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Study protocol for Smartphone Monitoring for Atrial fibrillation in Real-Time in India (SMART-India): a community-based screening and referral programme.

ABSTRACT: INTRODUCTION:Atrial fibrillation (AF), the world's most common arrhythmia, often goes undetected and untreated in low-resource communities, including India, where AF epidemiology is undefined. AF is an important risk factor for stroke, which plagues an estimated 1.6?million Indians annually. As such, early detection of AF and management of high-risk patients is critically important to decrease stroke burden in individuals with AF. This study aims to describe the epidemiology of AF in Anand District, Gujarat, India, characterise the clinical profile of individuals who are diagnosed with AF and determine the performance of two mobile technologies for community-based AF screening. METHODS:This observational study builds on findings from a previous feasibility study and leverages two novel technologies as well as an existing community health programme to perform door-to-door AF screening for 2000 people from 60 villages of Anand District, Gujarat, India using local health workers. A single-lead ECG and a pulse-based application is used to screen each individual for AF three times over a period of 5 days. Participants with suspected arrhythmias are followed up by study cardiologist who makes final diagnoses. Participants diagnosed with AF are initiated on treatment based on current anticoagulation guidelines and clinical reasoning. ANALYTICAL PLAN:Age-stratified and sex-stratified prevalence of AF in the Anand District will be calculated for sample and estimated for Anand distribution using survey design weights. Sociodemographic and clinical factors associated with AF will be evaluated using multivariable regression methods. Performance of each mobile technology in detecting AF will be evaluated using a 12-lead ECG interpretation as the gold standard. ETHICS AND DISSEMINATION:This protocol was approved separately by the Institutional Review Board of University of Massachusetts Medical School and the Human Research Ethics Committee at Charutar Arogya Mandal. The findings of this study will be disseminated through peer-reviewed journals and scientific conferences.

SUBMITTER: Soni A

PROVIDER: S-EPMC5736031 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Study protocol for Smartphone Monitoring for Atrial fibrillation in Real-Time in India (SMART-India): a community-based screening and referral programme.

Soni Apurv A Karna Sunil S Patel Harshil H Fahey Nisha N Raithatha Shyamsundar S Handorf Anna A Bostrom John J Bashar Syed S Talati Kandarp K Shah Ravi R Goldberg Robert J RJ Thanvi Sunil S Phatak Ajay Gajanan AG Allison Jeroan J JJ Chon Ki K Nimbalkar Somashekhar Marutirao SM McManus David D DD

BMJ open 20171214 12

<h4>Introduction</h4>Atrial fibrillation (AF), the world's most common arrhythmia, often goes undetected and untreated in low-resource communities, including India, where AF epidemiology is undefined. AF is an important risk factor for stroke, which plagues an estimated 1.6 million Indians annually. As such, early detection of AF and management of high-risk patients is critically important to decrease stroke burden in individuals with AF. This study aims to describe the epidemiology of AF in Ana ...[more]

PMID: 29247089

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Project description:ObjectiveTo assess whether atrial fibrillation (AF) self-screening stations in general practice waiting rooms improve AF screening, diagnosis, and stroke risk management.Design, settingIntervention study (planned duration: twelve weeks) in six New South Wales general practices (two in rural locations, four in greater metropolitan Sydney), undertaken during 28 August 2020 - 5 August 2021.ParticipantsPeople aged 65 years or more who had not previously been diagnosed with AF, and had appointments for face-to-face GP consultations. People with valvular AF were excluded.InterventionAF self-screening station and software, integrated with practice electronic medical record programs, that identified and invited participation by eligible patients, and exported single-lead electrocardiograms and automated evaluations to patients' medical records.Main outcome measuresScreening rate; incidence of newly diagnosed AF during intervention and pre-intervention periods; prescribing of guideline-recommended anticoagulant medications.ResultsAcross the six participating practices, 2835 of 7849 eligible patients (36.1%) had face-to-face GP appointments during the intervention period, of whom 1127 completed AF self-screening (39.8%; range by practice: 12-74%). AF was diagnosed in 49 screened patients (4.3%), 44 of whom (90%) had CHA2 DS2 -VA scores of 2 or more (high stroke risk). The incidence of newly diagnosed AF during the pre-intervention period was 11 cases per 1000 eligible patients; during the intervention period, it was 22 per 1000 eligible patients (screen-detected: 17 per 1000 eligible patients; otherwise detected: 4.6 per 1000 eligible patients). Prescribing of oral anticoagulation therapy for people newly diagnosed with AF and high stroke risk was similar during the pre-intervention (20 of 24, 83%) and intervention periods (46 of 54, 85%).ConclusionsAF self-screening in general practice waiting rooms is a feasible approach to increasing AF screening and diagnosis rates by reducing time barriers to screening by GPs. AF self-screening could reduce the number of AF-related strokes.Trial registrationAustralian New Zealand Clinical Trials Registry ACTRN12620000233921 (prospective).

Project description:BackgroundWe aimed to evaluate a novel method of atrial fibrillation (AF) screening using an iPhone camera to detect and analyze photoplethysmographic signals from the face without physical contact by extracting subtle beat-to-beat variations of skin color that reflect the cardiac pulsatile signal.Methods and resultsPatients admitted to the cardiology ward of the hospital for clinical reasons were recruited. Simultaneous facial and fingertip photoplethysmographic measurements were obtained from 217 hospital inpatients (mean age, 70.3±13.9 years; 71.4% men) facing the front camera and with an index finger covering the back camera of 2 independent iPhones before a 12-lead ECG was recorded. Backdrop and background light intensity was monitored during signal acquisition. Three successive 20-second (total, 60 seconds) recordings were acquired per patient and analyzed for heart rate regularity by Cardiio Rhythm (Cardiio Inc, Cambridge, MA) smartphone application. Pulse irregularity in ?1 photoplethysmographic readings or 3 uninterpretable photoplethysmographic readings were considered a positive AF screening result. AF was present on 12-lead ECG in 34.6% (n=75/217) patients. The Cardiio Rhythm facial photoplethysmographic application demonstrated high sensitivity (95%; 95% confidence interval, 87%-98%) and specificity (96%; 95% confidence interval, 91%-98%) in discriminating AF from sinus rhythm compared with 12-lead ECG. The positive and negative predictive values were 92% (95% confidence interval, 84%-96%) and 97% (95% confidence interval, 93%-99%), respectively.ConclusionsDetection of a facial photoplethysmographic signal to determine pulse irregularity attributable to AF is feasible. The Cardiio Rhythm smartphone application showed high sensitivity and specificity, with low negative likelihood ratio for AF from facial photoplethysmographic signals. The convenience of a contact-free approach is attractive for community screening and has the potential to be useful for distant AF screening.

Project description:BackgroundAtrial fibrillation is the most common recurrent arrhythmia in clinical practice, with most clinical events occurring outside the hospital. Low detection and nonadherence to guidelines are the primary obstacles to atrial fibrillation management. Photoplethysmography is a novel technology developed for atrial fibrillation screening. However, there has been limited validation of photoplethysmography-based smart devices for the detection of atrial fibrillation and its underlying clinical factors impacting detection.ObjectiveThis study aimed to explore the feasibility of photoplethysmography-based smart devices for the detection of atrial fibrillation in real-world settings.MethodsSubjects aged ≥18 years (n=361) were recruited from September 14 to October 16, 2018, for screening of atrial fibrillation with active measurement, initiated by the users, using photoplethysmography-based smart wearable devices (ie, a smart band or smart watches). Of these, 200 subjects were also automatically and periodically monitored for 14 days with a smart band. The baseline diagnosis of "suspected" atrial fibrillation was confirmed by electrocardiogram and physical examination. The sensitivity and accuracy of photoplethysmography-based smart devices for monitoring atrial fibrillation were evaluated.ResultsA total of 2353 active measurement signals and 23,864 periodic measurement signals were recorded. Eleven subjects were confirmed to have persistent atrial fibrillation, and 20 were confirmed to have paroxysmal atrial fibrillation. Smart devices demonstrated >91% predictive ability for atrial fibrillation. The sensitivity and specificity of devices in detecting atrial fibrillation among active recording of the 361 subjects were 100% and about 99%, respectively. For subjects with persistent atrial fibrillation, 127 (97.0%) active measurements and 2240 (99.2%) periodic measurements were identified as atrial fibrillation by the algorithm. For subjects with paroxysmal atrial fibrillation, 36 (17%) active measurements and 717 (19.8%) periodic measurements were identified as atrial fibrillation by the algorithm. All persistent atrial fibrillation cases could be detected as "atrial fibrillation episodes" by the photoplethysmography algorithm on the first monitoring day, while 14 (70%) patients with paroxysmal atrial fibrillation demonstrated "atrial fibrillation episodes" within the first 6 days. The average time to detect paroxysmal atrial fibrillation was 2 days (interquartile range: 1.25-5.75) by active measurement and 1 day (interquartile range: 1.00-2.00) by periodic measurement (P=.10). The first detection time of atrial fibrillation burden of <50% per 24 hours was 4 days by active measurement and 2 days by periodic measurementThe first detection time of atrial fibrillation burden of >50% per 24 hours was 1 day for both active and periodic measurements (active measurement: P=.02, periodic measurement: P=.03).ConclusionsPhotoplethysmography-based smart devices demonstrated good atrial fibrillation predictive ability in both active and periodic measurements. However, atrial fibrillation type could impact detection, resulting in increased monitoring time.Trial registrationChinese Clinical Trial Registry of the International Clinical Trials Registry Platform of the World Health Organization ChiCTR-OOC-17014138; http://www.chictr.org.cn/showprojen.aspx?proj=24191.

Dataset Information

Study protocol for Smartphone Monitoring for Atrial fibrillation in Real-Time in India (SMART-India): a community-based screening and referral programme.

Publications

Study protocol for <i>S</i>martphone <i>M</i>onitoring for <i>A</i>trial fibrillation in <i>R</i>eal-<i>T</i>ime in India (SMART-India): a community-based screening and referral programme.

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