Abstract Aims The last released European guidelines on the management of heart failure (HF) recommend in patients with chronic HF with reduced ejection fraction (HFrEF) a pharmacological approach based on four fundamental drugs to be rapidly implemented and then uptitrated to modify disease progression. The aim of the Optimization of Therapy in the Italian Management of Heart Failure (OPTIMA‐HF) registry is to collect data on chronic HF outpatients in different settings of care. In the present analysis, we report the first analysis of the OPTIMA‐HF registry, focusing on the real‐life use of guideline‐directed medical therapy in patients affected by HFrEF. Methods OPTIMA‐HF is an observational, cross‐sectional, multicentre, real‐life Italian registry conducted in two different clinical settings: HF outpatients' clinics of Italian hospitals and community HF outpatients' services. The study comprises a T0 phase—retrospective data collection, in which data of consecutive HF outpatients seen between January and October 2022 were collected; an educational activity phase; and a T1 phase—prospective data collection, in which data of consecutive HF outpatients seen between September 2023 and November 2023 were collected. In the present analysis, we describe the T0 phase focusing on HFrEF drug prescription rates, types, doses, combination therapy, the presence of contraindications and reasons of non‐optimized treatment. Results Twenty‐nine centres enrolled 2110 HF patients, of which 1390 (65.9%) had HFrEF [69.5 ± 11.9 years, 76.2% males, 4.1 years since HF diagnosis, median ejection fraction (EF) 33%]. Among HFrEF patients, 89.1% were on treatment with renin–angiotensin–aldosterone system inhibitor (RAASi)/angiotensin receptor neprilysin inhibitor (ARNI) (72% ARNI and 17.1% RAASi), 95.1% with beta‐blockers, 75.8% with mineralocorticoid receptor antagonists (MRA) and 63.2% with sodium/glucose cotransporter 2 inhibitors (SGLT2i). Despite high prescription rates, a non‐negligible number of patients with no contraindications were not treated with each specific drug. Patients taking all four drug classes, as recommended by guidelines, were mere 46.9%. Regarding doses, a still low number of patients on RAASi/ARNI and beta‐blockers were treated with a dose ≥50% of the target doses recommended by the European guidelines. Conclusions The OPTIMA‐HF registry reported that HFrEF fundamental drugs are prescribed in most Italian patients; however, <50% of patients receive optimal combination therapy, and still not a satisfying number of patients receive target doses. Strategies to improve implementation of guideline‐directed medical therapy are needed to improve HF prognosis.

Heart Failure (HF) poses a challenge for our health systems, and early detection of Worsening HF (WHF), defined as a deterioration in symptoms and clinical and instrumental signs of HF, is vital to improving prognosis. Predicting WHF in a phase that is currently undiagnosable by physicians would enable prompt treatment of such events in patients at a higher risk of WHF. Although the role of Artificial Intelligence in cardiovascular diseases is becoming part of clinical practice, especially for diagnostic and prognostic purposes, its usage is often considered not completely reliable due to the incapacity of these models to provide a valid explanation about their output results. Physicians are often reluctant to make decisions based on unjustified results and see these models as black boxes. This study aims to develop a novel diagnostic model capable of predicting WHF while also providing an easy interpretation of the outcomes. We propose a threshold-based binary classifier built on a mathematical model derived from the Genetic Programming approach. This model clearly indicates that WHF is closely linked to creatinine, sPAP, and CAD, even though the relationship of these variables and WHF is almost complex. However, the proposed mathematical model allows for providing a 3D graphical representation, which medical staff can use to better understand the clinical situation of patients. Experiments conducted using retrospectively collected data from 519 patients treated at the HF Clinic of the University Hospital of Salerno have demonstrated the effectiveness of our model, surpassing the most commonly used machine learning algorithms. Indeed, the proposed GP-based classifier achieved a 96% average score for all considered evaluation metrics and fully supported the controls of medical staff. Our solution has the potential to impact clinical practice for HF by identifying patients at high risk of WHF and facilitating more rapid diagnosis, targeted treatment, and a reduction in hospitalizations.

Chronic kidney disease (CKD) and cardiovascular disease (CVD) are highly prevalent conditions, each significantly contributing to the global burden of morbidity and mortality. CVD and CKD share a great number of common risk factors, such as hypertension, diabetes, obesity, and smoking, among others. Their relationship extends beyond these factors, encompassing intricate interplay between the two systems. Within this complex network of pathophysiological processes, vitamin D has emerged as a potential linchpin, exerting influence over diverse physiological pathways implicated in both CKD and CVD. In recent years, scientific exploration has unveiled a close connection between these two prevalent conditions and vitamin D, a crucial hormone traditionally recognized for its role in bone health. This article aims to provide an extensive review of vitamin D’s multifaceted and expanding actions concerning its involvement in CKD and CVD.

Sodium-Glucose Cotransporter-2 inhibitors (SGLT2i) represent a deep revolution of the therapeutic approach to heart failure (HF), preventing its insurgence but also improving the management of the disease and slowing its natural progression. To date, few studies have explored the effectiveness of SGLT2i and, in particular, Dapagliflozin in a real-world population. Therefore, in this observational prospective study, we evaluated Dapagliflozin's effectiveness in a real-world HF population categorized in the different hemodynamic profiles. From January 2022 to June 2023, we enrolled 240 patients with chronic HF and reduced ejection fraction (HFrEF) on optimal medical therapy, according to 2021 ESC guidelines, that added treatment with Dapagliflozin from the HF Clinics of 6 Italian University Hospitals. Clinical, biochemical, and echocardiographic parameters were collected before and after 6 months of Dapagliflozin introduction. Moreover, the HFrEF population was classified according to hemodynamic profiles (A: SV ≥ 35 ml/m2; E/e′ < 15; B: SV ≥ 35 ml/m2; E/e′ ≥ 15; C: SV < 35 ml/m2; E/e′ < 15; D: SV < 35 ml/m2; E/e′ ≥ 15). Then, we compared the Dapagliflozin population with two retrospective HF cohorts, hereinafter referred to as Guide Line 2012 (GL 2012) group and Guide Line 2016 (GL 2016) group, in accordance with the HF ESC guidelines in force at the time of patients enrolment. Precisely, we evaluated the changes to baseline in clinical, functional, biochemical, and echocardiographic parameters and compared them to the GL 2012 and GL 2016 groups. Dapagliflozin population (67.18 ± 11.11 years) showed a significant improvement in the echocardiographic and functional parameters (left ventricular ejection fraction [LVEF], LV end-diastolic volume [LVEDV], LVEDV index, stroke volume index [SVi], left atrium volume index [LAVi], filling pressure [E/e′ ratio], tricuspid annular plane systolic excursion [TAPSE], tricuspid annular S′ velocity [RVs’], fractional area change [FAC], inferior vena cava [IVC diameter], pulmonary artery systolic pressure [sPAP], NYHA class, and quality of life) compared to baseline. In particular, TAPSE and right ventricle diameter (RVD1) ameliorate in congestive profiles (B and D); accordingly, the furosemide dose significantly decreased in these profiles. Comparing the three populations, the analysis of echocardiographic parameters (baseline vs follow-up) highlighted a significant decrease of sPAP in the Dapagliflozin population (p < 0.05), while no changes were recorded in the GL 2012 and GL 2016 population. Moreover, at the baseline evaluation, the GL 2012 and 2016 groups needed a higher significant dose of furosemide compared to Dapagliflozin group. Finally, Dapagliflozin patients had significantly fewer rehospitalizations (1.25

AbstractAimsWe report the results of a real‐world study based on heart failure (HF) patients' continuous remote monitoring strategy using the CardioMEMS system to assess the impact of this device on healthcare outcomes, costs, and patients' management and quality of life.Methods and resultsWe enrolled seven patients (69.00 ± 4.88 years; 71.43% men) with HF, implanted with CardioMEMS, and daily remote monitored to optimize both tailored adjustments of home therapy and/or hospital infusions of levosimendan. We recorded clinical, pharmacological, biochemical, and echocardiographic parameters and data on hospitalizations, emergency room access, visits, and costs. Following the implantation of CardioMEMS, we observed a 50% reduction in the total number of hospitalizations and a 68.7% reduction in the number of days in the hospital. Accordingly, improved patient quality of life was recorded with EQ‐5D (pre 58.57 ± 10.29 vs. 1 year post 84.29 ± 19.02, P = 0.008). Echocardiographic data show a statistically significant improvement in both systolic pulmonary artery pressure (47.86 ± 8.67 vs. 35.14 ± 9.34, P = 0.022) and E/e′ (19.33 ± 5.04 vs. 12.58 ± 3.53, P = 0.023). The Quantikine® HS High‐Sensitivity Kit determined elevated interleukin‐6 values at enrolment in all patients, with a statistically significant reduction after 6 months (P = 0.0211). From an economic point of view, the net savings, including the cost of CardioMEMS, were on average €1580 per patient during the entire period of observation, while the analysis performed 12 months after the implant vs. 12 months before showed a net saving of €860 per patient. The ad hoc analysis performed on the levosimendan infusions resulted in 315 days of hospital avoidance and a saving of €205 158 for the seven patients enrolled during the observation period.ConclusionsThis innovative strategy prevents unplanned access to the hospital and contributes to the efficient use of healthcare facilities, human resources, and costs.

Aim: The interest in machine learning-based algorithms in the cardiovascular field is rapidly growing, especially for diagnostic and prognostic purposes. Recent evidence has demonstrated that certain electrocardiographic (ECG) parameters are predominantly associated with systolic function, estimated as left ventricular ejection fraction (LVEF) by echocardiography, albeit with still relatively low accuracy. Consequently, this study aims to develop an AI-based model capable of predicting LVEF from ECG data in an Italian population. Methods: Within the SOLOMAX project, we collected paired ECG-Echocardiography exams from 105 patients (64.82±16.02y;62.86%male). Precisely, we excluded patients with atrial fibrillation at the time of the ECG, PMK or electrostimulated rhythm, valve prostheses, previous cardiac surgery, O2 therapy or COPD, previous ablation or invasive electrophysiology procedures, currently hospitalized for Takotsubo or ACS, heart failure exacerbation, inotropic therapy, ACS over the last 3 months. We recorded anthropometric, clinical, biochemical, ECG, and Echocardiography parameters. The collected data was studied using AI-based techniques to create a new model to predict LVEF from ECG. Using an approach based on evolutionary algorithms, genetic programming was used. This approach solves a symbolic regression problem through genetic algorithms and provides a mathematical model of the relationship between ECG parameters and LVEF. The formula obtained was then used to build a simple explainable classifier, which provides a global interpretation of the link between ECG parameters and LVEF. Results: The performance of the proposed approach and the reliability of the results were assessed using the k-fold cross-validation method and by estimating standard metrics derived from the confusion matrix associated with a binary classifier, that is, accuracy, sensitivity, specificity, precision, and F-Measure. The proposed approach consistently demonstrated its ability to distinguish patients with preserved LVEF from those with reduced LVEF. Each metric averaged across all experiments scored approximately 95%. Furthermore, in the expression generated by the AI model, the axes of the P, QRS, and T waves play a prominent role, as they are likely to provide a better interpretation of the three-dimensional cardiac geometry and, consequently, cardiac function. Conclusions: AI applied to ECG data can be used to create cost-effective diagnostic and predictive tools for assessing LVEF. Indeed, the obtained formula highlights the relationship between ECG parameters and LVEF, as well as its complexity, which can aid in detecting heart diseases.

Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors.

Arterial hypertension (AH) is a progressive issue that grows in importance with the increased average age of the world population. The potential role of artificial intelligence (AI) in its prevention and treatment is firmly recognized. Indeed, AI application allows personalized medicine and tailored treatment for each patient. Specifically, this article reviews the benefits of AI in AH management, pointing out diagnostic and therapeutic improvements without ignoring the limitations of this innovative scientific approach. Consequently, we conducted a detailed search on AI applications in AH: the articles (quantitative and qualitative) reviewed in this paper were obtained by searching journal databases such as PubMed and subject-specific professional websites, including Google Scholar. The search terms included artificial intelligence, artificial neural network, deep learning, machine learning, big data, arterial hypertension, blood pressure, blood pressure measurement, cardiovascular disease, and personalized medicine. Specifically, AI-based systems could help continuously monitor BP using wearable technologies; in particular, BP can be estimated from a photoplethysmograph (PPG) signal obtained from a smartphone or a smartwatch using DL. Furthermore, thanks to ML algorithms, it is possible to identify new hypertension genes for the early diagnosis of AH and the prevention of complications. Moreover, integrating AI with omics-based technologies will lead to the definition of the trajectory of the hypertensive patient and the use of the most appropriate drug. However, AI is not free from technical issues and biases, such as over/underfitting, the "black-box" nature of many ML algorithms, and patient data privacy. In conclusion, AI-based systems will change clinical practice for AH by identifying patient trajectories for new, personalized care plans and predicting patients' risks and necessary therapy adjustments due to changes in disease progression and/or therapy response.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic continues to be a global challenge due to resulting morbidity and mortality. Cardiovascular (CV) involvement is a crucial complication in coronavirus disease 2019 (COVID-19), and no strategies are available to prevent or specifically address CV events in COVID-19 patients. The identification of molecular partners contributing to CV manifestations in COVID-19 patients is crucial for providing early biomarkers, prognostic predictors, and new therapeutic targets. The current report will focus on the role of microRNAs (miRNAs) in CV complications associated with COVID-19. Indeed, miRNAs have been proposed as valuable biomarkers and predictors of both cardiac and vascular damage occurring in SARS-CoV-2 infection. SIGNIFICANCE STATEMENT: It is essential to identify the molecular mediators of coronavirus disease 2019 (COVID-19) cardiovascular (CV) complications. This report focused on the role of microRNAs in CV complications associated with COVID-19, discussing their potential use as biomarkers, prognostic predictors, and therapeutic targets.

Patients with acute coronary syndrome and multivessel disease experience several recurrent adverse events that lead to poor outcomes. Given the complexity of treating these patients, and the extremely high risk of long-term adverse events, the assessment of non-culprit lesions becomes crucial. Recently, two trials have shown a possible clinical benefit into treat non-culprit lesions using a fraction flow reserve (FFR)-guided approach, compared to culprit-lesion-only PCI. However, the most recent FLOW Evaluation to Guide Revascularization in Multivessel ST-elevation Myocardial Infarction (FLOWER-MI) trial did not show a benefit of the use of FFR-guided PCI compared to an angiography-guided approach. Otherwise, intracoronary imaging using optical coherence tomography (OCT), intravascular ultrasound (IVUS), or near-infrared spectroscopy (NIRS) could provide both quantitative and qualitative assessments of non-culprit lesions. Different studies have shown how the characterization of coronary lesions with intracoronary imaging could lead to clinical benefits in these peculiar group of patients. Moreover, non-invasive evaluations of NCLs have begun to take ground in this context, but more insights through adequately powered and designed studies are needed. The aim of this review is to outline the available techniques, both invasive and non-invasive, for the assessment of multivessel disease in patients with STEMI, and to provide a systematic guidance on the assessment and approach to these patients.

Abstract Introduction Double-chambered right ventricle (DCRV) is a rare congenital heart defect with right ventricular outflow tract (RVOT) obstruction. The right ventricle (RV) is divided into anatomically proximal high-pressure and distal low-pressure chambers by abnormal muscle bundle. DCRV is frequently associated with others congenital heart defects, particularly ventricular septal defects (VSDs). Although its typically presents during childhood and adolescence, it can also present in adulthood. Case Presentation An 84-years-old woman was admitted to our hospital, in emergency department, with a 30-days history of worsening dyspnea and exercise intolerance. She was Ukrainian and did not speak Italian or English. The patient past medical history was unknow except for untreated bilateral glaucoma complicated by blindness. Vital signs were notable for tachycardia, tachypnea (respiratory rate, 28/minute), blood pressure of 118/76 mm Hg, SpO2 of 91%. Physical exam revealed left-sided parasternal systolic murmurs, abolished vesicular murmur at lung bases and jugular vein distension with hepatojugular reflux. The ECG showed atrial fibrillation. Chest X-ray showed moderate cardiomegaly, bilateral pleural effusions, and pulmonary congestion. Transthoracic echocardiography (TTE) was performed and revealed a normal-sized left ventricle with mildly reduced left ventricle ejection fraction (EF 48%), left atrial enlargement, biventricular hypertrophy with asymmetrical interventricular septal hypertrophy. Also, we found massive right atrium and enlarged right ventricle with reduced longitudinal contractility (TAPSE of 13 mm and tricuspid annular tissue Doppler S’ velocity = 7.0 cm/sec). Color flow Doppler in parasternal short-axis view revealed a turbulent systolic flow into the right ventricle. Continuous-wave spectral Doppler analysis showed a peak velocity of 5.6 m/ sec corresponding to a peak gradient of 120 mmHg. Real time 3D-TTE confirmed the of mid-ventricular obstruction due to abnormal trabecular tissue. Therapy including diuretics, beta-blockers and anticoagulants was started. Subsequently, a transesophageal echocardiography (TOE) confirmed the presence of an anomalous mid-ventricular muscle bundle and revealed an associated small sub-aortic ventricular septal defect (VSD) leading to the diagnosis of acute RV failure due to double-chambered RV with VSD and atrial fibrillation. Due to the high risk of complications, patient was considered not amenable for surgery. She was discharged on medical therapy. Conclusions We report a rare case of DCRV and VSD diagnosed in an elderly patient. Due to its rarity, DCRV continues to be misdiagnosed, especially in adulthood. Three-dimensional echocardiography and TOE were most useful tool to define diagnosis and pathophysiology in such an elderly and non-compliant patient.

Though the acute effects of SARS-CoV-2 infection have been extensively reported, the long-term effects are less well described. Specifically, while clinicians endure to battle COVID-19, we also need to develop broad strategies to manage post-COVID-19 symptoms and encourage those affected to seek suitable care. This review addresses the possible involvement of the lung, heart and brain in post-viral syndromes and describes suggested management of post-COVID-19 syndrome. Post-COVID-19 respiratory manifestations comprise coughing and shortness of breath. Furthermore, arrhythmias, palpitations, hypotension, increased heart rate, venous thromboembolic diseases, myocarditis and acute heart failure are usual cardiovascular events. Among neurological manifestations, headache, peripheral neuropathy symptoms, memory issues, lack of concentration and sleep disorders are most commonly observed with varying frequencies. Finally, mental health issues affecting mental abilities and mood fluctuations, namely anxiety and depression, are frequently seen. Finally, long COVID is a complex syndrome with protracted heterogeneous symptoms, and patients who experience post-COVID-19 sequelae require personalized treatment as well as ongoing support.

Background: Sacubitril/valsartan improves outcome in patients with heart failure (HF) with reduced left ventricular (LV) ejection fraction (EF, HFrEF). However, little is known about possible mechanisms underlying this favourable effect. Purpose: To assess changes in echocardiographically-derived hemodynamic profiles induced by sacubitril/valsartan and their impact on outcome. Methods: In this multicenter, open-label study, 727 HFrEF outpatients underwent comprehensive echocardiography at baseline (before starting sacubitril/valsartan) and after 12 months. Estimated LV filling pressure (E/e') and cardiac index (CI, l/min/m(2)) were combined to determine 4 hemodynamic profiles: profile-A (normal-flow/ normal-pressure); profile-B (low-flow/normal-pressure); profile-C: (normal-flow/high-pressure); profile-D: (low-flow/high-pressure). Changes among categories were recorded, and their associations with rates of the composite of death/HF-hospitalization were assessed by multivariable Cox analysis. Results: At baseline, 29% had profile-A, 15% had profile-B, 32% profile-C, and 24% profile-D. After 12 months, the hemodynamic profile improved in 53% of patients (all profile-A achievers, or profile-D patients achieving either C or B profile), while it remained unchanged in 39% patients and worsened in 9%. Prevalence of improved profile progressively increased with increasing dose of sacubitril/valsartan (P < 0.0001). After the second echocardiography, patients were followed up 12.6 +/- 7.6 months: event-rate was lower in patients with improved profile (12.3%, 95%CI: 9.4-16.1) compared to patients in whom hemodynamic profile remained unchanged (29.9%, 24.0-37.3) or worsened (31.2%, 20.7-46.9, P < 0.0001). Improved hemodynamic profile was associated with favourable outcome independent of LVEF and other covariates (HR 0.65, 95%CI: 0.45-0.95, P < 0.05). Conclusion: In HFrEF patients, the beneficial prognostic effects of sacubitril/valsartan are associated with improvement in hemodynamic conditions.

Abstract Background Congenitally corrected transposition of great arteries (ccTGA) is an uncommon complex congenital heart disease with atrio-ventricular and ventriculo-arterial connections discondance. ccTGA may be associated with a situs solitus or situs inversus (34% of cases). Situs inversus is a mirror image of normal with the systemic ventricle situated on the right side. Instead, dextrocardia represents 20% of cases. Case clinic and discussion Came to our observation a 61 years old female, symptomatic for dyspnea on exertion (NYHA II). She had no past medical history of cardiovascular events. In anamnesis two full-term pregnancies without complications. Transthoracic echocardiogram found atrioventricular and ventriculo-arterial discordance in absence of significant valvulopathy. Cardiac computed tomography showed pulmonary veins linked to right atrium, superior and inferior cava veins connected to the left atrium; right atrium with tricuspid valve was connected to a morphologically left ventricle and left atrium with mitral valve was linked to morphologically right ventricle; pulmonary artery was connected to morphologically left ventricle instead aorta with aortic valve was linked to morphologically right ventricle. Cardiac MRI confirmed cctga in situs viscerum inversus, mild subpulmonary stenosis, moderate dilatation of arterial pulmonary trunk, and also intramyocardial late gadolinium enhancement due to fibrosis involving anterior and inferior interventricular junctions. Cardiac Holter monitoring showed sinus rhythm with some brief phases of low atrial rhythm, monomorphic isolated ventricular extrasystoles in absence of significant hyperkinetic or hypokinetic arrhythmias. CcTGA represents approximately 0.5% of all congenital heart disease. If undiagnosed in childhood, people usually become symptomatic during the first decades of life. Dyspnea, syncope and fatigue are the most frequent symptoms detected. Cardiac conduction disorders such as atrioventricular blocks are common due to the abnormal development of cardiac structures. Quality of life and its expectancy are related to the latency of the onset of heart failure symptoms. Only few patients remain asymptomatic beyond 50 years old. Symptoms and signs are frequently due to right sided (systemic) heart dysfunction and tricuspid valve insufficiency. A particular clinical situation worthy of attention is pregnancy because of the hemodynamic imbalance occurring. In fact, cardiac output increases of 40–50% above baseline determining an augmentation of stroke volume and heart rate. For these reasons, echo surveillance is needed every 4-8 weeks because of the increased risk of acute heart failure. An accurate assessment of heart rhythm has to be done due to the known predisposition to bradyarrhythmic and tachyarrhythmic events in ccTGA. Conclusions CcTGA patients require a strict cardiological follow up with echocardiographic assessment and periodic heart rhythm monitoring, in order to early detect worsening of cardiac function and significant abnormalities of the rhythm.

Abstract Background Large cardiovascular (CV) trials enrolling patients with type 2 diabetes showed that sodium glucose co-transporter-2 inhibitors (SGLT2i) significantly decreased heart failure (HF) hospitalization, both in patients with or without a history of HF. Accordingly, DAPA-HF (Dapagliflozin and Prevention of Adverse Outcomes in Heart Failure) demonstrated the efficacy of dapagliflozin, for the reduction of CV death/HF hospitalization in patients with HF with reduced ejection fraction (HFrEF) regardless of type 2 diabetes status. However, there are still few real-word data and it is still not well known how early the clinical benefits are after the introduction of the drug into therapy; consequently, we aimed to evaluate the effect of dapagliflozin three months after its introduction in therapy in our real-world population. Methods From February 2022 to September 2022 we introduced Dapagliflozin in 23 HFrEF patients’ therapy and we collected data of 11 patients (66.78±3.96 years; 89% men) at 3-months-FU. Specifically, on the first visit we collected the clinical, laboratory and echocardiographic parameters and dapagliflozin was added to optimal medical therapy of patients; then, the patients were evaluated after 3 months (follow-up). Results At follow-up, all patients were free from side effects and we did not record statistically significant differences in laboratory parameters and/or blood pressure values. As regards the echocardiographic parameters, there was an improvement in FE (28.11±2.95 vs 37.00±5.71%, p0.17), PAPS (46.89±3.94 vs 37.63±5.27mmHg, p0.17), and LVEDVind (75.34±10.58 vs 57.20±13.55 ml/m2, p0.30), although not statistically significant. Moreover, we observed a statistically significant reduction in the diameter of the inferior vena cava (18.89±1.78 vs 11.5±1.15 mm, p<0.01), in NYHA class (2.78±0.15 vs 2±0, p<0.001), in basal SO2 (95.67±0.78 vs 97.67±0.47%, p0.04) and an improvement of quality of life (EQ5Dtot 73.33±4.71 vs 86.67±4.41, p0.05; pain/discomfort 3.67±0.29 vs 4.78±0.22, p<0.01; anxiety/depression 3.67±0.29 vs 4.67±0.24, p0.0163). Finally, we recorded a not statistically significant reduction in the amount of mineralocorticoid receptor antagonists (MRAs) at follow-up (62.5±12.5 vs 37.5±5.59 mg of eplerenone, p0.09). Conclusions Dapagliflozin improved symptoms, and quality of life in patients with HFrEF of our real world population already after 12 weeks, accordingly with previous data of DEFINE-HF trial. Moreover, already after 3 months was possible to record improvements in the echocardiographic parameters, even if they are not statistically significant. Certainly, it will be necessary to continue with the study to evaluate these results on a larger sample.

Abstract Aim Echo‐derived haemodynamic classification, based on forward‐flow and left ventricular (LV) filling pressure (LVFP) correlates, has been proposed to phenotype patients with heart failure and reduced ejection fraction (HFrEF). To assess the prognostic relevance of baseline echocardiographically defined haemodynamic profile in ambulatory HFrEF patients before starting sacubitril/valsartan. Methods and results In our multicentre, open‐label study, HFrEF outpatients were classified into 4 groups according to the combination of forward flow (cardiac index; CI:< or ≥2.0 L/min/m2) and early transmitral Doppler velocity/early diastolic annular velocity ratio (E/e′: ≥ or <15): Profile‐A: normal‐flow, normal‐pressure; Profile‐B: low‐flow, normal‐pressure; Profile‐C: normal‐flow, high‐pressure; Profile‐D: low‐flow, high‐pressure. Patients were started on sacubitril/valsartan and followed‐up for 12.3 months (median). Rates of the composite of death/HF‐hospitalization were assessed by multivariable Cox proportional‐hazards models. Twelve sites enrolled 727 patients (64 ± 12 year old; LVEF: 29.8 ± 6.2%). Profile‐D had more comorbidities and worse renal and LV function. Target dose of sacubitril/valsartan (97/103 mg BID) was more likely reached in Profile‐A (34%) than other profiles (B: 32%, C: 24%, D: 28%, P < 0.001). Event‐rate (per 100 patients per year) progressively increased from Profile‐A to Profile‐D (12.0%, 16.4%, 22.9%, and 35.2%, respectively, P < 0.0001). By covariate‐adjusted Cox model, profiles with low forward‐flow (B and D) remained associated with poor outcome (P < 0.01). Adding this categorization to MAGGIC‐score and natriuretic peptides, provided significant continuous net reclassification improvement (0.329; P < 0.001). Intermediate and high‐dose sacubitril/valsartan reduced the event's risk independently of haemodynamic profile. Conclusions Echocardiographically‐derived haemodynamic classification identifies ambulatory HFrEF patients with different risk profiles. In real‐world HFrEF outpatients, sacubitril/valsartan is effective in improving outcome across different haemodynamic profiles.

COVID-19 infection evokes various systemic alterations that push patients not only towards severe acute respiratory syndrome but causes an important metabolic dysregulation with following multi-organ alteration and potentially poor outcome. To discover novel potential biomarkers able to predict disease's severity and patient's outcome, in this study we applied untargeted lipidomics, by a reversed phase ultra-high performance liquid chromatography-trapped ion mobility mass spectrometry platform (RP-UHPLC-TIMS-MS), on blood samples collected at hospital admission in an Italian cohort of COVID-19 patients (45 mild, 54 severe, 21 controls). In a subset of patients, we also collected a second blood sample in correspondence of clinical phenotype modification (longitudinal population). Plasma lipid profiles revealed several lipids significantly modified in COVID-19 patients with respect to controls and able to discern between mild and severe clinical phenotype. Severe patients were characterized by a progressive decrease in the levels of LPCs, LPC-Os, PC-Os, and, on the contrary, an increase in overall TGs, PEs, and Ceramides. A machine learning model was built by using both the entire dataset and with a restricted lipid panel dataset, delivering comparable results in predicting severity (AUC= 0.777, CI: 0.639-0.904) and outcome (AUC= 0.789, CI: 0.658-0.910). Finally, re-building the model with 25 longitudinal (t1) samples, this resulted in 21 patients correctly classified. In conclusion, this study highlights specific lipid profiles that could be used monitor the possible trajectory of COVID-19 patients at hospital admission, which could be used in targeted approaches.

Abstract Background Heart failure (HF) alternates phases of stability and phases of exacerbation, with a progressive decline in the patient's functional capacity and quality of life; the need to anticipate and improve the effectiveness of management of HF exacerbation has led to the development of several remote monitoring tools. We report our experience with CardioMEMS HF system (implantable device to monitor changes in pulmonary artery diastolic pressure (PAPd) as early indicator of the onset of worsening HF) in order to optimize the pharmaceutical treatments strategy (e.g. Levosimendan infusion) and to assess the impact on hospital resources consumption and costs. Methods We enrolled 7 patients (69.00±4.88 years; 30% female) with end-stage HF, implanted with CardioMEMS and daily monitored remotely, in order to optimize both tailored adjustment of home therapy and infusions of Levosimendan. More in detail, if the cardiologist detected a tendency for PAPd to rise, patients were contacted for home therapeutic changes. If no further changes were possible, the patient was hospitalized for the infusion of Levosimendan. In order to calculate the impact of this remote monitoring strategy on resources consumption, we collected data on hospitalizations (e.g. causes, numbers, length, high-cost drugs and costs) taking into account the same number of months pre and post-CardioMEMS implant for each patient. Results Following the implantation of CardioMEMS we observed a 45% reduction in the total number of hospitalizations and a 62% reduction in the days of hospitalization (from a total of 421 days before implantation to a total of 159 days post implantation in the observation period). From an economic point of view, a significant hospital cost reduction was recorded in terms of both hospitalization costs (HF related re-hospitalization and CardioMEMS's implant related cost) and drugs infusion costs (hospital stay and drug costs); more specifically, the total savings for the 7 patients are around € 236,000 and total days of hospitalization avoided are around 500 days including the hospitalizations avoided for drugs infusion. Accordingly, was recorded an improvement in patients’ quality of life measured with EQ5D (pre-implantation 75.17±2.06 vs post-implantation 108.60±8.70, p 0.0078). Conclusions Our preliminary results support the usefulness of this system in the remote management of the HF patients and in the re-hospitalization reduction both for exacerbation and drug management. In fact, the parameters’ monitoring through the CardioMEMS device allows a personalized management of drug therapy; more precisely, considering the drug Levosimendan, instead of a periodic standard timing for infusion, a patient-tailored timing of infusion was applied. In conclusion, our innovative strategy contributes to achieve the organizational efficiency of the healthcare facilities, as well as to the adequate use and allocation of financial and human resources with a better outcome for HF patients.