Predicting trauma patient mortality: ICD [or ICD-10-AM] versus AIS based approaches

• Published in: ANZ journal of surgery 2010-11, Vol.80 (11), p.802-806
• Main Authors: Willis, Cameron D., Gabbe, Belinda J., Jolley, Damien, Harrison, James E., Cameron, Peter A.
• Format: Article
• Language: English
• Subjects: Abbreviated Injury Scale; Area Under Curve; Australia; Confidence Intervals; Databases, Factual; Female; Glasgow Coma Scale; Humans; injury; Injury Severity Score; International Classification of Diseases; Male; Mortality; Multivariate Analysis; Predictions; Predictive Value of Tests; Registries; Sensitivity and Specificity; Survival Analysis; Trauma care; wound; Wounds and Injuries - classification; Wounds and Injuries - mortality
• ISSN: 1445-1433; 1445-2197
• DOI: 10.1111/j.1445-2197.2010.05432.x

Background:  The International Classification of Diseases Injury Severity Score (ICISS) has been proposed as an International Classification of Diseases (ICD)‐10‐based alternative to mortality prediction tools that use Abbreviated Injury Scale (AIS) data, including the Trauma and Injury Severity Score (TRISS). To date, studies have not examined the performance of ICISS using Australian trauma registry data. This study aimed to compare the performance of ICISS with other mortality prediction tools in an Australian trauma registry. Methods:  This was a retrospective review of prospectively collected data from the Victorian State Trauma Registry. A training dataset was created for model development and a validation dataset for evaluation. The multiplicative ICISS model was compared with a worst injury ICISS approach, Victorian TRISS (V‐TRISS, using local coefficients), maximum AIS severity and a multivariable model including ICD‐10‐AM codes as predictors. Models were investigated for discrimination (C‐statistic) and calibration (Hosmer–Lemeshow statistic). Results:  The multivariable approach had the highest level of discrimination (C‐statistic 0.90) and calibration (H–L 7.65, P= 0.468). Worst injury ICISS, V‐TRISS and maximum AIS had similar performance. The multiplicative ICISS produced the lowest level of discrimination (C‐statistic 0.80) and poorest calibration (H–L 50.23, P < 0.001). Conclusions:  The performance of ICISS may be affected by the data used to develop estimates, the ICD version employed, the methods for deriving estimates and the inclusion of covariates. In this analysis, a multivariable approach using ICD‐10‐AM codes was the best‐performing method. A multivariable ICISS approach may therefore be a useful alternative to AIS‐based methods and may have comparable predictive performance to locally derived TRISS models.