Left Atrial Myopathy and Atrial Arrhythmia Recurrence After Pulmonary Vein Isolation in Patients With Minimal Left Atrial Low Voltage Areas.

Background: Atrial low voltage areas (LVAs) provide the substrate for atrial fibrillation (AF). In AF patients with minimal left atrial (LA) LVAs, this substrate has not been well characterised. We determined whether LA myopathy is present in AF patients with minimal LA LVAs (mLVA) by evaluating LA mechanical function and blood biomarkers of structural remodelling.

Therapeutic-dose heparin combined with antiplatelets in noncritically ill patients with COVID-19: a secondary analysis of a multiplatform randomized controlled trial.

Background: Therapeutic-dose heparin improves outcomes in noncritically ill patients hospitalized for COVID-19. The effect of antiplatelet exposure in addition to therapeutic-dose heparin is unknown.

Objectives: To evaluate the effect of antiplatelet exposure in addition to therapeutic-dose heparin on survival without organ support.

Tocilizumab, sarilumab and anakinra in critically ill patients with COVID-19: a randomised, controlled, open-label, adaptive platform trial.

Introduction: Tocilizumab improves outcomes in critically ill patients with COVID-19. Whether other immune-modulator strategies are equally effective or better is unknown.

Methods: We investigated treatment with tocilizumab, sarilumab, anakinra and no immune modulator in these patients.

The Economic, Political, and Societal Consequences of Disrupting Health Care Delivery with Artificial Intelligence.

Artificial intelligence (AI) is poised to become a significant disruptive force in healthcare delivery, setting new standards by automating routine tasks and introducing AI-informed care models that could redefine the roles of physicians. However, this transformation presents significant challenges, including potential overdiagnosis, increased costs to consumers, environmental impacts, and distributional consequences as market power is transferred from current entities (e.g.

Evidence-based personalised medicine in critical care: a framework for quantifying and applying individualised treatment effects in patients who are critically ill.

Clinicians aim to provide treatments that will result in the best outcome for each patient. Ideally, treatment decisions are based on evidence from randomised clinical trials. Randomised trials conventionally report an aggregated difference in outcomes between patients in each group, known as an average treatment effect.

Impact of Selective Peroxisome Proliferator-Activated Receptor (PPAR)-α Modulators and Fibrates on Microvascular Disease: Is There Still Room?

Purpose Of Review: This review examines the role of fibrates and the selective PPAR-alpha modulators (SPPARM-α), pemafibrate, in diabetic microvascular disease. It reviews their potential to mitigate residual risk in retinopathy, nephropathy, neuropathy and peripheral vascular disease.

Recent Findings: These pharmacotherapies, beyond their lipid-lowering effects, may exert anti-inflammatory, antioxidant, and endothelial-protective actions.

Epidemiology and Prognostic Significance of Acute Noncardiac Organ Dysfunction Across Cardiogenic Shock Subtypes.

Background: The epidemiology and prognostic significance of acute noncardiac organ dysfunction across cardiogenic shock (CS) subtypes are not well-defined.

Methods: CS admissions from 2017 to 2022 in the Critical Care Cardiology Trials Network Registry were classified as acute myocardial infarction-related CS (AMI-CS), acute-on-chronic heart failure-related CS (AoC HF-CS), or de novo HF-CS, and categorized as having at least moderate respiratory, kidney, liver, and/or neurological dysfunction using established criteria. Burden of organ dysfunction was defined as no noncardiac organ dysfunction (NOD), single organ dysfunction, or multiorgan dysfunction (≥2) (MOD).

Heart failure decompensation with cardiogenic shock exhibits distinct sequential inflammatory profiles.

Aims: The inflammatory profile of cardiogenic shock (CS) after myocardial infarction affects outcomes; however, little is known about the impact of inflammatory changes in CS caused by acute decompensated heart failure (ADHF-CS). We measured levels of inflammatory cytokines in patients with ADHF-CS admitted to a cardiac intensive care unit (CICU).

Methods: We identified patients admitted to our CICU with ADHF-CS who had consented to having biospecimens stored.

Identifying biomarker-driven subphenotypes of cardiogenic shock: analysis of prospective cohorts and randomized controlled trials.

Background: Cardiogenic shock (CS) is a heterogeneous clinical syndrome, making it challenging to predict patient trajectory and response to treatment. This study aims to identify biological/molecular CS subphenotypes, evaluate their association with outcome, and explore their impact on heterogeneity of treatment effect (ShockCO-OP, NCT06376318).

Methods: We used unsupervised clustering to integrate plasma biomarker data from two prospective cohorts of CS patients: CardShock (N = 205 [2010-2012, NCT01374867]) and the French and European Outcome reGistry in Intensive Care Units (FROG-ICU) (N = 228 [2011-2013, NCT01367093]) to determine the optimal number of classes.

Diverse NKT cells regulate early inflammation and neurological outcomes after cardiac arrest and resuscitation.

Neurological injury drives most deaths and morbidity among patients hospitalized for out-of-hospital cardiac arrest (OHCA). Despite its clinical importance, there are no effective pharmacological therapies targeting post-cardiac arrest (CA) neurological injury. Here, we analyzed circulating immune cells from a large cohort of patients with OHCA, finding that lymphopenia independently associated with poor neurological outcomes.

Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure.

Aims: Hospitalized patients with heart failure (HF) are a heterogeneous population, with multiple phenotypes proposed. Prior studies have not examined the biological phenotypes of critically ill patients with HF admitted to the contemporary cardiac intensive care unit (CICU). We aimed to leverage unsupervised machine learning to identify previously unknown HF phenotypes in a large and diverse cohort of patients with HF admitted to the CICU.