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Volume 3 Supplement 1

ESICM LIVES 2015

  • Poster presentation
  • Open Access

End-of-life decision making for cancer patients in an intensive care unit

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Intensive Care Medicine Experimental20153 (Suppl 1) :A651

https://doi.org/10.1186/2197-425X-3-S1-A651

  • Published:

Keywords

  • Intensive Care Unit
  • Hematological Malignancy
  • Hospital Mortality
  • Multivariate Logistic Regression Analysis
  • Life Support

Introduction

Patients with advanced malignances are at a high risk of developing complications that lead to an Intensive Care Unit (ICU) admission. Despite improvements in ICU-level care, mortality rates for some patients remain especially high. Limitation of therapy is an integral component of high-quality care of cancer patients in the ICU.

Objectives

Describe the practice and analyze associated factors of life-sustaining treatment in the 8-bed ICU of a cancer specialized center.

Methods

Retrospective surveillance of adult patients (aged more than 18 years) admitted to the ICU from January/2010 to December/2014. For patients with more than one admission, only the last one was analyzed. Patients were divided into two groups: withdrawing or withholding life support (WWLS), and full life support, as suggested in the literature. Predictive factors of WWLS were identified using multivariate logistic regression analysis.

Results

Among 1511 patients admitted to ICU, 1309 (86,6%) had solid tumors and 202 (13,4%) had some kind of hematological malignancy. A small group had received stem-cell transplant (4,9%). The median age was 62 (18, minimum and 90, maximum) years and 58% were male. Thirteen percent (196/1511) of patients had limitation of therapy (WWLS). We observed no difference in the annual prevalence during the study period (p=0,631). Primary reasons for the decision concerned malignancy status namely refractoriness to therapy and progressive disease. Hospital mortality was 39% (590/1511) and 33,2% of deaths occurred after WWLS. WWLS was independently associated with age, surgical status, length of mechanical ventilation, length of stay, APACHE score and organ failure (table 1).
Table 1

Multivariate logistic regression analysis.

Variable

Odds Ratio

95% CI

p

Age

1,020

0,899-2,533

0,008

Surgical status

0,257

0,137-0,483

< 0,001

Duration of mechanical ventilation

1,004

1,002-1,006

0,001

Length of stay in the ICU

0,948

0,902-0,996

0,036

APACHE ≥ 35

4,327

2,789-6,713

< 0,001

MODS

3,926

2,569-6,001

< 0,001

Conclusions

End-of-life practice has been a routine in our center during the last 5 years (13% of admissions). As demonstrated previously in general ICU, clinical parameters seem to be major determinants of WWLS decisions in cancer patients. Consensus statements may help physicians in the difficult task of end-of-life decision making.

Declarations

Grant Acknowledgment

APEMCIO.

Authors’ Affiliations

(1)
IPO - Porto, Porto, Portugal

References

  1. Azoulay E, et al: End-of-life practices in 282 intensive care units: data from the SAPS 3 database. Intensive Care Med. 2009, 35: 623-630. 10.1007/s00134-008-1310-6.PubMedView ArticleGoogle Scholar
  2. Sprung CL, et al: End-of-life practices in European intensive care units: the Ethicus Study. JAMA. 2003 Aug 13, 290 (6): 790-7. 10.1001/jama.290.6.790.PubMedView ArticleGoogle Scholar
  3. Truog RD, et al: Recommendations for end-of-life care in the intensive care unit: A consensus statement by the American College of Critical Care Medicine. Crit Care Med. 2008, 36 (3): 953-963. 10.1097/CCM.0B013E3181659096.PubMedView ArticleGoogle Scholar

Copyright

© Tavares et al.; 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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