Sex-specific data are limited regarding eligibility for hypolipidemic treatment. We aim to explore the sex-specific clinical utility of high sensitivity C-reactive protein(hsCRP) and carotid ultrasound as risk modifiers for hypolipidemic treatment in primary prevention for atherosclerotic cardiovascular disease(ASCVD). We aimed to explore these sex-specific trends in two pooled contemporary independent Greek cohorts (Athens Vascular Registry n=698, 50.9% women and Menopause Clinic n=373, 100% women) of individuals without overt ASCVD. Baseline ASCVD risk was estimated using the Systematic COronary Risk Evaluation-2(SCORE2) tools. Presence of carotid plaque and hsCRP≥2mg/L were integrated as risk-modifiers. Men had increased odds to achieve target LDL-C levels based on ASCVD risk(23.8% vs 17.7%, OR: 1.45 95%CI: 1.05-2.00, p=0.023, for men vs women). Additionally considering carotid plaque or high hsCRP levels did not change this association but reduced on-target LDL-C rate in both sexes. Women had decreased odds of being eligible for hypolipidemic treatment by ASCVD risk estimation(11.5% vs.26.4%, p<0.001) compared to men. The addition of carotid plaque presence or high hsCRP levels and their combination resulted in higher relative increase in hypolipidemic treatment eligibility in women(from 11.5% to 70.9% vs.26.4% to 61.4% for carotid plaque, from 11.5% to 38.5% vs.26.4% to 50.8% for hsCRP and from 11.5% to 79.1% vs.26.4% to 75% for their combination, all for women vs. men, p_interaction<0.001 for all) than men. Implementation of carotid plaque and hsCRP levels increases hypolipidemic treatment eligibility more prominently in women than men. The impact on clinical outcomes in these untreated patients merits further investigation.

Hospital-acquired infections (HAIs) constitute a critical challenge in intensive care units (ICUs). In Greece, data limitations due to incomplete electronic record implementation hinder advanced risk management. This study aims to compare the predictive performance of machine learning algorithms versus traditional techniques for HAI risk stratification in non-digitized ICU settings. We analyzed 1500 anonymized ICU patient records from two general hospitals in Athens, comparing Logistic Regression against LASSO, Random Forest, and Gradient Boosting Machine (GBM), while unsupervised K-means clustering identified latent patient profiles. Invasive devices were found to be the strongest predictors; GBM identified urinary catheter use as the dominant factor (Relative Influence Score: 36.66), and all models showed high performance convergence (AUC ≈ 0.85 to 0.919). K-means clustering (Silhouette width 0.47) revealed co-occurring risk factors, such as smoking and alcohol (r = 0.60). Combining machine learning with classical statistics enhances risk stratification, offering superior granularity in risk ranking to support targeted device stewardship and individualized prevention strategies in ICUs.