Patient-Led Mass Screening for Atrial Fibrillation in the Older Population Using Handheld Electrocardiographic Devices Integrated With a Clinician-Coordinated Remote Central Monitoring System: Protocol for a Randomized Controlled Trial and Process Evaluation.

BACKGROUND: Atrial fibrillation (AF) is common in older people and increases the risk of stroke. The feasibility and effectiveness of the implementation of a patient-led AF screening program for older people are unknown. OBJECTIVE: This study aims to examine the feasibility and effectiveness of an AF screening program comprising patient-led monitoring of single-lead electrocardiograms (ECGs) with clinician-coordinated central monitoring to diagnose AF among community-dwelling people aged ≥75 years in Australia. METHODS: This is a nationwide randomized controlled implementation trial conducted via the internet and remotely among 200 community-dwelling adults aged ≥75 years with no known AF. Randomization will be performed in a 1:1 allocation ratio for the intervention versus control. Intervention group participants will be enrolled in the monitoring program at randomization. They will receive a handheld single-lead ECG device and training on the self-recording of ECGs on weekdays and submit their ECGs via their smartphones. The control group participants will receive usual care from their general practitioners for the initial 6 months and then commence the 6-month monitoring program. The ECGs will be reviewed centrally by trained personnel. Participants and their general practitioners will be notified of AF and other clinically significant ECG abnormalities. RESULTS: This study will establish the feasibility and effectiveness of implementing the intervention in this patient population. The primary clinical outcome is the AF detection rate, and the primary feasibility outcome is the patient satisfaction score. Other outcomes include appropriate use of anticoagulant therapy, participant recruitment rate, program engagement (eg, frequency of ECG transmission), agreement in ECG interpretation between the device automatic algorithm and clinicians, the proportion of participants who complete the trial and number of dropouts, and the impact of frailty on feasibility and outcomes. We will conduct a qualitative evaluation to examine the barriers to and acceptability and enablers of implementation. Ethics approval was obtained from the human research ethics committee at the University of Sydney (project number 2020/680). The results will be disseminated via conventional scientific forums, including peer-reviewed publications and presentations at national and international conferences. CONCLUSIONS: By incorporating an integrated health care approach involving patient empowerment, centralized clinician-coordinated ECG monitoring, and facilitation of primary care and specialist services, it is possible to diagnose and treat AF early to reduce stroke risk. This study will provide new information on how to implement AF screening using digital health technology practicably and feasibly for older and frail populations residing in the community. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12621000184875; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380877. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34778.

Automatic data extraction from 24 hour blood pressure measurement reports of a large multicenter clinical trial.

BACKGROUND AND OBJECTIVES: Ambulatory blood pressure monitoring (ABPM) is usually reported in descriptive values such as circadian averages and standard deviations. Making use of the original, individual blood pressure measurements may be advantageous, particularly for research purposes, as this increases the flexibility of the analytical process, enables alternative statistical analyses and provide novel insights. Here we describe the development of a new multistep, hierarchical data extraction algorithm to collect raw data from .pdf reports and text files as part of a large multi-center clinical study. METHODS: Original reports were saved in a nested file system, from which they were automatically extracted, read and saved into databases with custom made programs written in Python 3. Data were further processed, cleaned and relevant descriptive statistics such as averages and standard deviations calculated according to a variety of definitions of day- and night-time. Additionally, data control mechanisms for manual review of the data and programmatic auto-detection of extraction errors was implemented as part of the project. RESULTS: The developed algorithm extracted 97% of the data automatically, the missing data consisted mostly of reports that were saved incorrectly or not formatted in the specified way. Manual checks comparing samples of the extracted data to original reports indicated a high level of accuracy of the extracted data, no errors introduced due to flaws in the extraction software were detected in the extracted dataset. CONCLUSIONS: The developed multistep, hierarchical data extraction algorithm facilitated collection from different file formats and paired with database cleaning and data processing steps led to an effective and accurate assembly of raw ABPM data for further and adjustable analyses. Manual work was minimized while data quality was ensured with standardized, reproducible procedures.