Alarm-inducing variability in cardiac postoperative data and the effects of prealarm delay.

OBJECTIVE

Our objective was to study the distribution of invasively measured hemodynamic data to enhance the reliability of patient monitor alarm systems.

METHODS

Monitoring data were collected, preprocessed off-line, and analyzed in 10 postcardiac surgery patients. The data were studied statistically to estimate the probability distributions, the probability of alarm at various probability levels in these distributions, the effect of the prealarm delay to the alarm probability, and the effect of preprocessing the monitoring data using one- or multidimensional median filtering.

RESULTS

Fifteen percent of all registered values fell outside of commonly applied alarm limits. Doubling the prealarm delay from 5 to 10 sec reduced the mean alarm rate by 26%. A further decrease of 8% in the alarm rate was observed when a multidimensional vector median filter was used to remove the variable value interdependencies.

CONCLUSIONS

Brief excursions beyond clinically optimal alarm limits were frequent and can occur without leading to significant degradation of the patient's state. Preprocessing can decrease the alarm rate effectively. Multidimensional preprocessing may produce more reliable alarms than one-dimensional processing.