The ICD-9-based illness severity score: a new model that outperforms both DRG and APR-DRG as predictors of survival and resource utilization.
OBJECTIVE
This project is designed to develop and validate a predictive model that is a useful benchmarking and quality of care assessment tool based on International Classification of Diseases, Ninth Revision (ICD-9), diagnoses and procedures. This model, the ICD-9-Based Illness Severity Score (ICISS), was developed from the Agency for Health Care Policy Research's Health Care Utilization Project database and is used to predict hospital survival, hospital length of stay, and hospital charges of injured patients admitted to University of North Carolina Hospitals. The study also compared the outcome predictions of ICISS with those of the long-established diagnosis-related groups (DRG) and the 3M product APR-DRG systems.


METHODS
We performed a retrospective study of 9,483 trauma patients at University of North Carolina Hospitals. A model was developed to predict survival, length of stay, and hospital charges. The accuracy of the model of survival was assessed using the area under the receiver-operating characteristics curve; the adjusted R2 statistic was used to judge the proportion of variation described by the models of length of stay and hospital charges.


RESULTS
ICISS proved to be superior to both DRG and APR-DRG in predicting survival of trauma patients: the area under the receiver-operating characteristics curve for prediction of hospital survival was 0.957 for ICISS, 0.707 for DRG, and 0.808 for APR-DRG. ICISS also outperformed DRG and APR-DRG in predicting hospital length of stay and hospital charges: the adjusted R2 for the ICISS length of stay model was 0.57, compared with the DRG length of stay model with adjusted R2 of 0.31 and the APR-DRG length of stay model with adjusted R2 of 0.35. The adjusted R2 for the ICISS hospital charges model was 0.67, compared with the DRG and APR-DRG hospital charges model R2 of 0.46 and 0.51, respectively (p < 0.001 in all cases).


CONCLUSIONS
This study demonstrates that an ICD-9-based predictive model (ICISS) can markedly outperform both DRG and APR-DRG as a predictor of survival, hospital length of stay, and hospital charges.
Department of Surgery, University of North Carolina at Chapel Hill 27599-7210, USA. rrutledg@med.unc.edu
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D003953:Diagnosis-Related Groups; D004194:Disease / Q000145:classification; D006295:Health Resources / Q000706:statistics & numerical data*; D017722:Hospital Charges; D006801:Humans; D007902:Length of Stay; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011379:Prognosis; D012189:Retrospective Studies; D012720:Severity of Illness Index; D015599:Trauma Severity Indices; D016896:Treatment Outcome; D014947:Wounds and Injuries / Q000145:classification* / Q000191:economics / Q000401:mortality