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.
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).
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).
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.
"The SRR measures the proportion of patients who survive after admission with a specific ICD-9- CM code  . The product of the worst 3 yields a Ps, which has been validated as predictive of mortality, morbidity, and resource use  . After these exclusions, the data set contained 27,313 pediatric patients, categorized as seriously injured. "
[Show abstract][Hide abstract] ABSTRACT: The purposes of the study were to compare the survival associated with treatment of seriously injured patients with pediatric trauma in Florida at designated trauma centers (DTCs) with nontrauma center (NCs) acute care hospitals and to evaluate differences in mortality between designated pediatric and nonpediatric trauma centers.
Trauma-related inpatient hospital discharge records from 1995 to 2004 were analyzed for children aged from 0 to 19 years. Age, sex, ethnicity, injury mechanism, discharge diagnoses, and severity as defined by the International Classification Injury Severity Score were analyzed, using mortality during hospitalization as the outcome measure. Children with central nervous system, spine, torso, and vascular injuries and burns were evaluated. Instrumental variable analysis was used to control for triage bias, and mortality was compared by probabilistic regression and bivariate probit modeling. Children treated at a DTC were compared with those treated at a nontrauma center. Within the population treated at a DTC, those treated at a DTC with pediatric capability were compared with those treated at a DTC without additional pediatric capability. Models were analyzed for children aged 0 to 19 years and 0 to 15 years.
For the 27,313 patients between ages 0 and 19 years, treatment in DTCs was associated with a 3.15% reduction in the probability of mortality (P < .0001, bivariate probit). The survival advantage for children aged 0 to 15 years was 1.6%, which is not statistically significant. Treatment of 16,607 children in a designated pediatric DTC, as opposed to a nonpediatric DTC, was associated with an additional 4.84% reduction in mortality in the 0- to 19-year age group and 4.5% in the 0 to 15 years group (P < .001, bivariate probit).
Optimal care of the seriously injured child requires both the extensive and immediate resources of a DTC as well as pediatric-specific specialty support.
Journal of Pediatric Surgery 02/2008; 43(1):212-21. DOI:10.1016/j.jpedsurg.2007.09.047 · 1.39 Impact Factor
"Even when serial physiologic assessments  or Bayesian analysis  were included, prognostication was still limited to relative risk of mortality, and therefore was not applicable to individual patients. Similarly, attempts at predicting multiple organ dysfunction , ventilator dependence , or duration of stay and hospital costs  have limited clinical value. In contrast, the prospectively validated SMART models presented here predicted important clinical end-points accurately in individual patients, with high levels of prognostic accuracy in most cases. "
[Show abstract][Hide abstract] ABSTRACT: Clinically useful predictions of end-organ function and failure in severe sepsis may be possible through analyzing the interactions among demographics, physiologic parameters, standard laboratory tests, and circulating markers of inflammation. The present study evaluated the ability of such a methodology, the Systemic Mediator Associated Response Test (SMART), to predict the clinical course of septic surgery patients from a database of medical and surgical patients with severe sepsis and/or septic shock.
Three hundred and three patients entered into the placebo arm of a multi-institutional sepsis study were randomly assigned to a model-building cohort (n = 200; 119 surgical) or to a predictive cohort (n = 103; 55 surgical). Using baseline and baseline plus serial measurements of physiologic data, standard laboratory tests, and plasma levels of IL-6, IL-8, and granulocyte colony-stimulating factor (GCSF), multivariate models were developed that predicted the presence or absence of pulmonary edema on chest radiography, and respiratory, renal, coagulation, hepatobiliary, or central nervous system dysfunction and shock in individual patients. Twenty-eight-day survival was predicted also in baseline plus serial data models. These models were validated prospectively by inserting baseline raw data from the 55 surgical patients in the predictive cohort into the models built on the comprehensive training cohort, and calculating the area under the curve (AUC) of predicted versus observed receiver operator characteristic (ROC) plots.
SMART predictions of physiologic, respiratory, metabolic, hepatic, renal, and hematologic function indicators were validated prospectively, frequently at clinically useful levels of accuracy. ROC AUC values above 0.700 were achieved in 30 out of 49 (61%) of SMART baseline models in predicting shock and organ failure up to 7 days in advance, and in 30 out of 54 (56%) of baseline plus serial data models.
SMART multivariate models accurately predict pathophysiology, shock, and organ failure in individual septic surgical patients. These prognostications may facilitate early treatment of end-organ dysfunction in surgical sepsis.
Critical Care 02/2000; 4(5):319-26. DOI:10.1186/cc715 · 4.48 Impact Factor
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