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J Crit Care. 2017 Apr;38:335-339. doi: 10.1016/j.jcrc.2016.11.031. Epub 2016 Nov 21.

Development and validation of the new ICNARC model for prediction of acute hospital mortality in adult critical care.

Author information

1
Intensive Care National Audit & Research Centre (ICNARC), London, UK.
2
Department of Intensive Care, Guy's and St Thomas' NHS Foundation Trust, London, UK.
3
Intensive Care National Audit & Research Centre (ICNARC), London, UK. Electronic address: david.harrison@icnarc.org.

Abstract

PURPOSE:

To develop and validate an improved risk model to predict acute hospital mortality for admissions to adult critical care units in the UK.

MATERIALS AND METHODS:

155,239 admissions to 232 adult critical care units in England, Wales and Northern Ireland between January and December 2012 were used to develop a risk model from a set of 38 candidate predictors. The model was validated using 90,017 admissions between January and September 2013.

RESULTS:

The final model incorporated 15 physiological predictors (modelled with continuous nonlinear models), age, dependency prior to hospital admission, chronic liver disease, metastatic disease, haematological malignancy, CPR prior to admission, location prior to admission/urgency of admission, primary reason for admission and interaction terms. The model was well calibrated and outperformed the current ICNARC model on measures of discrimination (area under the receiver operating characteristic curve 0.885 versus 0.869) and model fit (Brier's score 0.108 versus 0.115). On average, the new model reclassified patients into more appropriate risk categories (net reclassification improvement 19.9; P<0.0001). The model performed well across patient subgroups and in specialist critical care units.

CONCLUSIONS:

The risk model developed in this study showed excellent discrimination and calibration and when validated on a different period of time and across different types of critical care unit. This in turn allows improved accuracy of comparisons between UK critical care providers.

KEYWORDS:

Critical care; Hospital mortality; Intensive care units; Logistic models; Prognosis; Risk adjustment

PMID:
27899205
DOI:
10.1016/j.jcrc.2016.11.031
[Indexed for MEDLINE]

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