Literature DB >> 20016379

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

Sylvia Siebig1, Silvia Kuhls, Michael Imhoff, Ursula Gather, Jürgen Schölmerich, Christian E Wrede.   

Abstract

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.

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Year:  2010        PMID: 20016379     DOI: 10.1097/CCM.0b013e3181cb0888

Source DB:  PubMed          Journal:  Crit Care Med        ISSN: 0090-3493            Impact factor:   7.598


  43 in total

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3.  Game Theoretic Approach for Systematic Feature Selection; Application in False Alarm Detection in Intensive Care Units.

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9.  2017 ISHNE-HRS expert consensus statement on ambulatory ECG and external cardiac monitoring/telemetry.

Authors:  Jonathan S Steinberg; Niraj Varma; Iwona Cygankiewicz; Peter Aziz; Paweł Balsam; Adrian Baranchuk; Daniel J Cantillon; Polychronis Dilaveris; Sergio J Dubner; Nabil El-Sherif; Jaroslaw Krol; Malgorzata Kurpesa; Maria Teresa La Rovere; Suave S Lobodzinski; Emanuela T Locati; Suneet Mittal; Brian Olshansky; Ewa Piotrowicz; Leslie Saxon; Peter H Stone; Larisa Tereshchenko; Mintu P Turakhia; Gioia Turitto; Neil J Wimmer; Richard L Verrier; Wojciech Zareba; Ryszard Piotrowicz
Journal:  Ann Noninvasive Electrocardiol       Date:  2017-05       Impact factor: 1.468

10.  Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

Authors:  Lujie Chen; Artur Dubrawski; Donghan Wang; Madalina Fiterau; Mathieu Guillame-Bert; Eliezer Bose; Ata M Kaynar; David J Wallace; Jane Guttendorf; Gilles Clermont; Michael R Pinsky; Marilyn Hravnak
Journal:  Crit Care Med       Date:  2016-07       Impact factor: 7.598

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