Predicting survival, length of stay, and cost in the surgical intensive care unit: APACHE II versus ICISS.
BACKGROUND
Risk stratification of patients in the intensive care unit (ICU) is an important tool because it permits comparison of patient populations for research and quality control. Unfortunately, currently available scoring systems were developed primarily in medical ICUs and have only mediocre performance in surgical ICUs. Moreover, they are very expensive to purchase and use. We conceived a simple risk-stratification tool for the surgical ICU that uses readily available International Classification of Diseases, Ninth Revision, codes to predict outcome. Called ICISS (International Classification of Disease Illness Severity Score), our score is the product of the survival risk ratios (obtained from an independent data set) for all International Classification of Diseases, Ninth Revision, diagnosis codes.


METHODS
A total of 5,322 noncardiac patients admitted to a surgical ICU during an 8-year period had their Acute Physiology and Chronic Health Evaluation (APACHE) II scores compared with their ICISS as predictors of outcome (survival/nonsurvival, length of stay, and charges).


RESULTS
ICISS proved to be a much better predictor of survival than APACHE (receiver operating characteristic (ROC) APACHE = 0.806; Hosmer-Lemeshow (HL) APACHE = 22.56; ROC ICISS = 0.892; HL ICISS = 12.06) or the APACHE survival probability (ROC = 0.836; HL = 34.47). These differences were highly statistically significant (p < 0.001). ICISS was also better correlated with ICU length of stay (APACHE R2 = 0.06; ICISS R2 = 0.32) and ICU charges (APACHE R2 = 0.07; ICISS R2 = 0.39). When combined in a logistic model with ICISS, APACHE II added slightly to the predictive power of ICISS alone (combined ROC = 0.903) but degraded the calibration of the model (combined HL = 16.29; p = 0.038).


CONCLUSIONS
Because ICISS is both more accurate and much less expensive to calculate than APACHE II score, ICISS should replace APACHE II score as the standard risk stratification tool in surgical ICUs.
Osler
T M
TM
University of Vermont College of Medicine, Burlington, USA. turner.osler@vtmednet.org
Rogers
F B
FB
Glance
L G
LG
Cohen
M
M
Rutledge
R
R
Shackford
S R
SR
D018806:APACHE; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002138:Calibration; D002648:Child; D002675:Child, Preschool; D003953:Diagnosis-Related Groups / Q000145:classification / Q000191:economics; D005260:Female; D017722:Hospital Charges / Q000706:statistics & numerical data*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007362:Intensive Care Units / Q000191:economics* / Q000706:statistics & numerical data*; D007902:Length of Stay / Q000706:statistics & numerical data*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009657:North Carolina; D011183:Postoperative Complications / Q000191:economics* / Q000401:mortality*; D011237:Predictive Value of Tests; D012372:ROC Curve; D012307:Risk Factors; D012720:Severity of Illness Index; D016019:Survival Analysis