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In this multicenter, randomized trial comparing early rhythm control with usual care in patients with early atrial fibrillation and cardiovascular conditions, early rhythm control reduced the rate of death from cardiovascular causes and cardiovascular complications and did not affect the number of nights in the hospital.

Lung injury with features of acute respiratory distress syndrome (ARDS) appears to be the principal characteristic of severe acute respiratory syndrome coronavirus 2 infection.1 Recent guidance by the UK Intensive Care Society (ICS) advocates awake prone positioning to become standard of care for suspected or confirmed COVID-19, in patients requiring an FiO2 ≥28%0.2 These recommendations are extrapolated from physiological principles and clinical evidence obtained in a distinct study population—patients with severe ARDS undergoing invasive mechanical ventilation (IMV). Estimating the frequency and speed of phase transition as well as identifying pragmatic surrogate markers to predict disease phase may be useful in selecting those who could benefit the most from awake prone positioning. A systematic review and meta-analysis. Assessment of therapeutic interventions and lung protective ventilation in patients with moderate to severe acute respiratory distress syndrome: a systematic review and network meta-analysis.

Dilated cardiomyopathy (DCM) is a common yet challenging cardiac disease. Great strides have been made in improving DCM prognosis due to heart failure but sudden cardiac death (SCD) due to ventricular arrhythmias remains significant and challenging to predict. High-risk patients can be effectively managed with implantable cardioverter defibrillators (ICDs) but because identification of what is high risk is very limited, many patients unnecessarily experience the morbidity associated with an ICD implant and many others are not identified and have preventable mortality. Current guidelines recommend use of left ventricular ejection fraction and New York Heart Association class as the main markers of risk stratification to identify patients who would be at higher risk of SCD. However, when analysing the data from the trials that these recommendations are based on, the number of patients in whom an ICD delivers appropriate therapy is modest. In order to improve the effectiveness of therapy with an ICD, the patients who are most likely to benefit need to be identified. This review article presents the evidence behind current guideline-directed SCD risk markers and then explores new potential imaging, electrophysiological and genetic risk markers for SCD in DCM.

Objectives We assessed the CHA2DS2-VASc score for predicting hospital readmission risk and length of stay (LOS) in patients admitted with primary atrial fibrillation (AF). Methods This retrospective cohort study included patients with index admission for AF to Latakia’s tertiary center (May 2021–November 2023). Patients were followed 90 days to assess readmission. CHA2DS2-VASc was correlated with 90-day readmission, inpatient all-cause mortality, and LOS during index admission. Results In total, 717 patients were included; 320 (45%) were readmitted to the hospital within 90 days (58% men, 65% aged <65 years). Inpatient mortality was 4%; the median LOS was 2 days. There was an increase in the incident rate ratio (IRR) of LOS starting from a CHA2DS2-VASc of 2 (IRR: 2, 95% confidence interval [CI]: 1.7–2.2) to a score of >6 (IRR: 5, 95% CI: 1.8–10.7), compared with a score of 0. There was an incremental increase in the hazard ratio (HR) of readmission from a score of 1 (HR: 2.3, 95% CI: 1.3–4.1) to a score of >6 (HR: 41, 95% CI: 31–72) compared with a CHA2DS2-VASc of 0. Conclusion CHA2DS2-VASc could predict 90-day hospital readmission and LOS during the index admission in patients admitted with primary AF.

Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world. The arrhythmia and methods developed to cure it have been studied for several decades. However, professionals worldwide are still working to improve treatment quality. One novel technology that can be useful is a wearable device. The two most used recordings from these devices are photoplethysmogram (PPG) and electrocardiogram (ECG) signals. As the price lowers, these devices will become significant technology to increase sensitivity, for monitoring and for treatment quality support. This is important as AF can be challenging to detect in advance, especially during home monitoring. Modern artificial intelligence (AI) has the potential to respond to this challenge. AI has already achieved state of the art results in many applications, including bioengineering. In this perspective, we discuss wearable devices combined with AI for AF detection, an approach that enables a new era of possibilities for the future.

Despite significant progress in cardiovascular pharmacotherapy and interventional strategies, cardiovascular disease (CVD), in particular ischaemic heart disease, remains the leading cause of morbidity and mortality among women in the UK and worldwide. Women are underdiagnosed, undertreated and under-represented in clinical trials directed at management strategies for CVD, making their results less applicable to this subset. Women have additional sex-specific risk factors that put them at higher risk of future cardiovascular events. Psychosocial risk factors, socioeconomic deprivation and environmental factors have an augmented impact on women’s cardiovascular health, highlighting the need for a holistic approach to care that considers risk factors specifically related to female biology alongside the traditional risk factors. Importantly, in the UK, even in the context of a National Health Service, there exist significant regional variations in age-standardised mortality rates among patients with CVD. Given most CVDs are preventable, concerted efforts are necessary to address the unmet needs and ensure parity of care for women with CVD. The present consensus document, put together by the British Cardiovascular Society (BCS)’s affiliated societies, specifically portrays the current status on the sex-related differences in the diagnosis and treatment of each of the major CVD areas and proposes strategies to overcome the barriers in accessing diagnoses and treatments among women. This document aims at raising awareness of the scale of the current problem and hopes to stimulate a multifaceted approach to address sex disparities and enable future comprehensive sex- and gender-based research through collaboration across different affiliated societies within the BCS.

The ability to record and analyse electrical behaviour across the heart using optical and electrode mapping has revolutionised cardiac research. However, wider uptake of these technologies is constrained by the lack of multi-functional and robustly characterised analysis and mapping software. We present ElectroMap, an adaptable, high-throughput, open-source software for processing, analysis and mapping of complex electrophysiology datasets from diverse experimental models and acquisition modalities. Key innovation is development of standalone module for quantification of conduction velocity, employing multiple methodologies, currently not widely available to researchers. ElectroMap has also been designed to support multiple methodologies for accurate calculation of activation, repolarisation, arrhythmia detection, calcium handling and beat-to-beat heterogeneity. ElectroMap implements automated signal segmentation, ensemble averaging and integrates optogenetic approaches. Here we employ ElectroMap for analysis, mapping and detection of pro-arrhythmic phenomena in silico, in cellulo, animal model and in vivo patient datasets. We anticipate that ElectroMap will accelerate innovative cardiac research and enhance the uptake, application and interpretation of mapping technologies leading to novel approaches for arrhythmia prevention.

Deep learning (DL) models have shown promise for the accurate detection of atrial fibrillation (AF) from electrocardiogram/photoplethysmography (ECG/PPG) data, yet deploying these on resource-constrained wearable devices remains challenging. This study proposes integrating a customized channel attention mechanism to compress DL neural networks for AF detection, allowing the model to focus only on the most salient time-series features. The results demonstrate that applying compression through channel attention significantly reduces the total number of model parameters and file size while minimizing loss in detection accuracy. Notably, after compression, performance increases for certain model variants in key AF databases (ADB and C2017DB). Moreover, analyzing the learned channel attention distributions after training enhances the explainability of the AF detection models by highlighting the salient temporal ECG/PPG features most important for its diagnosis. Overall, this research establishes that integrating attention mechanisms is an effective strategy for compressing large DL models, making them deployable on low-power wearable devices. We show that this approach yields compressed, accurate, and explainable AF detectors ideal for wearables. Incorporating channel attention enables simpler yet more accurate algorithms that have the potential to provide clinicians with valuable insights into the salient temporal biomarkers of AF. Our findings highlight that the use of attention is an important direction for the future development of efficient, high-performing, and interpretable AF screening tools for wearable technology.

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Background The EQ-5D is one of the most commonly used tools to establish health-related quality of life (QoL). EQ-5D data in atrial fibrillation (AF) patients in the Middle East are lacking. Objectives This study aims to evaluate the reliability and validity of the Arabic version of the EQ-5D in AF inpatients in Syria. Methods The study involved patients admitted to the emergency department of Tishreen’s University Hospital in Latakia with AF as the primary diagnosis between the 1st of June 2021 and the 1st of June 2023. Arabic versions of the EQ-5D, EQ-VAS and SF36 questionnaires were administered to patients. Validation was done using convergent, discriminant, and known-groups validity, while reliability was conducted using EQ-5D retesting within 2–4 weeks. Results 432 participants were included in the study with a mean ± standard deviation of 63 ± 15. Males represented 242 (56%) of the participants. All hypotheses relating EQ-5D responses to external variables were satisfied. All three validation hypotheses demonstrated that the EQ-5D had the convergent, discriminant and known group validity to assess QoL in this cohort. The intraclass correlation coefficient (ICC) for test-retest reliability ranged between 0.74 and 0.88, while Cohen’s κ ranged between 0.72 and 0.86. Cronbach’s α value for internal consistency was 0.73. Conclusion The Arabic version of EQ-5D was valid and reliable in measuring QoL in AF inpatients in Syria. This validation opens the door for more widespread use of the EQ-5D in Arabic-speaking regions, facilitating better-informed healthcare decisions and improving patient care strategies in Syria and other Middle Eastern countries.