Harborview assessment for risk of mortality: an improved measure of injury severity on the basis of ICD-9-CM.

BACKGROUND There have been several attempts to develop a scoring system that can accurately reflect the severity of a trauma patient's injuries, particularly with respect to the effect of the injury on survival. Current methodologies require unreliable physiologic data for the assignment of a survival probability and fail to account for the potential synergism of different injury combinations. The purpose of this study was to develop a scoring system to better estimate probability of mortality on the basis of information that is readily available from the hospital discharge sheet and does not rely on physiologic data. METHODS Records from the trauma registry from an urban Level I trauma center were analyzed using logistic regression. Included in the regression were Internation Classification of Diseases-9th Rev (ICD-9CM) codes for anatomic injury, mechanism, intent, and preexisting medical conditions, as well as age. Two-way interaction terms for several combinations of injuries were also included in the regression model. The resulting Harborview Assessment for Risk of Mortality (HARM) score was then applied to an independent test data set and compared with Trauma and Injury Severity Score (TRISS) probability of survival and ICD-9-CM Injury Severity Score (ICISS) for ability to predict mortality using the area under the receiver operator characteristic curve. RESULTS The HARM score was based on analysis of 16,042 records (design set). When applied to an independent validation set of 15,957 records, the area under the receiver operator characteristic curve (AUC) for HARM was 0.9592. This represented significantly better discrimination than both TRISS probability of survival (AUC = 0.9473, p = 0.005) and ICISS (AUC = 0.9402, p = 0.001). HARM also had a better calibration (Hosmer-Lemeshow statistic [HL] = 19.74) than TRISS (HL = 55.71) and ICISS (HL = 709.19). Physiologic data were incomplete for 6,124 records (38%) of the validation set; TRISS could not be calculated at all for these records. CONCLUSION The HARM score is an effective tool for predicting probability of in-hospital mortality for trauma patients. It outperforms both the TRISS and ICD9-CM Injury Severity Score (ICISS) methodologies with respect to both discrimination and calibration, using information that is readily available from hospital discharge coding, and without requiring emergency department physiologic data.

[1]  E. Hannan,et al.  Predictors of mortality in adult patients with blunt injuries in New York State: a comparison of the Trauma and Injury Severity Score (TRISS) and the International Classification of Disease, Ninth Revision-based Injury Severity Score (ICISS). , 1999, The Journal of trauma.

[2]  K. Kochanek,et al.  Recommended framework for presenting injury mortality data. , 1997, MMWR. Recommendations and reports : Morbidity and mortality weekly report. Recommendations and reports.

[3]  T. Osler,et al.  Trauma registry injury coding is superfluous: a comparison of outcome prediction based on trauma registry International Classification of Diseases-Ninth Revision (ICD-9) and hospital information system ICD-9 codes. , 1997, The Journal of trauma.

[4]  T. Osler,et al.  Comparison of the Injury Severity Score and ICD-9 diagnosis codes as predictors of outcome in injury: analysis of 44,032 patients. , 1997, The Journal of trauma.

[5]  T. Osler,et al.  ICISS: an international classification of disease-9 based injury severity score. , 1996, The Journal of trauma.

[6]  S. Greenland Dose‐Response and Trend Analysis in Epidemiology: Alternatives to Categorical Analysis , 1995, Epidemiology.

[7]  R. Rutledge,et al.  Injury severity and probability of survival assessment in trauma patients using a predictive hierarchical network model derived from ICD-9 codes. , 1995, The Journal of trauma.

[8]  S. Fakhry,et al.  Injury severity grading in trauma patients: a simplified technique based upon ICD-9 coding. , 1993, The Journal of trauma.

[9]  T. Osler,et al.  Injury severity scoring: perspectives in development and future directions. , 1993, American journal of surgery.

[10]  C. Cayten,et al.  Comparison between TRISS and ASCOT methods in controlling for injury severity. , 1991, The Journal of trauma.

[11]  C. Magnant,et al.  Pre-existing disease in trauma patients: a predictor of fate independent of age and injury severity score. , 1991, The Journal of trauma.

[12]  E. Mackenzie,et al.  The effect of preexisting conditions on mortality in trauma patients. , 1990, JAMA.

[13]  T Gennarelli,et al.  A new characterization of injury severity. , 1990, The Journal of trauma.

[14]  D M Steinwachs,et al.  Classifying Trauma Severity Based on Hospital Discharge Diagnoses: Validation of an ICD-9CM to AIS-85 Conversion Table , 1989, Medical care.

[15]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[16]  H. Champion,et al.  Trauma score , 1981, Critical care medicine.

[17]  C. Metz Basic principles of ROC analysis. , 1978, Seminars in nuclear medicine.

[18]  B. Jennett,et al.  Assessment of coma and impaired consciousness. A practical scale. , 1974, Lancet.

[19]  W. Haddon,et al.  The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care. , 1974, The Journal of trauma.

[20]  Catharyn T. Liverman,et al.  Reducing the burden of injury : advancing prevention and treatment , 1999 .

[21]  E. Hannan,et al.  Validation of TRISS and ASCOT using a non-MTOS trauma registry. , 1995, The Journal of trauma.

[22]  W J Sacco,et al.  Injury severity scoring again. , 1995, The Journal of trauma.

[23]  R. Goris,et al.  [Revision of the trauma score]. , 1992, Nederlands tijdschrift voor geneeskunde.