These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform.
    Author: Aboukhalil A, Nielsen L, Saeed M, Mark RG, Clifford GD.
    Journal: J Biomed Inform; 2008 Jun; 41(3):442-51. PubMed ID: 18440873.
    Abstract:
    BACKGROUND: Over the past two decades, high false alarm (FA) rates have remained an important yet unresolved concern in the Intensive Care Unit (ICU). High FA rates lead to desensitization of the attending staff to such warnings, with associated slowing in response times and detrimental decreases in the quality of care for the patient. False arrhythmia alarms are commonly due to single channel ECG artifacts and low voltage signals, and therefore it is likely that the FA rates may be reduced if information from other independent signals is used to form a more robust hypothesis of the alarm's etiology. METHODS: A large multi-parameter ICU database (PhysioNet's MIMIC II database) was used to investigate the frequency of five categories of false critical ("red" or "life-threatening") ECG arrhythmia alarms produced by a commercial ICU monitoring system, namely: asystole, extreme bradycardia, extreme tachycardia, ventricular tachycardia and ventricular fibrillation/tachycardia. Non-critical ("yellow") arrhythmia alarms were not considered in this study. Multiple expert reviews of 5386 critical ECG arrhythmia alarms from a total of 447 adult patient records in the MIMIC II database were made using the associated 41,301 h of simultaneous ECG and arterial blood pressure (ABP) waveforms. An algorithm to suppress false critical ECG arrhythmia alarms using morphological and timing information derived from the ABP signal was then tested. RESULTS: An average of 42.7% of the critical ECG arrhythmia alarms were found to be false, with each of the five alarm categories having FA rates between 23.1% and 90.7%. The FA suppression algorithm was able to suppress 59.7% of the false alarms, with FA reduction rates as high as 93.5% for asystole and 81.0% for extreme bradycardia. FA reduction rates were lowest for extreme tachycardia (63.7%) and ventricular-related alarms (58.2% for ventricular fibrillation/tachycardia and 33.0% for ventricular tachycardia). True alarm (TA) reduction rates were all 0%, except for ventricular tachycardia alarms (9.4%). CONCLUSIONS: The FA suppression algorithm reduced the incidence of false critical ECG arrhythmia alarms from 42.7% to 17.2%, where simultaneous ECG and ABP data were available. The present algorithm demonstrated the potential of data fusion to reduce false ECG arrhythmia alarms in a clinical setting, but the non-zero TA reduction rate for ventricular tachycardia indicates the need for further refinement of the suppression strategy. To avoid suppressing any true alarms, the algorithm could be implemented for all alarms except ventricular tachycardia. Under these conditions the FA rate would be reduced from 42.7% to 22.7%. This implementation of the algorithm should be considered for prospective clinical evaluation. The public availability of a real-world ICU database of multi-parameter physiologic waveforms, together with their associated annotated alarms is a new and valuable research resource for algorithm developers.
    [Abstract] [Full Text] [Related] [New Search]