Literature DB >> 19656143

Predictability of survival models for waiting list and transplant patients: calculating LYFT.

R A Wolfe1, K P McCullough, A B Leichtman.   

Abstract

'Life years from transplant' (LYFT) is the extra years of life that a candidate can expect to achieve with a kidney transplant as compared to never receiving a kidney transplant at all. The LYFT component survival models (patient lifetimes with and without transplant, and graft lifetime) are comparable to or better predictors of long-term survival than are other predictive equations currently in use for organ allocation. Furthermore, these models are progressively more successful at predicting which of two patients will live longer as their medical characteristics (and thus predicted lifetimes) diverge. The C-statistics and the correlations for the three LYFT component equations have been validated using independent, nonoverlapping split-half random samples. Allocation policies based on these survival models could lead to substantial increases in the number of life years gained from the current donor pool.

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Year:  2009        PMID: 19656143     DOI: 10.1111/j.1600-6143.2009.02708.x

Source DB:  PubMed          Journal:  Am J Transplant        ISSN: 1600-6135            Impact factor:   8.086


  20 in total

1.  Enhancing the expanded criteria donor policy as an intervention to improve kidney allocation: is it actually a 'net-zero' model?

Authors:  J D Schold; Y N Hall
Journal:  Am J Transplant       Date:  2010-12       Impact factor: 8.086

2.  Marked variation of the association of ESRD duration before and after wait listing on kidney transplant outcomes.

Authors:  J D Schold; A R Sehgal; T R Srinivas; E D Poggio; S D Navaneethan; B Kaplan
Journal:  Am J Transplant       Date:  2010-07-20       Impact factor: 8.086

3.  Predictive Score for Posttransplantation Outcomes.

Authors:  Miklos Z Molnar; Danh V Nguyen; Yanjun Chen; Vanessa Ravel; Elani Streja; Mahesh Krishnan; Csaba P Kovesdy; Rajnish Mehrotra; Kamyar Kalantar-Zadeh
Journal:  Transplantation       Date:  2017-06       Impact factor: 4.939

4.  Sarcopenia and mortality after liver transplantation.

Authors:  Michael J Englesbe; Shaun P Patel; Kevin He; Raymond J Lynch; Douglas E Schaubel; Calista Harbaugh; Sven A Holcombe; Stewart C Wang; Dorry L Segev; Christopher J Sonnenday
Journal:  J Am Coll Surg       Date:  2010-06-26       Impact factor: 6.113

5.  The kidney allocation system does not appropriately stratify risk of pediatric donor kidneys: Implications for pediatric recipients.

Authors:  S M Nazarian; A W Peng; B Duggirala; M Gupta; T Bittermann; S Amaral; M H Levine
Journal:  Am J Transplant       Date:  2017-09-15       Impact factor: 8.086

6.  Personalizing the Kidney Transplant Decision: Who Doesn't Benefit from a Kidney Transplant?

Authors:  Deirdre Sawinski; David P Foley
Journal:  Clin J Am Soc Nephrol       Date:  2019-10-01       Impact factor: 8.237

7.  A Kidney Graft Survival Calculator that Accounts for Mismatches in Age, Sex, HLA, and Body Size.

Authors:  Valarie B Ashby; Alan B Leichtman; Michael A Rees; Peter X-K Song; Mathieu Bray; Wen Wang; John D Kalbfleisch
Journal:  Clin J Am Soc Nephrol       Date:  2017-06-08       Impact factor: 8.237

8.  Recipient age and time spent hospitalized in the year before and after kidney transplantation.

Authors:  Morgan E Grams; Mara A McAdams Demarco; Lauren M Kucirka; Dorry L Segev
Journal:  Transplantation       Date:  2012-10-15       Impact factor: 4.939

Review 9.  Increasing the pool of deceased donor organs for kidney transplantation.

Authors:  Jesse D Schold; Dorry L Segev
Journal:  Nat Rev Nephrol       Date:  2012-03-27       Impact factor: 28.314

10.  The prognostic value of kidney transplant center report cards.

Authors:  J D Schold; L D Buccini; E L G Heaphy; D A Goldfarb; A R Sehgal; J Fung; E D Poggio; M W Kattan
Journal:  Am J Transplant       Date:  2013-05-24       Impact factor: 8.086

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