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Device-detected subclinical atrial fibrillation (AF) refers to infrequent, short-lasting, asymptomatic AF that is detected only with long-term continuous monitoring. Subclinical AF is common and associated with an increased risk of stroke; however, the risk of stroke with subclinical AF is lower than for clinical AF, and very few patients with subclinical AF alone have been included in large AF anticoagulation trials. The net benefit of anticoagulation in patients with subclinical AF is unknown. ARTESiA is a prospective, multicenter, double-blind, randomized controlled trial, recruiting patients with subclinical AF detected by an implanted pacemaker, defibrillator, or cardiac monitor, and who have additional risk factors for stroke. Patients with clinical AF documented by surface electrocardiogram will be excluded from the study. Participants will be randomized to receive either apixaban (according to standard AF dosing) or aspirin 81mg daily. The primary outcome is the composite of stroke, transient ischemic attack with diffusion-weighted magnetic resonance imaging evidence of cerebral infarction, and systemic embolism. Approximately 4,000 patients will be enrolled from around 230 clinical sites, with an anticipated mean follow-up of 36months until 248 adjudicated primary outcome events have occurred. ARTESiA will determine whether oral anticoagulation therapy with apixaban compared with aspirin reduces the risk of stroke or systemic embolism in patients with subclinical AF and additional risk factors.

Rapid and early detection of SARS-CoV-2 infections, especially during the pre- or asymptomatic phase, could aid in reducing virus spread. Physiological parameters measured by wearable devices can be efficiently analysed to provide early detection of infections. The COVID-19 Remote Early Detection (COVID-RED) trial investigated the use of a wearable device (Ava bracelet) for improved early detection of SARS-CoV-2 infections in real-time. Prospective, single-blinded, two-period, two-sequence, randomised controlled crossover trial. Subjects wore a medical device and synced it with a mobile application in which they also reported symptoms. Subjects in the experimental condition received real-time infection indications based on an algorithm using both wearable device and self-reported symptom data, while subjects in the control arm received indications based on daily symptom-reporting only. Subjects were asked to get tested for SARS-CoV-2 when receiving an app-generated alert, and additionally underwent periodic SARS-CoV-2 serology testing. The overall and early detection performance of both algorithms was evaluated and compared using metrics such as sensitivity and specificity. A total of 17,825 subjects were randomised within the study. Subjects in the experimental condition received an alert significantly earlier than those in the control condition (median of 0 versus 7 days before a positive SARS-CoV-2 test). The experimental algorithm achieved high sensitivity (93.8-99.2%) but low specificity (0.8-4.2%) when detecting infections during a specified period, while the control algorithm achieved more moderate sensitivity (43.3-46.4%) and specificity (66.4-65.0%). When detecting infection on a given day, the experimental algorithm also achieved higher sensitivity compared to the control algorithm (45-52% versus 28-33%), but much lower specificity (38-50% versus 93-97%). Our findings highlight the potential role of wearable devices in early detection of SARS-CoV-2. The experimental algorithm overestimated infections, but future iterations could finetune the algorithm to improve specificity and enable it to differentiate between respiratory illnesses. Netherlands Trial Register number NL9320.

Biomarkers may help to improve our knowledge about the complex pathophysiology of atrial fibrillation (AF). In this study we sought to identify significant changes in biomarkers and clinical measures in patients with and without AF recurrence after electrical cardioversion. We measured 21 conventional and new biomarkers before and 30 days after electrical cardioversion and assessed the associations of changes in biomarker levels with rhythm status at follow-up. Significant between-group changes were observed for bone morphogenetic protein 10 (BMP10), N-terminal pro-B-type natriuretic peptide (NT-proBNP) and total bilirubin. Their respective changes were − 10.4%, − 62.0% and − 25.6% in patients with sinus rhythm, and 3.1%, 1.1% and − 9.4% in patients with recurrent AF, for a between-group difference of − 13.5% (95% confidence interval [CI] − 19.3% to − 7.6%; P < 0.001), − 63.1% (95% CI − 76.6% to − 49.6%; P < 0.001) and − 16.3% (95% CI − 27.9% to − 4.7%; P = 0.007). In multivariable models, the reductions of BMP10 and NT-proBNP were significantly associated with follow-up rhythm status (β coefficient per 1 − SD decrease, − 3.85; 95% CI − 6.34 to − 1.35; P = 0.003 for BMP10 and − 5.84; 95% CI − 10.22 to − 1.47; P = 0.009 for NT-proBNP. In conclusion, changes in BMP10 und NT-proBNP levels were independently associated with rhythm status after cardioversion, suggesting that these markers may be dependent on the actual heart rhythm.

This study aimed to analyse health related quality of life (HRQoL) for patients with different atrial fibrillation (AF) types and to identify patient characteristics, symptoms and comorbidities that influence HRQoL. We used baseline data from the Swiss Atrial Fibrillation (Swiss-AF) study, a prospective multicentre observational cohort study conducted in 13 clinical centres in Switzerland. Between April 2014 and August 2017, 2415 AF patients were recruited. Patients were included in this analysis if they had baseline HRQoL data as assessed with EQ-5D-based utilities and visual analogue scale (VAS) scores. Patient characteristics and HRQoL were described stratified by AF type. The impact of symptoms, comorbidities and socio-economic factors on HRQoL was analysed using multivariable regression analysis. Based on 2412 patients with available baseline HRQoL data, the lowest unadjusted mean HRQoL was found in patients with permanent AF regardless of whether measured with utilities (paroxysmal: 0.83, persistent: 0.84, permanent: 0.80, p<0.001) or VAS score (paroxysmal: 73.6, persistent: 72.8, permanent: 69.2, p<0.001). In multivariable analysis of utilities and VAS scores, higher European Heart Rhythm Association (EHRA) score, recurrent falls and several comorbidities showed a strong negative impact on HRQoL while AF type was no longer associated with HRQoL. Multiple factors turned out to influence HRQoL in AF patients. After controlling for several comorbidities, the EHRA score was one of the strongest predictors independent of AF type. The results may be valuable for better patient assessment and provide a reference point for further QoL and health economic analyses in AF populations.

Atrial fibrillation (AF) increases the risk of adverse cardiovascular events, yet the underlying biological mechanisms remain unclear. We evaluate a panel of 12 circulating biomarkers representing diverse pathophysiological pathways in 3817 AF patients to assess their association with adverse cardiovascular outcomes. We identify 5 biomarkers including D-dimer, growth differentiation factor 15 (GDF-15), interleukin-6 (IL-6), N-terminal pro-B-type natriuretic peptide (NT-proBNP), and high-sensitivity troponin T (hsTropT) that independently predict cardiovascular death, stroke, myocardial infarction, and systemic embolism, significantly enhancing predictive accuracy. Additionally, GDF-15, insulin-like growth factor-binding protein-7 (IGFBP-7), NT-proBNP, and hsTropT predict heart failure hospitalization, while GDF-15 and IL-6 are associated with major bleeding events. A biomarker model improves predictive accuracy for stroke and major bleeding compared to established clinical risk scores. Machine learning models incorporating these biomarkers demonstrate consistent improvements in risk stratification across most outcomes. In this work, we show that integrating biomarkers related to myocardial injury, inflammation, oxidative stress, and coagulation into both conventional and machine learning-based models refine prognosis and guide clinical decision-making in AF patients. In atrial fibrillation patients, circulating biomarkers reflecting inflammation, myocardial injury, and coagulation improve prediction of stroke, heart failure, and bleeding, outperforming clinical scores and enhance risk stratification.”.

BackgroundPremature ventricular contractions (PVCs) are associated with an increased risk of morbidity and mortality. Therefore, it was aimed to assess risk factors for the frequency of PVCs in young and healthy adults.MethodsOur population-based study included 2048 healthy adults from the general population aged 25–41 years. PVC frequency was determined by 24-hour Holter ECG. We performed multivariable regression analysis using stepwise backward selection to identify factors independently associated with PVC frequency.ResultsMedian age was 37 years, 953 (46.5%) were male. At least one PVC during the 24-hour monitoring period was observed in 69% of participants. Median number of detected PVCs was 2, the 95th percentile was 193. In multivariable regression analyses, we found 17 significant risk factors for PVC frequency. Low educational status (risk ratio (RR) 3.33; 95% CI 1.98 to 5.60), body height>median (1.58, 95% CI 1.11 to 2.24) and increasing levels of waist:hip ratio (2.15, 95% CI 1.77 to 2.61), N-terminal pro brain natriuretic peptide (1.52, 95% CI 1.30 to 1.76) and Sokolow-Lyon Index (1.38, 95% CI 1.15 to 1.66) (all p≤0.01) were associated with a higher PVC frequency. Physical activity (RR fourth vs first quartile 0.51, 95% CI 0.34 to 0.76) and increasing levels of haemoglobin (0.58, 95% CI 0.47 to 0.70) and glucagon-like peptide-1 (0.72, 95% CI 0.64 to 0.82) (all p<0.001) were related to a lower PVC frequency.ConclusionsPVC occurrence is common even in healthy low-risk individuals, and its frequency is associated with several covariates mainly related to cardiovascular risk factors, markers of cardiac structure and function and socioeconomic status.

Noncardiac surgery is associated with an inflammatory response. Whether increased inflammation in the perioperative period is associated with subsequent morbidity and mortality is unknown. MEDLINE, EMBASE, and CENTRAL were systematically searched from date of inception until May 2023. Longitudinal studies were included if they reported multivariable adjusted associations of biomarkers measured preoperatively and/or within 10 days after surgery with at least one prespecified adverse outcome in noncardiac surgery patients. Data were extracted independently and in duplicate. Risk estimates were pooled using DerSimonian-Laird random-effects models and reported as summary odds ratios (ORs) with 95% CIs. The outcomes were all-cause mortality and major adverse cardiovascular events. Fifty-two studies with a total of 121,849 patients were included. The median follow-up was 56 [IQR, 28–63] months and the average age was 57 (±3) years. Elevated preoperative C-reactive protein (CRP) levels were associated with a higher risk of mortality (OR 1.57, 95% CI 1.29–1.90, I2 = 93%, 28 studies). This association was stronger in non-cancer surgery populations (OR 2.10, 95% CI 1.92–2.31, I2 = 0%, 4 studies) when compared to cancer surgery populations (OR 1.51, 95% CI 1.26–1.81, I2 = 83%, 24 studies) (p for subgroup difference = 0.001). Similarly, higher postoperative CRP levels were associated with all-cause mortality (OR 1.61, 95% CI 1.17–2.20, I2 = 90%, 7 studies). Higher preoperative CRP levels were associated with major cardiovascular events (OR 2.11, 95% CI 1.51–2.94, I2 = 0%, 2 studies). Other preoperatively measured biomarkers associated with all-cause mortality were fibrinogen (OR 1.48, 95% CI 1.05–2.09, I2 = 52%, 5 studies), interleukin-6 (OR 1.17, 95% CI 1.07–1.28, I2 = 27%, 3 studies), and tumour necrosis factor-alpha (OR 1.37, 95% CI 1.16–1.61, I2 = 0%, 2 studies). Inflammatory biomarker levels in the perioperative period were associated with all-cause mortality and adverse cardiovascular events in patients undergoing noncardiac surgery. •In NCS patients, perioperative CRP associated with mortality and MACE.•Perioperative inflammatory biomarker levels associated with postoperative morbidity.•Anti-inflammatory treatment may reduce outcome risk in NCS patients.

Objective To assess the seasonality of cardiovascular risk factors (CVRF) in a large set of population-based studies. Methods Cross-sectional data from 24 population-based studies from 15 countries, with a total sample size of 237 979 subjects. CVRFs included Body Mass Index (BMI) and waist circumference; systolic (SBP) and diastolic (DBP) blood pressure; total, high (HDL) and low (LDL) density lipoprotein cholesterol; triglycerides and glucose levels. Within each study, all data were adjusted for age, gender and current smoking. For blood pressure, lipids and glucose levels, further adjustments on BMI and drug treatment were performed. Results In the Northern and Southern Hemispheres, CVRFs levels tended to be higher in winter and lower in summer months. These patterns were observed for most studies. In the Northern Hemisphere, the estimated seasonal variations were 0.26 kg/m2 for BMI, 0.6 cm for waist circumference, 2.9 mm Hg for SBP, 1.4 mm Hg for DBP, 0.02 mmol/L for triglycerides, 0.10 mmol/L for total cholesterol, 0.01 mmol/L for HDL cholesterol, 0.11 mmol/L for LDL cholesterol, and 0.07 mmol/L for glycaemia. Similar results were obtained when the analysis was restricted to studies collecting fasting blood samples. Similar seasonal variations were found for most CVRFs in the Southern Hemisphere, with the exception of waist circumference, HDL, and LDL cholesterol. Conclusions CVRFs show a seasonal pattern characterised by higher levels in winter, and lower levels in summer. This pattern could contribute to the seasonality of CV mortality.

ObjectivesWe investigated machinelearningbased identification of presymptomatic COVID-19 and detection of infection-related changes in physiology using a wearable device.DesignInterim analysis of a prospective cohort study.Setting, participants and interventionsParticipants from a national cohort study in Liechtenstein were included. Nightly they wore the Ava-bracelet that measured respiratory rate (RR), heart rate (HR), HR variability (HRV), wrist-skin temperature (WST) and skin perfusion. SARS-CoV-2 infection was diagnosed by molecular and/or serological assays.ResultsA total of 1.5 million hours of physiological data were recorded from 1163 participants (mean age 44±5.5 years). COVID-19 was confirmed in 127 participants of which, 66 (52%) had worn their device from baseline to symptom onset (SO) and were included in this analysis. Multi-level modelling revealed significant changes in five (RR, HR, HRV, HRV ratio and WST) device-measured physiological parameters during the incubation, presymptomatic, symptomatic and recovery periods of COVID-19 compared with baseline. The training set represented an 8-day long instance extracted from day 10 to day 2 before SO. The training set consisted of 40 days measurements from 66 participants. Based on a random split, the test set included 30% of participants and 70% were selected for the training set. The developed long short-term memory (LSTM) based recurrent neural network (RNN) algorithm had a recall (sensitivity) of 0.73 in the training set and 0.68 in the testing set when detecting COVID-19 up to 2 days prior to SO.ConclusionWearable sensor technology can enable COVID-19 detection during the presymptomatic period. Our proposed RNN algorithm identified 68% of COVID-19 positive participants 2 days prior to SO and will be further trained and validated in a randomised, single-blinded, two-period, two-sequence crossover trial. Trial registration number ISRCTN51255782; Pre-results.

Obstructive sleep apnea seems to have an important influence on the autonomic nervous system. In this study, we assessed the relations of sleep apnea–related parameters with 24-hour heart rate variability (HRV) in a large population of young and healthy adults. Participants aged 25 to 41 years with a body mass index <35 kg/m2 and without known obstructive sleep apnea were included in a prospective population-based cohort study. HRV was assessed using 24-hour electrocardiographic monitoring. The SD of all normal RR intervals (SDNN) was used as the main HRV variable. Apnea-Hypopnea Index (AHI) and oxygen desaturation index (ODI) were obtained from nighttime pulse oximetry with nasal airflow measurements. We defined sleep-related breathing disorders as an AHI ≥5 or an ODI ≥5. Multivariable regression models were constructed to assess the relation of HRV with either AHI or ODI. Median age of the 1,255 participants was 37 years, 47% were men, and 9.6% had an AHI ≥5. Linear inverse associations of SDNN across AHI and ODI groups were found (p for trend = 0.006 and 0.0004, respectively). The β coefficients (95% CI) for the relation between SDNN and elevated AHI were −0.20 (−0.40 to −0.11), p = 0.04 and −0.29 (−0.47 to −0.11), p = 0.002 for elevated ODI. After adjustment for 24-hour heart rate, the same β coefficients (95% CI) were −0.06 (−0.22 to 0.11), p = 0.51 and −0.14 (−0.30 to 0.01), p = 0.07, respectively. In conclusion, even early stages of sleep-related breathing disorders are inversely associated with HRV in young and healthy adults, suggesting that they are tightly linked with autonomic dysfunction. However, HRV and 24-hour heart rate seem to have common information.