Detection Of Atrial Fibrillation Research Articles

Diagnostic Accuracy of Apple Watch Electrocardiogram for Atrial Fibrillation: A Systematic Review and Meta-Analysis.

Electrocardiography (ECG) is the gold standard for the diagnosis of atrial fibrillation (AF). Recently, smartwatches like the Apple Watch have emerged as a promising, user-friendly device for rapid detection and diagnosis of AF, but the reliability and diagnostic accuracy still remain controversial. The purpose of this study was to perform a systematic review and diagnostic test accuracy meta-analysis evaluating the diagnostic performance of the Apple Watch ECG in detecting AF. The literature search was conducted on PubMed, Embase, and Cochrane Library through April 2024 for studies comparing the diagnostic accuracy of Apple Watch to standard 12-lead ECG. Statistical analysis was performed using R Software version 4.4.0 and OpenMeta[Analyst]. Pooled analyses of sensitivity, specificity, and area under the receiver operating characteristic curve were determined along with their 95%CIs. The quality of studies was analyzed using the QUADAS-2 tool. The meta-analysis included 11 studies comprising 4,241 participants. Their mean age was 62.56±3.92years, and 28% of the patients were females. The pooled sensitivity and specificity of the Apple Watch for detecting AF were 94.8% (95%CI: 91.7% to 96.8%; I2=67%) and 95% (95%CI: 88.6% to 97.8%; I2=88%), respectively. The area under the receiver operating characteristic curve was 0.96 (95%CI: 0.92-0.97). The Apple Watch ECG carries high accuracy in detecting atrial fibrillation, providing a convenientdiagnostic option for patients.

Biomarkers for the detection of covert atrial fibrillation after ischemic stroke: NT-proBNP or BNP

Biomarkers for the detection of covert atrial fibrillation after ischemic stroke: NT-proBNP or BNP

Smartphone-Based Applications for Atrial Fibrillation Detection: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy.

Background: Atrial fibrillation (AF) burden is strongly associated with an increased risk of stroke, which, in most cases, can be prevented through earlier detection of AF and the timely initiation of anticoagulation therapy. Smartphone devices can provide a simple, non-invasive, cost-effective early AF detection solution. Methods: PubMed, Embase, and Scopus databases were searched for studies comparing smartphone-based photoplethysmography (PPG) with standard electrocardiogram for AF detection. A bivariate random-effects model with a 95% confidence interval (CI) was applied to generate the summary receiver operating characteristic (SROC) curve. Results: Fourteen studies were included, comprising 5,090 patients with an AF prevalence of 31.6%. The pooled sensitivity and specificity were 0.96 (95% CI, 0.93-0.97) and 0.97 (95% CI, 0.95-0.98). The area under the SROC curve was 0.98 (95% CI, 0.94-0.99). The diagnostic odds ratio was 960 (95% CI, 439-2,104), with significant heterogeneity (I2 = 51%). The projected positive and negative predictive values were 66.5% and 99.7%, respectively, in the elderly population aged >65 years and 39.2% and 99.9% in the general population. Conclusion: Smartphone-based PPG demonstrated relatively high sensitivity and specificity and appears capable of ruling out AF. Patients aged >65 are more likely to benefit from AF screening.

Improving Atrial Fibrillation Detection Using a Shared Latent Space for ECG and PPG Signals

Improving Atrial Fibrillation Detection Using a Shared Latent Space for ECG and PPG Signals

Using left atrial strain values and correlating changes in left atrium area and volume size as an early predictor of atrial fibrillation: a prospective, single-centre case-control study

Abstract Background Atrial fibrillation (AF) is the most common type of arrhythmia and is characterised by chaotic, fast, and irregular electrical activity. AF detection is often incidental as patients may be asymptomatic. Turbulent blood flow and poor atrial contraction increase the likelihood of thrombus formation, and ultimately, increase stroke risk. New techniques focussing on LA strain analysis may be able to detect subtle changes within the LA, before gross cardiac structural and electrical remodelling has occurred. Aim Differences in LA strain values between three groups: normal sinus rhythm (NSR) and normotensive, NSR and hypertensive, and chronic AF. Correlations between LA strain to LA area and volume size were also investigated as well as the use of LA strain as an early predictor of AF. Methods A total of 72 participants were successfully recruited and allocated into one of three groups. 24 participants in the control group, 28 in the disease-control group; and 20 in the disease group. LA strain analysis was performed using the AFI LA atrial strain tool to obtain reservoir (LASr), conduit (LAScd) and contractile (LASct) strain from apical 4- and 2-chamber images. Area and volume (LAVMax) measurements were also obtained, allowing for Pearson’s Correlation Coefficient analysis comparing LA strain values to LA area and volume size. Linear regression was also performed to determine whether a reduction in LA strain may increase the risk of AF development. Results All data was found to have normal distribution. A One-Way ANOVA demonstrated that there were statistically significant differences in LA area, LASr (A4C, A2C and Biplane), and LAVMax (A4C, A2C and Biplane). When comparing mean LA area, volume and LASr between the control and disease groups, all p values < 0.001. Average Biplane LASr values are as follows: NSR and normotensive = 25.2% (±6.22%); NSR and hypertensive = 23.3% (± 618%); and AF group = 11.3% (±7.24%). This creates a scale demonstrating the significant reduction in LA strain in AF patients. Pearson’s Correlation indicated that there was a significantly moderate, negative correlation between Biplane LASr and LA area (r = -0.454, p < 0.001, N = 57), and between Biplane LASr and Biplane LAVMax (r = -0.580, p < 0.001, N = 72). Furthermore, a simple bivariate linear regression indicated that an increase in one point in research group type, would correspond, on average, with a decrease in Biplane LASr value by around 0.38 to 0.72 points. Suggesting that as LASr values decrease, the likelihood of AF increases. Conclusion LASr values are significantly different between patients in NSR and AF and LASr values correlate negatively with LA area and volume. It is possible to suggest that decreased LASr values, in patients with or without hypertension, can be used as a novel and alternative measure of atrial dysfunction in patients with risk factors for AF; LASr may be able to predict risk of AF development and subsequent progression.

A knowledge embedded multimodal pseudo-siamese model for atrial fibrillation detection

Atrial fibrillation (AF) is a common arrhythmia disease with a higher incidence rate. The diagnosis of AF is time-consuming. Although many ECG classification models have been proposed to assist in AF detection, they are prone to misclassifying indistinguishable noise signals, and the context information of long-term signals is also ignored, which impacts the performance of AF detection. Considering all the above problems, we propose a knowledge embedded multimodal pseudo-siamese model. The proposed model comprises a temporal-spatial pseudo-siamese network (TSPS-Net) and a knowledge embedded noise filter module. Firstly, a parallel siamese network architecture is proposed in TSPS-Net to process the multimodal representations. Secondly, a spatiotemporal collaborative fusion mechanism (STCFM) is proposed to fuse multimodal features. Finally, medical knowledge is introduced to design manual features, which are used to distinguish noise and fuse with deep features of ECG to obtain the accurate final result. The model’s performance is verified on the CinC 2017 dataset and the MIT-BIH AF dataset. Experimental results showed that the average accuracy achieved 82.17 and 99.11. The F1 score of our proposed model on the CinC 2017 dataset and MIT-BIH dataset was 0.787 and 0.970, respectively.

Mobile/wearable digital devices for care of active cancer patients: a survey from the ESC Council of Cardio-Oncology

Abstract Aims The Council of Cardio-Oncology of the European Society of Cardiology developed an on-line anonymous survey to provide an overall picture of the current practice on the use of mobile and wearable digital devices in cardio-oncology and the potential barriers to their large-scale applicability. Methods and results Between June 2023 and January 2024, an online anonymous questionnaire was completed by 220 healthcare professionals from 55 countries. The greatest number of respondents reported that mobile/wearable digital devices have a role in all active cancer patients for measuring heart rate (33.9%), blood pressure (34.4%), body temperature (32.0%), physical activity (42.4%), and sleep (31.2%). In the setting of atrial fibrillation detection, respondents were evenly split between applying these technologies in all patients (33.0%) or only in selected patients (33.0%). Regarding QTc interval monitoring, 30.6% reported that mobile/wearable digital devices play a role only in selected patients. The decision to use the device was taken by the patient in 56.6% of cases and the physician in 43.4%. The most important barrier reported to mobile/wearable device implementation in the setting of cardiac rhythm monitoring and QTc measurement was their cost (weighted average: 3.38 and 3.39, respectively). Conclusion Mobile/wearable digital devices are considered to play an important role in different settings of cardio-oncology, including monitoring of patients’ parameters and arrhythmia detection. Their role in monitoring physical activity and QTc interval appears more nuanced. The most important perceived barrier to mobile/wearable digital device implementation is considered their high cost.

Multiscale feature enhanced gating network for atrial fibrillation detection.

Atrial fibrillation (AF) is a significant cause of life-threatening heart disease due to its potential to lead to stroke and heart failure. Although deep learning-assisted diagnosis of AF based on ECG holds significance in clinical settings, it remains unsatisfactory due to insufficient consideration of noise and redundant features. In this work, we propose a novel multiscale feature-enhanced gating network (MFEG Net) for AF diagnosis. The network integrates multiscale convolution, adaptive feature enhancement (FE), and dynamic temporal processing. The multiscale convolution helps capture global and local information. The FE module consists of a soft-threshold residual shrinkage component, a dilated convolution module, and a Squeeze-and-Excitation (SE) module, eliminating redundant features and emphasizing effective features. The design allows the network to focus on the most relevant AF features, thereby enhancing its robustness and accuracy in the presence of noise and irrelevant information. The dynamic temporal module helps the network learn and recognize the time dependence associated with AF. The novel design endows the model with excellent robustness to cope with random noise in real-world environments. Compared with the state-of-the-art methods, our model exhibits excellent classification performance with an accuracy of 0.930, an F1 score of 0.883, and remarkable resilience to noise interference on the PhysioNet Challenge 2017 dataset. Moreover, the model was trained on the CinC2017 database and validated on the CPSC2018 database and AFDB database, achieving accuracies of 0.908 and 0.938, respectively. The excellent classification performance of MFEG Net, coupled with its robustness in processing noisy electrocardiogram signals, makes it a powerful method for automatic atrial fibrillation detection. This method has made significant progress over state-of-the-art methods and may alleviate the burden of manual diagnosis for clinical doctors.

Improving Atrial Fibrillation or Flutter Detection and Management by Smartphone-Based Photoplethysmography Rhythm Monitoring Following Cardiac Surgery: A Pragmatic Randomized Trial.

Atrial fibrillation (AF) or atrial flutter (AFL) after cardiac surgery are common and associated with adverse outcomes. The increased risk related to AF or AFL may extend beyond discharge. This study aims to determine whether photoplethysmography (PPG)-based smartphone monitoring to detect AF or AFL after hospital discharge following cardiac surgery improves AF management. The intervention group performed one-minute rhythm checks three times daily using a smartphone-based PPG application during six weeks after hospitalization for cardiac surgery. The primary outcome involved AF management interventions by independent physicians, including initiation of oral anticoagulation (OAC), direct cardioversion and up-titration or initiation of antiarrhythmic drugs. The study included 450 patients (mean [SD] age, 64.1 [9.2] years; 96 women [21.3%]; 130 patients with AF history [28.9%]; median [IQR] CHA2DS2-VASc score, 2 [1-3]), of whom 238 were randomized to PPG-based monitoring and 212 to usual care. AF/AFL was detected with PPG or electrocardiography in 44 patients (18.5%) in the monitoring group and 4 patients (1.9%) in the usual care group (OR 11.8; 95%CI, 4.2-33.3; P<.001), these were new detections in respectively 22 patients (9.2%) and 1 patient (0.5%) (OR 21.3; 95%CI, 2.9-166.7; P=.003). AF management interventions occurred in 24 patients (10.1%) in the monitoring group compared to 5 patients (2.4%) in the usual care group (odds ratio [OR]), 5.1; 95%CI, 1.8-14.4; P=.002). In unselected patients discharged home following cardiac surgery, PPG-based smartphone monitoring revealed significantly more AF/AFL which led to significantly more optimization of AF management.

Dynamic Data-Driven Management of Atrial Fibrillation With Implantable Cardiac Monitors: The MONITOR AF Study.

Implantable cardiac monitors (ICMs) provide valuable insights into managing atrial fibrillation (AF). Data suggest that ICMs increase AF detection, but their impact on management is still uncertain. To evaluate and compare the impact of ICMs on the clinical management of AF. MONITOR-AF (NCT06352060) was a retrospective, multi-center study of patients with AF between 2018 to 2021 who received an ICM or routine monitoring with electrocardiograms or long-term monitoring. Patients were followed for 12 months, making note of AF-related clinical outcomes. There were 2293 patients who received an ICM (n=1115) or routine monitoring (n=1178). Although comorbidities between ICM and non-ICM were significantly different, none of the AF-related characteristics were significantly different. Patients in the ICM group had more attempts at rhythm control with AAD (100% vs. 59.9%, p<0.001) and CA (91.7% vs. 59.7%, p<0.001). This led to higher freedom from AF at 12 months (86.0% vs. 61.8%, p<0.001) and freedom from anti-arrhythmic drugs (75.9% vs. 39.4%, p<0.001) and oral anticoagulation (69.6.8% vs. 39.4%, p<0.001) usage, and was associated with reduced rates of stroke (0.3% vs. 1.6%, p<0.001) and major bleeding (1.6% vs. 2.9%, p<0.001). Dynamic monitoring with ICM is associated with beneficial AF outcomes with improved freedom from AF at 12 months and fewer complications. Thus, ICM use should be considered for the management of chronic AF.

Recent advances in the tools and techniques for AI-aided diagnosis of atrial fibrillation.

Atrial fibrillation (AF) is recognized as a developing global epidemic responsible for a significant burden of morbidity and mortality. To counter this public health crisis, the advancement of artificial intelligence (AI)-aided tools and methodologies for the effective detection and monitoring of AF is becoming increasingly apparent. A unified strategy from the international research community is essential to develop effective intelligent tools and technologies to support the health professionals for effective surveillance and defense against AF. This review delves into the practical implications of AI-aided tools and techniques for AF detection across different clinical settings including screening, diagnosis, and ambulatory monitoring by reviewing the revolutionary research works. The key finding is that the advance in AI and its use for automatic detection of AF has achieved remarkable success, but collaboration between AI and human intelligence is required for trustworthy diagnostic of this life-threatening cardiac condition. Moreover, designing efficient and robust intelligent algorithms for onboard AF detection using portable and implementable computing devices with limited computation power and energy supply is a crucial research problem. As modern wearable devices are equipped with sophisticated embedded sensors, such as optical sensors and accelerometers, hence photoplethysmography and ballistocardiography signals could be explored as an affordable alternative to electrocardiography (ECG) signals for AF detection, particularly for the development of low-cost and miniature screening and monitoring devices.

Factors associated with the detection of atrial fibrillation in patients with embolic stroke of undetermined source

BackgroundDetection of atrial fibrillation (AF) in patients with embolic stroke of undetermined source (ESUS) is important for the secondary prevention of stroke. We investigated the factors associated with the detection of newly diagnosed AF in ESUS patients during follow-up.MethodsPatients with acute ischemic stroke classified as ESUS were included. All patients underwent transthoracic echocardiography and Holter to detect the source of embolism. Structural, electrophysiological markers of left atrial cardiopathy (i.e., left atrial enlargement [LAE], non-sustained tachycardia [NSAT]) as well as lesion patterns of ischemic stroke were examined. Implantable loop recorder (ILR) was implanted in selective patients. Sensitivity and positive predictive value analysis was used to assess the predictive value for AF detection.ResultsAmong 312 patients with ESUS, AF was detected in 24 (7.7%) patients during follow-up. Patients with AF had a higher prevalence of LAE, NSAT, and the imaging pattern of confluent plus additional lesions in a single vascular territory. Multivariable analysis showed that ILR implantation (hazards ratio 11.497 [95% confidence interval 3.795–34.818]), LAE (3.204 [1.096–9.370]), NSAT (4.070 [1.378–12.018]), and confluent plus additional lesions (4.977 [1.649–15.019]) were independent predictors of AF detection. The sensitivity of detecting AF in those with LAE, NSAT, or confluent plus additional lesions pattern was 91.7%. The positive predictive value of detecting AF in those with LAE, NSAT and confluent plus additional lesions pattern was 40.0%.ConclusionIn conclusion, patients with LAE, NSAT, or confluent plus additional lesions may benefit from ILR monitoring detecting new AF.

Excessive Supraventricular Ectopic Activity in Patients With Acute Ischemic Stroke Is Associated With Atrial Fibrillation Detection Within 24 Months After Stroke: A Predefined Analysis of the MonDAFIS Study.

Excessive supraventricular ectopic activity (ESVEA) is regarded as a risk marker for later atrial fibrillation (AF) detection. The investigator-initiated, prospective, open, multicenter MonDAFIS (Impact of Standardized Monitoring for Detection of Atrial Fibrillation in Ischemic Stroke) study randomized 3465 patients with acute ischemic stroke without known AF 1:1 to usual diagnostic procedures for AF detection or additive Holter monitoring in hospital for up to 7 days, analyzed in a core laboratory. Secondary study objectives include the comparison of recurrent stroke, myocardial infarction, major bleeding, and all-cause death within 24 months in patients with ESVEA (defined as ectopic supraventricular beats ≥480/day or atrial runs of 10-29 seconds or both) versus patients with newly diagnosed AF versus patients without ESVEA or AF (non-ESVEA/AF), randomized to the intervention group. Overall, 1435 (84.8%) of 1714 patients randomized to the intervention group had analyzable study ECG monitoring of at least 48 hours' duration within the first 72 hours of monitoring. ESVEA was detected in 363 (25.3%) patients, while AF was first detected in 48 (3.3%) patients. Within 24 months, AF was newly detected in 67 (18.5%) patients with ESVEA versus 60 (5.9%) patients without ESVEA/AF- (P<0.001). The composite outcome at 24 months was not different between patients with ESVEA and patients without ESVEA/AF (15.2% versus 12.6%; P=0.242). All-cause death was numerically higher in patients with ESVEA (6.6% versus 3.2%), but failed statistical significance (P=0.433) in multivariate analysis (including age, heart failure, stroke severity, and creatinine at baseline). ESVEA in the acute phase of ischemic stroke or transient ischemic attack is associated with AF detection during follow-up and therefore may be used to select patients for prolonged ECG monitoring. URL: https://www.clinicaltrials.gov; Unique identifier: NCT02204267.

Enhanced detection of atrial fibrillation in single-lead electrocardiograms using a Cloud-based artificial intelligence platform.

Although smartphone-based devices have been developed to record 1-lead electrocardiogram (ECG), existing solutions for automatic detection of atrial fibrillation (AF) often has poor positive predictive value. This study aimed to validate a Cloud-based deep-learning platform for automatic AF detection in a large cohort of patients using 1-lead ECG records. We analyzed 8528 patients with 30-second ECG records from a single-lead handheld ECG device. Ground truth for AF presence was established through a benchmark algorithm and expert manual labeling. The Willem Artificial Intelligence (AI) platform, not trained on these ECGs, was used for automatic arrhythmia detection, including AF. A rules-based algorithm was also used for comparison. An expert cardiology committee reviewed false positives and negatives, and performance metrics were computed. The AI platform achieved an accuracy of 96.1% (initial labels) and 96.4% (expert review), with sensitivities of 83.3% and 84.2%, and specificities of 97.3% and 97.6%, respectively. The positive predictive value was 75.2% and 78.0%, and the negative predictive value was 98.4%. Performance of the AI platform largely exceeded the performance of the rules-based algorithm for all metrics. The AI also detected other arrhythmias, such as premature ventricular complexes, premature atrial complexes along with 1-degree atrioventricular blocks. The result of this external validation indicates that the AI platform can match cardiologist-level accuracy in AF detection from 1-lead ECGs. Such tools are promising for AF screening and have the potential to improve accuracy in noncardiology expert health care professional interpretation and trigger further tests for effective patient management.

Risk-Based Screening of Atrial Fibrillation in General Practice (R-BEAT): A randomised Cross-over Trial.

The optimal approach to the diagnosis of atrial fibrillation in primary care is unclear. To determine if external loop recorder (ELR) screening improves atrial fibrillation detection in community dwelling adults with a CHA2DS2-VASc score of greater than two. Randomised cross-over clinical trial. Community dwelling adults ≥55 years with a CHA2DS2-VASc score of greater than two, who were deemed suitable for atrial fibrillation screening and oral anticoagulation by their general practitioner were randomly assigned to immediate or delayed ELR monitoring. The intervention period was ELR cardiac monitoring for 1 week and the usual care period was healthcare professional pulse screening and completion of electrocardiogram (ECG) or cardiac rhythm strip if pulse was identified as irregular. Of 488 participants randomized, 244 were assigned to the immediate monitoring period (intervention) and 244 were assigned to the delayed monitoring period. Mean (SD) age was 75.0 (7.0) years and 333 participants were women (68%). Atrial fibrillation was detected in 32 of 488 participants (6.6%) in the intervention period versus 5 of 488 (1%) in the usual care period (absolute difference, 5.53% (3.2%-7.9%), P < 0.001; number needed to screen 15 (11-23). Twelve cases (37.5%) of ELR-detected atrial fibrillation were greater than 24 hours in duration. Oral anticoagulation was initiated in all participants (n = 32). Among older community dwelling adults with a CHA2DS2-VASc score of greater than 2, screening with ELR for one week was associated with a 5.5% incremental detection of new atrial fibrillation over usual care. ClinicalTrials.gov Register: NCT03911986.

A Multi-Level Multiple Contrastive Learning Method for Single-Lead Electrocardiogram Atrial Fibrillation Detection.

Atrial fibrillation (AF) is the most common persistent arrhythmia, and it is crucial to develop generalizable automatic AF detection methods. However, supervised AF detection is often limited in performance due to the difficulty in obtaining labeled data. To address the gap between limited labeled data and the requirements for model robustness and generalization in single-lead ECG AF detection, we proposed a semi-supervised contrastive learning method named MLMCL for AF detection. The MLMCL method utilizes the multi-level feature representations of the encoder to perform multiple contrastive learning to fully exploit temporal consistency, channel consistency, and label consistency. Meanwhile, it combines labeled and unlabeled data for pre-training to obtain robust features for downstream tasks. In addition, it uses the domain knowledge in the field of AF diagnosis for domain knowledge augmentation to generate hard samples and improve the distinguishability of ECG representations. In the cross-dataset testing mode, MLMCL had better performance and good stability on different test sets, demonstrating its effectiveness and robustness in the AF detection task. The comparison results with existing studies show that MLMCL outperformed existing methods in external tests. The MLMCL method can be extended and applied to multi-lead scenarios and has reference significance for the development of contrastive learning methods for other arrhythmia.

The Efficacy of Artificial Intelligence in the Detection and Management of Atrial Fibrillation

The Efficacy of Artificial Intelligence in the Detection and Management of Atrial Fibrillation

Evidence Gaps and Lessons in the Early Detection of Atrial Fibrillation: A Prospective Study in a Primary Care Setting (PREFATE Study).

Background/Objectives: In Europe, the prevalence of AF is expected to increase 2.5-fold over the next 50 years with a lifetime risk of 1 in 3-5 individuals after the age of 55 years and a 34% rise in AF-related strokes. The PREFATE project investigates evidence gaps in the early detection of atrial fibrillation in high-risk populations within primary care. This study aims to estimate the prevalence of device-detected atrial fibrillation (DDAF) and assess the feasibility and impact of systematic screening in routine primary care. Methods: The prospective cohort study (NCT05772806) included 149 patients aged 65-85 years, identified as high-risk for AF. Participants underwent 14 days of cardiac rhythm monitoring using the Fibricheck® app (CE certificate number BE16/819942412), alongside evaluations with standard ECG and transthoracic echocardiography. The primary endpoint was a new AF diagnosis confirmed by ECG or Holter monitoring. Statistical analyses examined relationships between AF and clinical, echocardiographic, and biomarker variables. Results: A total of 18 cases (12.08%) were identified as positive for possible DDAF using FibriCheck® and 13 new cases of AF were diagnosed during follow-up, with a 71.4-fold higher probability of confirming AF in FibriCheck®-positive individuals than in FibriCheck®-negative individuals, resulting in a post-test odds of 87.7%. Significant echocardiographic markers of AF included reduced left atrial strain (<26%) and left atrial ejection fraction (<50%). MVP ECG risk scores ≥ 4 strongly predicted new AF diagnoses. However, inconsistencies in monitoring outcomes and limitations in current guidelines, particularly regarding AF burden, were observed. Conclusions: The study underscores the feasibility and utility of AF screening in primary care but identifies critical gaps in diagnostic criteria, anticoagulation thresholds, and guideline recommendations.

Effects of population-based screening for atrial fibrillation on quality of life.

Screening for atrial fibrillation is rising and may worsen or improve quality of life. We assessed quality of life (EQ-5D-5L) data in 6,004 participants with stroke risk factors randomised to usual care (n=4,503) or implantable loop recorder with anticoagulation upon detection of atrial fibrillation (n=1,501). Five domains (mobility, selfcare, usual activities, pain/discomfort, anxiety/depression) each scored from one to five were calculated into individual index scores (worst=-0.76, best=1.00). Changes in the index score and the visual analogue scale score (EQ VAS (0=worst, 100=best)) from baseline to year three were the primary outcomes, which were analysed using linear mixed models. Major problem was defined as a domain score ≥3 and analysed with logistic regression in year three. Of 6,004 participants, 5,733 (95 %) were alive after three years, and 5,162 (86 %) had complete EQ-5D-5L data. The baseline index score of 5,733 participants was 0.88 ±0.16, which decreased by -0.05 (-0.05; -0.04) in the control vs -0.04 (-0.05; -0.03) in the screening group after three years, and a baseline EQ VAS score of 78.4±16.2, which decreased by -6.06 (-6.54; -5.57) in control vs -5.18 (-5.95; -4.40) in the screening group after three years, with no significant difference between the groups (p=0.063 and p=0.056, respectively). The most frequent problem was major pain/discomfort (1,202 of 5,162 (23.3 %)), and any major problem occurred equally in the groups after three years (OR 0.91 (0.79;1.05)). A population-based, long-term, and continuous screening for atrial fibrillation in high-risk individuals did not translate into increased quality of life.

Association of Estimated Glomerular Filtration Rate (eGFR) and High-Sensitivity C-Reactive Protein (Hs-CRP) with the Risk of New-Onset Atrial Fibrillation in Patients with Diabetes.

Both renal function decline and systemic inflammation may synergistically increase the risk of atrial fibrillation (AF). This study investigates the association between estimated glomerular filtration rate (eGFR) and high-sensitivity C-reactive protein (hs-CRP) levels with the risk of new-onset AF in patients with diabetes mellitus. We included diabetic patients without AF who participated in physical exams in the Kailuan Study from 2006 to 2010. Participants were categorized into four groups based on baseline eGFR and hs-CRP levels: 1) high eGFR (≥60 mL/min/1.73m²) and low hs-CRP (<3 mg/L) (n=6,915), 2) high eGFR and high hs-CRP (≥3 mg/L) (n=3,154), 3) low eGFR (<60 mL/min/1.73m²) and low hs-CRP (n=4,638), 4) low eGFR and high hs-CRP (n=1,809). We employed multivariable Cox regression analysis to evaluate the relationships between eGFR, hs-CRP, and new-onset AF, adjusting for confounders including smoking status, alcohol consumption, blood pressure, fasting blood glucose (FBG), heart rate, lipid levels, body mass index (BMI), and medication usage. Competing risk analysis was also performed. Among 16,516 patients, 222 developed new-onset AF over a mean follow-up of 12.6 years. After adjusting for confounders, elevated hs-CRP and reduced eGFR were significantly associated with higher risk of new-onset AF compared to the high eGFR/low hs-CRP group. These findings remained consistent after excluding AF cases within the first 2-year. No significant interaction between eGFR and hs-CRP was observed (P=0.227). Subgroup analysis revealed that the combination of eGFR and hs-CRP had predictive value primarily in males under 60 years of age, individuals with FBG <9mmol/L, hypertension, and those not on hypoglycemic medications. In diabetic patients, decreased eGFR and elevated hs-CRP were independently linked to an increased risk of new-onset AF, emphasizing the importance of monitoring these factors for early detection and prevention of AF.