Obesity, a major risk factor for acute coronary syndrome (ACS), is a multifaceted disease with different metabolic phenotypes and sex-specific features. Here, we evaluated the long-term cardiovascular risk by different obesity/metabolic phenotypes and by sex in ACS patients. The occurrence of the composite outcome of death, nonfatal reinfarction with or without PCI and/or stroke was evaluated in 674 patients (504 men; 170 women), consecutively hospitalized for ACS and followed-up for 7 years, who were stratified in metabolically healthy (MHNW) and unhealthy normal weight (MUNW), and in metabolically healthy (MHO) and unhealthy obese (MUO) groups. At baseline, 54.6% of patients were included in the MHNW group, 26.4% in the MUNW, 5.9% in the MHO and 13.1% in the MUO, with no sex-differences in the distribution of phenotypes. The overall rate of major outcome (100 person-years) in the reference group (MHNW) was higher in men than in women (RR: 1.19 vs. 0.6). The Kaplan–Meier curves for cumulative survival free from cardiovascular events according to obesity/metabolic status diverged significantly according to sex (log rank test, p = 0.006), this effect being more prominent in men (log 11.20; p = 0.011), than in women (log 7.98; p = 0.047). Compared to MHNW, the risk increased in obese men (RR: 2.2; 95% 1.11–1.54 in MUO group), whereas in women the risk was confined to metabolically unhealthy subjects (RR: 3.2; 95% CI 1.23–9.98, MUNW group). Our data show a sex-specific impact of obesity phenotypes on long-term cardiovascular risk in patients hospitalized for ACS.

Low molecular weight heparin, enoxaparin, has been one of most used drugs to fight the SARS-CoV-2 pandemic. Pharmacological properties of heparin recognize its specific ability, as with other oligosaccharides and glycosaminoglycan, to bind several types of viruses during their pass through the extracellular matrix of the respiratory tract, as well as its anticoagulant activity to prevent venous thromboembolism. Antithrombotic actions of enoxaparin have been testified both for inpatients with COVID-19 in regular ward and for inpatients in Intensive Care Units (ICUs). Prophylactic doses seem to be able to prevent venous thromboembolism (VTE) in inpatients in the regular ward, while intermediate or therapeutic doses have been frequently adopted for inpatients with COVID-19 in ICU. On the other hand, although we reported several useful actions of heparin for inpatients with COVID-19, an increased rate of bleeding has been recorded, and it may be related to several conditions such as underlying diseases with increased risks of bleeding, increased doses or prolonged administration of heparin, personal trend to bleed, and so on.

To realize a machine learning (ML) model to estimate the dose of low molecular weight heparin to be administered, preventing thromboembolism events in COVID-19 patients with active cancer. Methods: We used a dataset comprising 131 patients with active cancer and COVID-19. We considered five ML models: logistic regression, decision tree, random forest, support vector machine and Gaussian naive Bayes. We decided to implement the logistic regression model for our study. A model with 19 variables was analyzed. Data were randomly split into training (70%) and testing (30%) sets. Model performance was assessed by confusion matrix metrics on the testing data for each model as positive predictive value, sensitivity and F1-score. Results: We showed that the five selected models outperformed classical statistical methods of predictive validity and logistic regression was the most effective, being able to classify with an accuracy of 81%. The most relevant result was finding a patient-proof where python function was able to obtain the exact dose of low weight molecular heparin to be administered and thereby to prevent the occurrence of VTE. Conclusions: The world of machine learning and artificial intelligence is constantly developing. The identification of a specific LMWH dose for preventing VTE in very high-risk populations, such as the COVID-19 and active cancer population, might improve with the use of new training ML-based algorithms. Larger studies are needed to confirm our exploratory results.

Background: During the SARS-CoV-2 pandemic, several biomarkers were shown to be helpful in determining the prognosis of COVID-19 patients. The aim of our study was to evaluate the prognostic value of N-terminal pro-Brain Natriuretic Peptide (NT-pro-BNP) in a cohort of patients with COVID-19. Methods: One-hundred and seven patients admitted to the Covid Hospital of Messina University between June 2022 and January 2023 were enrolled in our study. The demographic, clinical, biochemical, instrumental, and therapeutic parameters were recorded. The primary outcome was in-hospital mortality. A comparison between patients who recovered and were discharged and those who died during the hospitalization was performed. The independent parameters associated with in-hospital death were assessed by multivariable analysis and a stepwise regression logistic model. Results: A total of 27 events with an in-hospital mortality rate of 25.2% occurred during our study. Those who died during hospitalization were older, with lower GCS and PaO2/FiO2 ratio, elevated D-dimer values, INR, creatinine values and shorter PT (prothrombin time). They had an increased frequency of diagnosis of heart failure (p < 0.0001) and higher NT-pro-BNP values. A multivariate logistic regression analysis showed that higher NT-pro-BNP values and lower PT and PaO2/FiO2 at admission were independent predictors of mortality during hospitalization. Conclusions: This study shows that NT-pro-BNP levels, PT, and PaO2/FiO2 ratio are independently associated with in-hospital mortality in subjects with COVID-19 pneumonia. Further longitudinal studies are warranted to confirm the results of this study.

Objective To evaluate the specific effects of PCSK9 inhibitors (i.e. alirocumab and evolocumab) on major cardiovascular events (MACE) and lipid profile in patients with diabetes. Methods We conducted a systematic review of literature according to the PRISMA statement. A total of 8 randomized control trials (RCTs) enrolling 20 651 patients with diabetes were included. Mean follow-up was 51 weeks. We included RCTs which had compared the PCSK9i alirocumab and evolocumab with placebo in subjects with hypercholesterolemia and diabetes mellitus. Results MACE occurred in 8.7% of patients with diabetes randomized to PCSK9i vs.11.0% of those randomized to placebo. Thus, the use of alirocumab or evolocumab reduced MACE by 18% (relative risk [RR] 0.82, 95% confidence interval [CI] 0.74–0.90). Compared to control group, the use of PCSK9 inhibitors was associated with a significant % change from baseline in LDL-C (mean difference [MD]-58.48%; 95% CI: -63.73 to -53.22%), P &lt; 0.0001), HDL-C (MD5.21%; 95% CI: 3.26 to 7.17%), Triglycerides (MD-14.59%; 95% CI: -19.42 to -9.76%), non-HDL-C (MD -48.84%; 95% CI: -54.54 to -43.14%) and total Cholesterol (MD-33.76%; 95% CI: -38.71 to -28.8%). Moreover, a significant reduction of Lp(a) (MD-32.90%; 95% CI: -38.55 to -27.24%)and ApoB (MD-46.83%; 95% CI: -52.71 to -40.94%)were observed in PCSK9i group compared to placebo. Conclusions PCSK9i appear to be effective in reducing the risk of MACE and in improving lipid profiles of subjects with diabetes and dyslipidemia.