BACKGROUND: Subtle changes in monitored physiological signals might be used to guide clinical actions and give early warning of potential adverse events. Automated early warning systems could enhance the clinician's interpretation of data by instantaneously processing new information and presenting it within the context of previous observations. In this study, we tested algorithms for tracking the behaviour of dynamic physiological systems and automatically detecting key events over time. METHODS: Algorithms were activated in real-time during anaesthesia to run context-sensitive monitoring of six variables (end-tidal PCO(2), heart rate, exhaled minute ventilation, non-invasive arterial pressure, respiratory rate, and oxygen saturation), alongside standard physiological monitors. The clinical evaluation included real-time feedback on each change point (change in the physiological trend) detected by the algorithms and the completion of a usability questionnaire. RESULTS: Fifteen anaesthetists completed the evaluation during paediatric surgical cases. A total of 38 cases were evaluated, with a mean duration of 103 (102) min. The mean number of change points per case was 22.8 (23.4). Sixty-one per cent of all rated change points were considered clinically significant, and <7% were due to artifacts. CONCLUSIONS: The algorithms were able to detect a range of clinically significant physiological changes during paediatric anaesthesia, and were considered useful by participating anaesthetists. These findings indicate that automated detection of context-sensitive changes is possible and could be used by early warning systems during physiological monitoring. Further investigations are required to assess how this information can best be communicated to the anaesthetist.
BACKGROUND: Subtle changes in monitored physiological signals might be used to guide clinical actions and give early warning of potential adverse events. Automated early warning systems could enhance the clinician's interpretation of data by instantaneously processing new information and presenting it within the context of previous observations. In this study, we tested algorithms for tracking the behaviour of dynamic physiological systems and automatically detecting key events over time. METHODS: Algorithms were activated in real-time during anaesthesia to run context-sensitive monitoring of six variables (end-tidal PCO(2), heart rate, exhaled minute ventilation, non-invasive arterial pressure, respiratory rate, and oxygen saturation), alongside standard physiological monitors. The clinical evaluation included real-time feedback on each change point (change in the physiological trend) detected by the algorithms and the completion of a usability questionnaire. RESULTS: Fifteen anaesthetists completed the evaluation during paediatric surgical cases. A total of 38 cases were evaluated, with a mean duration of 103 (102) min. The mean number of change points per case was 22.8 (23.4). Sixty-one per cent of all rated change points were considered clinically significant, and <7% were due to artifacts. CONCLUSIONS: The algorithms were able to detect a range of clinically significant physiological changes during paediatric anaesthesia, and were considered useful by participating anaesthetists. These findings indicate that automated detection of context-sensitive changes is possible and could be used by early warning systems during physiological monitoring. Further investigations are required to assess how this information can best be communicated to the anaesthetist.
Authors: Pierre Barralon; Guy Dumont; Stephan K W Schwarz; William Magruder; J Mark Ansermino Journal: J Clin Monit Comput Date: 2009-04-10 Impact factor: 2.502