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

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Computing in Cardiology (39)977–980.
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.

Kayaalp M. ICU Outcome Predictions using Physiologic Trends in the First Two Days. Computing in Cardiology (39)977–980.