Defining the pattern of initiation of monomorphic ventricular tachycardia using the beat-to-beat intervals recorded on implantable cardioverter defibrillators from the RAFT study: A computer-based algorithm

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

Ambulatory monitoring is increasingly important for cardiovascular care but is often limited by the unpredictability of cardiovascular events, the intermittent nature of ambulatory monitors and the variable clinical significance of recorded data in patients. Technological advances in computing have led to the introduction of novel physiological biosignals that can increase the frequency at which abnormalities in cardiovascular parameters can be detected, making expert-level, automated diagnosis a reality. However, use of these biosignals for diagnosis also raises numerous concerns related to accuracy and actionability within clinical guidelines, in addition to medico-legal and ethical issues. Analytical methods such as machine learning can potentially increase the accuracy and improve the actionability of device-based diagnoses. Coupled with interoperability of data to widen access to all stakeholders, seamless connectivity (an internet of things) and maintenance of anonymity, this approach could ultimately facilitate near-real-time diagnosis and therapy. These tools are increasingly recognized by regulatory agencies and professional medical societies, but several technical and ethical issues remain. In this Review, we describe the current state of cardiovascular monitoring along the continuum from biosignal acquisition to the identification of novel biosensors and the development of analytical techniques and ultimately to regulatory and ethical issues. Furthermore, we outline new paradigms for cardiovascular monitoring.

Advances in cardiovascular monitoring technologies have resulted in an influx of consumer-targeted wearable sensors that have the potential to detect numerous heart conditions. In this Review, Krittanawong and colleagues describe processes involved in biosignal acquisition and analysis of cardiovascular monitors, as well as their associated ethical, regulatory and legal challenges.

Advances in the use of cardiovascular monitoring technologies, such as the development of novel portable sensors and machine learning algorithms that can provide near-real-time diagnosis, have the potential to provide personalized care.

Wearable sensor technologies can detect numerous biosignals, such as cardiac output, blood-pressure levels and heart rhythm, and can integrate multiple modalities.

The use of novel biosignals for diagnosis raises concerns regarding accuracy and actionability within clinical guidelines, in addition to medical, legal and ethical issues.

Machine learning-based interpretation of biosensor data can facilitate rapid evaluation of the haemodynamic consequences of heart failure or arrhythmias, but is limited by the presence of noise and training data that might not be representative of the real-world clinical setting.

The use of data derived from cardiovascular monitoring devices is associated with numerous challenges, such as data security, accessibility and ownership, in addition to other ethical and regulatory concerns.

Mechanism of spontaneous initiation of ventricular fibrillation in patients with implantable defibrillators

INTRODUCTION

To improve the mechanistic understanding of spontaneous initiation of ventricular fibrillation (VF), we characterized the patterns of premature ventricular complex (PVC) preceding spontaneous VF in primary and secondary implantable cardioverter-defibrillator (ICD) recipients.

METHODS AND RESULTS

A single-center, cross-sectional analysis of 1209 patients with primary and secondary prevention ICD identified 190 patients who received ICD therapy (firing or antitachycardia pacing) for VF or monomorphic ventricular tachycardia (MMVT). Initiation was quantified by the coupling interval (CI), the cycle length immediately preceding the CI (CL(-1)), the CI corrected by CL(-1) using Fridericia's formula (CIc), and the prematurity index (PI). In both VF (n = 44; 23%) and MMVT (n = 134; 71%), the most common pattern of initiation was late-coupled PVC, followed by the short-long-short pattern. The parameters such as pre-initiation median CL, CL(-1), CI, and PI were not significantly different between VF and MMVT for any patterns. At least some events (45% of VF and 63% of MMVT) had extremely long CIs beyond the QTc cut-off estimated from the CL(-1), suggestive of initiation by a train of multiple PVCs or nonsustained VT instead of a single PVC.

CONCLUSION

Some spontaneous VF events in ICD recipients appear to be initiated by a train of multiple PVC or nonsustained VT rather than a single PVC. This finding indicates that patterns of a single PVC are not an important determinant of VF initiation and thus account for conflicting results in previous studies.