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Dynamic prediction of pre-transplant survival in a multi-center study of pediatric acute liver failure

Donnell, Drew Michael S (2014) Dynamic prediction of pre-transplant survival in a multi-center study of pediatric acute liver failure. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

Acute liver failure (ALF) is a clinical syndrome characterized by the rapid onset of illness and disruption of critical hepatic processes. The natural history and clinical recognition of ALF in children differs considerably from that observed in adults largely due to both heightened etiologic variation and delayed onset of clinical encephalopathy within the pediatric population. Despite efforts to implement multidisciplinary management strategies and understand optimal timing of orthotopic liver transplantation (OLT), current prognostic models are unreliable and fail to identify high-risk patients. We propose a dynamic prediction model of pre-transplant survival for pediatric patients with ALF, specifically to inform the sequential medical decision making process and consequently improve clinical outcomes. Public Health Significance: Dynamic prediction models are of great interest to clinicians and patients alike, enabling well-informed decisions in light of the unpredictable nature of clinical and pathophysiological systems. Extensions of our model may be utilized to facilitate proper allocation of scarce resources, such as donor organs.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Donnell, Drew Michael Sdsd21@pitt.eduDSD21
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorChang, Chung-Chou Hchangj@pitt.eduCHANGJ
Committee MemberBryce, Cindy Lbryce99@pitt.eduBRYCE99
Committee MemberBuchanich, Jeanine Mjeanine@pitt.eduJEANINE
Committee MemberRoberts, Mark Smroberts@pitt.eduMROBERTS
Committee MemberSchaefer, Andrew Jschaefer@ie.pitt.eduSCHAEFER
Date: 27 June 2014
Date Type: Publication
Defense Date: 25 April 2014
Approval Date: 27 June 2014
Submission Date: 5 April 2014
Access Restriction: 4 year -- Restrict access to University of Pittsburgh for a period of 4 years.
Number of Pages: 197
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Biostatistics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Acute liver failure; dynamic prediction; landmark analysis; liver transplantation; medical decision making; personalized medicine; survival analysis.
Date Deposited: 27 Jun 2014 21:28
Last Modified: 01 May 2018 05:15
URI: http://d-scholarship.pitt.edu/id/eprint/20981

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