Recent innovations in intensive care unit risk-prediction models.
During the past 20 years, ICU risk-prediction models have undergone significant development, validation, and refinement. Among the general ICU severity of illness scoring systems, the Acute Physiology and Chronic Health Evaluation (APACHE), Mortality Prediction Model (MPM), and the Simplified Acute Physiology Score (SAPS) have become the most accepted and used. To risk-adjust patients with longer, more severe illnesses like sepsis and acute respiratory distress syndrome, several models of organ dysfunction or failure have become available, including the Multiple Organ Dysfunction Score (MODS), the Sequential Organ Failure Assessment (SOFA), and the Logistic Organ Dysfunction Score (LODS). Recent innovations in risk adjustment include automatic physiology and diagnostic variable retrieval and the use of artificial intelligence. These innovations have the potential of extending the uses of case-mix and severity-of-illness adjustment in the areas of clinical research, patient care, and administration. The challenges facing intensivists in the next few years are to further develop these models so that they can be used throughout the IUC stay to assess quality of care and to extend them to more specific patient groups such as the elderly and patients with chronic ICU courses.
Rosenberg
Andrew L
AL
Robert Wood Johnson Clinical Scholars Program, Department of Anesthesiology and Critical Care Medicine, University of Michigan, Ann Arbor, Michigan 48109-4270, USA. arosen@umich.edu
D001185:Artificial Intelligence; D003422:Critical Care; D003658:Decision Making, Computer-Assisted; D006305:Health Status Indicators; D017052:Hospital Mortality; D006801:Humans; D007256:Information Systems; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017063:Outcome Assessment, Health Care; D020379:Risk Adjustment / Q000379:methods* / Q000592:standards / Q000639:trends; D018570:Risk Assessment; D012720:Severity of Illness Index