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Background: Patients with adult congenital heart disease (ACHD) are a rapidly growing cardiovascular population with increasing health needs and co-morbidities. Furthermore, their management requires frequent and ongoing hospital visits which can be burdensome. Digital health and remote monitoring have been shown to have a vast potential to enhance delivery of healthcare for patients, reducing their need for travel to clinic appointments therefore reducing costs to the patient and the healthcare service., Methods: Patients over the age of 16 with a diagnosis of ACHD were invited to use the tailored digital application too. They were monitored for a period of 6 months. Information on patient demographics, time using the application, flagged events that prompted clinical reviews and their feedback through patient surveys were collected., Results: A total of 103 patients were enrolled and registered to use the digital application tool. There were 57 (56%) males, median age at the time of enrolment was 39 (16-73) years. The majority (96%) had a moderate or complex ACHD according to the ACC/AHA classification. There was a total of 7 modules that were completed on a weekly basis. The median length of a participant session was 2.2 min and the mean time to complete a module was 21 s. In total, 35 (67%) felt that the application helped them better manage their cardiac condition. Almost all (94%) of patients expressed that they would like to continue using the application beyond the pilot. There were 18 flagged events during the 6 month observation period, and 50% of received early clinical intervention., Conclusion: Application based remote monitoring in this select group was well received and potentially holds large benefit to patients both clinically and economically. There were no safety concerns in our pilot feasibility study. Our data may inform much needed and timely investment in digital health., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Aims: Growing evidence suggests that poor sleep health is associated with cardiovascular risk. However, research in this area often relies upon recollection dependent questionnaires or diaries. Accelerometers provide an alternative tool for measuring sleep parameters objectively. This study examines the association between wrist-worn accelerometer-derived sleep onset timing and cardiovascular disease (CVD)., Methods and Results: We derived sleep onset and waking up time from accelerometer data collected from 103 712 UK Biobank participants over a period of 7 days. From this, we examined the association between sleep onset timing and CVD incidence using a series of Cox proportional hazards models. A total of 3172 cases of CVD were reported during a mean follow-up period of 5.7 (±0.49) years. An age- and sex-controlled base analysis found that sleep onset time of 10:00 p.m.-10:59 p.m. was associated with the lowest CVD incidence. An additional model, controlling for sleep duration, sleep irregularity, and established CVD risk factors, did not attenuate this association, producing hazard ratios of 1.24 (95% confidence interval, 1.10-1.39; P  < 0.005), 1.12 (1.01-1.25; P   =  0.04), and 1.25 (1.02-1.52; P   =  0.03) for sleep onset <10:00 p.m., 11:00 p.m.-11:59 p.m., and ≥12:00 a.m., respectively, compared to 10:00 p.m.-10:59 p.m. Importantly, sensitivity analyses revealed this association with increased CVD risk was stronger in females, with only sleep onset <10:00 p.m. significant for males., Conclusions: Our findings suggest the possibility of a relationship between sleep onset timing and risk of developing CVD, particularly for women. We also demonstrate the potential utility of collecting information about sleep parameters via accelerometry-capable wearable devices, which may serve as novel cardiovascular risk indicators., (© The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Background: The emergence of COVID-19 resulted in postponement of nonemergent surgical procedures for cardiac patients in London. mHealth represented a potentially viable mechanism for highlighting deteriorating patients on the lengthened cardiac surgical waiting lists., Objective: To evaluate the deployment of a digital health solution to support continuous triaging of patients on a cardiac surgical waiting list., Method: An NHS trust utilized an app-based mHealth solution (Huma Therapeutics) to help gather vital information on patients awaiting cardiac surgery (valvular and coronary surgery). Patients at a tertiary cardiac center on a waiting list for elective surgery were given the option to be monitored remotely via a mobile app until their date of surgery. Patients were asked to enter their symptoms once a week. The clinical team monitored this information remotely, prompting intervention for those patients who needed it., Results: Five hundred and twenty-five patients were on boarded onto the app. Of the 525 patients using the solution, 51 (9.71%) were identified as at risk of deteriorating based on data captured via the remote patient monitoring platform and subsequently escalated to their respective consultant. 81.7% of patients input at least one symptom after they were on boarded on the platform., Discussion: Although not a generalizable study, this change in practice clearly demonstrates the feasibility and potential benefit digital remote patient monitoring can have in triaging large surgical wait lists, ensuring those that need care urgently receive it. We recommend further study into the potential beneficial outcomes from preoperative cardiac mHealth solutions., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Aims: Cardiovascular diseases (CVDs) are among the leading causes of death worldwide. Predictive scores providing personalized risk of developing CVD are increasingly used in clinical practice. Most scores, however, utilize a homogenous set of features and require the presence of a physician. The aim was to develop a new risk model (DiCAVA) using statistical and machine learning techniques that could be applied in a remote setting. A secondary goal was to identify new patient-centric variables that could be incorporated into CVD risk assessments., Methods and Results: Across 466 052 participants, Cox proportional hazards (CPH) and DeepSurv models were trained using 608 variables derived from the UK Biobank to investigate the 10-year risk of developing a CVD. Data-driven feature selection reduced the number of features to 47, after which reduced models were trained. Both models were compared to the Framingham score. The reduced CPH model achieved a c-index of 0.7443, whereas DeepSurv achieved a c-index of 0.7446. Both CPH and DeepSurv were superior in determining the CVD risk compared to Framingham score. Minimal difference was observed when cholesterol and blood pressure were excluded from the models (CPH: 0.741, DeepSurv: 0.739). The models show very good calibration and discrimination on the test data., Conclusion: We developed a cardiovascular risk model that has very good predictive capacity and encompasses new variables. The score could be incorporated into clinical practice and utilized in a remote setting, without the need of including cholesterol. Future studies will focus on external validation across heterogeneous samples., (© The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology.)