Correlating data from different sensors to increase the positive predictive value of alarms: an empiric assessment

Objectives: Alarm fatigue from high false alarm rate is a well described phenomenon in the intensive care unit (ICU). Progress to further reduce false alarms must employ a new strategy. Highly sensitive alarms invariably have a very high false alarm rate. Clinically useful alarms have a high Positive-Predictive Value. Our goal is to demonstrate one approach to suppressing false alarms using an algorithm that correlates information across sensors and replicates the ways that human evaluators discriminate artifact from real signal. Methods: After obtaining IRB approval and waiver of informed consent, a set of definitions, (hypovolemia, left ventricular shock, tamponade, hemodynamically significant ventricular tachycardia, and hemodynamically significant supraventricular tachycardia), were installed in the monitors in a 10 bed cardiothoracic ICU and evaluated over an 85 day study period. The logic of the algorithms was intended to replicate the logic of practitioners, and correlated information across sensors in a way similar to that used by practitioners. The performance of the alarms was evaluated via a daily interview with the ICU attending and review of the tracings recorded over the previous 24 hours in the monitor. True alarms and false alarms were identified by an expert clinician, and the performance of the algorithms evaluated using the standard definitions of sensitivity, specificity, positive predictive value, and negative predictive value. Results: Between 1 and 221 instances of defined events occurred over the duration of the study, and the positive predictive value of the definitions varied between 4.1% and 84%. Conclusions: Correlation of information across alarms can suppress artifact, increase the positive predictive value of alarms, and can employ more sophisticated definitions of alarm events than present single-sensor based systems.