Multicentric study of monitoring alarms in the adult intensive care unit (ICU): a descriptive analysis.

OBJECTIVES: To assess the relevance of current monitoring alarms as a warning system in the adult ICU. DESIGN: Prospective, observational study. SETTINGS: Two university hospital, and three general hospital, ICUs. PATIENTS: Hundred thirty-one patients, ventilated at admission, from different shifts (morning, evening, night) combined with different stages of stay, early (0-3 days), intermediate (4-6 days) and late (> 6 days). INTERVENTIONS: Experienced nurses were asked to record the patient's characteristics and, for each alarm event, the reason, type and consequence. MEASUREMENTS AND MAIN RESULTS: The mean age of the patients included was 59.8 +/- 16.4 and SAPS1 was 15.9 +/- 7.4. We recorded 1971 h of care. The shift distribution was 78 mornings, 85 evenings and 83 nights; the stage distribution was 88 early, 78 intermediate and 80 late. There were 3188 alarms, an average of one alarm every 37 min: 23.7% were due to staff manipulation, 17.5% to technical problems and 58.8% to the patients. Alarms originated from ventilators (37.8%), cardiovascular monitors (32.7%), pulse oximeters (14.9%) and capnography (13.5%). Of the alarms, 25.8% had a consequence such as sensor repositioning, suction, modification of the therapy (drug or ventilation). Only 5.9% of the alarms led to a physician's being called. The positive predictive value of an alarm was 27% and its negative predictive value was 99%. The sensitivity was 97% and the specificity 58%. CONCLUSIONS: The study confirms that the level of monitoring in ICUs generates a great number of false-positive alarms.

Towards symbolization using data-driven extraction of local trends for ICU monitoring.

We propose a methodology for the extraction of local trends from a stream of data. It has been designed to suit the needs of interpretation-oriented visualization and symbolization from ICU monitoring data. After giving implementation details for efficient computation of local trends, we propose the use of a characteristic analysis span for each variable. This characteristic span is obtained from a set of criteria that we compare and evaluate in regard of analysis of ICU monitoring data gathered within the Aiddaig project. The processing results in a rich visual representation and a framework for the local symbolization of the data stream based on its dynamics.

An architecture for online comparison and validation of processing methods and computerized guidelines in intensive care units.

Clinical decision support systems are a combination of software techniques to help the clinicians in their medical decision making process via functionalities ranging from basic signal analysis to therapeutic planning and computerized guidelines. The algorithms providing all these functionalities must be very carefully validated on real patient data and must be confronted to everyday clinical practice. One of the main problems when developing these techniques is the difficulty to obtain high-quality complete patient records, comprising data coming both from the biomedical equipment (high-frequency signals), and from numerous other sources (therapeutics, imagery, clinical actions, etc.). In this paper, we present an infrastructure for developing and testing such software algorithms. It is based on a bedside workstation where testing different algorithms simultaneously on real-time data is possible in the ward. It is completed by a collaborative portal enabling different teams to test their software algorithms on the same patient records, making comparisons and cross-validations more easily.

Computer-assisted evaluation of respiratory data in ventilated critically ill patients.

In intensive care unit, a lot of data are currently available but remain unused by nurses and residents because of complexity of analysis. We have developed a system for interpretation of respiratory data (RESPAID) in order to improve monitoring of patients under respiratory support and also to provide a high level of information. RESPAID is a real-time system which interprets quantitative and qualitative aspects of the usual respiratory data at different levels of information. Initial knowledge base was built from data given by four specialists in intensive care. Major attention was paid to different aspects of the system: monitor interface, user interface and time representation. Data are issued from standard respirators and/or monitors used in the intensive care unit. Informations provided by RESPAID are alarm identification, ventilator settings modification and proposal for physiological evolution of the patient or suspected complication. RESPAID runs on IBM PCAT3 with 1st class shell. It is currently in clinical validation procedure.

Introduction of knowledge bases in patient's data management system: role of the user interface.

As the number of signals and data to be handled grows in intensive care unit, it is necessary to design more powerful computing systems that integrate and summarize all this information. The manual input of data as e.g. clinical signs and drug prescription and the synthetic representation of these data requires an ever more sophisticated user interface. The introduction of knowledge bases in the data management allows to conceive contextual interfaces. The objective of this paper is to show the importance of the design of the user interface, in the daily use of clinical information system. Then we describe a methodology that uses the man-machine interaction to capture the clinician knowledge during the clinical practice. The different steps are the audit of the user's actions, the elaboration of statistic models allowing the definition of new knowledge, and the validation that is performed before complete integration. A part of this knowledge can be used to improve the user interface. Finally, we describe the implementation of these concepts on a UNIX platform using OSF/MOTIF graphical interface.

INFORM: integrated support for decisions and activities in intensive care.

Many medical decision support systems that have been developed in the past have failed to enter routine clinical practice. Often this is because the developers have failed to analyse in sufficient detail the precise user requirements, because they have produced a system which takes too narrow a view of the patient, or because the decision support facilities have not been sufficiently well integrated into the routine clinical data handling activities. In this paper we discuss how the AIM-INFORM project is setting out to deal with these issues, in the context of the provision of decision support in the intensive care unit.