Advances in screening for undiagnosed atrial fibrillation for stroke prevention and implications for patients with obstructive sleep apnoea: A literature review and research agenda

Atrial fibrillation (AF) is the most common type of sustained cardiac arrhythmia encountered in clinical practice, and its burden is expected to increase in most developed countries over the next few decades. Because AF can be silent, it is often not diagnosed until an AF-related complication occurs, such as stroke. AF is also associated with increased risk of heart failure, lower quality of life, and death. Anticoagulation has been shown to dramatically decrease embolic risk in the setting of atrial fibrillation, resulting in growing interest in early detection of previously undiagnosed AF. Newly developed monitoring devices have improved the detection of AF and have been recommended in guidelines for screening of AF in individuals aged 65 years and over. While screening is currently targeted to these older individuals, younger patients with obstructive sleep apnoea (OSA) are at higher risk of AF and stroke than the general population, indicating a need for targeted early detection of AF in this group. Compared to individuals without OSA, those with OSA are four times more likely to develop AF, and the risk of AF is strongly associated with OSA severity. The overall prevalence of AF among individuals with OSA remains unknown because of limitations related to study design and to the conventional methods previously used for AF detection. Recent and emerging technological advances may improve the detection of undiagnosed AF in high-risk population groups, such as those with OSA. In this clinical review, we discuss the methods of screening for AF and the applications of newer technologies for AF detection in patients with OSA. We conclude the review with a brief description of our research agenda in this area

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Synthesizer A Pattern Language for Designing Digital Modular Synthesis Software

Synthesizer is a pattern language for designing digital synthesizers using modular synthesis in software to generate sound. Software developed according to this pattern language emulates the abilities of an analog synthesizer. Modular synthesis is one of the oldest sound synthesis techniques. It was used in the earlies

Transfer learning artificial intelligence for automated detection of atrial fibrillation in patients undergoing evaluation for suspected obstructive sleep apnoea: A feasibility study

Background: Individuals with obstructive sleep apnoea (OSA) experience a higher burden of atrial fibrillation (AF) than the general population, and many cases of AF remain undetected. We tested the feasibility of an artificial intelligence (AI) approach to opportunistic detection of AF from single-lead electrocardiograms (ECGs) which are routinely recorded during in-laboratory polysomnographic sleep studies. Methods: Using transfer learning, an existing ECG AI model was applied to 1839 single-lead ECG traces recorded during in-laboratory sleep studies without any training of the algorithm. Manual review of all traces was performed by two trained clinicians who were blinded to each other\u27s review. Discrepancies between the two investigators were resolved by two cardiologists who were also unaware of each other\u27s scoring. The diagnostic accuracy of the AI algorithm was calculated against the results of the manual ECG review which were considered gold standard. Results: Manual review identified AF in 144 of the 1839 single-lead ECGs (7.8%). The AI detected all cases of manually confirmed AF (sensitivity = 100%, 95% CI: 97.5-100.0). The AI model misclassified many ECGs with artefacts as AF, resulting in a specificity of 76.0 (95% CI: 73.9-78.0), and an overall diagnostic accuracy of 77.9% (95% CI: 75.9%-97.8%). Conclusion: Transfer learning AI, without additional training, can be successfully applied to disparate ECG signals, with excellent negative predictive values, and can exclude AF among patients undergoing evaluation for suspected OSA. Further signal-specific training is likely to improve the AI\u27s specificity and decrease the need for manual verification