Michael Imhoff

Alarm algorithms in critical care monitoring

The alarms of medical devices are a matter of concern in critical and perioperative care. The frequent false alarms not only are 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 of alarm algorithms are urgently needed. We give an overview of the current clinical situation and the underlying problems and discuss different methods from statistics and computational science and their potential for clinical application.

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.

Knowledge Discovery and Knowledge Validation in Intensive Care

Operational protocols are a valuable means for quality control. However, developing operational protocols is a highly complex and costly task. We present an integrated approach involving both intelligent data analysis and knowledge acquisition from experts that support the development of operational protocols. The aim is to ensure high quality standards for the protocol through empirical validation during the development, as well as lower development cost through the use of machine learning and statistical techniques. We demonstrate our approach of integrating expert knowledge with data driven techniques based on our effort to develop an operational protocol for the hemodynamic system.

Medical device alarms

Abstract The high number of false positive alarms has long been known to be a serious problem in critical care medicine – yet it remains unresolved. At the same time, threats to patient safety due to missing or suppressed alarms are being reported. The purpose of this paper is to present results from a workshop titled “Too many alarms? Too few alarms?” organized by the Section Patient Monitoring and the Workgroup Alarms of the German Association of Biomedical Engineering of the Association for Electrical, Electronic and Information Technologies. The current situation regarding alarms and their problems in intensive care, such as lack of clinical relevance, alarm fatigue, workload increases due to clinically irrelevant alarms, usability problems in alarm systems, problems with manuals and training, and missing alarms due to operator error are outlined, followed by a discussion of solutions and strategies to improve the current situation. Finally, the need for more research and development, focusing on signal quality considerations, networking of medical devices at the bedside, diagnostic alarms and predictive warnings, usability of alarm systems, education of healthcare providers, creation of annotated clinical databases for testing, standardization efforts, and patient monitoring in the regular ward, are called for.

Statistical pattern detection in univariate time series of intensive care on-line monitoring data

Objectives: To determine how different mathematical time series approaches can be implemented for the detection of qualitative patterns in physiologic monitoring data, and which of these approaches could be suitable as a basis for future bedside time series analysis. Design: Off-line time series analysis. Setting: Surgical intensive care unit of a teaching hospital. Patients: 19 patients requiring hemodynamic monitoring with a pulmonary artery catheter. Interventions: None. Measurements and results: Hemodynamic data were acquired in 1-min intervals from a clinical information system and exported into statistical software for further analysis. Altogether, 134 time series for heart rate, mean arterial pressure, and mean pulmonary artery pressure were visually classified by a senior intensivist into five patterns: no change, outlier, temporary level change, permanent level change, and trend. The same series were analyzed with low-order autoregressive (AR) models and with phase space (PS) models. The resulting classifications from both models were compared to the initial classification. Outliers and level changes were detected in most instances with both methods. Trend detection could only be done indirectly. Both methods were more sensitive to pattern changes than they were clinically relevant. Especially with outlier detection, 95 % confidence intervals were too close. AR models require direct user interaction, whereas PS models offer opportunities for fully automated time series analysis in this context. Conclusion: Statistical patterns in univariate intensive care time series can reliably be detected with AR models and with PS models. For most bedside problems both methods are too sensitive. AR models are highly interactive, and both methods require that users have an explicit knowledge of statistics. While AR models and PS models can be extremely useful in the scientific off-line analysis, routine bedside clinical use cannot yet be recommended.

Noninvasive whole-body electrical bioimpedance cardiac output and invasive thermodilution cardiac output in high-risk surgical patients.

ObjectiveTo evaluate the reliability of whole-body impedance cardiography with two electrodes on either both wrists or one wrist and one ankle for the measurement of cardiac output compared with the thermodilution method. DesignProspective, clinical investigation SettingSurgical intensive care unit of a university-affiliated community hospital. PatientsSimultaneous cardiac output measurements by noninvasive whole-body impedance cardiography (nCO) and invasive thermodilution (thCO) in 22 high-risk surgical patients scheduled for extended surgery requiring perioperative pulmonary artery catheter monitoring. InterventionsNone. Measurements and Main ResultsA total of 109 sets of measurements consisting of 455 single comparison measurements between nCO and thCO were included in the analysis. The mean cardiac output difference between the two methods was 1.62 L/min with limits of agreement (2 sd) of ± 4.64 L/min. The inter-measurement variance was slightly higher for nCO. The correlation coefficient between nCO and thCO was r2 = 0.061 (p < .001) for single measurements and r2 = 0.083 (p < .002) for sets of three to six measurements. The two most predictive factors for between-method differences were the absolute thCO value (r2 = 0.13;p < .001) and whether or not a continuous nitroglycerin infusion was used (p < .05, Student’s t-test). ConclusionsAgreement between whole-body impedance cardiography and thermodilution in the measurement of cardiac output was unsatisfactory. Factors that can explain these differences are differences between the populations used for calibration of nCO and the study population, the influence of changing peripheral perfusion, and the effect of a supranormal hemodynamic state on the bioimpedance signal. Whole-body impedance cardiography cannot be recommended for assessing the hemodynamic state of high-risk surgical patients as studied in this investigation.

Reducing False Alarms of Intensive Care Online-Monitoring Systems: An Evaluation of Two Signal Extraction Algorithms

Online-monitoring systems in intensive care are affected by a high rate of false threshold alarms. These are caused by irrelevant noise and outliers in the measured time series data. The high false alarm rates can be lowered by separating relevant signals from noise and outliers online, in such a way that signal estimations, instead of raw measurements, are compared to the alarm limits. This paper presents a clinical validation study for two recently developed online signal filters. The filters are based on robust repeated median regression in moving windows of varying width. Validation is done offline using a large annotated reference database. The performance criteria are sensitivity and the proportion of false alarms suppressed by the signal filters.

The Identification of Multiple Outliers in Online Monitoring Data

We present a robust graphical procedure for routine detection of isolated and patchy outliers in univariate time series. This procedure is suitable for retrospective as well as for online identification of outliers. It is based on a phase space reconstruction of the time series which allows to regard the time series as a multivariate sample with identically distributed but non independent observations. Thus, multivariate outlier identifiers can be transferred into the context of time series which is done here. Some applications to online monitoring data from intensive care are given.

Time series analysis in intensive care medicine

Objectives: Time series analysis techniques facilitate statistical analysis of variables in the course of time. Continuous monitoring of the critically ill in intensive care offers an especially wide range of applications. In an open clinical study time series analysis was applied to the monitoring of lab variables after liver surgery, and to support clinical decision making in the treatment of acute respiratory distress syndrome.

The Crying Wolf: Still Crying?

Roland Fried, PhD† In 1994, Lawless compared the situation of alarms in the intensive care unit (ICU) with the boy who cried wolf in the famous fable by Aesop, alluding to the danger of desensitization of caregivers to true medical device alarms through the overwhelming number of false medical device alarms that he observed on a pediatric ICU. Alarm limits may be set dangerously broad, or alarms may even be completely disabled to reduce the nuisance from false alarms. Even at these settings, clinicians may tolerate an alarm for up to 10 min before taking action. This situation cries for immediate remedy. The sad reality, although, is that not much seems to have changed over the nearly 15 yr since Lawless’ publication. The current literature and ongoing research efforts (reviewed in Ref. 6) as well as recent data from our own group, show that still the vast majority of medical device alarms are false positives. Interestingly, there is no scarcity of research addressing the problem of medical device alarms. Many different approaches have been studied in the fields of statistics and artificial intelligence as well as biomedical and human factors engineering. Several approaches have shown efficacy and effectiveness in reducing the rate of false alarms in clinical study. Still, very little has been implemented in commercially available medical devices. In this situation Görges et al. promise hope in their article published in this issue of Anesthesia & Analgesia. In their study, they first acquired comprehensive clinical data on medical device alarms and then investigated two approaches to reduce the number of false-positive alarms. The authors must be commended for their efforts, as we know from other researchers and our own experience how much stamina it takes to acquire alarm data and consistently annotate sufficiently large numbers of medical device alarms. Görges et al. confirm that only the minority of medical device alarms are clinically relevant—in their study, 23% of all alarms. They also found that not only were six alarms activated per hour per bed, but also alarms were sounding 31⁄2 min per hour per bed. Extrapolating to a 10-bed ICU, this means that a false alarm is active, i.e., making some noise or “crying,” nearly 50% of the time, day and night, 24/7. These numbers are in line with other studies. If we keep in mind that it took the boy in Aesop’s fable only two false alarms to make the shepherds ignore the third but true and deadly alarm, the current situation of medical device alarms seems mindboggling. Of course, the study by Görges et al. has distinct weaknesses, most of which the authors diligently discuss: night shifts were not included in the study, the physical presence of the observer may have induced a Hawthorne effect, clinical annotations of alarms were subjective, and there may have been significant intraand interobserver variability. Moreover, clinical practice patterns in the study ICU may differ from other institutions, which may further affect the generalizability of the reported results, as may the differences in annotation schemata between different studies, as pointed out by the authors. But this is true for each and every clinical alarm study published as of today. And still, all studies come to similar conclusions despite their differences in methodology and clinical settings, actually strengthening rather than weakening our point about the inadequacy of current device alarms. From the *Department for Medical Informatics, Biometrics and Epidemiology, Ruhr-University, Bochum, Germany; and †Department of Statistics, Technical University Dortmund, Dortmund, Germany. Accepted for publication January 19, 2009. M.I. and R.F. have received research grants from the German Research Foundation (DFG SFB475). MI has received consulting fees from Draeger Medical and is managing member of Boston MedTech Advisors Europe. Address correspondence and reprint requests to Dr. Michael Imhoff, Am Pastorenwäldchen 2, D-44229 Dortmund, Germany. Address e-mail to mike@imhoff.de. Copyright