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Novacescu D, Latcu SC, Raica M, Baderca F, Dumitru CS, Daminescu L, Bardan R, Dema V, Croitor A, Cut TG, Cumpanas AA. Surgical Strategies for Renal Transplantation: A Pictorial Essay. J Clin Med 2024; 13:4188. [PMID: 39064228 PMCID: PMC11278135 DOI: 10.3390/jcm13144188] [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: 05/14/2024] [Revised: 07/13/2024] [Accepted: 07/16/2024] [Indexed: 07/28/2024] Open
Abstract
This pictorial essay aims to navigate through the complexities and challenges of renal transplantation (RT), by weaving together visual imagery with clinical insights within a comprehensive illustrative surgical guide. Herein, we provide a detailed visual exploration of the intricate anatomy and surgical processes necessary for both renal graft retrieval from the donor and also for an adequate implantation in the recipient. Regarding graft retrieval, after reviewing the relevant retroperitoneal surgical anatomy, and donor nephrectomy techniques, graft preservation and optimal backbench graft dissection principles were meticulously analyzed. Thereafter, the recipient surgical strategy for graft implantation was addressed, focusing on preoperative preparations, the site of implantation selection, exposure, operative bed dissection, graft revascularization, and urinary tract reconstruction. Careful donor and recipient selection, meticulous surgical execution, and rigorous postoperative management clearly hold a pivotal role in optimizing patient outcomes. Fostering a deeper understanding of the surgical nuances and clinical management practices that contribute to successful results post-RT, we hope to provide a useful practical tool for clinicians about to embark on the treacherous road of RT surgery. Innovative technologies and surgical practices that have already significantly improved the safety and effectiveness of RT stand testament to the importance of further scientific inquiry, conceptual developments, and clinical integration. Moving forward, it is essential that the medical community continues to refine these strategies and advocate for equitable access to transplantation, ensuring that advancements in the field translate into real-world benefits for all patients grappling with ESRD. The collaborative efforts of multidisciplinary teams are essential in addressing the complex clinical challenges associated with RT, with the ultimate goal of improving patient survival, enhancing graft longevity, and reducing healthcare disparities.
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Affiliation(s)
- Dorin Novacescu
- Department II of Microscopic Morphology, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, No. 2, 300041 Timisoara, Romania; (D.N.); (M.R.); (F.B.); (C.-S.D.)
- Angiogenesis Research Center, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, No. 2, 300041 Timisoara, Romania
| | - Silviu Constantin Latcu
- Doctoral School, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, Nr. 2, 300041 Timisoara, Romania; (V.D.); (A.C.)
- Department of Urology, “Pius Brinzeu” Timisoara County Emergency Hospital, Liviu Rebreanu Boulevard, Nr. 156, 300723 Timisoara, Romania; (L.D.); (R.B.); (A.A.C.)
- Department XV, Discipline of Urology, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, Nr. 2, 300041 Timisoara, Romania
| | - Marius Raica
- Department II of Microscopic Morphology, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, No. 2, 300041 Timisoara, Romania; (D.N.); (M.R.); (F.B.); (C.-S.D.)
- Angiogenesis Research Center, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, No. 2, 300041 Timisoara, Romania
| | - Flavia Baderca
- Department II of Microscopic Morphology, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, No. 2, 300041 Timisoara, Romania; (D.N.); (M.R.); (F.B.); (C.-S.D.)
- Angiogenesis Research Center, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, No. 2, 300041 Timisoara, Romania
| | - Cristina-Stefania Dumitru
- Department II of Microscopic Morphology, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, No. 2, 300041 Timisoara, Romania; (D.N.); (M.R.); (F.B.); (C.-S.D.)
- Angiogenesis Research Center, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, No. 2, 300041 Timisoara, Romania
| | - Liviu Daminescu
- Department of Urology, “Pius Brinzeu” Timisoara County Emergency Hospital, Liviu Rebreanu Boulevard, Nr. 156, 300723 Timisoara, Romania; (L.D.); (R.B.); (A.A.C.)
| | - Razvan Bardan
- Department of Urology, “Pius Brinzeu” Timisoara County Emergency Hospital, Liviu Rebreanu Boulevard, Nr. 156, 300723 Timisoara, Romania; (L.D.); (R.B.); (A.A.C.)
- Department XV, Discipline of Urology, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, Nr. 2, 300041 Timisoara, Romania
| | - Vlad Dema
- Doctoral School, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, Nr. 2, 300041 Timisoara, Romania; (V.D.); (A.C.)
- Department of Urology, “Pius Brinzeu” Timisoara County Emergency Hospital, Liviu Rebreanu Boulevard, Nr. 156, 300723 Timisoara, Romania; (L.D.); (R.B.); (A.A.C.)
- Department XV, Discipline of Urology, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, Nr. 2, 300041 Timisoara, Romania
| | - Alexei Croitor
- Doctoral School, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, Nr. 2, 300041 Timisoara, Romania; (V.D.); (A.C.)
- Department of Urology, “Pius Brinzeu” Timisoara County Emergency Hospital, Liviu Rebreanu Boulevard, Nr. 156, 300723 Timisoara, Romania; (L.D.); (R.B.); (A.A.C.)
- Department XV, Discipline of Urology, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, Nr. 2, 300041 Timisoara, Romania
| | - Talida Georgiana Cut
- Department XIII, Discipline of Infectious Diseases, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, Nr. 2, 300041 Timisoara, Romania;
- Center for Ethics in Human Genetic Identifications, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, Nr. 2, 300041 Timisoara, Romania
| | - Alin Adrian Cumpanas
- Department of Urology, “Pius Brinzeu” Timisoara County Emergency Hospital, Liviu Rebreanu Boulevard, Nr. 156, 300723 Timisoara, Romania; (L.D.); (R.B.); (A.A.C.)
- Department XV, Discipline of Urology, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, Nr. 2, 300041 Timisoara, Romania
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2
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Pérez Valdivia MÁ, Calvillo Arbizu J, Portero Barreña D, Castro de la Nuez P, López Jiménez V, Rodríguez Benot A, Mazuecos Blanca A, de Gracia Guindo MC, Bernal Blanco G, Gentil Govantes MÁ, Bedoya Pérez R, Rocha Castilla JL. Predicting Kidney Transplantation Outcomes from Donor and Recipient Characteristics at Time Zero: Development of a Mobile Application for Nephrologists. J Clin Med 2024; 13:1270. [PMID: 38592072 PMCID: PMC10932177 DOI: 10.3390/jcm13051270] [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: 01/10/2024] [Revised: 02/14/2024] [Accepted: 02/21/2024] [Indexed: 04/10/2024] Open
Abstract
(1) Background: We report on the development of a predictive tool that can estimate kidney transplant survival at time zero. (2) Methods: This was an observational, retrospective study including 5078 transplants. Death-censored graft and patient survivals were calculated. (3) Results: Graft loss was associated with donor age (hazard ratio [HR], 1.021, 95% confidence interval [CI] 1.018-1.024, p < 0.001), uncontrolled donation after circulatory death (DCD) (HR 1.576, 95% CI 1.241-2.047, p < 0.001) and controlled DCD (HR 1.567, 95% CI 1.372-1.812, p < 0.001), panel reactive antibody percentage (HR 1.009, 95% CI 1.007-1.011, p < 0.001), and previous transplants (HR 1.494, 95% CI 1.367-1.634, p < 0.001). Patient survival was associated with recipient age (> 60 years, HR 5.507, 95% CI 4.524-6.704, p < 0.001 vs. < 40 years), donor age (HR 1.019, 95% CI 1.016-1.023, p < 0.001), dialysis vintage (HR 1.0000263, 95% CI 1.000225-1.000301, p < 0.01), and male sex (HR 1.229, 95% CI 1.135-1.332, p < 0.001). The C-statistics for graft and patient survival were 0.666 (95% CI: 0.646, 0.686) and 0.726 (95% CI: 0.710-0.742), respectively. (4) Conclusions: We developed a mobile app to estimate survival at time zero, which can guide decisions for organ allocation.
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Affiliation(s)
| | - Jorge Calvillo Arbizu
- Biomedical Engineering Group, University of Sevilla, 41092 Sevilla, Spain;
- Department of Telematics Engineering, University of Sevilla, 41092 Sevilla, Spain;
| | | | | | | | | | | | | | - Gabriel Bernal Blanco
- Nephrology Service, Hospital Virgen del Rocío, 41013 Sevilla, Spain; (G.B.B.); (M.Á.G.G.); (J.L.R.C.)
| | | | - Rafael Bedoya Pérez
- Pediatric Nephrology Service, Hospital Virgen del Rocío, 41013 Sevilla, Spain;
| | - José Luis Rocha Castilla
- Nephrology Service, Hospital Virgen del Rocío, 41013 Sevilla, Spain; (G.B.B.); (M.Á.G.G.); (J.L.R.C.)
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3
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Nemati M, Zhang H, Sloma M, Bekbolsynov D, Wang H, Stepkowski S, Xu KS. Predicting kidney transplant survival using multiple feature representations for HLAs. Artif Intell Med 2023; 145:102675. [PMID: 37925205 DOI: 10.1016/j.artmed.2023.102675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 06/14/2023] [Accepted: 10/02/2023] [Indexed: 11/06/2023]
Abstract
Kidney transplantation can significantly enhance living standards for people suffering from end-stage renal disease. A significant factor that affects graft survival time (the time until the transplant fails and the patient requires another transplant) for kidney transplantation is the compatibility of the Human Leukocyte Antigens (HLAs) between the donor and recipient. In this paper, we propose 4 new biologically-relevant feature representations for incorporating HLA information into machine learning-based survival analysis algorithms. We evaluate our proposed HLA feature representations on a database of over 100,000 transplants and find that they improve prediction accuracy by about 1%, modest at the patient level but potentially significant at a societal level. Accurate prediction of survival times can improve transplant survival outcomes, enabling better allocation of donors to recipients and reducing the number of re-transplants due to graft failure with poorly matched donors.
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Affiliation(s)
- Mohammadreza Nemati
- Department of Electrical Engineering and Computer Science, University of Toledo, 2801 W Bancroft St, Toledo, 43606, OH, United States; Department of Computer and Data Sciences, Case Western Reserve University, 10900 Euclid Ave, Cleveland, 44106, OH, United States
| | - Haonan Zhang
- Department of Electrical Engineering and Computer Science, University of Toledo, 2801 W Bancroft St, Toledo, 43606, OH, United States
| | - Michael Sloma
- Department of Electrical Engineering and Computer Science, University of Toledo, 2801 W Bancroft St, Toledo, 43606, OH, United States
| | - Dulat Bekbolsynov
- Department of Medical Microbiology and Immunology, University of Toledo, United States
| | - Hong Wang
- Department of Engineering Technology, University of Toledo, United States
| | - Stanislaw Stepkowski
- Department of Medical Microbiology and Immunology, University of Toledo, United States
| | - Kevin S Xu
- Department of Electrical Engineering and Computer Science, University of Toledo, 2801 W Bancroft St, Toledo, 43606, OH, United States; Department of Computer and Data Sciences, Case Western Reserve University, 10900 Euclid Ave, Cleveland, 44106, OH, United States.
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4
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Frutos MÁ, Crespo M, Valentín MDLO, Alonso-Melgar Á, Alonso J, Fernández C, García-Erauzkin G, González E, González-Rinne AM, Guirado L, Gutiérrez-Dalmau A, Huguet J, Moral JLLD, Musquera M, Paredes D, Redondo D, Revuelta I, Hofstadt CJVD, Alcaraz A, Alonso-Hernández Á, Alonso M, Bernabeu P, Bernal G, Breda A, Cabello M, Caro-Oleas JL, Cid J, Diekmann F, Espinosa L, Facundo C, García M, Gil-Vernet S, Lozano M, Mahillo B, Martínez MJ, Miranda B, Oppenheimer F, Palou E, Pérez-Saez MJ, Peri L, Rodríguez O, Santiago C, Tabernero G, Hernández D, Domínguez-Gil B, Pascual J. Recommendations for living donor kidney transplantation. Nefrologia 2022; 42 Suppl 2:5-132. [PMID: 36503720 DOI: 10.1016/j.nefroe.2022.07.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 10/26/2021] [Indexed: 06/17/2023] Open
Abstract
This Guide for Living Donor Kidney Transplantation (LDKT) has been prepared with the sponsorship of the Spanish Society of Nephrology (SEN), the Spanish Transplant Society (SET), and the Spanish National Transplant Organization (ONT). It updates evidence to offer the best chronic renal failure treatment when a potential living donor is available. The core aim of this Guide is to supply clinicians who evaluate living donors and transplant recipients with the best decision-making tools, to optimise their outcomes. Moreover, the role of living donors in the current KT context should recover the level of importance it had until recently. To this end the new forms of incompatible HLA and/or ABO donation, as well as the paired donation which is possible in several hospitals with experience in LDKT, offer additional ways to treat renal patients with an incompatible donor. Good results in terms of patient and graft survival have expanded the range of circumstances under which living renal donors are accepted. Older donors are now accepted, as are others with factors that affect the decision, such as a borderline clinical history or alterations, which when evaluated may lead to an additional number of transplantations. This Guide does not forget that LDKT may lead to risk for the donor. Pre-donation evaluation has to centre on the problems which may arise over the short or long-term, and these have to be described to the potential donor so that they are able take them into account. Experience over recent years has led to progress in risk analysis, to protect donors' health. This aspect always has to be taken into account by LDKT programmes when evaluating potential donors. Finally, this Guide has been designed to aid decision-making, with recommendations and suggestions when uncertainties arise in pre-donation studies. Its overarching aim is to ensure that informed consent is based on high quality studies and information supplied to donors and recipients, offering the strongest possible guarantees.
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Affiliation(s)
| | - Marta Crespo
- Nephrology Department, Hospital del Mar, Barcelona, Spain
| | | | | | - Juana Alonso
- Nephrology Department, Hospital Regional Universitario de Málaga, Spain
| | | | | | - Esther González
- Nephrology Department, Hospital Universitario 12 Octubre, Spain
| | | | - Lluis Guirado
- Nephrology Department, Fundacio Puigvert, Barcelona, Spain
| | | | - Jorge Huguet
- RT Surgical Team, Fundació Puigvert, Barcelona, Spain
| | | | - Mireia Musquera
- Urology Department, Hospital Clinic Universitari, Barcelona, Spain
| | - David Paredes
- Donation and Transplantation Coordination Department, Hospital Clinic Universitari, Barcelona, Spain
| | | | - Ignacio Revuelta
- Nephrology and RT Department, Hospital Clinic Universitari, Barcelona, Spain
| | | | - Antonio Alcaraz
- Urology Department, Hospital Clinic Universitari, Barcelona, Spain
| | | | - Manuel Alonso
- Regional Transplantation Coordination, Seville, Spain
| | | | - Gabriel Bernal
- Nephrology Department, Hospital Universitario Virgen del Rocío, Seville, Spain
| | - Alberto Breda
- RT Surgical Team, Fundació Puigvert, Barcelona, Spain
| | - Mercedes Cabello
- Nephrology Department, Hospital Regional Universitario de Málaga, Spain
| | | | - Joan Cid
- Apheresis and Cell Therapy Unit, Haemotherapy and Haemostasis Department, Hospital Clinic Universitari, Barcelona, Spain
| | - Fritz Diekmann
- Nephrology and RT Department, Hospital Clinic Universitari, Barcelona, Spain
| | - Laura Espinosa
- Paediatric Nephrology Department, Hospital La Paz, Madrid, Spain
| | - Carme Facundo
- Nephrology Department, Fundacio Puigvert, Barcelona, Spain
| | | | | | - Miquel Lozano
- Apheresis and Cell Therapy Unit, Haemotherapy and Haemostasis Department, Hospital Clinic Universitari, Barcelona, Spain
| | | | | | | | | | - Eduard Palou
- Immunology Department, Hospital Clinic i Universitari, Barcelona, Spain
| | | | - Lluis Peri
- Urology Department, Hospital Clinic Universitari, Barcelona, Spain
| | | | | | | | - Domingo Hernández
- Nephrology Department, Hospital Regional Universitario de Málaga, Spain
| | | | - Julio Pascual
- Nephrology Department, Hospital del Mar, Barcelona, Spain.
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Wang Z, Shen L, Wang J, Huang J, Tao H, Zhou X. Prognostic analysis of m6A-related genes as potential biomarkers in idiopathic pulmonary fibrosis. Front Genet 2022; 13:1059325. [PMID: 36523766 PMCID: PMC9744785 DOI: 10.3389/fgene.2022.1059325] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 11/07/2022] [Indexed: 10/28/2023] Open
Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive, fatal lung disease with limited treatment options. N6-methyladenosine (m6A) is a reversible RNA modification and has been implicated in various biological processes. However, there are few studies on m6A in IPF. This project mainly explores the prognostic value of m6A-related genes as potential biomarkers in IPF, in order to establish a set of accurate prognostic prediction model. In this study, we used GSE28042 dataset in GEO database to screen out 218 m6A-related candidate genes with high IPF correlation and high differential expression through differentially expressed gene analysis, WGCNA and m6A correlation analysis. The genes associated with the prognosis of IPF were screened out by univariate Cox regression analysis, LASSO analysis, and multivariate Cox regression analysis, and the multivariate Cox model of prognostic risk of related genes was constructed. We found that RBM11, RBM47, RIC3, TRAF5 and ZNF14 were key genes in our model. Finally, the prognostic prediction ability and independent prognostic characteristics of the risk model were evaluated by survival analysis and independent prognostic analysis, and verified by the GSE93606 dataset, which proved that the prognostic risk model we constructed has a strong and stable prediction efficiency.
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Affiliation(s)
- Zhiqiang Wang
- Department of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Department of Biochemistry and Molecular Biology, Medical College, Soochow University, Suzhou, China
| | - Lanyu Shen
- Department of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Junjie Wang
- Department of Biochemistry and Molecular Biology, Medical College, Soochow University, Suzhou, China
| | - Jiaqian Huang
- Department of Biochemistry and Molecular Biology, Medical College, Soochow University, Suzhou, China
| | - Huimin Tao
- Department of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiumin Zhou
- Department of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
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Shi G, Wang J, Wang W, Chen M, Liu X, Zheng Y, Fu Y, Wang M, Zhang X. Prognostic analysis of m6A-related lncRNAs as potential biomarkers in intrahepatic cholangiocarcinom. Front Genet 2022; 13:982707. [PMID: 36160000 PMCID: PMC9493306 DOI: 10.3389/fgene.2022.982707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
Intrahepatic cholangiocarcinoma (iCCA) patients had no obvious symptoms at early stage and poor postoperative survival. Therefore, the establishment of an iCCA prognostic prediction model to carry out refined management of iCCA patients is expected to improve the survival of the iCCA patient population. In this paper, we analyzed the expression profiling data of patients from 32 iCCA tissues and eight paracancerous tissues in The Cancer Genome Atlas (TCGA) database. Perl software was used to separate M6A-related genes and lncRNAs from expression matrix files obtained from the TCGA database. The differentially expressed lncRNAs in the iCCA samples and the normal samples were screened out by differential analysis using the R package limma, and the m6A-related lncRNAs were further screened by Pearson correlation analysis. WGCNA clustering analysis constructs a random network to extract the module genes most related to iCCA, and take the intersection of differentially expressed lncRNAs related to m6A. Univariate Cox screening was performed for the intersection lncRNAs that had significant influence on the prognosis of iCCA patients, and further screening was performed by LASSO method and multivariate Cox regression analysis. Risk model was constructed and prognostic ability was evaluated according to risk score. In conclusion, we identified four m6A-related lncRNAs with potential prognostic value in iCCA, and established a novel m6A-related lncRNA-based prognostic model, which can be used as an independent prognostic factor to predict the prognosis of clinical patients.
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Affiliation(s)
- Guodong Shi
- Department of Biochemistry and Molecular Biology, Medical College, Soochow University, Suzhou, China
| | - Junjie Wang
- Department of Biochemistry and Molecular Biology, Medical College, Soochow University, Suzhou, China
| | - Weiqi Wang
- Department of Biochemistry and Molecular Biology, Medical College, Soochow University, Suzhou, China
| | - Min Chen
- Department of Biochemistry and Molecular Biology, Medical College, Soochow University, Suzhou, China
| | - Xiaoxuan Liu
- Department of Biochemistry and Molecular Biology, Medical College, Soochow University, Suzhou, China
| | - Yufan Zheng
- Department of Biochemistry and Molecular Biology, Medical College, Soochow University, Suzhou, China
| | - Yi Fu
- Department of Human Anatomy, Histology and Embryology, Medical College, Soochow University, Suzhou, China
| | - Minghua Wang
- Department of Biochemistry and Molecular Biology, Medical College, Soochow University, Suzhou, China
- *Correspondence: Minghua Wang, ; Xiaojie Zhang,
| | - Xiaojie Zhang
- Department of Experimental Center, Medical College, Soochow University, Suzhou, China
- *Correspondence: Minghua Wang, ; Xiaojie Zhang,
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7
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Recomendaciones para el trasplante renal de donante vivo. Nefrologia 2022. [DOI: 10.1016/j.nefro.2021.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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8
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Graham CN, Watson C, Barlev A, Stevenson M, Dharnidharka VR. Mean lifetime survival estimates following solid organ transplantation in the US and UK. J Med Econ 2022; 25:230-237. [PMID: 35068310 DOI: 10.1080/13696998.2022.2033050] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
AIMS Accurately estimating mean survival after solid organ transplant (SOT) is crucial for efficient healthcare resource allocation decisions. However, registry-based post-transplant recipient survival estimates vary greatly and are incomplete. Often, the methods used in lifetime survival extrapolation may not fit complex transplant data and therefore alternative methods are required. We aimed to explore the flexible cubic spline methodology as a meaningful alternative for estimating lifetime survival following SOT. METHODS Survival analyses were conducted in kidney, liver, heart, and lung transplant recipients. Mean survival was estimated using flexible cubic splines on the hazard scale fitted with three knots, based on where hazards changed direction, clinical advice, and best-fit curve using Akaike and Bayesian information criterion. The tail was extrapolated when data were no longer available. Extrapolation tails were compared with general population mortality, using age-matched life table hazards, and the highest hazards were taken at all times. RESULTS We found that mean survival post-transplant was longest for kidney transplants (US: 22.79 years; UK: 26.58 years), followed by liver (US: 20.90 years; UK: 20.38 years), heart (US: 14.82 years; UK: 15.85 years), and lung (US: 9.28 years; UK: 9.21 years). A sensitivity analysis using two knots found differences in survival ranging from -1.30 to +4.83 years across SOTs examined. LIMITATIONS This study does not represent individual patient survival, survival by age groups, multiple-organ transplants, or assess factors that may impact overall or organ survival. CONCLUSIONS Our study estimates reflect real-world survival following SOTs and demonstrate the importance of including long-term hazards in survival estimations. These lifetime survival estimates can be used by decision-makers in situations where means are preferred over medians (e.g. population projections, budgetary estimates, and cost-effectiveness models) and can thus offer a meaningful alternative to the estimates used and accepted in current practice.
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Affiliation(s)
| | - Crystal Watson
- Health Economics, Atara Biotherapeutics, South San Francisco, CA, USA
| | - Arie Barlev
- Health Economics, Atara Biotherapeutics, South San Francisco, CA, USA
| | - Matt Stevenson
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Vikas R Dharnidharka
- St. Louis Children's Hospital, Washington University School of Medicine, St. Louis, MO, USA
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9
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Atthakomol P, Manosroi W, Phinyo P, Pipanmekaporn T, Vaseenon T, Rojanasthien S. Predicting Survival in Thai Patients After Low Impact Hip Fracture Using Flexible Parametric Modelling: A Retrospective Cohort Study. J Clin Densitom 2021; 24:603-612. [PMID: 33541776 DOI: 10.1016/j.jocd.2021.01.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 01/15/2021] [Accepted: 01/20/2021] [Indexed: 12/23/2022]
Abstract
Predictive post-hip fracture mortality models have been presented for specific time points (in-hospital, 30-days or 1-year) and most provide marginal predictions based on the patient's risk group. However, the predictive model for individual survival probability following hip fracture is not available. This study aimed to develop a flexible parametric model for predicting individual survival probability for hip fracture patients. In this retrospective study, the medical charts of 765 Thai patients admitted to hospital with a hip fracture resulting from low-impact injury from January 2014 to December 2018 were reviewed. Predictors for all-cause mortality were identified using flexible parametric survival analysis and were used to develop the predictive model. The model was calibrated using a calibration graph and discrimination performance was evaluated using the C-statistic. Internal validity was assessed using bootstrapping. The overall mortality rate of the hip fracture patients was 14%. Predictors significantly associated with survival after hip fracture were age, active malignancy, dementia or Alzheimer's disease, chronic obstructive pulmonary disorder, diabetes mellitus, hemoglobin concentration, eGFR<30 mL/min/1.73m2 and operative treatments. The model-predicted survival was similar to that actually observed in the very low survival group in the first year after hip fracture. In bootstrapping, the apparent C-statistic and the test C-statistic of the reduced model were 0.79 (95% CI 0.77-0.81) and 0.79 (95% CI 0.78-0.80), respectively. The flexible survival model provides good predictive power for individual survival probability at any given time point within the first year after hip fracture and would be an easy to use tool in clinical practice.
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Affiliation(s)
- Pichitchai Atthakomol
- Department of Orthopaedics, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand; Musculoskeletal Science and Translational Research Center, Chiang Mai University, Chiang Mai, Thailand
| | - Worapaka Manosroi
- Division of Endocrinology, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.
| | - Phichayut Phinyo
- Department of Family Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand; Center for Clinical Epidemiology and Clinical Statistics, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Tanyong Pipanmekaporn
- Department of Anesthesiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand; Center for Clinical Epidemiology and Clinical Statistics, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Tanawat Vaseenon
- Department of Orthopaedics, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Sattaya Rojanasthien
- Department of Orthopaedics, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
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10
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Taherkhani N, Sepehri MM, Khasha R, Shafaghi S. Determining the Level of Importance of Variables in Predicting Kidney Transplant Survival Based on a Novel Ranking Method. Transplantation 2021; 105:2307-2315. [PMID: 33534528 DOI: 10.1097/tp.0000000000003623] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Kidney transplantation is the best alternative treatment for end-stage renal disease. To optimal use of donated kidneys, graft predicted survival can be used as a factor to allocate kidneys. The performance of prediction techniques is highly dependent on the correct selection of predictors. Hence, the main objective of this research is to propose a novel method for ranking the effective variables for predicting the kidney transplant survival. METHODS Five classification models were used to classify kidney recipients in long- and short-term survival classes. Synthetic minority oversampling and random undersampling were used to overcome the imbalanced class problem. In dealing with missing values, 2 approaches were used (eliminating and imputing them). All variables were categorized into 4 levels. The ranking was evaluated using the sensitivity analysis approach. RESULTS Thirty-four of the 41 variables were identified as important variables, of which, 5 variables were categorized in very important level ("Recipient creatinine at discharge," "Recipient dialysis time," "Donor history of diabetes," "Donor kidney biopsy," and "Donor cause of death"), 17 variables in important level, and 12 variables in the low important level. CONCLUSIONS In this study, we identify new variables that have not been addressed in any of the previous studies (eg, AGE_DIF and MATCH_GEN). On the other hand, in kidney allocation systems, 2 main criteria are considered: equity and utility. One of the utility subcriteria is the graft survival. Our study findings can be used in the design of systems to predict the graft survival.
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Affiliation(s)
- Nasrin Taherkhani
- Faculty Member of Computer Engineering, Payam-e-Noor University, Saveh, Iran
| | - Mohammad Mehdi Sepehri
- Department of Healthcare Systems Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
| | - Roghaye Khasha
- Center of Excellence in Healthcare Systems Engineering, Tarbiat Modares University, Tehran, Iran
| | - Shadi Shafaghi
- Lung Transplantation Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
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11
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Hou Y, Lu Q, Lu Z, Xu F, Cao Z, Li S, Yang H, Zhao Y, Wang Y. Frailty Phenotype and Mortality: A Prospective Cohort Study. J Am Med Dir Assoc 2021; 23:182-185.e13. [PMID: 34582780 DOI: 10.1016/j.jamda.2021.08.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 08/16/2021] [Accepted: 08/20/2021] [Indexed: 11/25/2022]
Affiliation(s)
- Yabing Hou
- School of Public Health, Tianjin Medical University, Tianjin, China
| | - Qi Lu
- School of nurse, Tianjin Medical University, Tianjin, China
| | - Zuolin Lu
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Fusheng Xu
- School of Public Health, Tianjin Medical University, Tianjin, China
| | - Zhi Cao
- Department of Epidemiology and Health Statistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Shu Li
- School of Public Health, Tianjin Medical University, Tianjin, China
| | - Hongxi Yang
- School of Public Health, Tianjin Medical University, Tianjin, China
| | - Yue Zhao
- School of nurse, Tianjin Medical University, Tianjin, China
| | - Yaogang Wang
- School of Public Health, Tianjin Medical University, Tianjin, China.
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12
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Nemati M, Zhang H, Sloma M, Bekbolsynov D, Wang H, Stepkowski S, Xu KS. Predicting Kidney Transplant Survival using Multiple Feature Representations for HLAs. ARTIFICIAL INTELLIGENCE IN MEDICINE. CONFERENCE ON ARTIFICIAL INTELLIGENCE IN MEDICINE (2005- ) 2021; 12721:51-60. [PMID: 34179894 PMCID: PMC8224462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Kidney transplantation can significantly enhance living standards for people suffering from end-stage renal disease. A significant factor that affects graft survival time (the time until the transplant fails and the patient requires another transplant) for kidney transplantation is the compatibility of the Human Leukocyte Antigens (HLAs) between the donor and recipient. In this paper, we propose new biologically-relevant feature representations for incorporating HLA information into machine learning-based survival analysis algorithms. We evaluate our proposed HLA feature representations on a database of over 100,000 transplants and find that they improve prediction accuracy by about 1%, modest at the patient level but potentially significant at a societal level. Accurate prediction of survival times can improve transplant survival outcomes, enabling better allocation of donors to recipients and reducing the number of re-transplants due to graft failure with poorly matched donors.
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Affiliation(s)
- Mohammadreza Nemati
- Department of Electrical Engineering and Computer Science, University of Toledo, 2801 W Bancroft St, Toledo, OH, USA 43606
| | - Haonan Zhang
- Department of Electrical Engineering and Computer Science, University of Toledo, 2801 W Bancroft St, Toledo, OH, USA 43606
| | - Michael Sloma
- Department of Electrical Engineering and Computer Science, University of Toledo, 2801 W Bancroft St, Toledo, OH, USA 43606
| | - Dulat Bekbolsynov
- Department of Medical Microbiology and Immunology, University of Toledo
| | - Hong Wang
- Department of Engineering Technology, University of Toledo
| | | | - Kevin S. Xu
- Department of Electrical Engineering and Computer Science, University of Toledo, 2801 W Bancroft St, Toledo, OH, USA 43606
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13
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Nemati M, Zhang H, Sloma M, Bekbolsynov D, Wang H, Stepkowski S, Xu KS. Predicting Kidney Transplant Survival Using Multiple Feature Representations for HLAs. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-77211-6_6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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14
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Bamoulid J, Frimat M, Courivaud C, Crepin T, Gaiffe E, Hazzan M, Ducloux D. A simple score to predict early death after kidney transplantation. Eur J Clin Invest 2020; 50:e13312. [PMID: 32533894 DOI: 10.1111/eci.13312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 06/02/2020] [Accepted: 06/02/2020] [Indexed: 11/29/2022]
Abstract
BACKGROUND Few studies have focused on risk stratification for premature death after transplantation. However, stratification of individual risk is an essential step in personalized care. MATERIAL AND METHODS We have developed a risk score of early post-transplant death (ORLY score) in a prospective multicentre cohort including 942 patients and validated our model in a retrospective independent replication cohort including 874 patients. RESULTS 60 patients (6.4%) from the prospective cohort died during the first three-year post-transplant. Age, male gender, diabetes, dialysis duration and chronic respiratory failure were associated with early post-transplant death. The multivariable model exhibited good discrimination ability (C-index = 0.78, 95%CI [0.75-0.81]). ORLY score highly predicted early death after transplantation (1.34; 95%CI, 1.22 to 1.48 for each increase of 1 point in score; P < .001). The predictive value of the score in the validation cohort was close to that observed in the experimental cohort (1.41; 95%CI, 1.27 to 1.56 for each increase of 1 point in score; P < .001). Merging the two cohorts, four categories of risk could be individualized: low, 0-5 (n = 522, mean risk, 1%); intermediate, 6-7 (n = 739, mean risk 4.7%); moderate, 8-10 (n = 429, mean risk 10%); and high risk 11-15 (n = 132, mean risk 19%). CONCLUSIONS The ORLY score discriminates patients with high risk of early death.
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Affiliation(s)
- Jamal Bamoulid
- INSERM, UMR1098, Federation hospitalo-universitaire INCREASE, Besançon, France.,Department of Nephrology, Dialysis, and Renal Transplantation, CHU Besançon, Besançon, France
| | - Marie Frimat
- Service de Néphrologie - CHRU de Lille - Université de Lille - UMR 995, Lille, France
| | - Cécile Courivaud
- INSERM, UMR1098, Federation hospitalo-universitaire INCREASE, Besançon, France.,Department of Nephrology, Dialysis, and Renal Transplantation, CHU Besançon, Besançon, France
| | - Thomas Crepin
- INSERM, UMR1098, Federation hospitalo-universitaire INCREASE, Besançon, France.,Department of Nephrology, Dialysis, and Renal Transplantation, CHU Besançon, Besançon, France
| | - Emilie Gaiffe
- INSERM, UMR1098, Federation hospitalo-universitaire INCREASE, Besançon, France
| | - Marc Hazzan
- Service de Néphrologie - CHRU de Lille - Université de Lille - UMR 995, Lille, France
| | - Didier Ducloux
- INSERM, UMR1098, Federation hospitalo-universitaire INCREASE, Besançon, France.,Department of Nephrology, Dialysis, and Renal Transplantation, CHU Besançon, Besançon, France
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15
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Equity-Efficiency Trade-offs Associated With Alternative Approaches to Deceased Donor Kidney Allocation: A Patient-level Simulation. Transplantation 2020; 104:795-803. [PMID: 31403554 PMCID: PMC7147404 DOI: 10.1097/tp.0000000000002910] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Supplemental Digital Content is available in the text. Background. The number of patients waiting to receive a kidney transplant outstrips the supply of donor organs. We sought to quantify trade-offs associated with different approaches to deceased donor kidney allocation in terms of quality-adjusted life years (QALYs), costs, and access to transplantation. Methods. An individual patient simulation model was developed to compare 5 different approaches to kidney allocation, including the 2006 UK National Kidney Allocation Scheme (NKAS) and a QALY maximization approach designed to maximize health gains from a limited supply of donor organs. We used various sources of patient-level data to develop multivariable regression models to predict survival, health state utilities, and costs. We simulated the allocation of kidneys from 2200 deceased donors to a waiting list of 5500 patients and produced estimates of total lifetime costs and QALYs for each allocation scheme. Results. Among patients who received a transplant, the QALY maximization approach generated 48 045 QALYs and cost £681 million, while the 2006 NKAS generated 44 040 QALYs and cost £625 million. When also taking into consideration outcomes for patients who were not prioritized to receive a transplant, the 2006 NKAS produced higher total QALYs and costs and an incremental cost-effectiveness ratio of £110 741/QALY compared with the QALY maximization approach. Conclusions. Compared with the 2006 NKAS, a QALY maximization approach makes more efficient use of deceased donor kidneys but reduces access to transplantation for older patients and results in greater inequity in the distribution of health gains between patients who receive a transplant and patients who remain on the waiting list.
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16
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The First Asian Kidney Transplantation Prediction Models for Long-term Patient and Allograft Survival. Transplantation 2020; 104:1048-1057. [DOI: 10.1097/tp.0000000000002918] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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17
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Projected 20- and 30-Year Outcomes for Pediatric Liver Transplant Recipients in the United States. J Pediatr Gastroenterol Nutr 2020; 70:356-363. [PMID: 31880667 PMCID: PMC8573715 DOI: 10.1097/mpg.0000000000002592] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
BACKGROUND Observed long-term outcomes no longer reflect the survival trajectory facing pediatric liver transplant (LT) recipients today. We aimed to use national registry data and parametric models to project 20- and 30-year post-transplant outcomes for recently transplanted pediatric LT recipients. METHODS We conducted a retrospective cohort study of 13,442 first-time pediatric (age <18) LT recipients using 1987 to 2018 Scientific Registry of Transplant Recipients data. We validated the proposed method (ie, to project long-term patient and graft survival using parametric survival models and short-term data) in 2 historic cohorts (1987-1996 and 1997-2006) and estimated long-term projections among patients transplanted between 2007 and 2018. Projections were stratified by raft type, recipient age, and indication for transplant. RESULTS Parsimonious parametric models with Weibull distribution can be applied to post-transplant data and used to project long-term outcomes for pediatric LT recipients beyond observed data. Projected 20-year patient survival for pediatric LT recipients transplanted in 2007 to 2018 was 84.0% (95% confidence interval 81.5-85.8), compared to observed 20-year survival of 72.8% and 63.6% among those transplanted in 1997 to 2006 and 1987 to 1996, respectively. Projected 30-year survival for pediatric LT recipients in 2007 to 2018 was 80.1% (75.2-82.7), compared to projected 30-year survival of 68.6% (66.1-70.9) in the 1997 to 2006 cohort and observed 30-year survival of 57.5% in the 1987 to 1996 cohort. Twenty- and 30-year patient and graft survival varied slightly by recipient age, graft type, and indication for transplant. CONCLUSIONS Projected long-term outcomes for recently transplanted pediatric LT recipients are excellent, reflective of substantial improvements in medical care, and informative for physician-patient education and decision making in the current era.
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18
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Chen J, Mikhail DM, Sharma H, Jevnikar J, Cooper M, Luke PP, Sener A. Donor age is the most important predictor of long term graft function in donation after cardiac death simultaneous pancreas-kidney transplantation: A retrospective study. Am J Surg 2019. [DOI: 10.1016/j.amjsurg.2019.02.024 and 21=21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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19
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Chen J, Mikhail DM, Sharma H, Jevnikar J, Cooper M, Luke PP, Sener A. Donor age is the most important predictor of long term graft function in donation after cardiac death simultaneous pancreas-kidney transplantation: A retrospective study. Am J Surg 2019; 218:978-987. [DOI: 10.1016/j.amjsurg.2019.02.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Revised: 02/05/2019] [Accepted: 02/14/2019] [Indexed: 10/27/2022]
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20
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Donor age is the most important predictor of long term graft function in donation after cardiac death simultaneous pancreas-kidney transplantation: A retrospective study. Am J Surg 2019. [DOI: 10.1016/j.amjsurg.2019.02.024 and 67=89] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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21
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Dienemann T, Schellenberg J, Heller K, Daniel C, Amann K, Hilgers KF, Schiffer M, Weidemann A. Association of donor hypertension and recipient renal function in living donor kidney transplantation: A single‐center retrospective study. Clin Transplant 2019; 33:e13697. [DOI: 10.1111/ctr.13697] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 07/09/2019] [Accepted: 08/14/2019] [Indexed: 11/30/2022]
Affiliation(s)
- Thomas Dienemann
- Medizinische Klinik 4 Nephrologie und Hypertensiologie Universitätsklinikum Erlangen Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Germany
| | - Jana Schellenberg
- Medizinische Klinik 4 Nephrologie und Hypertensiologie Universitätsklinikum Erlangen Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Germany
| | - Katharina Heller
- Medizinische Klinik 4 Nephrologie und Hypertensiologie Universitätsklinikum Erlangen Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Germany
| | - Christoph Daniel
- Abteilung für Nephropathologie Pathologisches Institut Universitätsklinikum Erlangen Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Germany
| | - Kerstin Amann
- Abteilung für Nephropathologie Pathologisches Institut Universitätsklinikum Erlangen Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Germany
| | - Karl Friedrich Hilgers
- Medizinische Klinik 4 Nephrologie und Hypertensiologie Universitätsklinikum Erlangen Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Germany
| | - Mario Schiffer
- Medizinische Klinik 4 Nephrologie und Hypertensiologie Universitätsklinikum Erlangen Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Germany
| | - Alexander Weidemann
- Medizinische Klinik 4 Nephrologie und Hypertensiologie Universitätsklinikum Erlangen Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Germany
- Medizinische Klinik 1 Nephrologie Transplantation und Internistische Intensivmedizin Krankenhaus Köln Merheim Klinikum der Universität Witten‐Herdecke Cologne Germany
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22
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Taherkhani N, Sepehri MM, Shafaghi S, Khatibi T. Identification and weighting of kidney allocation criteria: a novel multi-expert fuzzy method. BMC Med Inform Decis Mak 2019; 19:182. [PMID: 31492132 PMCID: PMC6729045 DOI: 10.1186/s12911-019-0892-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Accepted: 08/09/2019] [Indexed: 02/05/2023] Open
Abstract
Background Kidney allocation is a multi-criteria and complex decision-making problem, which should also consider ethical issues in addition to the medical aspects. Leading countries in this field use a point scoring system to allocate kidneys. Hence, the purpose of this study is to identify and weight the kidney allocation criteria considering the balance between utility and equity. Methods To do this, a new fuzzy hybrid approach is proposed, which consists of two steps: In the first step, Fuzzy Delphi Method (FDM) is used to identify the effective criteria in the kidney allocation algorithm. In the second step, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) is employed to determine the weight of the criteria. Results The results showed that the highest weight belongs to “Medical emergency” criterion and the lowest weight to “5 HLA mismatches”, which is similar to Euro-transplant kidney allocation system (ETKAS). The developed method is evaluated in two steps. First, the proposed model is implemented using a real case study from the Iranian Kidney Allocation System. It was shown that the proposed model has the potential to improve allocation outcome. Second, the proposed model’s superiority to the current model is approved by the experts using the results display in the profile matrix. Finally, sensitivity analysis is performed to check the robustness of the proposed model. Conclusions This paper contributes to the kidney allocation literature by doing the following: (a) developing a comprehensive framework for identification and weightings of criteria for kidney allocation, (b) using, for the first time, the IF-AHP technique to consider hesitancy of decision makers and uncertainty in organ allocation, and (c) proposing an appropriate framework for the countries that intend to improve or modify their organ allocation system. Electronic supplementary material The online version of this article (10.1186/s12911-019-0892-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nasrin Taherkhani
- Group of Information Technology, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, 1411713116, Iran
| | - Mohammad Mehdi Sepehri
- Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, 1411713116, Iran.
| | - Shadi Shafaghi
- Lung Transplantation Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLDD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Toktam Khatibi
- Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, 1411713116, Iran
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23
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Mark E, Goldsman D, Gurbaxani B, Keskinocak P, Sokol J. Using machine learning and an ensemble of methods to predict kidney transplant survival. PLoS One 2019; 14:e0209068. [PMID: 30625130 PMCID: PMC6326487 DOI: 10.1371/journal.pone.0209068] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2018] [Accepted: 11/29/2018] [Indexed: 01/08/2023] Open
Abstract
We used an ensemble of statistical methods to build a model that predicts kidney transplant survival and identifies important predictive variables. The proposed model achieved better performance, measured by Harrell’s concordance index, than the Estimated Post Transplant Survival model used in the kidney allocation system in the U.S., and other models published recently in the literature. The model has a five-year concordance index of 0.724 (in comparison, the concordance index is 0.697 for the Estimated Post Transplant Survival model, the state of the art currently in use). It combines predictions from random survival forests with a Cox proportional hazards model. The rankings of importance for the model’s variables differ by transplant recipient age. Better survival predictions could eventually lead to more efficient allocation of kidneys and improve patient outcomes.
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Affiliation(s)
- Ethan Mark
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- * E-mail:
| | - David Goldsman
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Brian Gurbaxani
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Pinar Keskinocak
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Joel Sokol
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
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24
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Zhu Z, Huang S, Zhao Q, Tang Y, Zhang Z, Wang L, Ju W, Guo Z, He X. Receiving Hypertensive Donor Grafts Is Associated with Inferior Prognosis in Simultaneous Liver-Kidney Transplantation Recipients. Med Sci Monit 2018; 24:2391-2403. [PMID: 29676390 PMCID: PMC5928915 DOI: 10.12659/msm.909706] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Background The impact of hypertensive (HTN) donor grafts on the prognosis of simultaneous liver-kidney transplantation (SLKT) patient is not known, and an applicable risk scoring system for SLKT patient survival is lacking. This study aimed to evaluate the impact of donor HTN on patient survival of SLKT recipients and to identify independent risk factors. Material/Methods Data from 3844 adult SLKT recipients receiving deceased donor grafts from March 2002 to December 2014 in the Scientific Registry of Transplant Recipients (SRTR) database were retrospectively analyzed. Kaplan-Meier analysis was used to compare patient and graft survival. Multivariate Cox proportional hazard models were built to identify independent risk factors associated with patient and graft survival. Results SLKT patients receiving HTN donor grafts had significantly shorter 5-year patient survival and kidney graft survival rates than did those receiving non-HTN donor grafts (50.1% vs. 63.2%, p<0.0001 and 45.4% vs. 67.8%, p<0.0001, respectively). Multivariate analysis identified HTN donor, donor age, donation after cardiac death, cold ischemia time, recipient age, recipient condition at transplant, recipient hepatitis C infection, need for life support, and recipient pre-transplant albumin level as independent risk factors associated with inferior patient survival in SLKT recipients. A risk scoring model that predicted excellent stratification of prognostic subgroups was established (AUC, 0.762; 95% CI, 0.739–0.785). Conclusions An SLKT patient receiving a graft from an HTN donor has an inferior prognosis. A risk scoring system applicable to patient survival in SLKT recipients was developed.
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Affiliation(s)
- Zebin Zhu
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China (mainland).,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, Guangdong, China (mainland).,Guangdong Provincial International Cooperation Base of Science and Technology, Guangzhou, Guangdong, China (mainland)
| | - Shanzhou Huang
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China (mainland).,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, Guangdong, China (mainland).,Guangdong Provincial International Cooperation Base of Science and Technology, Guangzhou, Guangdong, China (mainland)
| | - Qiang Zhao
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China (mainland).,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, Guangdong, China (mainland).,Guangdong Provincial International Cooperation Base of Science and Technology, Guangzhou, Guangdong, China (mainland)
| | - Yunhua Tang
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China (mainland).,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, Guangdong, China (mainland).,Guangdong Provincial International Cooperation Base of Science and Technology, Guangzhou, Guangdong, China (mainland)
| | - Zhiheng Zhang
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China (mainland).,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, Guangdong, China (mainland).,Guangdong Provincial International Cooperation Base of Science and Technology, Guangzhou, Guangdong, China (mainland)
| | - Linhe Wang
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China (mainland).,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, Guangdong, China (mainland).,Guangdong Provincial International Cooperation Base of Science and Technology, Guangzhou, Guangdong, China (mainland)
| | - Weiqiang Ju
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China (mainland).,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, Guangdong, China (mainland).,Guangdong Provincial International Cooperation Base of Science and Technology, Guangzhou, Guangdong, China (mainland)
| | - Zhiyong Guo
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China (mainland).,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, Guangdong, China (mainland).,Guangdong Provincial International Cooperation Base of Science and Technology, Guangzhou, Guangdong, China (mainland)
| | - Xiaoshun He
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China (mainland).,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, Guangdong, China (mainland).,Guangdong Provincial International Cooperation Base of Science and Technology, Guangzhou, Guangdong, China (mainland)
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Ng R, Kornas K, Sutradhar R, Wodchis WP, Rosella LC. The current application of the Royston-Parmar model for prognostic modeling in health research: a scoping review. Diagn Progn Res 2018; 2:4. [PMID: 31093554 PMCID: PMC6460777 DOI: 10.1186/s41512-018-0026-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 01/30/2018] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Prognostic models incorporating survival analysis predict the risk (i.e., probability) of experiencing a future event over a specific time period. In 2002, Royston and Parmar described a type of flexible parametric survival model called the Royston-Parmar model in Statistics in Medicine, a model which fits a restricted cubic spline to flexibly model the baseline log cumulative hazard on the proportional hazards scale. This feature permits absolute measures of effect (e.g., hazard rates) to be estimated at all time points, an important feature when using the model. The Royston-Parmar model can also incorporate time-dependent effects and be used on different scales (e.g., proportional odds, probit). These features make the Royston-Parmar model attractive for prediction, yet their current uptake for prognostic modeling is unknown. Thus, the objectives were to conduct a scoping review of how the Royston-Parmar model has been applied to prognostic models in health research, to raise awareness of the model, to identify gaps in current reporting, and to offer model building considerations and reporting suggestions for other researchers. METHODS Five electronic databases and gray literature indexed in web sources from 2001 to 2016 were searched to identify articles for inclusion in the scoping review. Two reviewers independently screened 1429 articles, and after applying exclusion criteria through a two-step screening process, data from 12 studies were abstracted. RESULTS Since 2001, only 12 studies were identified that used the Royston-Parmar model in some capacity for prognostic modeling, 10 of which used the model as the basis for their prognostic model. The restricted cubic spline varied across studies in the number of interior knots (range 1 to 6), and only three studies reported knot placement. Three studies provided details about the baseline function, with two studies using a figure and the third providing coefficients. However, no studies provided adequate information on their restricted cubic spline to permit others to validate or completely use the model. CONCLUSIONS Despite the advantages of the Royston-Parmar model for prognostic models, they are not widely used in health research. Better reporting of details about the restricted cubic spline is needed, so the prognostic model can be used and validated by others. REGISTRATION The protocol was registered with Open Science Framework (https://osf.io/r3232/).
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Affiliation(s)
- Ryan Ng
- 0000 0001 2157 2938grid.17063.33Dalla Lana School of Public Health, University of Toronto, 155 College St, Toronto, ON M5T 3M7 Canada
| | - Kathy Kornas
- 0000 0001 2157 2938grid.17063.33Dalla Lana School of Public Health, University of Toronto, 155 College St, Toronto, ON M5T 3M7 Canada
| | - Rinku Sutradhar
- 0000 0000 8849 1617grid.418647.8Institute for Clinical Evaluative Sciences, 2075 Bayview Ave, Toronto, ON M4N 3M5 Canada
| | - Walter P. Wodchis
- 0000 0000 8849 1617grid.418647.8Institute for Clinical Evaluative Sciences, 2075 Bayview Ave, Toronto, ON M4N 3M5 Canada
- 0000 0001 2157 2938grid.17063.33Institute of Health Policy, Management and Evaluation, University of Toronto, 155 College Street, Toronto, ON M5T 3M6 Canada
| | - Laura C. Rosella
- 0000 0001 2157 2938grid.17063.33Dalla Lana School of Public Health, University of Toronto, 155 College St, Toronto, ON M5T 3M7 Canada
- 0000 0000 8849 1617grid.418647.8Institute for Clinical Evaluative Sciences, 2075 Bayview Ave, Toronto, ON M4N 3M5 Canada
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