Outcome prediction in critical care: the Acute Physiology and Chronic Health Evaluation models.
OBJECTIVE
A new generation of predictive models for critically ill patients was described between 2005 and 2008. This review will give details of the latest version of the Acute Physiology and Chronic Health Evaluation (APACHE) predictive models, and discuss it in relation to recent critical care outcome studies. We also compare APACHE IV with other systems and address the issue of model complexity.


RESULTS
APACHE IV required the remodeling of over 40 equations. These new models calibrate better to contemporary data than older versions of APACHE and there is good predictive accuracy within diagnostic subgroups. Physiology accounts for 66% and diagnosis for 17% of the APACHE IV mortality model's predictive power. Thus, physiology and diagnosis account for 83% of the accuracy of APACHE IV.


CONCLUSIONS
Predictive models have a modest window of applicability, and therefore must be revalidated frequently. This was shown to be true for APACHE III, and hence a major reestimation of models was carried out to generate APACHE IV. Although overall model accuracy is important, it is also imperative that predictive models work well within diagnostic subgroups.
Zimmerman
Jack E
JE
Anesthesia and Critical Care Medicine, The George Washington University, Washington, District of Columbia, USA.
Kramer
Andrew A
AA
D018806:APACHE; D003422:Critical Care; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods*; D012720:Severity of Illness Index