SURGERY 187 DURING THE PAST TWO DECADES profound economic and technologic changes in the health care delivery system, including pressures for cost containment and quality enhancement, have brought about a growing interest in objective measurement of patient outcomes. 1-8 There is an increasing demand for valid and reliable informa- tion about the relative quality and effectiveness of health care provided by institutions and physi- cians. A just and unbiased comparison of outcomes remains a scientific challenge. 9-11 Correctly assessing illness severity is critical to accurate judgment of patient outcomes and efforts designed to improve the quality of patient care. Although there are several illness severity adjustment methods currently in use, all have important limitations. 12,13 Previous work by Rutledge and Osler 14-18 developed a straightfor- ward and statistically powerful methodology that has been shown to accurately predict patient survival, hospital length of stay (LOS), and hospital charges in trauma patients (International Classification of Diseases 9th Revision [ICD-9] Based Illness Severity Score [ICISS]). This study was an effort to assess the ability of the ICISS methodology to predict hospital patient survival, LOS, and total hospital charges in all patients admitted to all of the hospitals in an entire state. It was the primary hypothesis of the study that ICISS would accurately predict these patient outcomes, thus further validating the ICISS methodology as a broadly useful case-mix tool. METHODS Data source. Data for this study were obtained from a commercial medical data and information company that develops and markets clinical and Illness severity adjustment for outcomes analysis: Validation of the ICISS methodology in all 821,455 patients hospitalized in North Carolina in 1996 Robert Rutledge, MD, FACS, Turner Osler, MD, FACS, and Sharon Kromhout-Schiro, PhD, Chapel Hill, NC Background. Previous work has demonstrated that the International Classification of Diseases 9th Revision (ICD-9) Based Illness Severity Score (ICISS) methodology developed by Rutledge and Osler can perform well in this role as a severity adjustment tool in trauma patients. The purpose of the present study was to extend this previous work to determine the ability of ICISS to predict outcomes in all types of hospi- talized patients. Methods. The ICISS methodology was used to derive predictions of survival, length of hospital stay, and hospital charges in the entire study population. Results. A total of 821,455 hospitalized patients in North Carolina in 1996 had complete data available for analysis. The overall hospital mortality rate was 2.9% . ICISS was an accurate predictor of hospital survival in all hospitalized patients (accuracy 95.9% , sensitivity 97.2% , and specificity 52.7% .) The area of the receiver operator characteristic curve was 0.93. By adding age to the model, the area under the receiver operator characteristic curve increased to 0.95. ICISS also explained a large amount of the vari- ance in hospital stay and charges (R 2 = 0.38 and 0.56, respectively, P < .0001). Conclusions. This study extends previous work suggesting that ICISS may be an important improvement over other presently available severity adjustment models. If these findings are confirmed in comparison with other predictive tools, ICISS may find an important place in assessing illness severity. (Surgery 1998;124:187-96.) From the Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC Presented at the Fifty-ninth Annual Meeting of the Society of University Surgeons, Milwaukee, Wis, Feb 12-14, 1998. Reprint requests: Robert Rutledge, MD, FACS, Associate Professor of Surgery, Department of Surgery, University of North Carolina at Chapel Hill, Campus Box 7210, Burnett- Womack Building, Chapel Hill, NC 27599-7210. Copyright © 1998 by Mosby, Inc. 0039-6060/ 97/ $5.00 + 0 11/ 6/ 90563