Dynamic microsimulation to model multiple outcomes in cohorts of critically ill patients.

BACKGROUND: Existing intensive care unit (ICU) prediction tools forecast single outcomes, (e.g., risk of death) and do not provide information on timing. OBJECTIVE: To build a model that predicts the temporal patterns of multiple outcomes, such as survival, organ dysfunction, and ICU length of stay, from the profile of organ dysfunction observed on admission. DESIGN: Dynamic microsimulation of a cohort of ICU patients. SETTING: 49Forty-nine ICUs in 11 countries. PATIENTS: One thousand four hundred and forty-nine patients admitted to the ICU in May 1995. INTERVENTIONS: None. MODEL CONSTRUCTION: We developed the model on all patients (n=989) from 37 randomly-selected ICUs using daily Sequential Organ Function Assessment (SOFA) scores. We validated the model on all patients (n=460) from the remaining 12 ICUs, comparing predicted-to-actual ICU mortality, SOFA scores, and ICU length of stay (LOS). MAIN RESULTS: In the validation cohort, the predicted and actual mortality were 20.1% (95%CI: 16.2%-24.0%) and 19.9% at 30 days. The predicted and actual mean ICU LOS were 7.7 (7.0-8.3) and 8.1 (7.4-8.8) days, leading to a 5.5% underestimation of total ICU bed-days. The predicted and actual cumulative SOFA scores per patient were 45.2 (39.8-50.6) and 48.2 (41.6-54.8). Predicted and actual mean daily SOFA scores were close (5.1 vs 5.5, P=0.32). Several organ-organ interactions were significant. Cardiovascular dysfunction was most, and neurological dysfunction was least, linked to scores in other organ systems. CONCLUSIONS: Dynamic microsimulation can predict the time course of multiple short-term outcomes in cohorts of critical illness from the profile of organ dysfunction observed on admission. Such a technique may prove practical as a prediction tool that evaluates ICU performance on additional dimensions besides the risk of death.