[Coronary artery disease risk stratification prior to kidney transplantation]. / Valutazione del rischio cardiovascolare nei candidati al trapianto renale.

Patients with end-stage renal disease have a high risk of cardiovascular events, and are often referred for non-invasive screening prior to kidney transplantation. Several European and North American professional organizations have issued guidelines on what tests to perform and which patients may benefit most from them. There is some discrepancy between the various guidelines and their application varies broadly across medical centers and countries. In this review, we briefly discuss the advantages and disadvantages of the non-invasive imaging methodologies that can be used to risk stratify patients prior to transplant. We conclude by offering some practical recommendations on how to approach risk stratification in this population of patients.

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.

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.