Cardiovascular Reasons for Access to a Tertiary Oncological Emergency Service: The CARILLON Study.

BACKGROUND: The prevalence of acute cardiovascular diseases (CVDs) in cancer patients is steadily increasing and represents a significant reason for admission to the emergency department (ED). METHODS: We conducted a prospective observational study, enrolling consecutive patients with cancer presenting to a tertiary oncological ED and consequently admitted to the oncology ward. Two groups of patients were identified based on main symptoms that lead to ED presentation: symptoms potentially related to CVD vs. symptoms potentially not related to CVD. The aims of the study were to describe the prevalence of symptoms potentially related to CVD in this specific setting and to evaluate the prevalence of definite CV diagnoses at discharge. Secondary endpoints were new intercurrent in-hospital CV events occurrence, length of stay in the oncology ward, and mid-term mortality for all-cause. RESULTS: A total of 469 patients (51.8% female, median age 68.0 [59.1-76.3]) were enrolled. One hundred and eighty-six out of 469 (39.7%) presented to the ED with symptoms potentially related to CVD. Baseline characteristics were substantially similar between the two study groups. A discharge diagnosis of CVD was confirmed in 24/186 (12.9%) patients presenting with symptoms potentially related to CVD and in no patients presenting without symptoms potentially related to CVD (p < 0.01). During a median follow-up of 3.4 (1.2-6.5) months, 204 (43.5%) patients died (incidence rate of 10.1 per 100 person/months). No differences were found between study groups in terms of all-cause mortality (hazard ratio [HR]: 0.85, 95% confidence interval [CI] 0.64-1.12), new in-hospital CV events (HR: 1.03, 95% CI 0.77-1.37), and length of stay (p = 0.57). CONCLUSIONS: In a contemporary cohort of cancer patients presenting to a tertiary oncological ED and admitted to an oncology ward, symptoms potentially related to CVD were present in around 40% of patients, but only a minority were actually diagnosed with an acute CVD.

Three-Dimensional Automated, Machine-Learning-Based Left Heart Chamber Metrics: Associations with Prevalent Vascular Risk Factors and Cardiovascular Diseases.

Background. Three-dimensional transthoracic echocardiography (3DE) powered by artificial intelligence provides accurate left chamber quantification in good accordance with cardiac magnetic resonance and has the potential to revolutionize our clinical practice. Aims. To evaluate the association and the independent value of dynamic heart model (DHM)-derived left atrial (LA) and left ventricular (LV) metrics with prevalent vascular risk factors (VRFs) and cardiovascular diseases (CVDs) in a large, unselected population. Materials and Methods. We estimated the association of DHM metrics with VRFs (hypertension, diabetes) and CVDs (atrial fibrillation, stroke, ischemic heart disease, cardiomyopathies, >moderate valvular heart disease/prosthesis), stratified by prevalent disease status: participants without VRFs or CVDs (healthy), with at least one VRFs but without CVDs, and with at least one CVDs. Results. We retrospectively included 1069 subjects (median age 62 [IQR 49−74]; 50.6% women). When comparing VRFs with the healthy, significant difference in maximum and minimum indexed atrial volume (LAVi max and LAVi min), left atrial ejection fraction (LAEF), left ventricular mass/left ventricular end-diastolic volume ratio, and left ventricular global function index (LVGFI) were recorded (p < 0.05). In the adjusted logistic regression, LAVi min, LAEF, LV ejection fraction, and LVGFI showed the most robust association (OR 3.03 [95% CI 2.48−3.70], 0.45 [95% CI 0.39−0.51], 0.28 [95% CI 0.22−0.35], and 0.22 [95% CI 0.16−0.28], respectively, with CVDs. Conclusions. The present data suggested that novel 3DE left heart chamber metrics by DHM such as LAEF, LAVi min, and LVGFI can refine our echocardiographic disease discrimination capacity.

Digital literacy as a potential barrier to implementation of cardiology tele-visits after COVID-19 pandemic: the INFO-COVID survey.

BACKGROUND: During the COVID-19 pandemic, the implementation of telemedicine has represented a new potential option for outpatient care. The aim of our study was to evaluate digital literacy among cardiology outpatients. METHODS: From March to June 2020, a survey on telehealth among cardiology outpatients was performed. Digital literacy was investigated through six main domains: age; sex; educational level; internet access; availability of internet sources; knowledge and use of teleconference software programs. RESULTS: The study included 1067 patients, median age 70 years, 41.3% females. The majority of the patients (58.0%) had a secondary school degree, but among patients aged ≥ 75 years old the most represented educational level was primary school or none. Overall, for internet access, there was a splitting between "never" (42.1%) and "every day" (41.0%), while only 2.7% answered "at least 1/month" and 14.2% "at least 1/week". In the total population, the most used devices for internet access were smartphones (59.0%), and WhatsApp represented the most used app (57.3%). Internet users were younger compared to non-internet users (63 vs. 78 years old, respectively) and with a higher educational level. Age and educational level were associated with non-use of internet (age-per 10-year increase odds ratio (OR) = 3.07, 95% CI: 2.54-3.71, secondary school OR = 0.18, 95% CI: 0.12-0.26, university OR = 0.05, 95% CI: 0.02-0.10). CONCLUSIONS: Telemedicine represents an appealing option to implement medical practice, and for its development it is important to address the gaps in patients' digital skills, with age and educational level being key factors in this setting.

Device-detected atrial high rate episodes and the risk of stroke/thrombo-embolism and atrial fibrillation incidence: a systematic review and meta-analysis.

BACKGROUND: Atrial High Rate Episodes (AHRE) are asymptomatic atrial tachy-arrhythmias detected through continuous monitoring with a cardiac implantable electronic device. The risks of stroke/Thromboembolic (TE) events and incident clinical Atrial Fibrillation (AF) associated with AHRE varies markedly. OBJECTIVES: To assess the relationship between AHRE and TE events, and between AHRE and incident clinical AF. METHODS: This systematic review and meta-analysis was conducted following the PRISMA recommendations. PubMed, Scopus, and Google Scholar were searched from inception to 18/02/2021 for studies reporting TE events and incident clinical AF in patients with AHRE, as compared with patients without. RESULTS: Ten out of 8081 records fulfilled the inclusion criteria, for a total of 37 266 patients. Seven out of ten studies excluded patients with prior history of clinical AF (4961 patients), embracing the most recent definition of AHRE. The risk ratio (RR) for TE events in AHRE patients was 2.13 (95% CI: 1.53-2.95, I2: 0%). The incidence of clinical AF was reported in four studies excluding patients with a history of clinical AF (3574 patients). The RR for incident clinical AF was 3.34 (95%CI: 1.89-5.90, I2: 73%). CONCLUSIONS: AHRE are significantly associated with systemic thromboembolism and incident clinical AF. Further studies are needed to improve patients' risk stratification and management.

Remote monitoring and telemedicine in heart failure: implementation and benefits.

PURPOSE OF REVIEW: Remote monitoring (RM) of cardiac implantable electronic devices (CIEDs) is recommended as part of the individualized multidisciplinary follow-up of heart failure (HF) patients. Aim of this article is to critically review recent findings on RM, highlighting potential benefits and barriers to its implementation. RECENT FINDINGS: Device-based RM is useful in the early detection of CIEDs technical issues and cardiac arrhythmias. Moreover, RM allows the continuous monitoring of several patients' clinical parameters associated with impending HF decompensation, but there is still uncertainty regarding its effectiveness in reducing mortality and hospitalizations. Implementation of RM strategies, together with a proactive physicians' attitude towards clinical actions in response to RM data reception, will make RM a more valuable tool, potentially leading to better outcomes.

Clinical Value of Complex Echocardiographic Left Ventricular Hypertrophy Classification Based on Concentricity, Mass, and Volume Quantification.

Echocardiography is the most validated, non-invasive and used approach to assess left ventricular hypertrophy (LVH). Alternative methods, specifically magnetic resonance imaging, provide high cost and practical challenges in large scale clinical application. To include a wide range of physiological and pathological conditions, LVH should be considered in conjunction with the LV remodeling assessment. The universally known 2-group classification of LVH only considers the estimation of LV mass and relative wall thickness (RWT) to be classifying variables. However, knowledge of the 2-group patterns provides particularly limited incremental prognostic information beyond LVH. Conversely, LV enlargement conveys independent prognostic utility beyond LV mass for incident heart failure. Therefore, a 4-group LVH subdivision based on LV mass, LV volume, and RWT has been recently suggested. This novel LVH classification is characterized by distinct differences in cardiac function, allowing clinicians to distinguish between different LV hemodynamic stress adaptations in various cardiovascular diseases. The new 4-group LVH classification has the advantage of optimizing the LVH diagnostic approach and the potential to improve the identification of maladaptive responses that warrant targeted therapy. In this review, we summarize the current knowledge on clinical value of this refinement of the LVH classification, emphasizing the role of echocardiography in applying contemporary proposed indexation methods and partition values.

Echocardiographic Left Ventricular Mass Assessment: Correlation between 2D-Derived Linear Dimensions and 3-Dimensional Automated, Machine Learning-Based Methods in Unselected Patients.

A recently developed algorithm for 3D analysis based on machine learning (ML) principles detects left ventricular (LV) mass without any human interaction. We retrospectively studied the correlation between 2D-derived linear dimensions using the ASE/EACVI-recommended formula and 3D automated, ML-based methods (Philips HeartModel) regarding LV mass quantification in unselected patients undergoing echocardiography. We included 130 patients (mean age 60 ± 18 years; 45% women). There was only discrete agreement between 2D and 3D measurements of LV mass (r = 0.662, r2 = 0.348, p < 0.001). The automated algorithm yielded an overestimation of LV mass compared to the linear method (Bland-Altman positive bias of 13.1 g with 95% limits of the agreement at 4.5 to 21.6 g, p = 0.003, ICC 0.78 (95%CI 0.68-8.4). There was a significant proportional bias (Beta -0.22, t = -2.9) p = 0.005, the variance of the difference varied across the range of LV mass. When the published cut-offs for LV mass abnormality were used, the observed proportion of overall agreement was 77% (kappa = 0.32, p < 0.001). In consecutive patients undergoing echocardiography for any indications, LV mass assessment by 3D analysis using a novel ML-based algorithm showed systematic differences and wide limits of agreements compared with quantification by ASE/EACVI- recommended formula when the current cut-offs and partition values were applied.