 30.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
|
23
|
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
|
24
|
Shahmoradi L, Langarizadeh M, Pourmand G, Fard ZA, Borhani A. Comparing Three Data Mining Methods to Predict Kidney Transplant Survival. Acta Inform Med 2016; 24:322-327. [PMID: 28163356 PMCID: PMC5256037 DOI: 10.5455/aim.2016.24.322-327] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Accepted: 10/08/2016] [Indexed: 11/03/2022] Open
Abstract
INTRODUCTION One of the most important complications of post-transplant is rejection. Analyzing survival is one of the areas of medical prognosis and data mining, as an effective approach, has the capacity of analyzing and estimating outcomes in advance through discovering appropriate models among data. The present study aims at comparing the effectiveness of C5.0 algorithms, neural network and C&RTree to predict kidney transplant survival before transplant. METHOD To detect factors effective in predicting transplant survival, information needs analysis was performed via a researcher-made questionnaire. A checklist was prepared and data of 513 kidney disease patient files were extracted from Sina Urology Research Center. Following CRISP methodology for data mining, IBM SPSS Modeler 14.2, C5.0, C&RTree algorithms and neural network were used. RESULTS Body Mass Index (BMI), cause of renal dysfunction and duration of dialysis were evaluated in all three models as the most effective factors in transplant survival. C5.0 algorithm with the highest validity (96.77%) was the first in estimating kidney transplant survival in patients followed by C&RTree (83.7%) and neural network (79.5%) models. CONCLUSION Among the three models, C5.0 algorithm was the top model with high validity that confirms its strength in predicting survival. The most effective kidney transplant survival factors were detected in this study; therefore, duration of transplant survival (year) can be determined considering the regulations set for a new sample with specific characteristics.
Collapse
Affiliation(s)
- Leila Shahmoradi
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Mostafa Langarizadeh
- Department of Health Information Management, School of Health Management and Information Science, Iran University of Medical Sciences, Tehran, Iran
| | - Gholamreza Pourmand
- Urology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Ziba Aghsaei Fard
- Urology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Alireza Borhani
- Urology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
25
|
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.
Collapse
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
| |
Collapse
|
26
|
Wojciuk B, Myślak M, Pabisiak K, Ciechanowski K, Giedrys-Kalemba S. Epidemiology of infections in kidney transplant recipients - data miner's approach. Transpl Int 2015; 28:729-37. [DOI: 10.1111/tri.12536] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2014] [Revised: 04/25/2014] [Accepted: 01/30/2015] [Indexed: 12/01/2022]
Affiliation(s)
- Bartosz Wojciuk
- Department of Microbiology and Immunological Diagnostics; previously Department of Microbiology and Immunology; Pomeranian Medical University; Szczecin Poland
| | - Marek Myślak
- Clinic of Nephrology, Transplantation and Internal Medicine; Pomeranian Medical University; Szczecin Poland
| | - Krzysztof Pabisiak
- Clinic of Nephrology, Transplantation and Internal Medicine; Pomeranian Medical University; Szczecin Poland
| | - Kazimierz Ciechanowski
- Clinic of Nephrology, Transplantation and Internal Medicine; Pomeranian Medical University; Szczecin Poland
| | - Stefania Giedrys-Kalemba
- Department of Microbiology and Immunological Diagnostics; previously Department of Microbiology and Immunology; Pomeranian Medical University; Szczecin Poland
| |
Collapse
|
27
|
Carrillo-de-Albornoz A, Figuero E, Herrera D, Cuesta P, Bascones-Martínez A. Gingival changes during pregnancy: III. Impact of clinical, microbiological, immunological and socio-demographic factors on gingival inflammation. J Clin Periodontol 2011; 39:272-83. [DOI: 10.1111/j.1600-051x.2011.01800.x] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/12/2011] [Indexed: 11/26/2022]
Affiliation(s)
| | - Elena Figuero
- Section of Periodontology; School of Dentistry, Complutense University of Madrid; Madrid; Spain
| | - David Herrera
- Section of Periodontology; School of Dentistry, Complutense University of Madrid; Madrid; Spain
| | - Pedro Cuesta
- Section of Statistics; Research Support Center. Complutense University of Madrid; Madrid; Spain
| | | |
Collapse
|