RESUMO
The over 400,000 cardiac surgeries performed in the United States each year hold a risk for the postoperative complication of arrhythmias. Currently, bedside monitoring of surface electrocardiogram leads is used to interpret arrhythmias despite the evidence that atrial electrograms (AEGs) offer superior rhythm discrimination. This hesitancy to use the AEG may be due to a lack of training for practitioners in interpreting AEGs; therefore, our goal was to create an algorithm for the diagnosis of tachyarrhythmia using an AEG that can be utilized by any health care practitioner. Our algorithm classifies the most prevalent type of tachyarrhythmias following cardiac surgery. To allow rhythm identification, we categorized them based on their atrial to ventricular signal ratio, which is uniquely apparent on AEGs. Other considerations were given to rhythm regularity, consistency, P-wave axis, and rate. The algorithm includes the most common postoperative arrhythmias differentiated based on a unique branch-point approach, which walks through the steps in arrhythmia discrimination. Both rendered and collected AEGs are included as references for further understanding and interpretation of tachyarrhythmias. The utility of AEGs for rhythm discrimination post-cardiac surgery is established and recent technology can provide real-time and continuous monitoring; however, practitioner training may be inadequate. To bridge this divide, we created an algorithm so that existing atrial wires can be better used for an enhanced rhythm interpretation via AEGs.