Authors: Woods, A. W.¹; MacKirdy, F. N.²; Livingston, B. M.³; Norrie, J.³; Howie, J. C.²

Source: Anaesthesia, Volume 55, Number 11, November 2000 , pp. 1058-1065(8)

Publisher: Wiley-Blackwell

Abstract:

The most recent edition of the Acute Physiology and Chronic Health Evaluation provides a prediction of intensive care unit length of stay in addition to the probability of hospital mortality. Intensive care length of stay is an important determinant of intensive care costs and may be an important indicator of quality of care. Data were collected from 22 Scottish intensive care units over a 2-year period to allow comparison of actual intensive care unit length of stay with that predicted by the Acute Physiology and Chronic Health Evaluation III system. Correlation between actual and predicted stay for individual patients was poor. However, performance of the model for patients, grouped either by predicted length of stay or by intensive care unit, indicated that the model stratified patient groups appropriately while demonstrating a consistent bias. Length of stay in Scottish intensive care units was found to be consistently lower than that predicted by a model which is based on intensive care practice in the USA. Variations in severity of illness in intensive care unit populations cannot readily explain differences in intensive care unit length of stay. The availability of a model capable of predicting length of intensive care stay, based on data reflecting practice in the UK, would compliment current methods of assessing effectiveness of intensive care.

Keywords: Intensive care unit; length of stay; Scoring systems; APACHE

Document Type: Research article

Affiliations: 1: Specialist Registrar in Anaesthesia, and 2: Consultant in Anaesthesia and Intensive Care, Department of Anaesthetics, Victoria Infirmary, Langside Road, Glasgow, UK Project Director, and 3: Research Fellow, Scottish Intensive Care Society Audit Group, Department of Anaesthetics, Langside Road, Glasgow, UK Senior Statistician, Robertson Centre for Biostatistics, Boyd Orr Building, University of Glasgow, University Avenue, Glasgow, UK

Publication date: 2000-11-01

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