Coronary computed tomography angiography (CCTA) is the primary investigation for stable chest pain. Despite approximately 80% of individuals undergoing CCTA not having obstructive coronary disease, this group contributes to two-thirds of major adverse cardiovascular events (MACE). Assessment of coronary inflammation using perivascular fat attenuation index (FAI) and AI-derived risk scores (AI-Risk) has demonstrated enhanced risk prediction beyond traditional clinical and CCTA parameters. We aimed to assess if FAI and AI-Risk alter risk prediction and clinical management in those with no evidence of coronary artery disease. Consecutive patients undergoing CCTA who had a CAD-RADS score = 0 went on to have fat attenuation index (FAI) calculation and AI-Risk (CaRi-HeartÒ) at a single centre over a 3-year period were recruited. Conventional risk scores for non-fatal and fatal myocardial infarctions (QRISK3 and SCORE, respectively) were compared to AI-Risk. Clinical management decisions based on risk factors and CCTA results were recorded. FAI and AI-Risk scores were then provided, and the resultant clinical management decision recorded. Seventy-nine patients were included in the study (n=79, male 72%, 53 years, Table 1). AI-Risk reclassified risk in 47% and 37% of patients compared with QRISK3 and SCORE, respectively (Table 2 & 3). Clinical management was changed in 32% of patients following AI-Risk analysis (Figure 1). FAI and AI-Risk scores in those with no conventional evidence of coronary artery disease changed risk prediction in around half of individuals and changed clinical management in around a third.

Heart failure (HF) remains a leading cause of recurrent hospitalisations worldwide, largely driven by acute episodes of decompensation. Early identification of impending decompensation could enable timely intervention and potentially prevent costly admissions. Non-invasive wearable devices have emerged as promising tools for continuously monitoring physiological parameters and detecting early signs of deterioration. This review summarises recent advances in wearable technologies designed to predict HF decompensation and appraises their ability to generate clinically useful alerts. It will examine various modalities designed to monitor different aspects of cardiorespiratory physiology that have the potential to detect abnormalities preceding heart failure decompensation. Broadly, these devices either monitor physical activity capacity and cardiac function or monitor changes in pulmonary fluid congestion. We will also cover evidence exploring whether these devices can generate timely alerts for interventions to improve patient outcomes and reduce hospitalisations. However, despite advances in these technologies, challenges remain regarding their accuracy and usability for remote monitoring, as well as concerns with data storage, processing, patient adherence, and integration into existing healthcare workflows. While current limitations exist, previous results warrant further research into this area, with a focus on larger randomised trials, exploring both single- and multi-sensor systems, using artificial intelligence and cost-effectiveness analysis. Overall, non-invasive wearables represent an opportunity to create a more proactive approach to HF management, with the potential to shift the paradigm from reactive treatment to anticipatory care.

Background:Eosinophilic granulomatosis with polyangiitis (EGPA) is a rare vasculitis associated with significant cardiac morbidity and mortality. This case report presents the diagnostic and management challenges of EGPA-related arrhythmias in a remote general hospital setting. Case summary:A 64-year-old Caucasian male presented with an indolent prodrome of fatigue, shortness of breath and anorexia, that culminated in an acute presentation with pulmonary embolism. His complicated clinical course included intracranial haemorrhage and refractory ventricular arrhythmias. Eosinophilia and sub-endocardial hypoattenuation observed on chest computed tomography were key findings that led to the diagnosis of EGPA. Multiple anti-arrhythmic therapies were required as temporary measures whilst control of the underlying eosinophilic inflammation was achieved.Once stable, the patient was transferred to a tertiary cardiac centre for further investigation and cardioverter-defibrillator implantation. With EGPA now well controlled, he has experienced no further ventricular arrhythmias and has fully recovered. Conclusion:Cardiac complications of EGPA, including ventricular arrhythmias, are difficult to manage without concurrent immunosuppression, which may itself further destabilize cardiac electrophysiology. The role of multiple imaging modalities in the diagnosis and monitoring of EGPA is emphasized, with cardiac magnetic resonance imaging playing a crucial role in detecting sub-endocardial fibrosis.

Aims Electrocardiogram (ECG) interpretation is an essential skill across multiple medical disciplines; yet, studies have consistently identified deficiencies in the interpretive performance of healthcare professionals linked to a variety of educational and technological factors. Despite the established correlation between noise interference and erroneous diagnoses, research evaluating the impacts of digital denoising software on clinical ECG interpretation proficiency is lacking. Methods and results Forty-eight participants from a variety of medical professions and experience levels were prospectively recruited for this study. Participants' capabilities in classifying common cardiac rhythms were evaluated using a sequential blinded and semi-blinded interpretation protocol on a challenging set of single-lead ECG signals (42 x 10 s) pre- and post-denoising with robust, cloud-based ECG processing software. Participants' ECG rhythm interpretation performance was greatest when raw and denoised signals were viewed in a combined format that enabled comparative evaluation. The combined view resulted in a 4.9% increase in mean rhythm classification accuracy (raw: 75.7% +/- 14.5% vs. combined: 80.6% +/- 12.5%, P = 0.0087), a 6.2% improvement in mean five-point graded confidence score (raw: 4.05 +/- 0.58 vs. combined: 4.30 +/- 0.48, P < 0.001), and 9.7% reduction in the mean proportion of undiagnosable data (raw: 14.2% +/- 8.2% vs. combined: 4.5% +/- 2.4%, P < 0.001), relative to raw signals alone. Participants also had a predominantly positive perception of denoising as it related to revealing previously unseen pathologies, improving ECG readability, and reducing time to diagnosis. Conclusion Our findings have demonstrated that digital denoising software improves the efficacy of rhythm interpretation on single-lead ECGs, particularly when raw and denoised signals are provided in a combined viewing format, warranting further investigation into the impact of such technology on clinical decision-making and patient outcomes.

Panel A) A 12-lead electrocardiogram post return of spontaneous circulation.(Panel B) Echocardiogram still from an apical focused view demonstrating multiple bands across the left ventricle.(Panel C) Cine clip demonstrating hyperdynamic focal septal wall motion abnormality due to a left ventricular band.(Panel D) Invasive angiogram demonstrating normal coronary arteries.(Panel E) Cardiac magnetic resonance imaging in horizontal long-axis view demonstrating mid-wall late gadolinium enhancement.

Objective: Modern lifestyles are triggering stress at a disproportionate rate for longer periods of time. Chronic or long-lasting stress can pose a risk to our health. Despite advances in physiological recording methods, mental stress remains challenging to quantify and monitor. Methods: We describe an Internet of Medical Things (IoMT) device with electrocardiogram (ECG) recording features. The recorded ECG signal is processed on-the-fly to calculate, in real time, heart rate (HR), HR variability, energy expenditure, and mental stress. Data are sent to an online platform using a standard Internet of Things (IoT) publish-subscribe messaging transport protocol for continuous monitoring. Results: The system functionality is first validated by performing hardware-in-the-loop measurements connected to a patient simulator. We, then, monitored induced stress by recording ECG in subjects using liquid metal electrodes performing a plank walking task in a virtual reality (VR) environment with high heights exposure. The results demonstrate our IoMT system’s ability to provide accurate ECG metrics using novel liquid metal electrodes by detecting continuously increased stress values in a VR setting and at home. Conclusion: The IoMT measurement device presented provides a novel strategy for monitoring stress in real time. Significance: Our work provides the opportunity for future research on psychological stress and emotion regulation within daily life and the physiological mechanisms through which it influences the health of both children and adults.

This case report reflects on a delayed diagnosis for a 27-year-old woman who reported chest pain and shortness of breath to the emergency department. The treating clinician reflects upon how cognitive biases influenced their diagnostic process and how multiple missed opportunities resulted in missteps. Using artificial intelligence (AI) tools for clinical decision-making, we suggest how AI could augment the clinician, and in this case, delayed diagnosis avoided. Incorporating AI tools into clinical decision-making brings potential benefits, including improved diagnostic accuracy and addressing human factors contributing to medical errors. For example, they may support a real-time interpretation of medical imaging and assist clinicians in generating a differential diagnosis in ensuring that critical diagnoses are considered. However, it is vital to be aware of the potential pitfalls associated with the use of AI, such as automation bias, input data quality issues, limited clinician training in interpreting AI methods, and the legal and ethical considerations associated with their use. The report draws attention to the utility of AI clinical decision-support tools in overcoming human cognitive biases. It also emphasizes the importance of clinicians developing skills needed to steward the adoption of AI tools in healthcare and serve as patient advocates, ensuring safe and effective use of health data.

External biometrics such as thumbprint and facial recognition have become standard tools for securing our digital devices and protecting our data. These systems, however, are potentially prone to copying and cybercrime access. Researchers have therefore explored internal biometrics, such as the electrical patterns within an electrocardiogram (ECG). The heart's electrical signals carry sufficient distinctiveness to allow the ECG to be used as an internal biometric for user authentication and identification. Using the ECG in this way has many potential advantages and limitations. This article reviews the history of ECG biometrics and explores some of the technical and security considerations. It also explores current and future uses of the ECG as an internal biometric.

Andrew R.J. Mitchell | Consultant Cardiologist at Jersey General Hospital, Saint Helier, Jersey; Honorary Consultant Cardiologist at Oxford University Hospitals NHS Foundation Trust, UK

Background: Technological advances have led to electrocardiograph (ECG) functionality becoming increasingly accessible in wearable health devices, which has the potential to vastly expand the clinician's ability to monitor, diagnose, and manage cardiac health conditions. However, achieving the high signal quality necessary to make an accurate and confident diagnosis is inherently challenging on consumer device-acquired ECGs. Effective signal conditioning is crucial to make ECG data from wearable devices clinically actionable. Objective: This study evaluates the heart rate (HR) performance of ECG data collected on the HeartKey (R) Test Watch, a single lead, dry electrode wrist wearable, against data acquired on two criterion devices: the Bittium (R) Faros 180, a gold standard wet electrode ambulatory monitoring device, and the HeartKey Chest Module. Methods: ECG data was simultaneously acquired on three devices during a multi-stage protocol (sitting, walking, standing) designed to reflect the motion noise of real-life scenarios. Raw ECGs from the HeartKey Test Watch and HeartKey Chest Module were processed through HeartKey software, and the accuracy of the outputted heart rate data was compared to that of the criterion device at each stage of the protocol. A beat rejection analysis was performed to provide insight into the degree of high-frequency noise present in ECGs recorded on the HeartKey Test Watch. Results: Data acquired on the HeartKey Test Watch and processed by HeartKey software generated HR metrics that closely matched that of the criterion devices throughout the protocol. Bland-Altman analysis showed a mean absolute HR difference of 0.74, 1.21, 0.80 bpm during the sitting, walking, and standing stages respectively, which is within the +/- 10% or +/- 5 bpm range required by ANSI EC13. ECG data from the HeartKey Test Watch had a higher beat rejection rate relative to the HeartKey Chest Module (8.5% vs -0%) due to the excessive high-frequency noise generated during the motion-based protocol. Conclusion: HeartKey software demonstrated highly accurate HR performance, comparable to that of the criterion Faros device, when processing challenging ECG data acquired on a single lead, dry electrode wrist wearable during both non-motion and motion-based protocols.

Atrial fibrillation (AF) and diabetes are increasingly prevalent worldwide, both increasing stroke risk. AF can be detected by patient-led electrocardiogram (ECG) screening applications. Understanding patients' views around AF screening is important when considering recommendations, and this study explores these views where there is an existing diagnosis of diabetes. Nine semi-structured qualitative interviews were conducted with participants from a previous screening study (using a mobile ECG device), who were identified with AF. Thematic analysis was completed using NVivo 12 Plus software and themes were identified within each research question for clarity. Themes were identified in four groups: 1. patients' understanding of AF - the 'concept of irregularity' and 'consideration of consequence'; 2. views on screening - 'screening as a resource-intensive initiative', 'fear of outcomes from screening' and 'expectations of screening reliability'; 3. views on incorporating screening into routine care - 'importance of screening convenience'; and 4. views on the screening tool - 'technology as a barrier' and 'feasibility of the mobile ECG recording device for screening'. In conclusion, eliciting patients' views has demonstrated the need for clear and concise information around the delivery of an AF diagnosis. Screening initiatives should factor in location, convenience, personnel, and cost, all of which were important for promoting screening inclusion.

Heart disease affects much of the world’s population, yet many people have no idea that they could have something wrong with them. An opportunity therefore exists for targeted screening for conditions such as cardiovascular disease, heart rhythm changes, valvular heart disease, structural abnormalities, and more subtle, rarer inherited heart conditions. At the same time, the rapid development of digital health technologies and clinical support systems is providing patients and their doctors access to augmented intelligence solutions to diagnose these conditions. This article will focus on how the emerging field of digital health technology can aid screening for heart disease and explore its usefulness in disease specific and population specific groups.

BACKGROUND:Electrocardiogram (ECG) signal conditioning is a vital step in the ECG signal processing chain that ensures effective noise removal and accurate feature extraction. OBJECTIVE:This study evaluates the performance of the FDA 510 (k) cleared HeartKey Signal Conditioning and QRS peak detection algorithms on a range of annotated public and proprietary ECG databases (HeartKey is a UK Registered Trademark of B-Secur Ltd). METHODS:Seven hundred fifty-one raw ECG files from a broad range of use cases were individually passed through the HeartKey signal processing engine. The algorithms include several advanced filtering steps to enable significant noise removal and accurate identification of the QRS complex. QRS detection statistics were generated against the annotated ECG files. RESULTS:HeartKey displayed robust performance across 14 ECG databases (seven public, seven proprietary), covering a range of healthy and unhealthy patient data, wet and dry electrode types, various lead configurations, hardware sources, and stationary/ambulatory recordings from clinical and non-clinical settings. Over the NSR, MIT-BIH, AHA, and MIT-AF public databases, average QRS Se and PPV values of 98.90% and 99.08% were achieved. Adaptable performance (Se 93.26%, PPV 90.53%) was similarly observed on the challenging NST database. Crucially, HeartKey's performance effectively translated to the dry electrode space, with an average QRS Se of 99.22% and PPV of 99.00% observed over eight dry electrode databases representing various use cases, including two challenging motion-based collection protocols. CONCLUSION:HeartKey demonstrated robust signal conditioning and QRS detection performance across the broad range of tested ECG signals. It should be emphasized that in no way have the algorithms been altered or trained to optimize performance on a given database, meaning that HeartKey is potentially a universal solution capable of maintaining a high level of performance across a broad range of clinical and everyday use cases.

Prevalence of atrial fibrillation (AF) and diabetes is increasing worldwide. Diabetes is a risk factor for AF and both increase stroke risk. Previous AF screening studies have recruited highrisk patient groups, but not with diabetes as the target group. This study aims to determine whether people with diabetes have a higher prevalence of AF than the general population and investigate whether determinants, such as diabetes duration or diabetes control, add to AF risk. In a cross-sectional screening study, patients with diabetes were recruited via their GP surgeries or a diabetes centre. A 30-second single-lead electrocardiogram (ECG) was recorded using the Kardia® device, along with physiological measurements and details relating to risk factor variables. There were 300 participants recruited and 16 patients identified with AF (5.3% prevalence). This demonstrated a significantly greater likelihood of AF than the background population (p=0.043). People with diabetes and AF were significantly older than those who only had diabetes. More people with type 2 diabetes had AF than people with type 1. Prediction of AF diagnosis by age, sex, diabetes type, diabetes duration and level of control revealed only age as a significant predictor. In conclusion, these findings add to existing data around the association of these chronic conditions, supporting AF screening in this high-risk group, particularly in those of older age. This can contribute to appropriate management of both conditions in combination, not least with regards to stroke prevention.

Background Wearable devices capable of measuring health metrics are becoming increasingly prevalent. Most work has investigated the potential for these devices in the context of atrial fibrillation, our case highlights the potential of wearable devices across a wider range of arrhythmia. Case presentation A 51-year-old woman was referred to the cardiology clinic for an assessment of symptoms of intermittent exertional shortness of breath and palpitation. The patient was otherwise fit and well, took limited alcohol and no caffeine, and was a never smoker. There was no family history of heart disease. Physical examination in clinic was unremarkable, and a 12-lead electrocardiogram (ECG), seven-day ambulatory ECG, exercise stress ECG, and trans-thoracic echocardiogram were all normal. During a severe episode the patient recorded an ECG using an Apple Watch (Apple Inc, California, USA). This was forwarded to the patient’s cardiologist, who suspected a broad complex tachycardia and organised an urgent follow-up appointment. A further 72-h Holter ECG monitor showed frequent sustained periods of monomorphic ventricular tachycardia, confirming the watch findings. The patient was started on beta blocker therapy with a rapid improvement in symptoms. Conclusions Current smartwatch technology can reliably identify irregular rhythms and can distinguish atrial fibrillation from sinus rhythm, with emerging evidence supporting detection of other cardiovascular diseases, including medical emergencies. There may also be a role for wearable devices in screening young populations for predictors of sudden cardiac death. At present device outputs require clinician interpretation, but in the future patients may present to primary or secondary care with a firm diagnosis of arrhythmia and may already be making wearable device guided behaviour changes.

Background Virtual reality is increasingly being used as an adjunct or replacement to pharmacological analgesia and sedation during medical procedures. Methods and results We report the successful use of a virtual reality device in a highly anxious patient undergoing lumbar puncture. Conclusion The case demonstrates how virtual reality technology may benefit patients undergoing invasive procedures such as lumbar puncture. Virtual reality may, therefore, offer an alternative or adjunct to sedation and analgesia and may reduce the amount of pharmacological therapy required.

Background: Endocarditis of an implanted cardiac device is difficult to diagnose but has a high morbidity and mortality if left untreated. We present a case of culture negative endocarditis due to Fusobacterium species detected using molecular methods. Case report: An 81-year-old female presents with chest pain and breathlessness two months after aortic valve replacement and permanent pacemaker implantation. Fevers, hypoxia, and a single splinter haemorrhage were noted. Transoesophageal echocardiography demonstrated a single vegetation. Blood cultures were negative, but 16S ribosomal RNA matching Fusobacterium species was detected in serum. Antimicrobials were rationalised and the patient made a complete recovery. Conclusion: Infective endocarditis is a life-threatening condition which patients with cardiac devices in-situ are particularly susceptible to. There should be a low threshold for transoesophageal echocardiography when cardiac device endocarditis is suspected. Molecular methods such as polymerase chain reaction and serology are valuable when assessing culture negative endocarditis.