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PUBLIC RELEASE DATE:
16-Sep-2008

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Contact: Rebecca Walton
press@plos.org
PLOS

New model predicts long-term survival of critically ill patients

The long term survival of critically ill patients may now be predicted, using a new model which has been developed by Clinical Associate Professor Ho and his co-investigators at Royal Perth Hospital and the University of Western Australia, according to a recent publication "Estimating long term survival of critically ill patients: the PREDICT model".

The study, published in the open-access journal PLoS ONE, used clinical and long term survival data of a heterogenous group of 11,930 patients admitted to the Intensive Care Unit at Royal Perth Hospital in Western Australia. This Intensive Care Unit admits patients of all specialties and captures over 40% of all critically ill patients in Western Australia.

The new model uses seven commonly collected clinical variables within the first 5 days of a critical illness to estimate the long term survival rate of critically unwell patients that come through the Intensive Care Unit. The PREDICT model - Predicted Risk, Existing Diseases, and Intensive Care Therapy, uses criteria such as age, gender, co-morbidity, severity of illness and intensity of intensive care therapy to predict a patient's likely long term survival up to 15 years after the onset of a critical illness.

This prognostic model suggests that age (50%) and co-morbidity (27%) of a seriously ill patient has a much more profound discriminative effect on a patient's long term survival rate, than the severity of the acute illness (20%) itself.

This model extends the existing knowledge about the prognosis of critically ill patients beyond five years and may provide a very useful framework for clinicians, patients, and researchers when the long term prognosis of a critically ill patient is considered.

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Contact:

Dr. K.M. Ho
ICU, Royal Perth Hospital
Email: Kwok.Ho@health.wa.gov.au

Citation: Ho KM, Knuiman M, Finn J, Webb SA (2008) Estimating Long-Term Survival of Critically Ill Patients: The PREDICT Model. PLoS ONE 3(9): e3226. doi:10.1371/journal.pone.0003226

PLEASE ADD THE LINK TO THE PUBLISHED ARTICLE IN ONLINE VERSIONS OF YOUR REPORT (URL live from Sep 17): http://dx.plos.org/10.1371/journal.pone.0003226

PRESS-ONLY PREVIEW: http://www.plos.org/press/pone-03-09-ho.pdf



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