Background: Diagnosing atrial fibrillation ( AF ) before ischemic stroke occurs is a priority for stroke prevention in AF . Smartphone camera–based photoplethysmographic ( PPG ) pulse waveform measurement discriminates between different heart rhythms, but its ability to diagnose AF in real‐world situations has not been adequately investigated. We sought to assess the diagnostic performance of a standalone smartphone PPG application, Cardiio Rhythm, for AF screening in primary care setting. Methods and Results: Patients with hypertension, with diabetes mellitus, and/or aged ≥65 years were recruited. A single‐lead ECG was recorded by using the AliveCor heart monitor with tracings reviewed subsequently by 2 cardiologists to provide the reference standard. PPG measurements were performed by using the Cardiio Rhythm smartphone application. AF was diagnosed in 28 (2.76%) of 1013 participants. The diagnostic sensitivity of the Cardiio Rhythm for AF detection was 92.9% (95% CI ] 77–99%) and was higher than that of the AliveCor automated algorithm (71.4% [95% CI 51–87%]). The specificities of Cardiio Rhythm and the AliveCor automated algorithm were comparable (97.7% [95% CI : 97–99%] versus 99.4% [95% CI 99–100%]). The positive predictive value of the Cardiio Rhythm was lower than that of the AliveCor automated algorithm (53.1% [95% CI 38–67%] versus 76.9% [95% CI 56–91%]); both had a very high negative predictive value (99.8% [95% CI 99–100%] versus 99.2% [95% CI 98–100%]). Conclusions: The Cardiio Rhythm smartphone PPG application provides an accurate and reliable means to detect AF in patients at risk of developing AF and has the potential to enable population‐based screening for AF .

Background and Purpose—: Little is known about the impact of quality of anticoagulation control, as reflected by time in therapeutic range (TTR), on the effectiveness and safety of warfarin therapy in Chinese patients with atrial fibrillation. We investigated the risks of ischemic stroke and intracranial hemorrhage (ICH) in relation to warfarin at various TTRs in a real-world cohort of Chinese patients with atrial fibrillation receiving warfarin and compared with those on dabigatran, aspirin, and no therapy. Methods—: This is an observational study. Results—: Of 8754 Chinese patients with atrial fibrillation and CHA ₂ DS ₂ -VASc ≥1 (79.5±9.2 years; CHA ₂ DS ₂ -VASc, 4.1±1.5; and Hypertension, Abnormal Renal/Liver Function, Stroke, Bleeding History or Predisposition, Labile International Normalized Ratio, Elderly (>65 years), Drugs/Alcohol Concomitantly [HAS-BLED], 2.2±0.9), 16.3% received warfarin, 41.1% aspirin, 4.5% dabigatran, and 38.1% received no therapy. The incidence of ischemic stroke was highest in patients with no therapy (10.38%/y), followed by patients on aspirin (7.95%/y). The incidence of stroke decreased progressively with increasing TTR quartiles (56.2%) from 7.34%/y (first quartile) to 3.10%/y (fourth quartile). Patients on dabigatran had the lowest incidence of stroke among all groups (2.24%/y). The incidence of ICH was lowest in patients on dabigatran (0.32%/y) compared with those on warfarin (0.90%/y), aspirin (0.80%/y), and no therapy (0.53%/y). ICH incidence decreased with increasing TTR from 1.37%/y (first quartile) to 0.74%/y (fourth quartile). Conclusions—: In Chinese patients with atrial fibrillation, the benefits of warfarin therapy for stroke prevention and ICH risk are closely dependent on the quality of anticoagulation, as reflected by TTR. Even at the top TTR quartile, warfarin was associated with a higher stroke and ICH risk than dabigatran.

Introduction There is an outbreak of COVID-19 worldwide. As there is no effective therapy or vaccine yet, rigorous implementation of traditional public health measures such as isolation and quarantine remains the most effective tool to control the outbreak. When an asymptomatic individual with COVID-19 exposure is being quarantined, it is necessary to perform temperature and symptom surveillance. As such surveillance is intermittent in nature and highly dependent on self-discipline, it has limited effectiveness. Advances in biosensor technologies made it possible to continuously monitor physiological parameters using wearable biosensors with a variety of form factors. Objective To explore the potential of using wearable biosensors to continuously monitor multidimensional physiological parameters for early detection of COVID-19 clinical progression. Method This randomised controlled open-labelled trial will involve 200–1000 asymptomatic subjects with close COVID-19 contact under mandatory quarantine at designated facilities in Hong Kong. Subjects will be randomised to receive a remote monitoring strategy (intervention group) or standard strategy (control group) in a 1:1 ratio during the 14 day-quarantine period. In addition to fever and symptom surveillance in the control group, subjects in the intervention group will wear wearable biosensors on their arms to continuously monitor skin temperature, respiratory rate, blood pressure, pulse rate, blood oxygen saturation and daily activities. These physiological parameters will be transferred in real time to a smartphone application called Biovitals Sentinel. These data will then be processed using a cloud-based multivariate physiology analytics engine called Biovitals to detect subtle physiological changes. The results will be displayed on a web-based dashboard for clinicians’ review. The primary outcome is the time to diagnosis of COVID-19. Ethics and dissemination Ethical approval has been obtained from institutional review boards at the study sites. Results will be published in peer-reviewed journals.

Although the incidence of venous thromboembolism (VTE) in Asian populations is lower than in Western countries, the overall burden of VTE in Asia has been considerably underestimated. Factors that may explain the lower prevalence of VTE in Asian populations relative to Western populations include the limited availability of epidemiological data in Asia, ethnic differences in the genetic predisposition to VTE, underdiagnoses, low awareness toward thrombotic disease, and possibly less symptomatic VTE in Asian patients. The clinical assessment, diagnostic testing, and therapeutic considerations for VTE are, in general, the same in Asian populations as they are in Western populations. The management of VTE is based upon balancing the treatment benefits against the risk of bleeding. This is an especially important consideration for Asian populations because of increased risk of intracranial hemorrhage with vitamin K antagonists. Non-vitamin K antagonist oral anticoagulants have shown advantages over current treatment modalities with respect to bleeding outcomes in major phase 3 clinical trials, including in Asian populations. Although anticoagulant therapy has been shown to reduce the risk of postoperative VTE in Western populations, VTE prophylaxis is not administered routinely in Asian countries. Despite advances in the management of VTE, data in Asian populations on the incidence, prevalence, recurrence, risk factors, and management of bleeding complications are limited and there is need for increased awareness. To that end, this review summarizes the available data on the epidemiology, risk stratification, diagnosis, and treatment considerations in the management of VTE in Asia.

Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild COVID-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 32 years; male: 52.9%) with mild COVID-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 (NEWS2) were 0.59 ± 0.7. 1231 manual measurement of physiology parameters were performed during hospital stay (median 15 days). Physiology parameters obtained from wearable biosensors correlated well with manual measurement including pulse rate (r = 0.96, p < 0.0001) and oxygen saturation (r = 0.87, p < 0.0001). A machine learning-derived index reflecting overall health status, Biovitals Index (BI), was generated by autonomous analysis of physiology parameters, symptoms, and other medical data. Daily BI was linearly associated with respiratory tract viral load (p < 0.0001) and NEWS2 (r = 0.75, p < 0.001). BI was superior to NEWS2 in predicting clinical worsening events (sensitivity 94.1% and specificity 88.9%) and prolonged hospitalization (sensitivity 66.7% and specificity 72.7%). Wearable biosensors coupled with machine learning-derived health index allowed automated detection of clinical deterioration.