Objective: To validate cardiovascular alarms in critically ill patients in an experimental setting by generating a database of physiologic data and clinical alarm annotations, and report the current rate of alarms and their clinical validity. Currently, monitoring of physiologic parameters in critically ill patients is performed by alarm systems with high sensitivity, but low specificity. As a consequence, a multitude of alarms with potentially negative impact on the quality of care is generated.
Design: Prospective, observational, clinical study.
Setting: Medical intensive care unit of a university hospital.
Data Source: Data from different medical intensive care unit patients were collected between January 2006 and May 2007.
Measurements and Main Results: Physiologic data at 1-sec intervals, monitor alarms, and alarm settings were extracted from the surveillance network. Video recordings were annotated with respect to alarm relevance and technical validity by an experienced physician. During 982 hrs of observation, 5934 alarms were annotated, corresponding to six alarms per hour. About 40% of all alarms did not correctly describe the patient condition and were classified as technically false; 68% of those were caused by manipulation. Only 885 (15%) of all alarms were considered clinically relevant. Most of the generated alarms were threshold alarms (70%) and were related to arterial blood pressure (45%).
Conclusion: This study used a new approach of off-line, video-based physician annotations, showing that even with modern monitoring systems most alarms are not clinically relevant. As the majority of alarms are simple threshold alarms, statistical methods may be suitable to help reduce the number of false-positive alarms. Our study is also intended to develop a reference database of annotated monitoring alarms for further application to alarm algorithm research.
From the Department of Internal Medicine I (SS, JS, CEW), Hospital of the University of Regensburg, Regensburg, Germany; Department of Statistics (SK, UG), TU Dortmund University, Dortmund, Germany; Department of Medical Informatics (MI), Biometrics and Epidemiology, Ruhr-University, Bochum, Germany; and the Department of Emergency Medicine (CEW), Helios-Klinikum Berlin-Buch, Berlin, Germany.
This work was supported, in part, by the German Research Foundation (DFG) (Reduction of Complexity in Multivariate Data Structures, SFB 475).
Dr. Imhoff has received consulting honoraria from Draeger Medical and is a managing member of Boston MedTech Advisors Europe GmbH, a medical technology consulting firm. The remaining authors have not disclosed any potential conflicts of interest.
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