Silvia Kuhls

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.

Noise in intensive care units. Do the alarms for subspecialties differ

INTRODUCTION Cardiovascular monitoring alarms are frequent in intensive care units (ICUs) and lead to noise levels often exceeding 80 dB. The aim of this study was to evaluate if there are relevant differences between ICUs with different subspecialties in the frequency and distribution of alarm signals, their occurrence during the day, the types of alarms and the underlying vital parameters. METHODS All alarm signals of the cardiovascular monitoring systems from randomly chosen patients at five different ICUs of the university hospital of Regensburg were evaluated. RESULTS No significant differences between the ICUs regarding the frequency of alarm signals and only slight differences in the time distribution could be recognized (p=0.02). The most frequent alarm signals were from threshold alarms (61%) followed by technical alarms. The majority of alarms generated were related to invasive arterial blood pressure measurement. CONCLUSIONS The frequency and distribution of ICU alarm signals seem to be comparable on different ICUs. Therefore, implementation of universal concepts for alarm reduction seems to be applicable regardless of the subspecialty of the ICU.

Lärm auf der Intensivstation

INTRODUCTION Cardiovascular monitoring alarms are frequent in intensive care units (ICUs) and lead to noise levels often exceeding 80 dB. The aim of this study was to evaluate if there are relevant differences between ICUs with different subspecialties in the frequency and distribution of alarm signals, their occurrence during the day, the types of alarms and the underlying vital parameters. METHODS All alarm signals of the cardiovascular monitoring systems from randomly chosen patients at five different ICUs of the university hospital of Regensburg were evaluated. RESULTS No significant differences between the ICUs regarding the frequency of alarm signals and only slight differences in the time distribution could be recognized (p=0.02). The most frequent alarm signals were from threshold alarms (61%) followed by technical alarms. The majority of alarms generated were related to invasive arterial blood pressure measurement. CONCLUSIONS The frequency and distribution of ICU alarm signals seem to be comparable on different ICUs. Therefore, implementation of universal concepts for alarm reduction seems to be applicable regardless of the subspecialty of the ICU.

Entwicklung einer Eingabemaske für die Erfassung klinischer Annotationen

Im Rahmen des Projektes ?Zeitreihenanalytische Methoden zur Behandlung von Online-Monitoring-Daten aus der Intensivmedizin? im Sonderforschungsbereich 475 wird eine klinische Studie zur Evaluierung und zum Vergleich von Alarm-Algorithmen fur die Patientenuberwachung auf Intensivstationen durchgefuhrt. Die Studie erhebt klinisch annotierte Daten. Die durch ein Monitoringgerat aufgezeichneten Patientendaten fur die Vitalparameter und die aufgetretenen Alarme werden dazu retrospektiv analysiert. Alle Annotationen werden mit Hilfe einer speziell zu diesem Zweck entwickelten Eingabemaske erfasst, die in dieser Arbeit beschrieben wird.

Lärm auf der Intensivstation@@@Noise in intensive care units: Unterscheiden sich die Alarme in den Fachdisziplinen?@@@Do the alarms for subspecialties differ?

INTRODUCTION Cardiovascular monitoring alarms are frequent in intensive care units (ICUs) and lead to noise levels often exceeding 80 dB. The aim of this study was to evaluate if there are relevant differences between ICUs with different subspecialties in the frequency and distribution of alarm signals, their occurrence during the day, the types of alarms and the underlying vital parameters. METHODS All alarm signals of the cardiovascular monitoring systems from randomly chosen patients at five different ICUs of the university hospital of Regensburg were evaluated. RESULTS No significant differences between the ICUs regarding the frequency of alarm signals and only slight differences in the time distribution could be recognized (p=0.02). The most frequent alarm signals were from threshold alarms (61%) followed by technical alarms. The majority of alarms generated were related to invasive arterial blood pressure measurement. CONCLUSIONS The frequency and distribution of ICU alarm signals seem to be comparable on different ICUs. Therefore, implementation of universal concepts for alarm reduction seems to be applicable regardless of the subspecialty of the ICU.

Analyzing Associations in Multivariate Binary Time Series

We analyze multivariate binary time series using a mixed parameterization in terms of the conditional expectations given the past and the pairwise canonical interactions among contemporaneous variables. This allows consistent inference on the influence of past variables even if the contemporaneous associations are misspecified. Particularly, we can detect and test Granger non-causalities since they correspond to zero parameter values.