UCSF

Background: Critical care nurses are exposed to an excessive number of clinical alarms in the patient care environment. Prior studies show that the vast majority of arrhythmia alarms are false and can lead to alarm fatigue. Patients with both true and false arrhythmia alarms present a unique challenge, because exposure to false alarms can desensitize nurses to true alarms and may lead to missed true events. Objectives: The purpose of this study was to examine patients with both true and false arrhythmia alarms and determine: (1) frequency of patients with true and false arrhythmia alarms in a sample of 461 intensive care unit (ICU) patients, (2) patient, clinical, and electrocardiographic characteristics associated with both true and false alarms, and (3) the number and types of true and false arrhythmia alarms (e.g., asystole, ventricular fibrillation, ventricular tachycardia, accelerated ventricular rhythm, pause, and ventricular bradycardia). Methods: This was a secondary analysis utilizing data from the UCSF Alarm Study. Results: In 461 ICU patients, 71 (15%) patients had a total of 10,699 true and false arrhythmias alarms: 1,348 (13%) were true and 9,351 (87%) were false. Compared to patients without true and false alarms, the presence of ventricular pacemaker, bundle branch block, altered mental status, mechanical ventilation, left ventricular assist device, and cardiac ICU admission were associated with the presence of true and false alarms (p = 0.001). Patients with both true and false alarms had longer length of ICU stay than patients without both true and false alarms (208 hours versus 75 hours; p = 0.001). Accelerated ventricular rhythm was the most common alarm (37%), followed by ventricular tachycardia (31%). Conclusion: We found that 15% of ICU patients have both true and false alarms and the vast majority are false. Further research is needed to build and test improved ECG arrhythmia algorithms that adjust for clinical and ECG characteristics associated with false alarms. Nurses’ awareness of these characteristics may help them identify patients at risk for excessive alarms and aid in developing unit specific solutions to decrease alarm fatigue.