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      Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Kidney Transplant Recipients With DGF and Associated Outcomes


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          Data and transplant community opinion on delayed graft function (DGF), and its impact on outcomes, remains varied. An unsupervised machine learning consensus clustering approach was applied to categorize the clinical phenotypes of kidney transplant (KT) recipients with DGF using OPTN/UNOS data. DGF was observed in 20.9% ( n = 17,073) of KT and most kidneys had a KDPI score <85%. Four distinct clusters were identified. Cluster 1 recipients were young, high PRA re-transplants. Cluster 2 recipients were older diabetics and more likely to receive higher KDPI kidneys. Cluster 3 recipients were young, black, and non-diabetic; they received lower KDPI kidneys. Cluster 4 recipients were middle-aged, had diabetes or hypertension and received well-matched standard KDPI kidneys. By cluster, one-year patient survival was 95.7%, 92.5%, 97.2% and 94.3% ( p < 0.001); one-year graft survival was 89.7%, 87.1%, 91.6%, and 88.7% ( p < 0.001). There were no differences between clusters after accounting for death-censored graft loss ( p = 0.08). Clinically meaningful differences in recipient characteristics were noted between clusters, however, after accounting for death and return to dialysis, there were no differences in death-censored graft loss. Greater emphasis on recipient comorbidities as contributors to DGF and outcomes may help improve utilization of DGF at-risk kidneys.

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          mice: Multivariate Imputation by Chained Equations inR

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            ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking

            Summary: Unsupervised class discovery is a highly useful technique in cancer research, where intrinsic groups sharing biological characteristics may exist but are unknown. The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R and extends it with new functionality and visualizations including item tracking, item-consensus and cluster-consensus plots. These new features provide users with detailed information that enable more specific decisions in unsupervised class discovery. Availability: ConsensusClusterPlus is open source software, written in R, under GPL-2, and available through the Bioconductor project (http://www.bioconductor.org/). Contact: mwilkers@med.unc.edu Supplementary Information: Supplementary data are available at Bioinformatics online.
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              Association between delayed graft function and allograft and patient survival: a systematic review and meta-analysis.

              Delayed graft function (DGF) is a common complication of renal transplantation. The short-term consequences of DGF are well known, but the long-term relationship between DGF and patient and graft survival is controversial in the published literature. We conducted a systematic review and meta-analysis to precisely estimate these relationships. We performed a literature search for original studies published through March 2007 pertaining to long-term (>6 months) outcomes of DGF. The primary outcome was graft survival. Secondary outcomes were patient survival, acute rejection and kidney function. When compared to patients without DGF, patients with DGF had a 41% increased risk of graft loss (RR 1.41, 95% CI 1.27-1.56) at 3.2 years of follow-up. There was no significant relationship between DGF and patient survival at 5 years (RR 1.14, 95% CI 0.94-1.39). The mean creatinine in the non-DGF group was 1.6 mg/dl. Patients with DGF had a higher mean serum creatinine (0.66 mg/dl, 95% CI 0.57-0.74) compared to patients without DGF at 3.5 years of follow-up. DGF was associated with a 38% relative increase in the risk of acute rejection (RR 1.38, 95% CI 1.29-1.47). The results of this meta-analysis emphasize and quantify the long-term detrimental association between DGF and important graft outcomes like graft survival, acute rejection and renal function. Efforts to prevent and treat DGF should be aggressively investigated in order to improve graft survival given the deficit in the number of kidney donors.

                Author and article information

                Transpl Int
                Transpl Int
                Transpl Int
                Transplant International
                Frontiers Media S.A.
                08 December 2022
                : 35
                : 10810
                [1] 1 Division of Transplant Surgery , Mayo Clinic , Phoenix, AZ, United States
                [2] 2 Division of Nephrology and Hypertension , Department of Medicine , Mayo Clinic , Rochester, MN, United States
                [3] 3 Renal Transplant Program , University of Missouri-Kansas City School of Medicine , Saint Luke’s Health System , Kansas City, MO, United States
                [4] 4 Department of Military and Community Medicine , Phramongkutklao College of Medicine , Bangkok, Thailand
                [5] 5 Department of Internal Medicine , Faculty of Medicine , Thammasat University , Pathum Thani, Thailand
                [6] 6 Medstar Georgetown Transplant Institute , Georgetown University , Washington, DC, United States
                Author notes
                *Correspondence: Caroline C. Jadlowiec, jadlowiec.caroline@ 123456mayo.edu
                Copyright © 2022 Jadlowiec, Thongprayoon, Leeaphorn, Kaewput, Pattharanitima, Cooper and Cheungpasitporn.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 30 July 2022
                : 14 November 2022
                Health Archive
                Original Research

                kidney transplant,delayed graft function,clustering,machine learning,artificial intelligence


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