Literature DB >> 31313464

Predicting chance of liver transplantation for pediatric wait-list candidates.

Xun Luo1, Douglas B Mogul2, Allan B Massie1,3, Tanveen Ishaque1, John F P Bridges4, Dorry L Segev1,3.   

Abstract

Information about wait-list time has been reported as one of the single most frequently asked questions by individuals awaiting a transplant but data regarding wait-list time have not been processed in a useful way for pediatric candidates. To predict chance of receiving a DDLT, we identified 6471 pediatric (<18 years), non status-1A, liver-only transplant candidates between 2006 and 2017 from the SRTR. Cox regression with shared frailty for DSA level effect was used to model the association of blood type, weight, allocation PELD and MELD, and DSA with chance of DDLT. Jackknife technique was used for validation. Median (interquartile range) wait-list time was 100 (34-309) days. Non-O Blood type, higher PELD/MELD score at listing, and DSA were associated with increased chance of DDLT, while age 1-5 years and 10-18 years was associated with lower chance of DDLT (P < 0.001 for all variables). Our model accurately predicted chance of transplant (C-statistic = 0.68) and was able to predict DDLT at specific follow-up times (eg, 3 months). This model can serve as the basis for an online tool that would provide useful information for pediatric wait-list candidates.
© 2019 Wiley Periodicals, Inc.

Entities:  

Keywords:  calculator; deceased donor liver transplant; pediatric; prediction

Year:  2019        PMID: 31313464      PMCID: PMC6824918          DOI: 10.1111/petr.13542

Source DB:  PubMed          Journal:  Pediatr Transplant        ISSN: 1397-3142


  22 in total

1.  Prediction versus aetiology: common pitfalls and how to avoid them.

Authors:  Merel van Diepen; Chava L Ramspek; Kitty J Jager; Carmine Zoccali; Friedo W Dekker
Journal:  Nephrol Dial Transplant       Date:  2017-04-01       Impact factor: 5.992

2.  Regional variation and use of exception letters for cadaveric liver allocation in children with chronic liver disease.

Authors:  Paolo R Salvalaggio; Katie Neighbors; Susan Kelly; Karan M Emerick; Kishore Iyer; Riccardo A Superina; Peter F Whitington; Estella M Alonso
Journal:  Am J Transplant       Date:  2005-08       Impact factor: 8.086

3.  Predicting Outcomes on the Liver Transplant Waiting List in the United States: Accounting for Large Regional Variation in Organ Availability and Priority Allocation Points.

Authors:  Allyson Hart; David P Schladt; Jessica Zeglin; Joshua Pyke; W Ray Kim; John R Lake; John P Roberts; Ryutaro Hirose; David C Mulligan; Bertram L Kasiske; Jon J Snyder; Ajay K Israni
Journal:  Transplantation       Date:  2016-10       Impact factor: 4.939

4.  Randomized trial of an uncertainty self-management telephone intervention for patients awaiting liver transplant.

Authors:  Donald E Bailey; Cristina C Hendrix; Karen E Steinhauser; Karen M Stechuchak; Laura S Porter; Julie Hudson; Maren K Olsen; Andrew Muir; Sarah Lowman; Andrea DiMartini; Laurel Williams Salonen; James A Tulsky
Journal:  Patient Educ Couns       Date:  2016-10-18

5.  What patients and members of their support networks ask about transplant program data.

Authors:  Cory R Schaffhausen; Marilyn J Bruin; Daryl Chesley; Maureen McBride; Jon J Snyder; Bertram L Kasiske; Ajay K Israni
Journal:  Clin Transplant       Date:  2017-10-23       Impact factor: 2.863

6.  MELD Exceptions and Rates of Waiting List Outcomes.

Authors:  A B Massie; B Caffo; S E Gentry; E C Hall; D A Axelrod; K L Lentine; M A Schnitzler; A Gheorghian; P R Salvalaggio; D L Segev
Journal:  Am J Transplant       Date:  2011-09-15       Impact factor: 8.086

7.  Addressing geographic disparities in liver transplantation through redistricting.

Authors:  S E Gentry; A B Massie; S W Cheek; K L Lentine; E H Chow; C E Wickliffe; N Dzebashvili; P R Salvalaggio; M A Schnitzler; D A Axelrod; D L Segev
Journal:  Am J Transplant       Date:  2013-07-09       Impact factor: 8.086

Review 8.  Big data in organ transplantation: registries and administrative claims.

Authors:  A B Massie; L M Kucirka; L M Kuricka; D L Segev
Journal:  Am J Transplant       Date:  2014-08       Impact factor: 8.086

9.  Frequency or probability? A qualitative study of risk communication formats used in health care.

Authors:  M M Schapira; A B Nattinger; C A McHorney
Journal:  Med Decis Making       Date:  2001 Nov-Dec       Impact factor: 2.583

10.  Factors that affect deceased donor liver transplantation rates in the United States in addition to the Model for End-stage Liver Disease score.

Authors:  Pratima Sharma; Douglas E Schaubel; Emily E Messersmith; Mary K Guidinger; Robert M Merion
Journal:  Liver Transpl       Date:  2012-12       Impact factor: 5.799

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  1 in total

1.  Living Donor Versus Deceased Donor Pediatric Liver Transplantation: A Systematic Review and Meta-analysis.

Authors:  Arianna Barbetta; Chanté Butler; Sarah Barhouma; Rachel Hogen; Brittany Rocque; Cameron Goldbeck; Hannah Schilperoort; Glenda Meeberg; James Shapiro; Yong K Kwon; Rohit Kohli; Juliet Emamaullee
Journal:  Transplant Direct       Date:  2021-09-20
  1 in total

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