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Abstract
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BACKGROUND: The International Classification of Disease Injury Severity Score (ICISS) provides an efficient method to determine injury severity in hospitalised injury patients. Injury severity metrics are of particular interest for the tracking of road transport injury rates and trends. The aims of this study were to calculate ICISS using linked morbidity and mortality datasets and to compare predictive ability of various methods and metrics.
METHODS: This was a retrospective analysis of Admitted Patient Data Collection records from New South Wales, Australia, linked with mortality data. Using a split sample approach, design data (2008-2014; n=1 035 174 periods of care) was used to derive survival risk ratios and calculate various ICISS scales based on in-hospital death and 3-month death. These scales were applied to testing data (2015-2017; n=575 306). Logistic regression modelling was used to determine model discrimination and calibration.
RESULTS: There were 12 347 (1.19%) in-hospital deaths and 29 275 (2.83%) 3-month deaths in the design data. Model discrimination ranged from acceptable to excellent (area under the curve 0.75-0.88). Serious injury (ICISS≤0.941) rates in the testing data varied, with a range of 10%-31% depending on the methodology. The 'worst injury' ICISS was always superior to 'multiplicative injury' ICISS in model discrimination and calibration.
CONCLUSIONS: In-hospital death and 3-month death were used to generate ICISS; the former is recommended for settings with a focus on short-term threat to life, such as in trauma care settings. The 3-month death approach is recommended for outcomes beyond immediate clinical care, such as injury compensation schemes.
Language: en |