Aims Most patients who receive implantable cardioverter defibrillators (ICDs) for primary prevention do not receive therapy during the lifespan of the ICD, whilst up to 50% of sudden cardiac death (SCD) occur in individuals who are considered low risk by conventional criteria. Machine learning offers a novel approach to risk stratification for ICD assignment. Methods and results Systematic search was performed in MEDLINE, Embase, Emcare, CINAHL, Cochrane Library, OpenGrey, MedrXiv, arXiv, Scopus, and Web of Science. Studies modelling SCD risk prediction within days to years using machine learning were eligible for inclusion. Transparency and quality of reporting (TRIPOD) and risk of bias (PROBAST) were assessed. A total of 4356 studies were screened with 11 meeting the inclusion criteria with heterogeneous populations, methods, and outcome measures preventing meta-analysis. The study size ranged from 122 to 124 097 participants. Input data sources included demographic, clinical, electrocardiogram, electrophysiological, imaging, and genetic data ranging from 4 to 72 variables per model. The most common outcome metric reported was the area under the receiver operator characteristic (n = 7) ranging between 0.71 and 0.96. In six studies comparing machine learning models and regression, machine learning improved performance in five. No studies adhered to a reporting standard. Five of the papers were at high risk of bias. Conclusion Machine learning for SCD prediction has been under-applied and incorrectly implemented but is ripe for future investigation. It may have some incremental utility in predicting SCD over traditional models. The development of reporting standards for machine learning is required to improve the quality of evidence reporting in the field.

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Background Atrial fibrillation (AF) hospital admissions represent significant AF related treatment costs nationally. In the year 2019–2020 our hospital reported 1,333 admissions with a primary diagnosis of AF, with a 10% annual increase. A virtual ambulatory AF ward providing multidisciplinary care with remote hospital-level monitoring could reshape the future model of AF management. Methods An AF virtual ward was implemented at our UK tertiary centre, as a proof-of-concept model of care. Patients admitted with a primary diagnosis of AF satisfying the AF virtual ward (AFVW) entry criteria (i.e., haemodynamically stable, HR <140 bpm with other acute conditions excluded) were given access to a single lead ECG recording device, a Bluetooth integrated blood pressure machine and pulse oximeter with instruction to record daily ECGs, blood pressure readings, oxygen saturations and fill an online AF symptom questionnaire via a smart phone or electronic tablet. Data were uploaded to an integrated digital platform for review by the clinical team who undertook twice daily virtual ward rounds. Medication adjustment was arranged through the hospital pharmacy. Data was collected prospectively for patients admitted to the AF virtual ward between 31 January and 11 March 2022. Outcomes included length of hospital stay, admission avoidance and re-admissions. Re-admission avoidance was assessed using the index admission criteria as a parameter for re-admission likelihood. Patients' satisfaction was assessed using the NHS family and friends' test (FFT). Results Over the 6-week period a total of 14 patients were enrolled. One patient was unable to be onboarded because of technology related anxiety with 13 patients onboarded to the virtual ward, 30.7% (n=4) did not have smart phones and were provided with electronic tablets. The age on admission was 64±10 years (mean±SD) with the oldest at 78 years of age. All patients were in AF with a mean heart rate of 122±24 bpm, and 38.5% (n=5) were discharged from the virtual ward in sinus rhythm. One patient was onboarded directly from pacemaker clinic and hence hospital admission was completely avoided, and 5 re-admissions were avoided for 3 patients. One patient required brief readmission due to persistent tachycardia requiring acute cardioversion. The FFT yielded 100% positive responses among patients. Conclusion This proof-of-concept is a first real world experience of a virtual ward for hospital patients with fast AF. It demonstrates a promising new telemedicine-based care model and with clear appetite among both patients and health professionals. This model of care has the potential to reduce the financial and backlog pressures caused by AF admissions without compromising patients' care or safety. Work is ongoing to further confirm the safety and cost-effectiveness upon further progress in a larger patient cohort. Funding Acknowledgement Type of funding sources: None.

BackgroundAtrial fibrillation (AF) represents a growing healthcare challenge, mainly driven by acute hospitalisations. Virtual wards could be the way forward to manage acute AF patients through remote monitoring, especially with the rise in global access to digital telecommunication and the growing acceptance of telemedicine post-COVID-19.MethodsAn AF virtual ward was implemented as a proof-of-concept care model. Patients presenting acutely with AF or atrial flutter and rapid ventricular response to the hospital were onboarded to the virtual ward and managed at home through remote ECG-monitoring and ‘virtual’ ward rounds, after being given access to a single-lead ECG device, a blood pressure monitor and pulse oximeter with instructions to record daily ECGs, blood pressure, oxygen saturations and to complete an online AF symptom questionnaire. Data were uploaded to a digital platform for daily review by the clinical team. Primary outcomes included admission avoidance, readmission avoidance and patient satisfaction. Safety outcomes included unplanned discharge from the virtual ward, cardiovascular mortality and all-cause mortality.ResultsThere were 50 admissions to the virtual ward between January and August 2022. Twenty-four of them avoided initial hospital admission as patients were directly enrolled to the virtual ward from outpatient settings. A further 25 readmissions were appropriately prevented during virtual surveillance. Patient satisfaction questionnaires yielded 100% positive responses among participants. There were three unplanned discharges from the virtual ward requiring hospitalisation. Mean heart rate on admission to the virtual ward and discharge was 122±26 and 82±27 bpm respectively. A rhythm control strategy was pursued in 82% (n=41) and 20% (n=10) required 3 or more remote pharmacological interventions.ConclusionThis is a first real-world experience of an AF virtual ward that heralds a potential means for reducing AF hospitalisations and the associated financial burden, without compromising on patients’ care or safety.