Objective: To develop predictive equations, estimating the probability that an individual intensive care unit (ICU) patient will receive life support within the next 24 hrs.
Design: Prospective, multicenter, inception cohort study.
Setting: Forty-two ICUs in 40 U.S. hospitals, including 26 that were randomly selected and 14 volunteer hospitals, primarily university or large tertiary care centers.
Patients: A consecutive sample of 17,440 ICU admissions.
Interventions: None.
Measurements and main results: A series of multivariate equations were developed to create daily estimates of probability of life support in the next 24 hrs. These equations used demographic, physiologic, and treatment information obtained at the time of ICU admission and during the first 7 ICU days. The most important determinants of next day risk for life support were the current day's therapy and Acute Physiology Score of the Acute Physiology and Chronic Health Evaluation (APACHE) III score. Other predictor variables included diagnosis, age, chronic health status, emergency surgery, previous day Acute Physiology Score, and hospital stay and location before ICU admission. The cross-validated ICU day 1, 2, and 3 predictive equations had receiver operating characteristic areas of 0.90. Survival, ICU readmission rate, and the number and type of therapies received by patients predicted at < 10% risk for active treatment suggest that discharge of patients meeting these criteria to an intermediate care unit or hospital ward could reduce ICU bed demand without compromising patient safety.
Conclusions: Accurate, objective predictions of next day risk for life support can be developed, using readily available patient information. Supplementing physician judgment with these objective risk assessments deserves evaluation for the role of these assessments in enhancing patient safety and improving ICU resource utilization.