Collection of annotated data in a clinical validation study for alarm algorithms in intensive care--a methodologic framework.

INTRODUCTION: Monitoring of physiologic parameters in critically ill patients is currently performed by threshold alarm systems with high sensitivity but low specificity. As a consequence, a multitude of alarms are generated, leading to an impaired clinical value of these alarms due to reduced alertness of the intensive care unit (ICU) staff. To evaluate a new alarm procedure, we currently generate a database of physiologic data and clinical alarm annotations. METHODS: Data collection is taking place at a 12-bed medical ICU. Patients with monitoring of at least heart rate, invasive arterial blood pressure, and oxygen saturation are included in the study. Numerical physiologic data at 1-second intervals, monitor alarms, and alarm settings are extracted from the surveillance network. Bedside video recordings are performed with network surveillance cameras. RESULTS: Based on the extracted data and the video recordings, alarms are clinically annotated by an experienced physician. The alarms are categorized according to their technical validity and clinical relevance by a taxonomy system that can be broadly applicable. Preliminary results showed that only 17% of the alarms were classified as relevant, and 44% were technically false. DISCUSSION: The presented system for collecting real-time bedside monitoring data in conjunction with video-assisted annotations of clinically relevant events is the first allowing the assessment of 24-hour periods and reduces the bias usually created by bedside observers in comparable studies. It constitutes the basis for the development and evaluation of "smart" alarm algorithms, which may help to reduce the number of alarms at the ICU, thereby improving patient safety.

Intensive care unit alarms--how many do we need?

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.

Smart alarms from medical devices in the OR and ICU.

Alarms in medical devices are a matter of concern in critical and perioperative care. The high rate of false alarms is not only a nuisance for patients and caregivers, but can also compromise patient safety and effectiveness of care. The development of alarm systems has lagged behind the technological advances of medical devices over the last 20 years. From a clinical perspective, major improvements in alarm algorithms are urgently needed. This review gives an overview of the current clinical situation and the underlying problems, and discusses different methods from statistics and computational science and their potential for clinical application. Some examples of the application of new alarm algorithms to clinical data are presented.

Robust filters for intensive care monitoring: beyond the running median.

Current alarm systems in intensive care units create a very high rate of false positive alarms because most of them simply compare physiological measurements to fixed thresholds. An improvement can be expected when the actual measurements are replaced by smoothed estimates of the underlying signal. However, classical filtering procedures are not appropriate for signal extraction, as standard assumptions, such as stationarity, do no hold here: the time series measured often show long periods without change, but also upward or downward trends, sudden shifts and numerous large measurement artefacts. Alternative approaches are needed to extract the relevant information from the data, i.e., the underlying signal of the monitored variables and the relevant patterns of change, such as abrupt shifts and trends. This article reviews recent research on filter-based online signal extraction methods designed for application in intensive care.

Dimension reduction for physiological variables using graphical modeling.

In intensive care, physiological variables of the critically ill are measured and recorded in short time intervals. The proper extraction and interpretation of the essential information contained in this flood of data can hardly be done by experience alone. Typically, decision making in intensive care is based on only a few selected variables. Alternatively, for a dimension reduction statistical latent variable techniques like principal component analysis or factor analysis can be applied. However, the interpretation of latent components extracted by these methods may be difficult. A more refined analysis is needed to provide suitable bedside decision support. Graphical models based on partial correlations provide information on the relationships among physiological variables that is helpful for variable selection and for identifying interpretable latent components. In a comparative study we investigate how much of the variability of the observed multivariate physiological time series can be explained by variable selection, by standard principal component analysis and by extracting latent compo-nents from groups of variables identified in a graphical model.

Extraction of a local linear trend from physiological time series.

We discuss methods for robust signal extraction from noisy physiological time series as measured in intensive care. The aim is a method which allows a fast and reliable de-noising of the data and separation of artifacts from relevant changes in the patients condition. For approximating local linear trends we use robust regression estimators. We examine the performance of the L1 regression, the repeated median and the least median of squares for this task.

Detecting relationships between physiological variables using graphical models.

In intensive care physiological variables of the critically ill are measured and recorded in short time intervals. The proper extraction and interpretation of the information contained in this flood of information can hardly be done by experience alone. Intelligent alarm systems are needed to provide suitable bedside decision support. So far there is no commonly accepted standard for detecting the actual clinical state from the patient record. We use the statistical methodology of graphical models based on partial correlations for detecting time-varying relationships between physiological variables. Graphical models provide information on the relationships among physiological variables that is helpful e.g. for variable selection. Separate analyses for different pathophysiological states show that distinct clinical states are characterized by distinct partial correlation structures. Hence, this technique can provide new insights into physiological mechanisms.

Graphical models for multivariate time series from intensive care monitoring.

Nowadays physicians are confronted with high-dimensional data generated by clinical information systems. The proper extraction and interpretation of the information contained in such massive data sets, which are often observed with high sampling frequencies, can hardly be done by experience only. This yields new perspectives of data recording and also sets a new challenge for statistical methodology. Recently graphical models have been developed for analysing the partial correlations between the components of multivariate time series. We apply this technique to the haemodynamic system of critically ill patients monitored in intensive care. In this way we can appraise the practical value of the new procedure by re-identifying known associations within the haemodynamic system. From separate analyses for different pathophysiological states we can even conclude that distinct clinical states are characterized by distinct partial correlation structures. Hence, this technique seems useful for automatic statistical analysis of high-dimensional physiological time series and it can provide new insights into physiological mechanisms. Moreover, we can use it to achieve an adequate dimension reduction of the variables needed for online monitoring at the bedside.

Functional cerebral asymmetries during the menstrual cycle: a cross-sectional and longitudinal analysis.

This study aims at answering two basic questions regarding the mechanisms with which hormones modulate functional cerebral asymmetries. Which steroids or gonadotropins fluctuating during the menstrual cycle affect perceptual asymmetries? Can these effects be demonstrated in a cross-sectional (follicular and midluteal cycle phases analyzed) and a longitudinal design, in which the continuous hormone and asymmetry fluctuations were measured over a time course of 6 weeks? To answer these questions, 12 spontaneously cycling right-handed women participated in an experiment in which their levels of progesterone, estradiol, testosterone, LH, and FSH were assessed every 3 days by blood-sample based radioimmunoassays (RIAs). At the same points in time their asymmetries were analyzed with visual half-field (VHF) techniques using a lexical decision, a figure recognition, and a face discrimination task. Both cross-sectional and longitudinal analyzes showed that an increase of progesterone is related to a reduction in asymmetries in a figure recognition task by increasing the performance of the left-hemisphere which is less specialized for this task. Cross-sectionally, estradiol was shown to have significant relationships to the accuracy and the response speed of both hemispheres. However, since these effects were in the same direction, asymmetry was not affected. This was not the case in the longitudinal design, where estradiol affected the asymmetry in the lexical decision and the figural comparison task. Overall, these data show that hormonal fluctuations within the menstrual cycle have important impacts on functional cerebral asymmetries. The effect of progesterone was highly reliable and could be shown in both analysis schemes. By contrast, estradiol mainly, but not exclusively, affected both hemispheres in the same direction.