Literature DB >> 27646676

A clinical tool to risk stratify potential kidney transplant recipients and predict severe adverse events.

Rachel M Nygaard1, Anne-Marie Sirany1, Elizabeth A Wyman1, Jennifer Bodner2, Chad J Richardson1, Arthur L Ney1, Mark D Odland1, Mark J Hill3.   

Abstract

Preoperative risk assessment of potential kidney transplant recipients often fails to adequately balance risk related to underlying comorbidities with the beneficial impact of kidney transplantation. We sought to develop a simple scoring system based on factors known at the time of patient assessment for placement on the waitlist to predict likelihood of severe adverse events 1 year post-transplant. The tool includes four components: age, cardiopulmonary factors, functional status, and metabolic factors. Pre-transplant factors strongly associated with severe adverse events include diabetic (OR: 3.76, P<.001), coronary artery disease (OR: 3.45, P<.001), history of CABG/PCI (OR 3.1, P=.001), and peripheral vascular disease (OR 2.74, P=.008).The score was evaluated by calculation of concordance index. The C statistic of 0.74 for the risk stratification group was considered good discrimination in the validation cohort (N=127) compared to the development cohort (N=368). The pre-transplant risk group was highly predictive of severe adverse events (OR 2.36, P<.001). Patients stratified into the above average-risk group were four times more likely to experience severe adverse events compared to average-risk patients, while patients in the high-risk group were nearly 11 times more likely to experience severe adverse events. The pre-transplant risk stratification tool is a simple scoring scheme using easily obtained preoperative characteristics that can meaningfully stratify patients in terms of post-transplant risk and may ultimately guide patient selection and inform the counseling of potential kidney transplant recipients.
© 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Entities:  

Keywords:  adverse event; age; cardiovascular; clinical tool; functional status; graft survival; kidney transplant; metabolic; outcome; recipient selection; renal transplant; risk stratify

Mesh:

Year:  2016        PMID: 27646676     DOI: 10.1111/ctr.12848

Source DB:  PubMed          Journal:  Clin Transplant        ISSN: 0902-0063            Impact factor:   2.863


  1 in total

1.  Prediction of risk of depressive symptoms in menopausal women based on hot flash and sweating symptoms: a multicentre study.

Authors:  Yanwei Zheng; Yibei Zhou; Jiangshan Hu; Jieping Zhu; Qi Hua; Minfang Tao
Journal:  Clin Interv Aging       Date:  2017-11-23       Impact factor: 4.458

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.