Summary: iRhythm Technologies presented new data at the American Heart Association’s 2024 Scientific Sessions, highlighting the benefits of long-term continuous monitoring for detecting arrhythmias. The findings span three areas: identifying arrhythmia patterns during sleep and activity, leveraging digital tools to boost patient compliance, and assessing the economic impact of early arrhythmia detection in patients with type 2 diabetes and COPD. Studies demonstrated the effectiveness of the Zio system in tracking sleep-related arrhythmias, enhancing patient engagement through digital interventions, and potentially reducing healthcare costs in at-risk populations.
Key Takeaways:
- Distinct Arrhythmia Patterns Linked to Sleep and Activity: Continuous monitoring with the Zio system identified specific arrhythmias more likely to occur during sleep, such as pauses and third-degree heart block, providing insights for personalized arrhythmia management.
- Digital Tools Improve Patient Compliance: Using digital interventions like the MyZio app and SMS notifications significantly boosted patient compliance, with device return rates reaching nearly 95% among users.
- Economic Benefits of Early Detection in Chronic Conditions: Early arrhythmia detection in patients with type 2 diabetes and COPD may reduce acute care costs by preventing serious outcomes, underscoring the economic value of long-term monitoring.
iRhythm Technologies presented new findings at the American Heart Association’s 2024 Scientific Sessions, demonstrating the clinical and economic value of long-term continuous monitoring for detecting arrhythmias, including insights into patterns during sleep and activity to optimize patient outcomes.
The five studies presented by iRhythm span three focus areas for long-term continuous monitoring: evaluating arrhythmia patterns during periods of sleep and activity, patient engagement and satisfaction through digital tools and patient-centered product enhancements, and assessing the potential healthcare resource and economic impact of early arrhythmia detection in patients with type 2 diabetes and chronic obstructive pulmonary disease (COPD).
“These new findings underscore iRhythm’s commitment to rigorous scientific evidence,” says Mintu Turakhia, MD, iRhythm’s chief medical and scientific officer and executive vice president of product innovation, in a release. “Our data demonstrates the significant health economic benefits of early arrhythmia detection in often-overlooked conditions like diabetes and COPD, highlights greater patient engagement through our patient-centered digital tools that complement our services, and reveals distinct arrhythmia patterns associated with sleep and activity.”
Algorithm Tracks Sleep and Activity
Two studies assessed the feasibility and clinical utility of using the Zio system to monitor arrhythmias in relation to sleep and activity patterns. Analyzing and classifying arrhythmia occurrences during sleep and physical exertion provides insights that may inform more personalized arrhythmia management.
In one of the studies, Determining the accuracy of sleep and activity patterns in patients undergoing long-term ambulatory ECG monitoring, researchers sought to develop and assess the performance of an algorithm to classify periods of sleep, activity (>2mph walking), and inactivity using a novel ambulatory ECG (AECG) patch (Zio monitor) with embedded accelerometry.
A prospective clinical study enrolled participants across four American Academy of Sleep Medicine-qualified sleep centers to support algorithm training and validation. Eighty-one study participants wore the Zio monitor AECG patch and a commercially available actigraphy reference device simultaneously over a 14-day study period, which included in-clinic overnight polysomnography (PSG) sleep testing and a six-minute walk test.
Data acquired were split into training (n=40) and validation (n=41) sets. Feature and model selection utilized five-fold cross-validation on the training set, focusing on total activity and body angle. Algorithm sensitivity and specificity (assessed over one-minute epochs vs PSG reference) in sleep detection were 88.8% and 54.0%, respectively for the validation set. Sensitivity and specificity in activity detection were 97.0% and 100%, respectively.
Study authors concluded the assessment of sleep and activity during AECG is feasible, with performance comparable to US Food and Drug Administration (FDA)-cleared actigraphy and consumer devices. This feature offers insights into patient wellness patterns, highlighting its potential for personalized healthcare monitoring.
Arrhythmia Patterns Linked to Sleep
For the other study, Characterization of arrhythmia occurrence during sleep and activity in patients undergoing long-term continuous ambulatory ECG monitoring, researchers sought to quantify the occurrence of arrhythmias detected by long-term (≤14 days) continuous ambulatory ECG monitoring during periods of sleep, activity, and inactivity.
The analysis included 23,962 patients (57.7% female, age 60.9±18.0 years) who underwent monitoring with a next-generation long-term continuous monitoring (Zio monitor) device. An Al algorithm previously developed and validated was used to classify periods of sleep and activity using long-term continuous monitoring accelerometry data. Rhythms were classified by an FDA-cleared deep learning algorithm, confirmed by a cardiographic technician, and time-aligned to the algorithm-generated sleep/wake and activity/inactivity labels.
Odds ratios (OR) associated with time in arrhythmia for sleep and activity periods were calculated by rhythm type. Among the rhythms having the highest association with sleep (vs wake) were pause (OR=2.58; 95% CI 2.55-2.60) and third-degree heart block, (OR=1.37; 95% CI 1.37-1.37).
Notably, the analysis identified ventricular tachycardia as among the arrhythmias least likely to occur during sleep (OR=0.51; 95% Cl 0.50-0.51). Ventricular tachycardia and third-degree heart block had the highest OR associated with periods of activity.
Results demonstrate the feasibility of integrating sleep and activity labeling with long-term continuous monitoring findings and the potential to give context to arrhythmias, such as onset or termination during sleep, wake, or exertion.
Additionally, two studies validated the impact of digital health tools on improving patient compliance with timely device return and demonstrated the value of using patient-centric feedback to guide enhancements in the latest Zio monitor.
One study, Digital engagement with a patient smartphone app and text messaging is associated with increased compliance in patients undergoing long-term continuous ambulatory cardiac monitoring, sought to determine if two optional direct-to-patient digital interventions, the MyZio smartphone app and short messages services (SMS) text notifications impact patient compliance (ie, activation, wear, and device return within 45 days) in patients who self-applied and activated a Zio 14-day patch-based long-term continuous ambulatory monitoring device shipped directly to their home.
Distribution of the use of digital tools and compliance outcomes was evaluated in 169,131 patients. Device activation, usage, and return compliance was highest (94.8%) when both the app and text messaging were used vs. 74.6% in cases where neither digital intervention was used.
Opting in to SMS text was associated with compliance improvement vs no digital intervention but was inferior to app use. These data support the use of patient digital health interventions in home-based diagnostics and underscore the importance of post-implementation evaluation of outcomes.
The other study, Feasibility of point-of-wear patient satisfaction surveys to validate patient-centered product enhancements: results from over 300,000 patients for long-term ambulatory cardiac monitoring, sought to understand the feasibility and value of collecting patient survey data at the point of care to assess quality improvements associated with use of a novel 14-day patch-based long-term continuous ambulatory ECG monitor.
Specifically, the study compared product experience and patient satisfaction associated with the prior generation long-term continuous monitor (Zio XT) to that of a next-generation, FDA-cleared long-term continuous monitoring product (Zio monitor) designed with patient-centered features, including a more breathable adhesive, waterproof housing, thinner profile, and lighter weight.
Among 334,054 respondents, the new long-term continuous monitor was associated with a greater proportion of affirmative responses across all survey categories, including a 14-percentage point improvement in wear comfort as compared to the prior generation device (79.1% vs 64.7%, p<0.001).
The finding demonstrated patient survey data for post-market quality assessment is feasible for digital health technologies, in this case leading to over 300,000 total respondents in one year. Measures of patient satisfaction were higher with the new device, which may be due to patient-centered product enhancements.
Arrhythmia Detection in Type 2 Diabetes and COPD
One retrospective analysis of medical claims data, Real world evidence on health care resources utilization and economic burden of arrhythmias in patients with diabetes and COPD, examined the healthcare resource burden and medical costs of managing undiagnosed and untreated arrhythmias in patients with type 2 diabetes and COPD.
The analysis was conducted by Eversana, and the preliminary findings suggest that early detection with arrhythmia monitoring devices has the combined potential to help prevent serious outcomes like stroke and heart failure and significantly reduce acute care utilization and related costs in these populations.
These data, presented at the American Heart Association’s 2024 Scientific Sessions, are part of iRhythm’s clinical evidence program, encompassing over 100 original research publications and insights from over 1.5 billion hours of curated heartbeat data.
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