The researchers classified the clinical data for each variable into categories. For example, a patient's facial expression was categorized as "relaxed," "grimacing and moaning," or "grimacing and crying." A patient's noncardiac sympathetic stability was classified as "warm and dry skin," "flushed and sweaty," or "pale and sweaty."
They also recorded each patient's score on the motor activity and assessment scale (MAAS), which is used by clinicians to evaluate level of sedation on a scale of zero to six. In the MAAS system, a score of zero represents an "unresponsive patient," three represents a "calm and cooperative patient," and six represents a "dangerously agitated patient." The MAAS score is subjective and can result in inconsistencies and variability in sedation administration.
Using a Bayesian network, the researchers used the clinical data to compute the probability that a patient was agitated. Twelve-thousand measurements collected from patients admitted to the ICU at the Northeast Georgia Medical Center between during a one-year period were used to train the Bayesian network and the remaining 3,000 were used to test it.
In 18 percent of the test cases, the computer classified a patient as "agitated" but the MAAS score described the same patient as "not agitated." In five percent of the test cases, the computer classified a patient as "not agitated," whereas the MAAS score indicated "agitated." These probabilities signify an 18 percent false-positive rate and a five percent false-negative rate.
"This level of performance would allow a significant reduction in the workload of the intensive care unit nurse, but it would in no way replace the nurse as the ultimate judge of the adequacy of sedation," said Bailey. "However, by relieving the nurse of some of the work associated with titration of sedation, it would allow the nurse to better focus on other aspects of his or her demanding job."
|Contact: Abby Robinson|
Georgia Institute of Technology Research News