Cardiac Amyloidosis (CA) pertains to the cardiac involvement of a group of diseases, in which misfolded proteins deposit in tissues and cause progressive organ damage. The vast majority of CA cases are caused by light chain amyloidosis (AL) and transthyretin amyloidosis (ATTR). The increased awareness of these diseases has led to an increment of newly diagnosed cases each year. We performed multiple searches on MEDLINE, EMBASE and the Cochrane Database of Systematic Reviews. Several search terms were used, such as “cardiac amyloidosis”, “diagnostic modalities cardiac amyloidosis” and “staging cardiac amyloidosis”. Emphasis was given on original articles describing novel diagnostic and staging approaches to the disease. Imaging techniques are indispensable to diagnosing CA. Novel ultrasonographic techniques boast high sensitivity and specificity for the disease. Nuclear imaging has repeatedly proved its worth in the diagnostic procedure, with efforts now focusing on standardization and quantification of amyloid load. Because the latter would be invaluable for any staging system, those spearheading research in magnetic resonance imaging of the disease are also trying to come up with accurate tools to quantify amyloid burden. Staging tools are currently being developed and validated for ATTR CA, in the spirit of the acclaimed Mayo Staging System for AL. Cardiac involvement confers significant morbidity and mortality in all types of amyloidosis. Great effort is made to reduce the time to diagnosis, as treatment in the initial stages of the disease is tied to better prognosis. The results of these efforts are highly sensitive and specific diagnostic modalities that are also reasonably cost effective.

Background: Heart failure (HF) remains a major public health challenge, while HF self-care is particularly challenging. Mobile health (mHealth)–based interventions taking advantage of smartphone technology have shown particular promise in increasing the quality of self-care among these patients, and in turn improving the outcomes of their disease. Objective: The objective of this study was to co-develop with physicians, patients with HF, and their caregivers a patient-oriented mHealth app, perform usability assessment, and investigate its effect on the quality of life of patients with HF and rate of hospitalizations in a pilot study. Methods: The development of an mHealth app (The Hellenic Educational Self-care and Support Heart Failure app [ThessHF app]) was evidence based, including features based on previous clinically tested mHealth interventions and selected by a panel of HF expert physicians and discussed with patients with HF. At the end of alpha development, the app was rated by mHealth experts with the Mobile Application Rating Scale (MARS). The beta version was tested by patients with HF, who rated its design and content by means of the Post-Study System Usability Questionnaire (PSSUQ). Subsequently, a prospective pilot study (THESS-HF [THe Effect of a Specialized Smartphone app on Heart Failure patients’ quality of self-care, quality of life and hospitalization rate]) was performed to investigate the effect of app use on patients with HF over a 3-month follow-up period. The primary endpoint was patients’ quality of life, which was measured with the Kansas City Cardiomyopathy Questionnaire (KCCQ) and the 5-level EQ-5D version (EQ-5D-5L). The secondary endpoints were the European Heart Failure Self-care Behavior Scale (EHFScBS) score and the hospitalization rate. Results: A systematic review of mHealth-based HF interventions and expert panel suggestions yielded 18 separate app features, most of which were incorporated into the ThessHF app. A total of 14 patients and 5 mHealth experts evaluated the app. The results demonstrated a very good user experience (overall PSSUQ score 2.37 [SD 0.63], where 1 is the best, and a median MARS score of 4.55/5). Finally, 30 patients (male: n=26, 87%) participated in the THESS-HF pilot study (mean age 68.7 [SD 12.4] years). A significant increase in the quality of self-care was noted according to the EHFScBS, which increased by 4.4% (SD 7.2%) (P=.002). The mean quality of life increased nonsignificantly after 3 months according to both KCCQ (mean increase 5.8 [SD 15] points, P=.054) and EQ-5D-5L (mean increase 5.6% [SD 15.6%], P=.06) scores. The hospitalization rate for the follow-up duration was 3%. Conclusions: The need for telehealth services and remote self-care management in HF is of vital importance, especially in periods such as the COVID-19 pandemic. We developed a user-friendly mHealth app to promote remote self-care support in HF. In this pilot study, the use of the ThessHF app was associated with an increase in the quality of self-care. A future multicenter study will investigate the effect of the app use on long-term outcomes in patients with HF.

The aim of this systematic review and meta-analysis is to investigate the capacity of pre-interventional left atrial strain (LAS) to predict AF recurrence after catheter ablation by using all relative published data. Inter-vendor variability regarding different ultrasound stations and strain analysis software suites was taken into consideration. The research was performed according to PRISMA guidelines. The Cochrane database, MEDLINE and EMBASE were searched for studies assessing with echocardiography LAS prior to catheter ablation of AF cases. The systematic research yielded 10 studies (2 retrospective and 8 prospective, 880 patients in total). LAS differed significantly between the patients with AF recurrence (AFR) and those with no AF recurrence (nAFR) during the follow-up period (LASAFR: 17.5 ± 8.7% vs. LASnAFR: 24.1 ± 9.5%, p<0.00001). A pooled cut-off value of 21.9% for LAS was extracted for the prediction of ablation success. Regarding inter-vendor variability, sub-group analyses were able to be performed for studies using GE and TomTec software. The difference in LASAFR and LASnAFR remained significant (p<0.00001 and p<0.0001 for TomTec and GE, respectively), while significant inter-vendor difference in absolute strain values was also detected (p<0.0001 for both AFR and nAFR groups). LAS prior to catheter ablation is consistently lower in patients that experience AF recurrence. Its incorporation in clinical practice would assist physicians detect patients that require closer follow-up. Inter-vendor variability appears to be considerable and steps must be taken to document it thoroughly and mitigate it if possible.

Atrial fibrillation (AF) is the most common arrhythmia. myAlgos is an mHealth disease management system consisting of physician-oriented platform and patient-oriented smartphone app. Our purpose was to assess the usability of myAlgos by physicians and patients and the effect of myAlgos on the quality of life (QoL) in patients with paroxysmal AF (PAF). Physicians rated the platform with the Post-Study System Usability Questionnaire (PSSUQ). Patients rated the app with the mHealth App Usability Questionnaire (MAUQ). The e-medicine Platform for Optimizing the Workflow in hEaRt Diseases (emPOWERD-AF) study investigated the effect of myAlgos in PAF patients randomized to full/control version. QoL was measured by the Atrial Fibrillation Effect on QualiTy-of-life (AFEQT) and 5-level EQ-5D (EQ-5D-5L) questionnaires. myAlgos got a PSSUQ score of 2.52 ± 0.36 by five physicians and a MAUQ score of 79.9% by 33 patients. In emPOWERD-AF, 80 patients were randomized 1:1 (58.1 ± 8.7 years, 66% male). The median AFEQT change at 6 months was +2.63% in full version users and −1.63% in controls ( p < .001). The myAlgos platform and app were easy-to-use and improved QoL in patients.

Early identification of patients at risk for paroxysmal atrial fibrillation (PAF) is essential to attain optimal treatment and a favorable prognosis. We compared the performance of a beat-to-beat (B2B) P-wave analysis with that of standard P-wave indices (SPWIs) in identifying patients prone to PAF. To this end, 12-lead ECG and 10 min vectorcardiogram (VCG) recordings were obtained from 33 consecutive, antiarrhythmic therapy naïve patients, with a short history of low burden PAF, and from 56 age- and sex-matched individuals with no AF history. For both groups, SPWIs were calculated, while the VCG recordings were analyzed on a B2B basis, and the P-waves were classified to a primary or secondary morphology. Wavelet transform was used to further analyze P-wave signals of main morphology. Univariate analysis revealed that none of the SPWIs performed acceptably in PAF detection, while five B2B features reached an AUC above 0.7. Moreover, multivariate logistic regression analysis was used to develop two classifiers—one based on B2B analysis derived features and one using only SPWIs. The B2B classifier was found to be superior to SPWIs classifier; B2B AUC: 0.849 (0.754–0.917) vs. SPWIs AUC: 0.721 (0.613–0.813), p value: 0.041. Therefore, in the studied population, the proposed B2B P-wave analysis outperforms SPWIs in detecting patients with PAF while in sinus rhythm. This can be used in further clinical trials regarding the prognosis of such patients.

The identification of patients prone to atrial fibrillation (AF) relapse after catheter ablation is essential for better patient selection and risk stratification. The current prospective cohort study aims to validate a novel P-wave index based on beat-to-beat (B2B) P-wave morphological and wavelet analysis designed to detect patients with low burden AF as a predictor of AF recurrence within a year after successful catheter ablation. From a total of 138 consecutive patients scheduled for AF ablation, 12-lead ECG and 10 min vectorcardiogram (VCG) recordings were obtained. Univariate analysis revealed that patients with higher B2B P-wave index had a two-fold risk for AF recurrence (HR: 2.35, 95% CI: 1.24–4.44, p: 0.010), along with prolonged P-wave, interatrial block, early AF recurrence, female gender, heart failure history, previous stroke, and CHA₂DS₂-VASc score. Multivariate analysis of assessable predictors before ablation revealed that B2B P-wave index, along with heart failure history and a history of previous stroke or transient ischemic attack, are independent predicting factors of atrial fibrillation recurrence. Further studies are needed to assess the predictive value of the B2B index with greater accuracy and evaluate a possible relationship with atrial substrate analysis.