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Identifying critically ill patients who benefit the most from nutrition therapy: the development and initial validation of a novel risk assessment tool

TLDR
This scoring algorithm may be helpful in identifying critically ill patients most likely to benefit from aggressive nutrition therapy in the intensive care unit (ICU), and based on the statistical significance in the multivariable model, the final score used all candidate variables except BMI.
Abstract
To develop a scoring method for quantifying nutrition risk in the intensive care unit (ICU). A prospective, observational study of patients expected to stay > 24 hours. We collected data for key variables considered for inclusion in the score which included: age, baseline APACHE II, baseline SOFA score, number of comorbidities, days from hospital admission to ICU admission, Body Mass Index (BMI) < 20, estimated % oral intake in the week prior, weight loss in the last 3 months and serum interleukin-6 (IL-6), procalcitonin (PCT), and C-reactive protein (CRP) levels. Approximate quintiles of each variable were assigned points based on the strength of their association with 28 day mortality. A total of 597 patients were enrolled in this study. Based on the statistical significance in the multivariable model, the final score used all candidate variables except BMI, CRP, PCT, estimated percentage oral intake and weight loss. As the score increased, so did mortality rate and duration of mechanical ventilation. Logistic regression demonstrated that nutritional adequacy modifies the association between the score and 28 day mortality (p = 0.01). This scoring algorithm may be helpful in identifying critically ill patients most likely to benefit from aggressive nutrition therapy.

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Citations
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The Relationship between Serum Concentrations of Pro- and Anti-Inflammatory Cytokines and Nutritional Status in Patients with Traumatic Head Injury in the Intensive Care Unit

TL;DR: THI patients who had high serum levels of studied cytokines were more prone to develop a reduction of nutritional status in terms of BMI, FBM, LBM MAUAC and APM over the course of time from patient admission until day 13 of ICU admission.
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Impact of Enteral Feeding on Vasoactive Support in Septic Shock: A Retrospective Observational Study.

TL;DR: The median vasopressor dose did not increase by ≥50% during the first 24 hours of EN, which suggests early EN delivered during septic shock is not associated with worsening hemodynamic instability.
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Association Between Enteral Feeding, Weight Status, and Mortality in a Medical Intensive Care Unit

TL;DR: The finding of a positive association between an order for enteral feeding and survival supportsEnteral feeding of patients in medical intensive care units and the beneficial effect of enteral feed appears to apply to patients regardless of body mass index.
References
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Book

Applied Logistic Regression

TL;DR: Hosmer and Lemeshow as discussed by the authors provide an accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets.
Journal ArticleDOI

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TL;DR: Applied Logistic Regression, Third Edition provides an easily accessible introduction to the logistic regression model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.
Journal ArticleDOI

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TL;DR: The form and validation results of APACHE II, a severity of disease classification system that uses a point score based upon initial values of 12 routine physiologic measurements, age, and previous health status, are presented.
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Regression modeling strategies : with applications to linear models, logistic regression, and survival analysis

TL;DR: In this article, the authors present a case study in least squares fitting and interpretation of a linear model, where they use nonparametric transformations of X and Y to fit a linear regression model.
Journal ArticleDOI

A note on a general definition of the coefficient of determination

TL;DR: In this article, a generalization of the coefficient of determination R2 to general regression models is discussed, and a modification of an earlier definition to allow for discrete models is proposed.
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