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.


METHODS
Dynamic microsimulation of a cohort of ICU patients.


METHODS
49Forty-nine ICUs in 11 countries.


METHODS
One thousand four hundred and forty-nine patients admitted to the ICU in May 1995.


METHODS
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).


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.
Clermont
Gilles
G
Room 606B, Scaife Hall, Critical Care Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261, USA. clermontg@ccm.upmc.edu
Kaplan
Vladimir
V
Moreno
Rui
R
Vincent
Jean-Louis
JL
Linde-Zwirble
Walter T
WT
Hout
Ben Van
BV
Angus
Derek C
DC
D015331:Cohort Studies; D003198:Computer Simulation; D003422:Critical Care; D005544:Forecasting / Q000379:methods*; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D007902:Length of Stay; D016015:Logistic Models; D009102:Multiple Organ Failure / Q000401:mortality*; D011237:Predictive Value of Tests; D011897:Random Allocation