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Quality and outcomes reporting in trauma using International Statistical Classification for Diseases, Ninth Revision
Efstathios Karamanos1, Alexander Van Esbroeck2, Sanjay Mohanty1
1Department of Surgery, Henry Ford Hospital, Detroit, Michigan.
Background:
Use of the trauma and injury severity score (TRISS) for quality and outcomes assessment is challenged by the need for laborious collection of demographic and physiological data. We hypothesize that a novel stratification approach based on International Statistical Classification for Diseases, Ninth Revision (ICD-9) data that are readily available for trauma patients provides a more accurate and more easily obtainable alternative to TRISS with the potential for widespread use.
Methods:
Data from the ACS National Trauma Data Bank were used to train and evaluate a regularized logistic regression model for mortality and linear regression models for hospital length of stay (HLOS) and intensive care unit length of stay (ILOS) using ICD-9 diagnostic and procedural codes. Model training was performed on data from 2008 (n = 124,625) and evaluation on data from 2009 (n = 120,079). The discrimination and calibration of each model based on ICD-9 codes were compared with those of TRISS.
Results:
The mortality model using ICD-9 codes was comparable with that of TRISS in terms of the area under the receiver operating characteristic curve (0.922 versus 0.921, P = not significant.) and achieved better results in terms of both integrated discrimination improvement (0.106, P < 0.001) and Hosmer-Lemeshow chi-squared value (294.15 versus 2043.20). The HLOS and ILOS models using ICD-9 codes also demonstrated improvements in both R(2) (0.64 versus 0.30 for HLOS, 0.68 versus 0.34 for ILOS) and root mean-squared error (7.06 versus 8.62 for HLOS, 4.15 versus 9.54 for ILOS).
Conclusions:
Use of ICD-9 codes for stratification provides a more accurate and more broadly applicable approach to quality and outcomes assessment in trauma patients than the labor-intensive gold standard of TRISS.
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