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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.
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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
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Guo S, Jia D, Liu X, Gao L, Wang H, Chen C, Wu Y. The positive efficacy of dexmedetomidine on the clinical outcomes of patients undergoing renal transplantation: evidence from meta-analysis. Aging (Albany NY) 2023; 15:14192-14209. [PMID: 38085644 PMCID: PMC10756127 DOI: 10.18632/aging.205296] [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: 06/15/2023] [Accepted: 11/02/2023] [Indexed: 12/21/2023]
Abstract
INTRODUCTION Whether dexmedetomidine (DEX), an anesthetic adjuvant, can improve renal transplant outcomes is not clear. METHODS We systematically identified clinical trials in which DEX was administered in renal transplantation (RT). On November 1, 2022, we searched The Cochrane Library, MEDLINE, EMBASE and https://www. CLINICALTRIALS gov/. The main outcomes were delayed graft function and acute rejection. RESULTS A total of seven studies were included in the meta-analysis. The results showed that compared with the control, DEX significantly reduced the occurrence of delayed graft function (RR 0.76; 95% CI 0.60-0.98), short-term serum creatinine [postoperative day (POD) 2: (MD -22.82; 95% CI -42.01 - -3.64)] and blood urea nitrogen [POD 2: (MD -2.90; 95% CI -5.10 - -0.70); POD 3: (MD 2.07; 95% CI -4.12 - -0.02)] levels, postoperative morphine consumption (MD -4.27; 95% CI -5.92 - -2.61) and the length of hospital stay (MD -0.85; 95% CI-1.47 - -0.23). However, DEX did not reduce the risk of postoperative acute rejection (RR 0.75; 95% CI 0.45-1.23). The results of the subgroup analysis showed that country type, donor type, and average age had a certain impact on the role of DEX. CONCLUSIONS DEX may improve the short-term clinical outcome of RT and shorten the length of hospital stay of patients.
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Affiliation(s)
- Shanshan Guo
- Department of Nephropathy, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, PR China
| | - Degong Jia
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, PR China
| | - Xueqi Liu
- Department of Nephropathy, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, PR China
| | - Li Gao
- Department of Nephropathy, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, PR China
| | - Huaying Wang
- Department of Nephropathy, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, PR China
| | - Chaoyi Chen
- Department of Nephropathy, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, PR China
| | - Yonggui Wu
- Department of Nephropathy, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, PR China
- Center for Scientific Research of Anhui Medical University, Hefei, Anhui 230022, PR China
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Quinino RM, Agena F, Modelli de Andrade LG, Furtado M, Chiavegatto Filho ADP, David-Neto E. A Machine Learning Prediction Model for Immediate Graft Function After Deceased Donor Kidney Transplantation. Transplantation 2023; 107:1380-1389. [PMID: 36872507 DOI: 10.1097/tp.0000000000004510] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Abstract
BACKGROUND After kidney transplantation (KTx), the graft can evolve from excellent immediate graft function (IGF) to total absence of function requiring dialysis. Recipients with IGF do not seem to benefit from using machine perfusion, an expensive procedure, in the long term when compared with cold storage. This study proposes to develop a prediction model for IGF in KTx deceased donor patients using machine learning algorithms. METHODS Unsensitized recipients who received their first KTx deceased donor between January 1, 2010, and December 31, 2019, were classified according to the conduct of renal function after transplantation. Variables related to the donor, recipient, kidney preservation, and immunology were used. The patients were randomly divided into 2 groups: 70% were assigned to the training and 30% to the test group. Popular machine learning algorithms were used: eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Gradient Boosting classifier, Logistic Regression, CatBoost classifier, AdaBoost classifier, and Random Forest classifier. Comparative performance analysis on the test dataset was performed using the results of the AUC values, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. RESULTS Of the 859 patients, 21.7% (n = 186) had IGF. The best predictive performance resulted from the eXtreme Gradient Boosting model (AUC, 0.78; 95% CI, 0.71-0.84; sensitivity, 0.64; specificity, 0.78). Five variables with the highest predictive value were identified. CONCLUSIONS Our results indicated the possibility of creating a model for the prediction of IGF, enhancing the selection of patients who would benefit from an expensive treatment, as in the case of machine perfusion preservation.
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Affiliation(s)
- Raquel M Quinino
- Renal Transplant Service, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | - Fabiana Agena
- Renal Transplant Service, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | | | - Mariane Furtado
- Department of Epidemiology, School of Public Health, University of São Paulo, São Paulo, Brazil
| | | | - Elias David-Neto
- Renal Transplant Service, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
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Wang J, Liu J, Wu W, Yang S, Liu L, Fu Q, Li J, Chen X, Deng R, Wu C, Long S, Zhang W, Zhang H, Mao H, Chen W. Combining Clinical Parameters and Acute Tubular Injury Grading Is Superior in Predicting the Prognosis of Deceased-Donor Kidney Transplantation: A 7-Year Observational Study. Front Immunol 2022; 13:912749. [PMID: 35844570 PMCID: PMC9279653 DOI: 10.3389/fimmu.2022.912749] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 05/16/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundWe developed a pragmatic dichotomous grading criterion to stratify the acute tubular injury (ATI) of deceased-donor kidneys. We intended to verify the predictive value of this criterion for the prognosis of deceased-donor kidney transplantation.MethodsThe allografts with ATI were classified into severe and mild groups. Severe ATI was defined as the presence of extreme and diffuse flattening of the tubular epithelial cells, or denudement of the tubular basement membrane. The clinical delayed graft function (DGF) risk index was calculated based on a regression model for posttransplant DGF using 17 clinical parameters related to donor–recipient characteristics.ResultsA total of 140 recipients were enrolled: 18 severe and 122 mild ATI. Compared with the mild ATI group, the severe ATI group had more donors after cardiac death, higher median donor terminal serum creatinine level (dScr), and longer median cold ischemia time. Severe ATI had a higher DGF rate (55.6% vs 14.6%, p < 0.001), longer DGF recovery time (49.6 vs 26.3 days, p < 0.001), and a lower estimated glomerular filtration rate (eGFR) at 1 month (23.5 vs 54.0 ml/min/1.73 m2, p < 0.001), 3 months (40.4 vs 59.0, p = 0.001), and 6 months after transplant (46.8 vs 60.3, p = 0.033). However, there was no significant difference in eGFR at 1 year or beyond, graft, and patient survival. The predictive value of combined dScr with ATI severity for DGF rate and DGF recovery time was superior to that of dScr alone. The predictive value of the combined DGF risk index with ATI severity for DGF was also better than that of the DGF risk index alone; however, the association of the DGF risk index with DGF recovery time was not identified. Chronic lesions including glomerulosclerosis, interstitial fibrosis, arterial intimal fibrosis, and arteriolar hyalinosis were associated with declined posttransplant 1-year eGFR.ConclusionBased on our pragmatic dichotomous grading criterion for ATI in a preimplantation biopsy, donor kidneys with severe ATI increased DGF risk, prolonged DGF recovery, and decreased short-term graft function but demonstrated favorable long-term graft function. Our grading method can offer additive valuable information for assessing donor kidneys with acute kidney injury and may act as an effective supplementary index of the Banff criteria.
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Affiliation(s)
- Jiali Wang
- Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Yuexiu District, Guangzhou, China
| | - Jinqi Liu
- Department of Pediatrics, Guangzhou Women and Children’s Medical Centre, Guangzhou, China
| | - Wenrui Wu
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shicong Yang
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Longshan Liu
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory on Organ Donation and Transplant Immunology, Guangzhou, Guangzhou, China
- Guangdong Provincial International Cooperation Base of Science and Technology (Organ Transplantation), Guangzhou, China
| | - Qian Fu
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jun Li
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xutao Chen
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ronghai Deng
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chenglin Wu
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Sizhe Long
- Center for Information Technology and Statistics, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wujun Zhang
- Center for Information Technology and Statistics, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Huanxi Zhang
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Wenfang Chen, ; Haiping Mao, ; Huanxi Zhang,
| | - Haiping Mao
- Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Yuexiu District, Guangzhou, China
- *Correspondence: Wenfang Chen, ; Haiping Mao, ; Huanxi Zhang,
| | - Wenfang Chen
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Wenfang Chen, ; Haiping Mao, ; Huanxi Zhang,
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Matas AJ, Helgeson E, Fieberg A, Leduc R, Gaston RS, Kasiske BL, Rush D, Hunsicker L, Cosio F, Grande JP, Cecka JM, Connett J, Mannon RB. Risk Prediction for Delayed Allograft Function: Analysis of the Deterioration of Kidney Allograft Function (DeKAF) Study Data. Transplantation 2022; 106:358-368. [PMID: 33675321 PMCID: PMC8380757 DOI: 10.1097/tp.0000000000003718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
BACKGROUND Delayed graft function (DGF) of a kidney transplant results in increased cost and complexity of management. For clinical care or a DGF trial, it would be ideal to accurately predict individual DGF risk and provide preemptive treatment. A calculator developed by Irish et al has been useful for predicting population but not individual risk. METHODS We analyzed the Irish calculator (IC) in the DeKAF prospective cohort (incidence of DGF = 20.4%) and investigated potential improvements. RESULTS We found that the predictive performance of the calculator in those meeting Irish inclusion criteria was comparable with that reported by Irish et al. For cohorts excluded by Irish: (a) in pump-perfused kidneys, the IC overestimated DGF risk; (b) in simultaneous pancreas kidney transplants, the DGF risk was exceptionally low. For all 3 cohorts, there was considerable overlap in IC scores between those with and those without DGF. Using a modified definition of DGF-excluding those with single dialysis in the first 24 h posttransplant-we found that the calculator had similar performance as with the traditional DGF definition. Studying whether DGF prediction could be improved, we found that recipient cardiovascular disease was strongly associated with DGF even after accounting for IC-predicted risk. CONCLUSIONS The IC can be a useful population guide for predicting DGF in the population for which it was intended but has limited scope in expanded populations (SPK, pump) and for individual risk prediction. DGF risk prediction can be improved by inclusion of recipient cardiovascular disease.
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Affiliation(s)
- Arthur J Matas
- Transplantation Division, Department of Surgery, University of Minnesota, Minneapolis, MN
| | - Erika Helgeson
- Biostatistics Division, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Ann Fieberg
- Biostatistics Division, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Robert Leduc
- Biostatistics Division, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Robert S Gaston
- Department of Medicine, University of Alabama, Birmingham, AL
| | | | - David Rush
- Department of Internal Medicine, University of Manitoba, Winnipeg, MB, Canada
| | | | - Fernando Cosio
- Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | - Joseph P Grande
- Division of Anatomic Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - J Michael Cecka
- Department of Pathology & Lab Medicine, David Geffen School of Medicine, University of California, UCLA Immunogenetics Center, Los Angeles, CA
| | - John Connett
- Biostatistics Division, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Roslyn B Mannon
- University of Nebraska Medical Center and VA Nebraska-Western Iowa Health Care System, Omaha, NE
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Zhang H, Fu Q, Liu J, Li J, Deng R, Wu C, Nie W, Chen X, Liu L, Wang C. Risk factors and outcomes of prolonged recovery from delayed graft function after deceased kidney transplantation. Ren Fail 2021; 42:792-798. [PMID: 32772773 PMCID: PMC7472517 DOI: 10.1080/0886022x.2020.1803084] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Objective We aimed to evaluate the effect of prolonged recovery from DGF on outcomes, using a new definition of DGF recovery time, among deceased donor kidney transplant recipients with DGF, and to examine the risk factors for prolonged recovery. Methods From 2007 to 2016, 91 deceased donor kidney transplant recipients with DGF were retrospectively analyzed. DGF recovery time was defined as the time from transplantation to achieve a stable estimated glomerular filtration rate (eGFR). Recipients with a DGF recovery time greater than or equal to the median were assigned to the prolonged recovery group, while the others were assigned to the rapid recovery group. Result The median DGF recovery time was 27 days. Donor terminal eGFR was significantly lower in the prolonged recovery group (n = 46) compared with the rapid recovery group (n = 45) (median 24.9 vs. 65.4 ml/min/1.73m2, p = 0.004). The eGFR at 1 year post-transplant in the prolonged recovery group was significantly lower than that in the rapid recovery group (50.6 ± 20.0 vs. 63.5 ± 21.4 ml/min/1.73m2, p = 0.005). The risk of adverse outcomes (acute rejection, pneumonia, graft failure, and death) was significantly greater in the prolonged recovery group (hazard ratio 2.604, 95% confidence interval 1.102–6.150, p = 0.029) compared with the rapid recovery group. Conclusion Decreased donor terminal eGFR is a risk factor for prolonged recovery from DGF after deceased kidney transplantation. Prolonged DGF recovery time is associated with reduced graft function at 1-year post-transplant, and poor transplant outcome.
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Affiliation(s)
- Huanxi Zhang
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qian Fu
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jinqi Liu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Jun Li
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ronghai Deng
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chenglin Wu
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Weijian Nie
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xutao Chen
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Longshan Liu
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory on Organ Donation and Transplant Immunology, Guangzhou, China.,Guangdong Provincial International Cooperation Base of Science and Technology (Organ Transplantation), Guangzhou, China
| | - Changxi Wang
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory on Organ Donation and Transplant Immunology, Guangzhou, China.,Guangdong Provincial International Cooperation Base of Science and Technology (Organ Transplantation), Guangzhou, China
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Xue W, Wang C, Chen J, Sun X, Wu X, Peng L, Chen Z, Qu Q, Zhang X, Fu Y, Dong Z, Chen Z, Feng G, Lin T, Men T, Yu L, Sun Q, Zhao Y, Zhou J, Zeng L, Zhao M, Tan J, Ye Q, Shi B, Ming Y, Zhu T, Sui W, Huang C, Fu Y. A prediction model of delayed graft function in deceased donor for renal transplant: a multi-center study from China. Ren Fail 2021; 43:520-529. [PMID: 33719820 PMCID: PMC7971200 DOI: 10.1080/0886022x.2021.1895838] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Background Kidneys obtained from deceased donors increase the incidence of delayed graft function (DGF) after renal transplantation. Here we investigated the influence of the risk factors of donors with DGF, and developed a donor risk scoring system for DGF prediction. Methods This retrospective study was conducted in 1807 deceased kidney donors and 3599 recipients who received donor kidneys via transplants in 29 centers in China. We quantified DGF associations with donor clinical characteristics. A donor risk scoring system was developed and validated using an independent sample set. Results The incidence of DGF from donors was 19.0%. Six of the donor characteristics analyzed, i.e., age, cause of death, history of hypertension, terminal serum creatinine, persistence of hypotension, and cardiopulmonary resuscitation (CPR) time were risk factors for DGF. A 49-point scoring system of donor risk was established for DGF prediction and exhibited a superior degree of discrimination. External validation of DGF prediction revealed area under the receiver-operating characteristic (AUC) curves of 0.7552. Conclusions Our study determined the deceased donor risk factors related to DGF after renal transplantation pertinent to the Chinese cohort. The scoring system developed here had superior diagnostic significance and consistency and can be used by clinicians to make evidence-based decisions on the quality of kidneys from deceased donors and guide renal transplantation therapy.
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Affiliation(s)
- Wujun Xue
- The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Institute of Organ Transplantation of Xi'an Jiaotong University, Xi'an, China
| | - Changxi Wang
- The First Affiliated Hospital Of Sun Yat-sen University, Guangzhou, China
| | - Jianghua Chen
- First Affiliated Hospital Of Zhejiang University Medical School, Hangzhou, China
| | - Xuyong Sun
- The Chinese People's Liberation Army 923 Hospital, Nanjing, China
| | - Xiaotong Wu
- Second People's Hospital of Shanxi Province, Taiyuan, China
| | - Longkai Peng
- The Second Xiangya Hospital Of Central South University, Changsha, China
| | - Zhishui Chen
- Tongji Hospital Affiliated to Tongji Medical College Huazhong University of Science and Technology, Wuhan, China
| | - Qingshan Qu
- People's Hospital of Zhengzhou City, Zhengzhou, China
| | - Xiaodong Zhang
- Beijing Chaoyang Hospital Affiliated to The Capital University of Medical Sciences, Beijing, China
| | - Yaowen Fu
- Bethune First Hospital of Jilin University, Changchun, China
| | - Zhen Dong
- The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zheng Chen
- The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Guiwen Feng
- The First Affiliated Hospital Of Zhengzhou University, Zhengzhou, China
| | - Tao Lin
- West China Hospital of Sichuan University, Chengdu, China
| | - Tongyi Men
- The First Affiliated Hospital of the First Shandong Medical University, Jinan, China
| | - Lixin Yu
- Nanfang Hospital of Southern Medical University, Guangzhou, China
| | - Qiquan Sun
- The Third Hospital Affiliated of Sun-Yet University, Guangzhou, China
| | - Yongheng Zhao
- Kunming City First People's Hospital, Kunming, China
| | | | - Li Zeng
- Changhai Hospital of Naval Military Medical University, Shanghai, China
| | - Ming Zhao
- Zhujiang Hospital, of Southern Medical University, Guangzhou, China
| | - Jianming Tan
- The 900 Hospital of Joint Logistic Support Department of PLA, Fuzhou, China
| | - Qifa Ye
- Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Bingyi Shi
- The Eighth Medical Center of People's Liberation Army General Hospital, Beijing, China
| | - Yingzi Ming
- The Third Xiangya Hospital of Central South University, Changsha, China
| | - Tongyu Zhu
- Zhongshan Hospital Affiliated to Fudan University, Shanghai, China
| | - Weiguo Sui
- The 924 Hospital of Joint Logistic Support Department of PLA, Guilin, China
| | - Chibing Huang
- The Second Affiliated Hospital of Army Military Medical University, Chongqing, China
| | - Yingxin Fu
- Tianjin First Central Hospital, Tianjin, China
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Automated En Masse Machine Learning Model Generation Shows Comparable Performance as Classic Regression Models for Predicting Delayed Graft Function in Renal Allografts. Transplantation 2021; 105:2646-2654. [PMID: 33560727 DOI: 10.1097/tp.0000000000003640] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Several groups have previously developed logistic regression models for predicting delayed graft function (DGF). In this study, we used an automated machine learning (ML) modeling pipeline to generate and optimize DGF prediction models en masse. METHODS Deceased donor renal transplants at our institution from 2010-2018 were included. Input data consisted of 21 donor features from United Network for Organ Sharing. A training set composed of ~50%/50% split in DGF-positive and DGF-negative cases was used to generate 400,869 models. Each model was based on one of seven ML algorithms (gradient boosting machine, k-nearest neighbor, logistic regression, neural network, naïve Bayes, random forest, support vector machine) with various combinations of feature sets and hyperparameter values. Each model's performance was based on a separate secondary test dataset and assessed by common statistical metrics. RESULTS The best performing models were based on neural network algorithms, with the highest area under the receiver operating characteristic (AUROC) curve of 0.7595. This model used 10 out of the original 21 donor features, including age, height, weight, ethnicity, serum creatinine, blood urea nitrogen, hypertension history, donation after cardiac death status, cause of death, and cold ischemia time. With the same donor data, the highest AUROC curve for logistic regression models was 0.7484, using all donor features. CONCLUSION Our automated en masse ML modeling approach was able to rapidly generate ML models for DGF prediction. The performance of the ML models was comparable to classic logistic regression models.
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Kawakita S, Beaumont JL, Jucaud V, Everly MJ. Personalized prediction of delayed graft function for recipients of deceased donor kidney transplants with machine learning. Sci Rep 2020; 10:18409. [PMID: 33110142 PMCID: PMC7591492 DOI: 10.1038/s41598-020-75473-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 10/15/2020] [Indexed: 02/06/2023] Open
Abstract
Machine learning (ML) has shown its potential to improve patient care over the last decade. In organ transplantation, delayed graft function (DGF) remains a major concern in deceased donor kidney transplantation (DDKT). To this end, we harnessed ML to build personalized prognostic models to predict DGF. Registry data were obtained on adult DDKT recipients for model development (n = 55,044) and validation (n = 6176). Incidence rates of DGF were 25.1% and 26.3% for the development and validation sets, respectively. Twenty-six predictors were identified via recursive feature elimination with random forest. Five widely-used ML algorithms-logistic regression (LR), elastic net, random forest, artificial neural network (ANN), and extreme gradient boosting (XGB) were trained and compared with a baseline LR model fitted with previously identified risk factors. The new ML models, particularly ANN with the area under the receiver operating characteristic curve (ROC-AUC) of 0.732 and XGB with ROC-AUC of 0.735, exhibited superior performance to the baseline model (ROC-AUC = 0.705). This study demonstrates the use of ML as a viable strategy to enable personalized risk quantification for medical applications. If successfully implemented, our models may aid in both risk quantification for DGF prevention clinical trials and personalized clinical decision making.
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Affiliation(s)
| | | | - Vadim Jucaud
- Terasaki Research Institute, Los Angeles, CA, USA
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10
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Li F, Hu L, Zhao X, Ge W, Pan H, Zhang W, Jiang Y, Xu X, Hou J, Pu J. The value of cystatin C and urinary and serum neutrophil gelatinase-associated lipocalin during the perioperative period of renal transplantation. Transl Androl Urol 2019; 8:432-441. [PMID: 31807420 DOI: 10.21037/tau.2019.08.12] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Background The perioperative management of renal transplantation is complex. Our research aimed to study the clinical value of cystatin-C (Cys-C) and urinary and serum neutrophil gelatinase-associated lipocalin (NGAL) during the perioperative period of renal transplantation. Methods We collected the clinical information of 47 renal transplantation patients. Urine and serum samples were collected daily until the second week and then weekly until discharge to determine serum NGAL (s-NGAL), urine NGAL (u-NGAL), serum creatinine (s-Cr), and Cys-C levels. Receiver-operating characteristic (ROC) analysis was used, and the area under the curve (AUC) was compared to evaluate the accuracy of the diagnosis of delayed graft function (DGF). Multivariable analysis was used to find the association between the markers and renal function at discharge. Results In our research, the value of Cys-C, serum NGAL, and urine NGAL were higher in DGF group. In the ROC analysis, Cys-C had the highest AUC (0.939) compared with s-NGAL (0.909), u-NGAL (0.856), and s-Cr (0.747). Multivariable analysis showed that Cys-C levels in the first week after the operation and cold ischemia time were independently associated with estimated glomerular filtration rate (eGFR) at discharge (P<0.05). Conclusions Our results showed that Cys-C, serum NGAL, and urine NGAL could reflect renal function sensitively. Cys-C had the highest sum of sensitivity and specificity at 4.77 mg/L, with a sensitivity of 0.818 and specificity of 0.889. The Cys-C level during the first week after the operation was independently associated with eGFR at discharge and could predict the short-term prognosis of renal transplantation patients.
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Affiliation(s)
- Feng Li
- Department of Urinary Surgery, The First Affiliated Hospital, Soochow University, Suzhou 215006, China
| | - Linkun Hu
- Department of Urinary Surgery, The First Affiliated Hospital, Soochow University, Suzhou 215006, China
| | - Xiaojun Zhao
- Department of Urinary Surgery, The First Affiliated Hospital, Soochow University, Suzhou 215006, China
| | - Wenqing Ge
- Department of Urinary Surgery, The First Affiliated Hospital, Soochow University, Suzhou 215006, China
| | - Hao Pan
- Department of Urinary Surgery, The First Affiliated Hospital, Soochow University, Suzhou 215006, China
| | - Wei Zhang
- Department of Urinary Surgery, The First Affiliated Hospital, Soochow University, Suzhou 215006, China
| | - Yufeng Jiang
- Department of Urinary Surgery, The First Affiliated Hospital, Soochow University, Suzhou 215006, China
| | - Xu Xu
- Department of Urinary Surgery, The First Affiliated Hospital, Soochow University, Suzhou 215006, China
| | - Jianquan Hou
- Department of Urinary Surgery, The First Affiliated Hospital, Soochow University, Suzhou 215006, China
| | - Jinxian Pu
- Department of Urinary Surgery, The First Affiliated Hospital, Soochow University, Suzhou 215006, China
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