Cardiovascular Implications of microRNAs in Coronavirus Disease 2019.

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.

Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects.

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.

An explainable model for predicting Worsening Heart Failure based on genetic programming.

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.

The favourable alliance between CardioMEMS and levosimendan in patients with advanced heart failure.

AIMS: We 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 RESULTS: We 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. CONCLUSIONS: This innovative strategy prevents unplanned access to the hospital and contributes to the efficient use of healthcare facilities, human resources, and costs.

Vitamin D: A Bridge between Kidney and Heart.

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.

Moving toward Precision Medicine in Acute Coronary Syndromes: A Multimodal Assessment of Non-Culprit Lesions.

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.

Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve.

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.

Acute heart failure: mechanisms and pre-clinical models-a Scientific Statement of the ESC Working Group on Myocardial Function.

While chronic heart failure (CHF) treatment has considerably improved patient prognosis and survival, the therapeutic management of acute heart failure (AHF) has remained virtually unchanged in the last decades. This is partly due to the scarcity of pre-clinical models for the pathophysiological assessment and, consequently, the limited knowledge of molecular mechanisms involved in the different AHF phenotypes. This scientific statement outlines the different trajectories from acute to CHF originating from the interaction between aetiology, genetic and environmental factors, and comorbidities. Furthermore, we discuss the potential molecular targets capable of unveiling new therapeutic perspectives to improve the outcome of the acute phase and counteracting the evolution towards CHF.

Post-COVID-19 Syndrome: Involvement and Interactions between Respiratory, Cardiovascular and Nervous Systems.

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.

Echocardiographically defined haemodynamic categorization predicts prognosis in ambulatory heart failure patients treated with sacubitril/valsartan.

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.

Benefit from sacubitril/valsartan is associated with hemodynamic improvement in heart failure with reduced ejection fraction: An echocardiographic study.

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/m2) 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.

Untargeted lipidomics reveals specific lipid profiles in COVID-19 patients with different severity from Campania region (Italy).

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.

Clinical and echocardiographic benefit of Sacubitril/Valsartan in a real-world population with HF with reduced ejection fraction.

The aim of this study was to evaluate the effects of Sacubitril/Valsartan (S/V) on clinical, laboratory and echocardiographic parameters and outcomes in a real-world population with heart failure with reduced ejection fraction (HFrEF). This was a prospective observational study enrolling patients with HFrEF undergoing treatment with S/V. The primary outcome was the composite of cardiac death and HF rehospitalization at 12 months follow-up; secondary outcomes were all-cause death, cardiac death and the occurrence of rehospitalization for worsening HF. The clinical outcome was compared with a retrospective cohort of 90 HFrEF patients treated with standard medical therapy. The study included 90 patients (66.1 ± 11.7 years) treated with S/V. The adjusted regression analysis showed a significantly lower risk for the primary outcome (HR:0.31; 95%CI, 0.11-0.83; p = 0.019) and for HF rehospitalization (HR:0.27; 95%CI, 0.08-0.94; p = 0.039) in S/V patients as compared to the control group. A significant improvement in NYHA class, left ventricular ejection fraction, left ventricular end systolic volume and systolic pulmonary arterial pressure was observed up to 6 months. S/V did not affect negatively renal function and was associated with a significantly lower dose of furosemide dose prescribed at 6- and 12-month follow-up. In this study, S/V reduced the risk of HF rehospitalization and cardiac death at 1 year in patients with HFrEF. S/V improved NYHA class, echocardiographic parameters and need of furosemide, and preserved renal function.