Intensive Care Society's APACHE II study in Britain and Ireland--I: Variations in case mix of adult admissions to general intensive care units and impact on outcome.

Rowan, Kathryn M; Kerr, John H.; Major, E.; McPherson, Klim; Short, Alasdair; Mp, Vessey

doi:10.1136/bmj.307.6910.972

Cited by 154 publications

(81 citation statements)

References 18 publications

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“…Although the same data set was used, the APACHE II results were different from those of the original APACHE II study carried out by Rowan et al [2][3][4][5]. This is because only a third of the total data set (i.e.…”

Section: Discussionmentioning

confidence: 95%

“…The study was conducted using the database collected for the ICS UK APACHE II study [2][3][4][5]. This data set contains the physiological variables required to calculate the APACHE II score and the hospital mortality for 8796 patients from 26 ICUs in the UK and Ireland.…”

Section: Data Collectionmentioning

confidence: 99%

“…The patient's status (alive/dead) at discharge from hospital was also recorded. The detailed definitions of the APACHE II system and the algorithms for calculating the APACHE II score and the probability of hospital death have been described in detail elsewhere [2][3][4][5].…”

Section: Data Collectionmentioning

confidence: 99%

“…It has been extensively validated in 26 intensive care units (ICUs) in the United Kingdom and Ireland in the Intensive Care Society (ICS) UK APACHE II study [2][3][4][5]. Further improvements on APACHE II were carried out in 1991 resulting in the APACHE III model which increased the prediction accuracy at the cost of increased data collection [6][7][8].…”

mentioning

confidence: 99%

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A comparison of ICU mortality prediction using the APACHE II scoring system and artificial neural networks

Wong¹,

Young

1999

Anaesthesia

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SummaryThe aim of this study was to compare the ability of artificial neural networks and the Acute Physiology and Chronic Health Evaluation II score to predict mortality in adult intensive care units. The same physiological variables were used in both predictive models to predict hospital mortality from a data set of 8796 patients collected from 26 adult intensive care units in the United Kingdom and Ireland as part of the Intensive Care Society study. The results from the two models were compared with the actual outcome. The overall prediction accuracy and the overall goodness-of-fit of all the models were assessed. Both predictive models showed similar goodnessof-fit and prediction discrimination. The overall predictive and classification performance of the artificial neural network developed matched and in some aspects was better than that of Acute Physiology and Chronic Health Evaluation II.

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Section: Discussionmentioning

confidence: 95%

Section: Data Collectionmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

mentioning

confidence: 99%

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A comparison of ICU mortality prediction using the APACHE II scoring system and artificial neural networks

Wong¹,

Young

1999

Anaesthesia

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“…When considering individual outcome prediction, the calibration at the highest risks of death is of interest. In the UK, Beck et al [13] have shown that the APACHE II system's calibration is better overall than that of the APACHE III and MPM models; even so calibration of the APACHE II system at the highest risks of death is not perfect as the system tends to overestimate actual mortality [14]. This is clearly not satisfactory when attempting to decide whether withdrawal of treatment is appropriate.…”

Section: Accuracymentioning

confidence: 99%

Uncertainty and scoring systems

Ridley

2002

Anaesthesia

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Summary Estimating risks for individual patients facilitates communication with patients, relatives and colleagues, and determines whether further treatment is futile. The process of estimating risks involves mathematics (i.e. scoring systems) and human experience and expertise. Understanding how risks are estimated is important because prognostication is an integral part of any medical specialty. In the USA, such treatment limitation or withdrawal decisions were made on only 7% of all intensive care unit patients but this represented 47% of all deaths on such units. In the UK, data reported by the Intensive Care National Audit and Research Centre suggest that although treatment limitation decisions are made on only 11.8% of patients, this accounts for over 50% of deaths on intensive care. Scoring systems offer a useful adjunct in identifying futility but there are important inherent weaknesses that limit their performance. This review aims to discuss some of these limitations.

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Respiratory Failure

MacNee¹

2000

Crofton and Douglas's Respiratory Diseases

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Intensive Care Society's APACHE II study in Britain and Ireland--I: Variations in case mix of adult admissions to general intensive care units and impact on outcome.

Abstract: Objectives-To describe the extent of variation in the case mix of adult admissions to

Cited by 154 publications

References 18 publications

A comparison of ICU mortality prediction using the APACHE II scoring system and artificial neural networks

A comparison of ICU mortality prediction using the APACHE II scoring system and artificial neural networks

Uncertainty and scoring systems

Respiratory Failure

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