Quantification of Myocardial Contraction Fraction with Three-Dimensional Automated, Machine-Learning-Based Left-Heart-Chamber Metrics: Diagnostic Utility in Hypertrophic Phenotypes and Normal Ejection Fraction.

Aims: The differentiation of left ventricular (LV) hypertrophic phenotypes is challenging in patients with normal ejection fraction (EF). The myocardial contraction fraction (MCF) is a simple dimensionless index useful for specifically identifying cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) when calculated by cardiac magnetic resonance. The purpose of this study was to evaluate the value of MCF measured by three-dimensional automated, machine-learning-based LV chamber metrics (dynamic heart model [DHM]) for the discrimination of different forms of hypertrophic phenotypes. Methods and Results: We analyzed the DHM LV metrics of patients with CA (n = 10), hypertrophic cardiomyopathy (HCM, n = 36), isolated hypertension (IH, n = 87), and 54 healthy controls. MCF was calculated by dividing LV stroke volume by LV myocardial volume. Compared with controls (median 61.95%, interquartile range 55.43-67.79%), mean values for MCF were significantly reduced in HCM-48.55% (43.46-54.86% p < 0.001)-and CA-40.92% (36.68-46.84% p < 0.002)-but not in IH-59.35% (53.22-64.93% p < 0.7). MCF showed a weak correlation with EF in the overall cohort (R2 = 0.136) and the four study subgroups (healthy adults, R2 = 0.039 IH, R2 = 0.089; HCM, R2 = 0.225; CA, R2 = 0.102). ROC analyses showed that MCF could differentiate between healthy adults and HCM (sensitivity 75.9%, specificity 77.8%, AUC 0.814) and between healthy adults and CA (sensitivity 87.0%, specificity 100%, AUC 0.959). The best cut-off values were 55.3% and 52.8%. Conclusions: The easily derived quantification of MCF by DHM can refine our echocardiographic discrimination capacity in patients with hypertrophic phenotype and normal EF. It should be added to the diagnostic workup of these patients.

Atrial fibrillation in vascular surgery: a systematic review and meta-analysis on prevalence, incidence and outcome implications.

AIMS: To know the prevalence of atrial fibrillation (AF), as well as the incidence of postoperative AF (POAF) in vascular surgery for arterial diseases and its outcome implications. METHODS: We performed a systematic review and meta-analysis following the PRISMA statement. RESULTS: After the selection process, we analyzed 44 records (30 for the prevalence of AF history and 14 for the incidence of POAF).The prevalence of history of AF was 11.5% [95% confidence interval (CI) 1-13.3] with high heterogeneity (I2 = 100%). Prevalence was higher in the case of endovascular procedures. History of AF was associated with a worse outcome in terms of in-hospital death [odds ratio (OR) 3.29; 95% CI 2.66-4.06; P < 0.0001; I2 94%] or stroke (OR 1.61; 95% CI 1.39-1.86; P < 0.0001; I2 91%).The pooled incidence of POAF was 3.6% (95% CI 2-6.4) with high heterogeneity (I2 = 100%). POAF risk was associated with older age (mean difference 4.67 years, 95% CI 2.38-6.96; P = 0.00007). The risk of POAF was lower in patients treated with endovascular procedures as compared with an open surgical procedure (OR 0.35; 95% CI 0.13-0.91; P = 0.03; I2 = 61%). CONCLUSIONS: In the setting of vascular surgery for arterial diseases a history of AF is found overall in 11.5% of patients, more frequently in the case of endovascular procedures, and is associated with worse outcomes in terms of short-term mortality and stroke.The incidence of POAF is overall 3.6%, and is lower in patients treated with an endovascular procedure as compared with open surgery procedures. The need for oral anticoagulants for preventing AF-related stroke should be evaluated with randomized clinical trials.

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.

Sustained right ventricular dysfunction in severe COVID-19: The role of disseminated intravascular coagulation.

BACKGROUND: Acute right ventricular (RV) failure is common in patients hospitalized with COVID-19. Compared to the conventional echocardiographic parameters, right ventricular longitudinal strain (RVLS) is more sensitive and accurate for the diagnosis of RV systolic dysfunction. OBJECTIVE: Our purpose was to investigate the sustained RV dysfunction echo-quantified by RVLS in patients recovered from severe COVID-19. Furthermore, we aimed to assess whether disseminated intravascular coagulation (DIC) has a key role to predict the impaired RV strain. METHODS: Of 198 consecutive COVID-19 patients hospitalized from March 1, 2020, to April 15, 2020, 45 selected patients who survived from severe COVID-19 were enrolled in the study and referred to our echo-lab for transthoracic echocardiography 6-months after discharge. RVLS was calculated as the mean of the strain values of RV free wall. DIC was defined with a validated scoring system: DIC score equal to or more than 5 is compatible with overt-DIC. Categories of acute respiratory distress syndrome (ARDS) were defined based on PaO2 /FiO2 ratio. RESULTS: A total 26 of 45 patients showed impaired RVLS at 6-months' follow-up. DIC score was significantly higher in patients with worse RVLS than in those with better RVLS (4.8 ± .5 vs. 3.6 ± .6, p =.03). Stages of ARDS did not modulate this relationship. Finally, overt-DIC results the only independent predictor of sustained RV dysfunction (OR 1.233, 95% CI 1.041-1.934, p =.043). CONCLUSIONS: Sustained RV impairment frequently occurs in patients recovered from severe COVID-19. DIC plays a key role, resulting in an independent predictor of sustained RV dysfunction.

DIGItal Health Literacy after COVID-19 Outbreak among Frail and Non-Frail Cardiology Patients: The DIGI-COVID Study.

BACKGROUND: Telemedicine requires either the use of digital tools or a minimum technological knowledge of the patients. Digital health literacy may influence the use of telemedicine in most patients, particularly those with frailty. We aimed to explore the association between frailty, the use of digital tools, and patients' digital health literacy. METHODS: We prospectively enrolled patients referred to arrhythmia outpatient clinics of our cardiology department from March to September 2022. Patients were divided according to frailty status as defined by the Edmonton Frail Scale (EFS) into robust, pre-frail, and frail. The degree of digital health literacy was assessed through the Digital Health Literacy Instrument (DHLI), which explores seven digital skill categories measured by 21 self-report questions. RESULTS: A total of 300 patients were enrolled (36.3% females, median age 75 (66-84)) and stratified according to frailty status as robust (EFS ≤ 5; 70.7%), pre-frail (EFS 6-7; 15.7%), and frail (EFS ≥ 8; 13.7%). Frail and pre-frail patients used digital tools less frequently and accessed the Internet less frequently compared to robust patients. In the logistic regression analysis, frail patients were significantly associated with the non-use of the Internet (adjusted odds ratio 2.58, 95% CI 1.92-5.61) compared to robust and pre-frail patients. Digital health literacy decreased as the level of frailty increased in all the digital domains examined. CONCLUSIONS: Frail patients are characterized by lower use of digital tools compared to robust patients, even though these patients would benefit the most from telemedicine. Digital skills were strongly influenced by frailty.