Predicting respiratory instability in the ICU.

Conf Proc IEEE Eng Med Biol Soc · Jan 2008

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%.

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- Colleen M Ennett, K P Lee, Larry J Eshelman, Brian Gross, Larry Nielsen, Joseph J Frassica, and Mohammed Saeed.
- Philips Research North America, Briarcliff Manor, NY, USA. colleen.ennett@philips.com
- Conf Proc IEEE Eng Med Biol Soc. 2008 Jan 1;2008:2848-51.
AbstractAcute 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%.

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Predicting respiratory instability in the ICU.

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