Computerized ventilator data selection: artifact rejection and data reduction

Objective: To determine acceptable strategies for automated data acquisition and artifact rejection from computerized ventilators using the Medical Information Bus. Design: Medical practitioners were surveyed to establish ‘clinically important’ ventilator events. A prospective study involving frequent data collection from ventilators was also conducted. Subjects: Data from 10 adult patients were collected every 10 seconds from a Puritan Bennett 7200A ventilator for a total of 617.1 hours. Interventions: Twelve different computerized data selection and artifact algorithms were tested and evaluated. Measurements and Main Results: Data derived from 12 data selection algorithms were compared with each other and with data manually charted by respiratory therapists into a computerized charting system. Ventilator setting data collected by the algorithms, such as FIO2, reduced the amount of data collected to about 25% compared to manually charted data. The amount of data collected for measured parameters, such as tidal volume, from the ventilator had large variability and many artifacts. Automated data capture and selection generally increased the amount of data collected compared to manual charting, for example for the 3 minute median the increase was a modest 1.2 times. Conclusion: Computerized methods for collecting ventilator setting data were relatively straightforward and more efficient than manual methods. However, the method for automated selection and presentation of observed measured parameters is much more difficult. Based on the findings and analysis presented here, the authors recommend recording ventilator setting data after they have existed for three minutes and measured parameters using a three minute median data selection strategy. Such an algorithm rejected most artifacts, required minimal computational time, had minimal time-delay, and provided clinically acceptable data acquisition. The results presented here are but a starting point in developing automated ventilator data selection strategies.

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