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Kathleen Daly a St
Thomas's Hospital, London SE1 7EH, b St George's Hospital, London SW17 0QT
Correspondence to: R W S Chang renechang{at}compuserve.com
Objective:
To develop a predictive model to triage
patients for discharge from intensive care units to reduce mortality
after discharge.
What is already known on this topic
What this study adds
Design:
Logistic regression analyses and modelling of
data from patients who were discharged from intensive care units.
Setting:
Guy's hospital intensive care unit and 19 other UK intensive care units from 1989 to 1998.
Participants:
5475 patients for the development of the
model and 8449 for validation.
Main outcome measures:
Mortality after discharge and
power of triage model.
Results:
Mortality after discharge from intensive care was up to 12.4%. The triage model identified patients at risk from
death on the ward with a sensitivity of 65.5% and specificity of
87.6%, and an area under the receiver operating curve of 0.86. Variables in the model were age, end stage disease, length of stay in
unit, cardiothoracic surgery, and physiology. In the validation dataset
the 34% of the patients identified as at risk had a discharge mortality of 25% compared with a 4% mortality among those not at risk.
Conclusions:
The discharge mortality of at risk
patients may be reduced by 39% if they remain in intensive care units
for another 48 hours. The discharge triage model to identify patients at risk from too early and inappropriate discharge from intensive care
may help doctors to make the difficult clinical decision of whom to
discharge to make room for a patient requiring urgent admission to the
unit. If confirmed, this study has implications on the provision of resources.
In the United Kingdom, the mortality of patients who die on the ward
after discharge from intensive care is unacceptably high (9% to
27%)
A triage model identifies patients at risk from inappropriate discharge
from intensive care
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