Predicting respiratory instability in the ICU.

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) contribute to the morbidity and mortality of intensive care patients worldwide, and have large associated human and financial costs. We identified a reference data set of 624 mechanically-ventilated patients in the MIMIC-II intensive care database with and without low PaO(2)/FiO(2) ratios (termed respiratory instability), and developed prediction algorithms for distinguishing these patients prior to the critical event. In the end, we had four rule sets using mean airway pressure, plateau pressure, total respiratory rate and oxygen saturation (SpO(2)), where the specificity/sensitivity rates were either 80%/60% or 90%/50%.

Development and evaluation of predictive alerts for hemodynamic instability in ICU patients.

This paper describes an algorithm for identifying ICU patients that are likely to become hemodynamically unstable. The algorithm consists of a set of rules that trigger alerts. Unlike most existing ICU alert mechanisms, it uses data from multiple sources and is often able to identify unstable patients earlier and with more accuracy than alerts based on a single threshold.