ICU Outcome Predictions using Physiologic Trends in the First Two Days.

Abstract

Aims: This study aims to accurately predict patient mortality in the ICU. Given all physiologic measurements in the first 48 hours of the ICU stay, the Bayesian model of the study predicts outcome with a posterior probability.

Methods: This study modeled the outcome as a binary random variable dependent on trends of daily physiologic measures of the patient, where trends were conditionally independent given the outcome. A two-day trend is a sequence of two discrete values, one for each day. Each value (low, medium, high or unmeasured) is a function of the arithmetic mean of that measure on the corresponding day.

Results: The prediction performance of the model was measured as the minimum of sensitivity and positive predictive values. The model yielded a score of 0.39 along with a Hosmer-Lemeshow H statistic of 36, which measures calibration. The perfect scores would be 1.0 and 0, respectively.

Conclusion: The prediction performance of the study was an improvement over the established ICU scoring metric SAPS-I, whose score was 0.32. Calibration of the model outputs was comparable to that of SAPS-I.