eHealth to improve patient outcome in rehabilitating myocardial infarction patients

Introduction: Cardiac rehabilitation is aimed at risk factor modification and improving quality of life. eHealth has a couple of potential benefits to improve this aim. The primary purpose of this review is to summarize available literature for eHealth strategies that have been investigated in randomized controlled trials in post-myocardial infarction (MI) patients. The second purpose of this review is to investigate the clinical effectiveness in post-MI patients. Areas covered: The literature was searched using PubMed. Randomized controlled trials (RCTs) describing interventions in patients that had experienced an ST-elevation myocardial infarction or non-ST acute coronary syndrome were eligible for inclusion. Fifteen full-texts were included and their results are described in this review. These RCTs described interventions that used remote coaching or remote monitoring in post-MI patients. Most interventions resulted in an improved cardiovascular risk profile. Remote coaching had a positive effect on activity and dietary intake. Expert opinion: eHealth might be clinically beneficial in post-MI patients, particularly for risk estimation. Moreover, eHealth as a tool for remote coaching on activity is a good addition to traditional cardiac rehabilitation programs. Further research needs to corroborate these findings

Accuracy of a Standalone Atrial Fibrillation Detection Algorithm Added to a Popular Wristband and Smartwatch: Prospective Diagnostic Accuracy Study

BACKGROUND: Silent paroxysmal atrial fibrillation (AF) may be difficult to diagnose, and AF burden is hard to establish. In contrast to conventional diagnostic devices, photoplethysmography (PPG)-driven smartwatches or wristbands allow for long-term continuous heart rhythm assessment. However, most smartwatches lack an integrated PPG-AF algorithm. Adding a standalone PPG-AF algorithm to these wrist devices might open new possibilities for AF screening and burden assessment. OBJECTIVE: The aim of this study was to assess the accuracy of a well-known standalone PPG-AF detection algorithm added to a popular wristband and smartwatch, with regard to discriminating AF and sinus rhythm, in a group of patients with AF before and after cardioversion (CV). METHODS: Consecutive consenting patients with AF admitted for CV in a large academic hospital in Amsterdam, the Netherlands, were asked to wear a Biostrap wristband or Fitbit Ionic smartwatch with Fibricheck algorithm add-on surrounding the procedure. A set of 1-min PPG measurements and 12-lead reference electrocardiograms was obtained before and after CV. Rhythm assessment by the PPG device-software combination was compared with the 12-lead electrocardiogram. RESULTS: A total of 78 patients were included in the Biostrap-Fibricheck cohort (156 measurement sets) and 73 patients in the Fitbit-Fibricheck cohort (143 measurement sets). Of the measurement sets, 19/156 (12%) and 7/143 (5%), respectively, were not classifiable by the PPG algorithm due to bad quality. The diagnostic performance in terms of sensitivity, specificity, positive predictive value, negative predictive value, and accuracy was 98%, 96%, 96%, 99%, 97%, and 97%, 100%, 100%, 97%, and 99%, respectively, at an AF prevalence of ~50%. CONCLUSIONS: This study demonstrates that the addition of a well-known standalone PPG-AF detection algorithm to a popular PPG smartwatch and wristband without integrated algorithm yields a high accuracy for the detection of AF, with an acceptable unclassifiable rate, in a semicontrolled environment

Cardiovascular magnetic resonance techniques for tissue characterization after acute myocardial injury

The annual incidence of hospital admission for acute myocardial infarction lies between 90 and 312 per 100 000 inhabitants in Europe. Despite advances in patient care 1 year mortality after ST-segment elevation myocardial infarction (STEMI) remains around 10%. Cardiovascular magnetic resonance imaging (CMR) has emerged as a robust imaging modality for assessing patients after acute myocardial injury. In addition to accurate assessment of left ventricular ejection fraction and volumes, CMR offers the unique ability of visualization of myocardial injury through a variety of imaging techniques such as late gadolinium enhancement and T2-weighted imaging. Furthermore, new parametric mapping techniques allow accurate quantification of myocardial injury and are currently being exploited in large trials aiming to augment risk management and treatment of STEMI patients. Of interest, CMR enables the detection of microvascular injury (MVI) which occurs in approximately 40% of STEMI patients and is a major independent predictor of mortality and heart failure. In this article, we review traditional and novel CMR techniques used for myocardial tissue characterization after acute myocardial injury, including the detection and quantification of MVI. Moreover, we discuss clinical scenarios of acute myocardial injury in which the tissue characterization techniques can be applied and we provide proposed imaging protocols tailored to each scenario

Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease

Purpose of Review: Artificial intelligence (AI) applications in (interventional) cardiology continue to emerge. This review summarizes the current state and future perspectives of AI for automated imaging analysis in invasive coronary angiography (ICA). Recent Findings: Recently, 12 studies on AI for automated imaging analysis In ICA have been published. In these studies, machine learning (ML) models have been developed for frame selection, segmentation, lesion assessment, and functional assessment of coronary flow. These ML models have been developed on monocenter datasets (in range 31–14,509 patients) and showed moderate to good performance. However, only three ML models were externally validated. Summary: Given the current pace of AI developments for the analysis of ICA, less-invasive, objective, and automated diagnosis of CAD can be expected in the near future. Further research on this technology in the catheterization laboratory may assist and improve treatment allocation, risk stratification, and cath lab logistics by integrating ICA analysis with other clinical characteristics