Critical Care

, 19:P522 | Cite as

Impact of age, physiological status and APACHE score on acceptance of patients to the ICU

  • L Terry
  • S Passey
  • D Porter
  • F Clark
  • R Matsa
Open Access
Poster presentation
  • 154 Downloads

Keywords

Functional Status Multivariate Regression Analysis Status Score Acute Illness Case Note 

Introduction

Evidence suggests that age, chronic health status and acute illness severity affect the decision-making of clinicians regarding admission to the ICU (ITU) [1, 2, 3]. This prospective service review assesses the impact of age, APACHE II score and WHO functional score towards admission acceptance or refusal to ITU in a tertiary-level facility.

Methods

Design: a planned prospective review of all referrals over a 14-day period. Data collection: review (LT, DP, SP) of case notes of patients referred to ITU with the following variables collected: age, sex, APACHE II scores, WHO functional status score, grade of referrer and source of referral. Data were collected on 37 patients: 22 accepted to ITU and 15 refused admission. Statistics: data were analyzed using GraphPad 6.05. Categorical variables were expressed as mean and standard error of mean. For unpaired variables, statistical significance is determined using unpaired t test. P < 0.05 is considered statistically significant.

Results

The WHO functional status was the most significant variable affecting admission (P < 0.001). The APACHE score of patients admitted to ITU was significantly lower than refused patients (P = 0.039). Patient age did not affect admission status (P = 0.15). See Table 1.

Table 1

 

Accepted

Refused

Pvalue

Mean age

56.5 ± 4.4

65.3 ± 3.6

0.15

Sex

73% male

56% male

N/A

WHO

0.45 ± 0.2

2.21 ± 0.3

<0.001

APACHE

13.5 ± 1.6

18.8 ± 2.0

0.039

Conclusion

This study was performed to assess decision-making for admission to the ITU in times of increased demand and limited availability. We want to validate our findings from this short pilot study in a larger prospective study. Multivariate regression analysis will be used to identify significant features that affect clinician decision making in critical care admission.

References

  1. 1.
    Reignier J, et al: Crit Care Med. 2008, 36: 2076-83. 10.1097/CCM.0b013e31817c0ea7.CrossRefPubMedGoogle Scholar
  2. 2.
    Garrouste-Orgeas M, et al: Crit Care Med. 2005, 33: 750-5. 10.1097/01.CCM.0000157752.26180.F1.CrossRefPubMedGoogle Scholar
  3. 3.
    Azoulay E, et al: Crit Care Med. 2001, 29: 2132-6. 10.1097/00003246-200111000-00014.CrossRefPubMedGoogle Scholar

Copyright information

© Terry et al.; licensee BioMed Central Ltd. 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • L Terry
    • 1
  • S Passey
    • 1
  • D Porter
    • 1
  • F Clark
    • 1
  • R Matsa
    • 1
  1. 1.Royal Stoke University HospitalStoke on TrentUK

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