Assessment of the impact of oxandrolone on outcomes in burn injured patients

•Temporal data mathematically links clinical predictors to outcomes in burn care.•These formulas can objectively compare outcomes across patients and burn units.•Oxandrolone improved prealbumin levels; it may limit catabolism in burn patients.•Using and tracking nutrition in patient care may improve...

Full description

Saved in:
Bibliographic Details
Published in:Burns 2019-06, Vol.45 (4), p.841-848
Main Authors: Dobbe, Logan, Clapp, Cody, Larumbe-Zabala, Eneko, Griswold, John
Format: Article
Language:English
Subjects:
Adult
Anabolic Agents - therapeutic use
Body Weight
Burn care
Burns - metabolism
Burns - pathology
Burns - therapy
Burns, Inhalation
Female
Humans
Length of Stay - statistics & numerical data
Longitudinal Studies
Male
Middle Aged
Nutrition
Oxandrolone
Oxandrolone - therapeutic use
Prealbumin - metabolism
Retrospective Studies
Trauma Severity Indices
Treatment Outcome
Young Adult
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Temporal data mathematically links clinical predictors to outcomes in burn care.•These formulas can objectively compare outcomes across patients and burn units.•Oxandrolone improved prealbumin levels; it may limit catabolism in burn patients.•Using and tracking nutrition in patient care may improve outcomes.•The limits of a retrospective regression analysis necessitate further research. The purpose of this study is to use a retrospective cohort of burn patients to evaluate the contribution of oxandrolone on burn care outcomes. Longitudinal clinical data is used to analyze outcomes from a new perspective. Our random-effects longitudinal regression analysis model used temporal clinical data to evaluate oxandrolone’s impact on outcomes (oxandrolone/non-oxandrolone n=50/11, median length of stay [LOS]=42.2/39.3, mean weight (kg)=192.2/207.6, mean initial prealbumin (mg/dL) 10.1/7.5). The resultant predictive models (p
ISSN:0305-4179
1879-1409
DOI:10.1016/j.burns.2018.11.001