Arterial Stiffness, Subendocardial Impairment, and 30-Day Readmission in Heart Failure Older Patients

Arterial stiffness and subendocardial perfusion impairment may play a significant role in heart failure (HF) outcomes. The aim of the study was to examine the main predictors of 30-day readmission in geriatric patients, hospitalized with HF, explore hemodynamical parameters, arterial stiffness indexes, and subendocardial viability ratio (SEVR). In total, 41 hospitalized patients, affected by HF, were included; they underwent clinical evaluation, routine laboratory testing, and echocardiography. At the time of admission, after the achievement of clinical stability (defined as switching from intravenous to oral diuretic therapy), and at discharge, arterial tonometry was performed to evaluate carotid-femoral pulse wave velocity (PWVcf) and SEVR (then corrected for hemoglobin concentration and oxygen saturation). Through the evaluations, a significant progressive decrease in PWVcf was described (17.79 ± 4.49, 13.54 ± 4.54, and 9.94 ± 3.73 m/s), even after adjustment for age, gender, mean arterial pressure (MAP) variation, and left ventricular ejection fraction (LVEF). A significant improvement was registered for both SEVR (83.48 ± 24.43, 97.94 ± 26.84, and 113.29 ± 38.02) and corrected SEVR (12.74 ± 4.69, 15.71 ± 5.30, and 18.55 ± 6.66) values, and it was still significant when adjusted for age, gender, MAP variation, and LVEF. After discharge, 26.8% of patients were readmitted within 30 days. In a multivariate binary logistic regression analysis, PWVcf at discharge was the only predictor of 30-day readmission (odds ratio [OR] 1.957, 95% CI 1.112-3.443). In conclusion, medical therapy seems to improve arterial stiffness and subendocardial perfusion in geriatric patients hospitalized with heart failure. Furthermore, PWVcf is a valid predictor of 30-day readmission. Its feasibility in clinical practice may provide an instrument to detect patients with HF at high risk of rehospitalization

Myocardial fibrosis and steatosis in patients with aortic stenosis: roles of myostatin and ceramides

Aortic stenosis (AS) involves progressive valve obstruction and a remodeling response of the left ventriculum (LV) with systolic and diastolic dysfunction. The roles of interstitial fibrosis and myocardial steatosis in LV dysfunction in AS have not been completely characterized. We enrolled 31 patients (19 women and 12 men) with severe AS undergoing elective aortic valve replacement. The subjects were clinically evaluated, and transthoracic echocardiography was performed pre-surgery. LV septal biopsies were obtained to assess fibrosis and apoptosis and fat deposition in myocytes (perilipin 5 (PLIN5)), or in the form of adipocytes within the heart (perilipin 1 (PLIN1)), the presence of ceramides and myostatin were assessed via immunohistochemistry. After BMI adjustment, we found a positive association between fibrosis and apoptotic cardiomyocytes, as well as fibrosis and the area covered by PLIN5. Apoptosis and PLIN5 were also significantly interrelated. LV fibrosis increased with a higher medium gradient (MG) and peak gradient (PG). Ceramides and myostatin levels were higher in patients within the higher MG and PG tertiles. In the linear regression analysis, increased fibrosis correlated with increased apoptosis and myostatin, independent from confounding factors. After adjustment for age and BMI, we found a positive relationship between PLIN5 and E/A and a negative correlation between septal S', global longitudinal strain (GLS), and fibrosis. Myostatin was inversely correlated with GLS and ejection fraction. Fibrosis and myocardial steatosis altogether contribute to ventricular dysfunction in severe AS. The association of myostatin and fibrosis with systolic dysfunction, as well as between myocardial steatosis and diastolic dysfunction, highlights potential therapeutic targets

Senescent adipocytes as potential effectors of muscle cells dysfunction: An in vitro model

Recently, there has been a growing body of evidence showing a negative effect of the white adipose tissue (WAT) dysfunction on the skeletal muscle function and quality. However, little is known about the effects of senescent adipocytes on muscle cells. Therefore, to explore potential mechanisms involved in age-related loss of muscle mass and function, we performed an in vitro experiment using conditioned medium obtained from cultures of mature and aged 3 T3-L1 adipocytes, as well as from cultures of dysfunctional adipocytes exposed to oxidative stress or high insulin doses, to treat C2C12 myocytes. The results from morphological measures indicated a significant decrease in diameter and fusion index of myotubes after treatment with medium of aged or stressed adipocytes. Aged and stressed adipocytes presented different morphological characteristics as well as a different gene expression profile of proinflammatory cytokines and ROS production. In myocytes treated with different adipocytes' conditioned media, we demonstrated a significant reduction of gene expression of myogenic differentiation markers as well as a significant increase of genes involved in atrophy. Finally, a significant reduction in protein synthesis as well as a significant increase of myostatin was found in muscle cells treated with medium of aged or stressed adipocytes compared to controls. In conclusion, these preliminary results suggest that aged adipocytes could influence negatively trophism, function and regenerative capacity of myocytes by a paracrine network of signaling

Kawasaki disease : an epidemiological study in central Italy

BACKGROUND: Kawasaki disease (KD) is a systemic vasculitis with an acute and self-limited course. The incidence of KD differs widely among ethnic groups and is higher in the Asian population. In Italy, no recent data are available. Our purpose is to define the epidemiology of Kawasaki disease in the years 2008-2013 in children aged\u2009<\u200914 years in the Italian regions of Tuscany and Emilia Romagna through administrative data. METHODS: We studied the epidemiology of KD in the years 2008-2013 in children 0-14 years old resident in Tuscany and in Emilia Romagna regions using hospital ICD-9 discharge codes with a thorough data cleaning for duplicates. RESULTS: The distribution of the KD patients across ages was similar for the two regions with a peak in the second year of life. When considering data of the two regions together, the rate of incidence was 17.6 for 100,000 children under 5 years. For both Regions the incidence rose slightly during the study period and had a seasonal distribution, with higher incidence in spring and winter. CONCLUSION: This is the first Italian study performed through the use of administrative data. Figures are in line but slightly higher than those published in other European countries

Neurological manifestations of Kawasaki disease and multisystem inflammatory syndrome in children associated with COVID-19: A comparison of two different clinical entities

Kawasaki disease (KD) is one of the most frequent idiopathic vasculitis in children, affecting medium- and small-sized vessels. Multisystem inflammatory syndrome in children (MIS-C) associated with COVID-19 has recently emerged as a new systemic hyperinflammatory condition affecting children some weeks after an acute COVID-19 infection. KD and MIS-C share different aspects and differ in many others: patients affected by MIS-C are usually older, with prominent gastrointestinal manifestations, diffuse adenopathy, extensive conjunctivitis, myocardial damage, leukopenia, and thrombocytopenia at the laboratory exams. Both conditions can present neurological complications. The aim of this manuscript is to provide a narrative review of neurological involvement in KD and MIS-C. A comprehensive review literature has been performed, and the main clinical features have been analyzed, contributing to neurological differential diagnosi

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods.MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions.ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models’ prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR.ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

How future surgery will benefit from SARS-COV-2-related measures: a SPIGC survey conveying the perspective of Italian surgeons

COVID-19 negatively affected surgical activity, but the potential benefits resulting from adopted measures remain unclear. The aim of this study was to evaluate the change in surgical activity and potential benefit from COVID-19 measures in perspective of Italian surgeons on behalf of SPIGC. A nationwide online survey on surgical practice before, during, and after COVID-19 pandemic was conducted in March-April 2022 (NCT:05323851). Effects of COVID-19 hospital-related measures on surgical patients' management and personal professional development across surgical specialties were explored. Data on demographics, pre-operative/peri-operative/post-operative management, and professional development were collected. Outcomes were matched with the corresponding volume. Four hundred and seventy-three respondents were included in final analysis across 14 surgical specialties. Since SARS-CoV-2 pandemic, application of telematic consultations (4.1% vs. 21.6%; p < 0.0001) and diagnostic evaluations (16.4% vs. 42.2%; p < 0.0001) increased. Elective surgical activities significantly reduced and surgeons opted more frequently for conservative management with a possible indication for elective (26.3% vs. 35.7%; p < 0.0001) or urgent (20.4% vs. 38.5%; p < 0.0001) surgery. All new COVID-related measures are perceived to be maintained in the future. Surgeons' personal education online increased from 12.6% (pre-COVID) to 86.6% (post-COVID; p < 0.0001). Online educational activities are considered a beneficial effect from COVID pandemic (56.4%). COVID-19 had a great impact on surgical specialties, with significant reduction of operation volume. However, some forced changes turned out to be benefits. Isolation measures pushed the use of telemedicine and telemetric devices for outpatient practice and favored communication for educational purposes and surgeon-patient/family communication. From the Italian surgeons' perspective, COVID-related measures will continue to influence future surgical clinical practice