iRhythm Unveils New Real-World Data at ACC.25 Demonstrating the Benefits of Zio® Long-Term Continuous Monitoring for Arrhythmia Detection

Core Insights - Two large studies involving over 1.1 million patients indicate that short-term Holter monitoring often fails to detect actionable arrhythmias, even in patients with reported daily symptoms, highlighting the limitations of current monitoring practices [1][4][7] Group 1: Study Findings - 64% of patients with daily symptoms and actionable arrhythmias were undetected within the first 48 hours of monitoring, suggesting that short-term Holter monitoring is inadequate [6][9] - Non-daily symptom patients had a higher arrhythmia yield of 80.9% compared to 69.1% for daily-symptom patients, indicating that symptom frequency does not correlate with arrhythmia burden [6] - The mean time to the first detected arrhythmia episode exceeded 48 hours across all types, reinforcing the inadequacy of short-term monitoring [6][9] Group 2: Clinical Implications - The findings challenge the assumption that frequent symptoms justify short-duration monitoring and emphasize the need for long-term continuous monitoring (LTCM) for better detection of arrhythmias [4][7] - Zio LTCM provides uninterrupted monitoring for up to 14 days, which can lead to more accurate and timely detection of actionable arrhythmias compared to Holter monitoring [7][8] - The CAMELOT study supports that Zio LTCM has the highest yield for arrhythmia diagnosis and the lowest likelihood of repeat testing among monitoring services [7] Group 3: Economic Impact - Approximately 16 million Americans experience arrhythmias, with undiagnosed atrial fibrillation alone costing the U.S. $3 billion annually, highlighting the financial burden of undetected arrhythmias [5] - The economic rationale for long-term continuous monitoring is underscored by the projected costs of heart failure reaching$70 billion by 2030 [5] Group 4: Company Background - iRhythm Technologies is a leading digital health care company focused on creating solutions for disease detection and prevention, utilizing wearable biosensors and cloud-based analytics [11]