Patient-centered novel e-health technology and services will lay the foundation for future healthcare systems and services to support health and welfare promotion. Yet, there is a lack of ways to incorporate novel technological innovations into easy-to-use, cost-effective and low workload treatment. The detection of atrial fibrillation (AF) paroxysms and its permanent form as well as the prevention of AF-related strokes are major challenges in cardiology today. AF is often silent or asymptomatic, but the risk of ischemic stroke seems to be similar regardless of the presence or absence of symptoms. CARE-DETECT algorithm development part I will investigate following topics: 1. The usefulness and validity of bed sensor and mobile phone application in rhythm disorder capture compared to gold standard ECG-holter monitoring (Faros ECG) 2. Accuracy of AF detection from PDL data 3. Technical development of algorithms to detect arrhythmia from data collected with these novel devices 4. Development of a pre-processing tool that will evaluate the collected data and generate a preliminary filtered report of the raw data to ease clinician's workload in data handling and rhythm evaluation. CARE-DETECT clinical trial (part II) proposal provides a new concept for low workload for healthcare personnel, high diagnostic yield in silent AF detection and AF burden evaluation. CARE-DETECT protocol proposal seeks to address following issues: 1. Can a combination of actively used smartphone application and passive monitoring with bed sensor (with upstream ECG) - compared to routine care - enhance the detection of AF in patients who are at increased risk of stroke and have undergone a cardiac procedure? 2. What is the actual AF burden in paroxysmal AF patients after the detection of new-onset AF? 3. Can a direct-to-consumer telehealth with integrated cloud-based telecardiology service for medical professionals improve the efficacy of silent AF detection and what is the AF burden in patients suffering of (asymptomatic) paroxysmal AF and secondarily what is the cost-effectiveness of these new screening methods? 4. Additionally, during the hospitalization phase of the study part II PDL data will be collected in the intervention group. PDL data will be analyzed offline with the purpose to develop new methods and will not be used to monitor treatment or for diagnosis.
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The rate of new AF during index hospitalization.
Timeframe: During hospital stay (assessed up to 10 days).
The rate of new AF burden during index hospitalization.
Timeframe: During hospital stay (assessed up to 10 days).
The rate of new AF during 3 months follow-up after index hospitalization.
Timeframe: During 3 months follow-up.
The rate of new AF burden during 3 months follow-up after index hospitalization.
Timeframe: During 3 months follow-up.
Algorithm development.
Timeframe: The part I trial during index hospitalization (assessed up to 48 hours). The part I trial with PDL may be continued among the part II interventional group during initial hospitalization (assessed up to 10 days) in order to collect additional data.
Number of adverse events related to PDL device.
Timeframe: The part I trial during index hospitalization (assessed up to 48 hours). The part I trial with PDL may be continued among the part II interventional group during initial hospitalization (assessed up to 10 days) in order to collect additional data.
Number of technical issues of the concept and interface.
Timeframe: The part I trial during index hospitalization (assessed up to 48 hours).
Number of practical issues of the concept and interface.
Timeframe: The part I trial during index hospitalization (assessed up to 48 hours).