A Program to Optimize the Detection of Paroxysmal Atrial Fibrillation: The RITMO Study

Abstract

Background: Atrial fibrillation (AF) is the most common sustained arrythmia, but still underdiagnosed especially among asymptomatic patients. Objectives: To evaluate a simple strategy to optimize the identification of AF. Methods: Asymptomatic patients aged 65 years or older, with hypertension or heart failure (HF), were included. Data were inserted into the REDCap software platform. Patients were assessed for the risk for AF using the Stroke Risk Analysis (SRA) mathematical algorithm, which was applied on a one-hour electrocardiogram (ECG). All patients at high risk for AF were instructed to follow a home ECG protocol for seven days using a portable Kardia 6 (OMRON, AliveCor®). The Kolmogorov-test was used to test the normality of quantitative variables; those with normal distribution were expressed as mean and standard deviation. A p<0.05 was set as statistically significant. Results: A total of 423 patients were assessed; 15 were excluded due to absence of SRA, yielding a sample of 408 patients. In 13 (3.2%), AF was identified, 120 (29.4%) were considered at high risk and 275 (67.4%) without increased risk for AF. Of the 120 high-risk patients, 111 successfully completed the seven-day protocol with Kardia; at least one episode of AF was identified in 43 patients. Conclusion: The strategy adopted in the RITMO study was shown to be effective in identifying AF in asymptomatic elderly patients with hypertension or HF, with an incidence of 13.7% (56/408).