Literature DB >> 15183207

Value and role of intensive care unit outcome prediction models in end-of-life decision making.

Amber E Barnato1, Derek C Angus.   

Abstract

In the United States, intensive care unit (ICU) admission at the end of life is commonplace. What is the value and role of ICU mortality prediction models for informing the utility of ICU care?In this article, we review the history, statistical underpinnings,and current deployment of these models in clinical care. We conclude that the use of outcome prediction models to ration care that is unlikely to provide an expected benefit is hampered by imperfect performance, the lack of real-time availability, failure to consider functional outcomes beyond survival, and physician resistance to the use of probabilistic information when death is guaranteed by the decision it informs. Among these barriers, the most important technical deficiency is the lack of automated information systems to provide outcome predictions to decision makers, and the most important research and policy agenda is to understand and address our national ambivalence toward rationing care based on any criterion.

Mesh:

Year:  2004        PMID: 15183207     DOI: 10.1016/j.ccc.2004.03.002

Source DB:  PubMed          Journal:  Crit Care Clin        ISSN: 0749-0704            Impact factor:   3.598


  13 in total

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2.  Hope, truth, and preparing for death: perspectives of surrogate decision makers.

Authors:  Latifat Apatira; Elizabeth A Boyd; Grace Malvar; Leah R Evans; John M Luce; Bernard Lo; Douglas B White
Journal:  Ann Intern Med       Date:  2008-12-16       Impact factor: 25.391

3.  Variation in decisions to forgo life-sustaining therapies in US ICUs.

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Journal:  Chest       Date:  2014-09       Impact factor: 9.410

4.  Is survival better at hospitals with higher "end-of-life" treatment intensity?

Authors:  Amber E Barnato; Chung-Chou H Chang; Max H Farrell; Judith R Lave; Mark S Roberts; Derek C Angus
Journal:  Med Care       Date:  2010-02       Impact factor: 2.983

5.  Surrogate decision-makers' perspectives on discussing prognosis in the face of uncertainty.

Authors:  Leah R Evans; Elizabeth A Boyd; Grace Malvar; Latifat Apatira; John M Luce; Bernard Lo; Douglas B White
Journal:  Am J Respir Crit Care Med       Date:  2008-10-17       Impact factor: 21.405

6.  Who should receive life support during a public health emergency? Using ethical principles to improve allocation decisions.

Authors:  Douglas B White; Mitchell H Katz; John M Luce; Bernard Lo
Journal:  Ann Intern Med       Date:  2009-01-20       Impact factor: 25.391

7.  The role of surgeon error in withdrawal of postoperative life support.

Authors:  Margaret L Schwarze; Andrew J Redmann; Karen J Brasel; G Caleb Alexander
Journal:  Ann Surg       Date:  2012-07       Impact factor: 12.969

Review 8.  Computerized decision support in adult and pediatric critical care.

Authors:  Cydni N Williams; Susan L Bratton; Eliotte L Hirshberg
Journal:  World J Crit Care Med       Date:  2013-11-04

9.  Age, risk, and life expectancy in Norwegian intensive care: a registry-based population modelling study.

Authors:  Frode Lindemark; Øystein A Haaland; Reidar Kvåle; Hans Flaatten; Kjell A Johansson
Journal:  PLoS One       Date:  2015-05-26       Impact factor: 3.240

10.  A calibration study of SAPS II with Norwegian intensive care registry data.

Authors:  O A Haaland; F Lindemark; H Flaatten; R Kvåle; K A Johansson
Journal:  Acta Anaesthesiol Scand       Date:  2014-05-12       Impact factor: 2.105

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