Project description:Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice, and its paroxysmal nature makes its detection challenging. In this trial, we evaluated a novel App for its accuracy to differentiate between patients in AF and patients in sinus rhythm (SR) using the plethysmographic sensor of an iPhone 4S and the integrated LED only.For signal acquisition, we used an iPhone 4S, positioned with the camera lens and LED light on the index fingertip. A 5 min video file was recorded with the pulse wave extracted from the green light spectrum of the signal. RR intervals were automatically identified. For discrimination between AF and SR, we tested three different statistical methods. Normalized root mean square of successive difference of RR intervals (nRMSSD), Shannon entropy (ShE), and SD1/SD2 index extracted from a Poincaré plot. Eighty patients were included in the study (40 patients in AF and 40 patients in SR at the time of examination). For discrimination between AF and SR, ShE yielded the highest sensitivity and specificity with 85 and 95%, respectively. Applying a tachogram filter resulted in an improved sensitivity of 87.5%, when combining ShE and nRMSSD, while specificity remained stable at 95%. A combination of SD1/SD2 index and nRMSSD led to further improvement and resulted in a sensitivity and specificity of 95%.The algorithm tested reliably discriminated between SR and AF based on pulse wave signals from a smartphone camera only. Implementation of this algorithm into a smartwatch is the next logical step.
Project description:Atrial fibrillation (AF) is a common and morbid arrhythmia. Stroke is a major hazard of AF and may be preventable with oral anticoagulation. Yet since AF is often asymptomatic, many individuals with AF may be unaware and do not receive treatment that could prevent a stroke. Screening for AF has gained substantial attention in recent years as several studies have demonstrated that screening is feasible. Advances in technology have enabled a variety of approaches to facilitate screening for AF using both medical-prescribed devices as well as consumer electronic devices capable of detecting AF. Yet controversy about the utility of AF screening remains owing to concerns about potential harms resulting from screening in the absence of randomized data demonstrating effectiveness of screening on outcomes such as stroke and bleeding. In this review, we summarize current literature, present technology, population-based screening considerations, and consensus guidelines addressing the role of AF screening in practice.
Project description:AimWe aimed to systematically review the available literature on mobile Health (mHealth) solutions, including handheld and wearable devices, implantable loop recorders (ILRs), as well as mobile platforms and support systems in atrial fibrillation (AF) detection and management.MethodsThis systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The electronic databases PubMed (NCBI), Embase (Ovid), and Cochrane were searched for articles published until 10 February 2021, inclusive. Given that the included studies varied widely in their design, interventions, comparators, and outcomes, no synthesis was undertaken, and we undertook a narrative review.ResultsWe found 208 studies, which were deemed potentially relevant. Of these studies included, 82, 46, and 49 studies aimed at validating handheld devices, wearables, and ILRs for AF detection and/or management, respectively, while 34 studies assessed mobile platforms/support systems. The diagnostic accuracy of mHealth solutions differs with respect to the type (handheld devices vs wearables vs ILRs) and technology used (electrocardiography vs photoplethysmography), as well as application setting (intermittent vs continuous, spot vs longitudinal assessment), and study population.ConclusionWhile the use of mHealth solutions in the detection and management of AF is becoming increasingly popular, its clinical implications merit further investigation and several barriers to widespread mHealth adaption in healthcare systems need to be overcome. Mobile health solutions for atrial fibrillation detection and management: a systematic review.
Project description:AimsAtrial fibrillation (AF) is the most common sustained arrhythmia and an important risk factor for stroke and heart failure. We aimed to conduct a systematic review of the literature and summarize the performance of mobile health (mHealth) devices in diagnosing and screening for AF.Methods and resultsWe conducted a systematic search of MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. Forty-three studies met the inclusion criteria and were divided into two groups: 28 studies aimed at validating smart devices for AF diagnosis, and 15 studies used smart devices to screen for AF. Evaluated technologies included smartphones, with photoplethysmographic (PPG) pulse waveform measurement or accelerometer sensors, smartbands, external electrodes that can provide a smartphone single-lead electrocardiogram (iECG), such as AliveCor, Zenicor and MyDiagnostick, and earlobe monitor. The accuracy of these devices depended on the technology and the population, AliveCor and smartphone PPG sensors being the most frequent systems analysed. The iECG provided by AliveCor demonstrated a sensitivity and specificity between 66.7% and 98.5% and 99.4% and 99.0%, respectively. The PPG sensors detected AF with a sensitivity of 85.0-100% and a specificity of 93.5-99.0%. The incidence of newly diagnosed arrhythmia ranged from 0.12% in a healthy population to 8% among hospitalized patients.ConclusionAlthough the evidence for clinical effectiveness is limited, these devices may be useful in detecting AF. While mHealth is growing in popularity, its clinical, economic, and policy implications merit further investigation. More head-to-head comparisons between mHealth and medical devices are needed to establish their comparative effectiveness.
Project description:BackgroundAtrial fibrillation (AF) is the most common arrhythmia, and its prevalence is increasing. Early diagnosis is important to reduce the risk of stroke. Mobile health (mHealth) devices, such as single-lead electrocardiogram (ECG) devices, have been introduced to the worldwide consumer market over the past decade. Recent studies have assessed the usability of these devices for detection of AF, but it remains unclear if the use of mHealth devices leads to a higher AF detection rate.ObjectiveThe goal of the research was to conduct a systematic review of the diagnostic detection rate of AF by mHealth devices compared with traditional outpatient follow-up. Study participants were aged 16 years or older and had an increased risk for an arrhythmia and an indication for ECG follow-up-for instance, after catheter ablation or presentation to the emergency department with palpitations or (near) syncope. The intervention was the use of an mHealth device, defined as a novel device for the diagnosis of rhythm disturbances, either a handheld electronic device or a patch-like device worn on the patient's chest. Control was standard (traditional) outpatient care, defined as follow-up via general practitioner or regular outpatient clinic visits with a standard 12-lead ECG or Holter monitoring. The main outcome measures were the odds ratio (OR) of AF detection rates.MethodsTwo reviewers screened the search results, extracted data, and performed a risk of bias assessment. A heterogeneity analysis was performed, forest plot made to summarize the results of the individual studies, and albatross plot made to allow the P values to be interpreted in the context of the study sample size.ResultsA total of 3384 articles were identified after a database search, and 14 studies with a 4617 study participants were selected. All studies but one showed a higher AF detection rate in the mHealth group compared with the control group (OR 1.00-35.71), with all RCTs showing statistically significant increases of AF detection (OR 1.54-19.16). Statistical heterogeneity between studies was considerable, with a Q of 34.1 and an I2 of 61.9, and therefore it was decided to not pool the results into a meta-analysis.ConclusionsAlthough the results of 13 of 14 studies support the effectiveness of mHealth interventions compared with standard care, study results could not be pooled due to considerable clinical and statistical heterogeneity. However, smartphone-connectable ECG devices provide patients with the ability to document a rhythm disturbance more easily than with standard care, which may increase empowerment and engagement with regard to their illness. Clinicians must beware of overdiagnosis of AF, as it is not yet clear when an mHealth-detected episode of AF must be deemed significant.
Project description:Atrial fibrillation (AF) is a chronic disease with an incidence increasing steeply by age and affecting more than 11 million patients in Europe and the United States. Diagnosing AF is essential for the prevention of stroke by oral anticoagulation. Opportunistic screening for AF in patients ?65 years of age is recommended by the European and Danish Societies of Cardiology. The study aim was to examine the detection rate of AF in consecutively screened patients in the primary care setting in Denmark. In an open, non-interventional, cluster, multicenter, cross-sectional, observational study patients ?65 years of age entering consecutively into general practice clinics were invited to nurse-assisted opportunistic screening for AF. The General Practice (GP) clinics participating were randomized to patient inclusion in three age groups: 65-74, 75-84, and ?85 years respectively. All patients underwent pulse palpation followed by 12-led electrocardiogram in case of irregular pulse. Two cardiologists validated all electrocardiogram examinations. Forty-nine general practice clinics recruited in total 970 patients split into three age groups; 480 patients (65-74 years), 372 (75-84 years), and 118 patients ?85 years of age. Co-morbidities increased by age with hypertension being most frequent. Eighty-seven patients (9%) were detected with an irregular pulse, representing 4.4%, 10.5% and 22.9%, respectively in the three age groups. Assessment of electrocardiograms by the GP showed suspicion of AF in 13 patients with final verification of electrocardiograms by cardiologists revealing 10 AF-patients. The highest detection rate of AF was found in the ?85 age group (3.39%) followed by the 65-74 age group (0.83%) and the 75-84 age group (0.54%). Opportunistic screening of AF in primary care is feasible and do result in the detection of new AF-patients. Close collaboration with cardiologists is advisable to avoid false positive screening results.
Project description:The aim of this study is to perform transcriptome analysis on mouse left atrium tissue after long-term ibrutinib treatment or cardiac CSK knockout, in order to compared the enriched gene clusters.
Project description:ObjectivesRecent technology advances have allowed for heart rhythm monitoring using single-lead ECG monitoring devices, which can be used for early diagnosis of atrial fibrillation (AF). We sought to investigate the AF detection rate using portable ECG devices compared with Holter monitoring.Setting, participants and outcome measuresWe searched the Medline, Embase and Scopus databases (conducted on 8 May 2017) using search terms related to AF screening and included studies with adults aged >18 years using portable ECG devices or Holter monitoring for AF detection. We excluded studies using implantable loop recorders and pacemakers. Using a random-effects model we calculated the overall AF detection rate. Meta-regression analysis was performed to explore potential sources for heterogeneity. Quality of reporting was assessed using the tool developed by Downs and Black.ResultsPortable ECG monitoring was used in 18 studies (n=117?436) and Holter monitoring was used in 36 studies (n=8498). The AF detection rate using portable ECG monitoring was 1.7% (95% CI 1.4 to 2.1), with significant heterogeneity between studies (p<0.001). There was a moderate linear relationship between total monitoring time and AF detection rate (r=0.65, p=0.003), and meta-regression identified total monitoring time (p=0.005) and body mass index (p=0.01) as potential contributors to heterogeneity. The detection rate (4.8%, 95%?CI 3.6% to 6.0%) in eight studies (n=10?199), which performed multiple ECG recordings was comparable to that with 24?hours Holter (4.6%, 95%?CI 3.5% to 5.7%). Intermittent recordings for 19?min total produced similar AF detection to 24?hours Holter monitoring.ConclusionPortable ECG devices may offer an efficient screening option for AF compared with 24?hours Holter monitoring.Prospero registration numberCRD42017061021.
Project description:This study will report the incidence of atrial fibrillation after elective colorectal cancer resection in the over 65 age group. This will be used to validate a risk model for the development of post-operative atrial fibrillation.
Eligible patients will undergo electrocardiogram based screening for atrial fibrillation, as well as brain natriuretic peptide tests prior to surgery. They will undergo 24 hour holter monitor prior to surgery, and at 30 and 90 days following surgery.
The primary outcome will be occurrence of atrial fibrillation within 90 days of surgery. Secondary outcomes include quality of life change, use of hospital services for atrial fibrillation, and complications of atrial fibrillation. This will be used to validate the pre-existing model for prediction of atrial fibrillation.
Project description:Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with an unfavorable prognosis, increasing the risk of stroke and death. Although traditionally associated with cardiovascular diseases, there is increasing evidence of high incidence of AF in patients with highly prevalent noncardiovascular diseases, such as cancer, sepsis, chronic obstructive pulmonary disease, obstructive sleep apnea and chronic kidney disease. Therefore, considerable number of patients has been affected by these comorbidities, leading to an increased risk of adverse outcomes.The authors performed a systematic review of the literature aiming to better elucidate the interaction between these conditions.Several mechanisms seem to contribute to the concomitant presence of AF and noncardiovascular diseases. Comorbidities, advanced age, autonomic dysfunction, electrolyte disturbance and inflammation are common to these conditions and may predispose to AF.The treatment of AF in these patients represents a clinical challenge, especially in terms of antithrombotic therapy, since the scores for stratification of thromboembolic risk, such as the CHADS2 and CHA2DS2VASc scores, and the scores for hemorrhagic risk, like the HAS-BLED score have limitations when applied in these conditions.The evidence in this area is still scarce and further investigations to elucidate aspects like epidemiology, pathogenesis, prevention and treatment of AF in noncardiovascular diseases are still needed.