Search Results for: Colesterolo e rischio cardiovascolare (CV): percorso diagnostico-terapeutico

Un prelievo scova i cibi a rischio e la gravità della reazione

Al via CVrisk-IT,stanziati 20 milioni dal ministero della Salute

Stroke, Volume 56, Issue Suppl_1, Page ATMP47-ATMP47, February 1, 2025. Background:Life’s Essential 8 (LE8) is a cardiovascular health (CVH) metric proposed by the American Heart Association (AHA) that includes blood glucose, blood pressure, lipid levels, diet, physical activity, nicotine exposure, body mass index, and sleep duration. LE8 is used as a tool to assess and improve CVH outcomes. Little is known about the association between LE8 and mortality after stroke.Methods:We included data from participants aged 20 and older with self-reported stroke who participated in the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. Data was linked with National Death Index mortality data through 2019. The association between poor and ideal LE8 scores and all-cause mortality and CV mortality rates after stroke were determined in 3 models: (1) unadjusted; (2) adjusted for sociodemographic factors (age, sex, race/ethnicity, poverty income ratio (PIR)) and (3) further adjusted for comorbidities (Charlson Comorbidity Index).Results:During the study period, 1,019 out of 26,670 individuals reported a prior stroke. When assessing mortality in those with poor CVH, although the direction of the hazard ratios (HR) suggested an increased risk of CV mortality with higher numbers of poor LS8 metrics, the associations were not significant in any of the models. However, for all-cause mortality, the risk increased with a higher number of poor CVH metrics. Possessing at least 4 poor CVH metrics was associated with a higher risk of all-cause mortality (Model 1 HR 1.69, 95%CI 1.13-2.52; Model 2 HR 2.25, 1.51-3.35; Model 3 HR 2.10, 1.39-3.20). Conversely, possessing at least 4 ideal metrics was associated with lower CV mortality in the unadjusted model, (HR 0.35, 0.15-0.85) but this association was no longer significant in Models 2 and 3. Possessing at least 4 ideal CVH characteristics was associated with lower all-cause mortality in all 3 models (Model 1 HR 0.50, 0.29-0.85; Model 2 HR 0.54, 0.31-0.92; Model 3 HR 0.56, 0.33-0.96). Higher LE8 scores were associated with lower CV mortality in Model 2 (HR per point LE8 score 0.98, 0.96-0.99) and Model 3 (0.98, 0.96-0.99) and all-cause mortality in Model 2 (0.98, 0.97-0.99) and Model 3 (0.98, 0.97-0.99).Conclusion:A higher LE8 score correlates with an increased risk of both CV and all-cause mortality in stroke survivors. Possession of at least 4 ideal CVH metrics is associated with a nearly two-thirds risk reduction in CV mortality and 50% risk reduction in all-cause mortality.

Stroke, Volume 56, Issue Suppl_1, Page A79-A79, February 1, 2025. Introduction:During imaging follow-up of unruptured intracranial aneurysms (IA), changes in cerebrovascular (CV) anatomy are commonly found. While the focus is on the IA site, clinical images capture evidence of vascular remodeling and longitudinal change in the Circle of Willis. This study hypothesizes an association between IA and broader CV changes. This study developed a CV longitudinal atlas (CV shape vs. age) to determine a shared trajectory of CV morphological change in IA patients and identified clinical risk factors associated with IA which affected this trajectory.Methods:A total of 405 longitudinal standard clinical magnetic resonance angiography (MRA) image studies were included in the study. On the average there were 3.68 ± 1.34 MRA studies for each IA patient, with follow-up time ranging from 2.5 to 14 years. The Deformetrica was used to analyze the shape changes in the DICOM images. Specifically, a machine learning diffeomorphic analysis of arterial shape with age was performed. A hybrid geodesic regression/Bayesian atlas 4D longitudinal atlas model that inferred the average trajectory of arterial change from patients with overlapping ages of follow-up was created. Parameters mapping individual IA patients to the average trajectory were then analyzed based on patient characteristics.Results:The general longitudinal atlas trajectory consisted of longer cerebral arteries as patient age increased. The Kruskal-Wallis test associated several factors related to IA patient medical histories with this pattern of change. Specifically, diabetes mellitus (p=0.016) and no previous subarachnoid hemorrhage (p=0.023) were associated with a faster median rate of CV change (1.5-fold and 2.7-fold, respectively). Hypertension (p=0.0004), atherosclerosis (p=0.001), and ICA IA (p=0.013) were associated with CV morphology more advanced along the CV-age trajectory.Conclusion:We developed the first longitudinal atlas model of CV morphological change over time. We used this new technique to investigate the association of IA location and related risk factors with the trajectory of CV change. We identified characteristics that affected the speed and degree of CV change in individual patients. Risk factors like diabetes and hypertension impact a variety of vascular diseases, and our findings indicate IA patients with these factors may have broad CV change and may benefit from review of the Circle of Willis, in addition to the IA site during MRA follow-up.

Stroke, Volume 56, Issue Suppl_1, Page AWMP65-AWMP65, February 1, 2025. Introduction:Embolic stroke of undetermined source (ESUS) may be associated with nonstenotic carotid artery plaques. Noncalcified plaque components such as intraplaque hemorrhage (IPH) as well as perivascular adipose tissue (PVAT) are associated with increased stroke risk, but the role of plaque calcifications is unclear. We examine a machine-learning approach using eXtreme Gradient Boosting (XGBoost) to classify carotid plaques as vulnerable or stable using non-calcified plaque features and calcification morphology.Methods:Patients with neck CT angiography and unilateral anterior circulation ESUS with calcific carotid plaque were retrospectively analyzed. Derived by a combination of manual scoring by a neuroradiologist blinded to stroke side and semi-automated plaque composition segmentation software (Elucid), plaque-level features included plaque thickness, ulceration, composition volumes (IPH, lipid-rich necrotic core, matrix) and PVAT. Calcification-level features, segmented/scored manually with 3D Slicer, included spotty calcification (arc 90% accuracy, superior to models exclusively trained with plaque-level or calcification-level features.

Stroke, Volume 56, Issue Suppl_1, Page AHUP5-AHUP5, February 1, 2025. Background:Life’s Essential 8 (LE8) is a cardiovascular health (CVH) metric proposed by the American Heart Association (AHA) that includes blood glucose, blood pressure, lipid levels, diet, physical activity, nicotine exposure, body mass index, and sleep duration. LE8 is used as a tool to assess and improve CVH outcomes. Little is known about the association between LE8 and mortality after stroke.Methods:We included data from participants aged 20 and older with self-reported stroke who participated in the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. Data was linked with National Death Index mortality data through 2019. The association between poor and ideal LE8 scores and all-cause mortality and CV mortality rates after stroke were determined in 3 models: (1) unadjusted; (2) adjusted for sociodemographic factors (age, sex, race/ethnicity, poverty income ratio (PIR)) and (3) further adjusted for comorbidities (Charlson Comorbidity Index).Results:During the study period, 1,019 out of 26,670 individuals reported a prior stroke. When assessing mortality in those with poor CVH, although the direction of the hazard ratios (HR) suggested an increased risk of CV mortality with higher numbers of poor LS8 metrics, the associations were not significant in any of the models. However, for all-cause mortality, the risk increased with a higher number of poor CVH metrics. Possessing at least 4 poor CVH metrics was associated with a higher risk of all-cause mortality (Model 1 HR 1.69, 95%CI 1.13-2.52; Model 2 HR 2.25, 1.51-3.35; Model 3 HR 2.10, 1.39-3.20). Conversely, possessing at least 4 ideal metrics was associated with lower CV mortality in the unadjusted model, (HR 0.35, 0.15-0.85) but this association was no longer significant in Models 2 and 3. Possessing at least 4 ideal CVH characteristics was associated with lower all-cause mortality in all 3 models (Model 1 HR 0.50, 0.29-0.85; Model 2 HR 0.54, 0.31-0.92; Model 3 HR 0.56, 0.33-0.96). Higher LE8 scores were associated with lower CV mortality in Model 2 (HR per point LE8 score 0.98, 0.96-0.99) and Model 3 (0.98, 0.96-0.99) and all-cause mortality in Model 2 (0.98, 0.97-0.99) and Model 3 (0.98, 0.97-0.99).Conclusion:A higher LE8 score correlates with an increased risk of both CV and all-cause mortality in stroke survivors. Possession of at least 4 ideal CVH metrics is associated with a nearly two-thirds risk reduction in CV mortality and 50% risk reduction in all-cause mortality.

L’inizio tardivo della fase Rem è associato a un maggior rischio di ammalarsi

Rispettata la scadenza del Pnrr. Mancano gli infermieri

Esperti, ‘da depressione ad Alzheimer si studiano nuovi usi”

Introduction
Propofol is a widely used sedative-hypnotic agent for critically ill patients requiring invasive mechanical ventilation (IMV). Despite its clinical benefits, propofol is associated with increased risks of hypertriglyceridemia. Early identification of patients at risk for propofol-associated hypertriglyceridemia is crucial for optimising sedation strategies and preventing adverse outcomes. Machine-learning (ML) models offer a promising approach for predicting individualised patient risks of propofol-associated hypertriglyceridemia.

Methods and analysis
We propose the development of an ML model aimed at predicting the risk of propofol-associated hypertriglyceridemia in ICU patients receiving IMV. The study will use retrospective data from four Mayo Clinic sites. Nested cross validation (CV) will be employed, with a tenfold inner CV loop for model tuning and selection as well as an outer loop using leave-one-site-out CV for external validation. Feature selection will be conducted using Boruta and least absolute shrinkage and selection operator-penalised logistic regression. Data preprocessing steps include missing data imputation, feature scaling and dimensionality reduction techniques. Six ML algorithms will be tuned and evaluated. Bayesian optimisation will be used for hyperparameter selection. Global model explainability will be assessed using permutation importance, and local model explainability will be assessed using SHapley Additive exPlanations.

Ethics and dissemination
The proposed ML model aims to provide a reliable and interpretable tool for clinicians to predict the risk of propofol-associated hypertriglyceridemia in ICU patients. The final model will be deployed in a web-based clinical risk calculator. The model development process and performance measures obtained during nested CV will be described in a study publication to be disseminated in a peer-reviewed journal. The proposed study has received ethics approval from the Mayo Clinic Institutional Review Board (IRB #23–0 07 416).

Anche alcuni vaccini, tanti composti potrebbero aiutare le cura

Sip, “terapie per milioni di italiani, c’è il rischio di discriminazione”

Nasem, rischio di mortalità in uomini -16% e in donne -23%