Machine learning for outcome prediction in patients with non-valvular atrial fibrillation from the GLORIA-AF registry.

Clinical risk scores that predict outcomes in patients with atrial fibrillation (AF) have modest predictive value. Machine learning (ML) may achieve greater results when predicting adverse outcomes in patients with recently diagnosed AF. Several ML models were tested and compared with current clinical risk scores on a cohort of 26,183 patients (mean age 70.

Initial blood pressure and adverse cardiac events following acute ischaemic stroke: An individual patient data pooled analysis from the VISTA database.

Background: Adverse cardiac events following ischaemic stroke (stroke-heart syndrome, SHS) pose a clinical challenge. We investigated the association between initial blood pressure at stroke presentation and the risk of SHS.

Methods: We utilised data from the Virtual International Stroke Trials Archive (VISTA).

Stroke-heart syndrome and early mortality in patients with acute ischaemic stroke using hierarchical cluster analysis: An individual patient data pooled analysis from the VISTA database.

Background: The patient clinical phenotypes at particularly high risk for early cardiac complications after a recent acute ischaemic stroke (AIS), that is, stroke-heart syndrome (SHS), remain poorly defined. We utilised hierarchical cluster analysis to identify specific phenotypic profiles associated with this risk.

Methods: We gathered data on patients with AIS from the Virtual International Stroke Trials Archive, a global repository of clinical trial data.

Systematic Review and Meta-Analysis of Prehospital Machine Learning Scores as Screening Tools for Early Detection of Large Vessel Occlusion in Patients With Suspected Stroke.

Background: Enhanced detection of large vessel occlusion (LVO) through machine learning (ML) for acute ischemic stroke appears promising. This systematic review explored the capabilities of ML models compared with prehospital stroke scales for LVO prediction.

Methods And Results: Six bibliographic databases were searched from inception until October 10, 2023.

Prediction of early death after atrial fibrillation diagnosis using a machine learning approach: A French nationwide cohort study.

Aims: Atrial fibrillation is associated with important mortality but the usual clinical risk factor based scores only modestly predict mortality. This study aimed to develop machine learning models for the prediction of death occurrence within the year following atrial fibrillation diagnosis and compare predictive ability against usual clinical risk scores.

Methods And Results: We used a nationwide cohort of 2,435,541 newly diagnosed atrial fibrillation patients seen in French hospitals from 2011 to 2019.

Predicting acute and long-term mortality in a cohort of pulmonary embolism patients using machine learning.

Background: Pulmonary embolism (PE) is a severe condition that causes significant mortality and morbidity. Due to its acute nature, scores have been developed to stratify patients at high risk of 30-day mortality. Here we develop a machine-learning based score to predict 30-day, 90-day, and 365-day mortality in PE patients.

Phenotypes and outcomes in non-anticoagulated patients with atrial fibrillation: An unsupervised cluster analysis.

Background: Patients with atrial fibrillation are characterized by great clinical heterogeneity and complexity. The usual classifications may not adequately characterize this population. Data-driven cluster analysis reveals different possible patient classifications.

Clinical Phenotypes and Atrial Fibrillation Recurrences After Catheter Ablation: An Unsupervised Cluster Analysis.

Catheter ablation (CA) is a well-established treatment of atrial fibrillation (AF). Data-driven cluster analysis is able to better distinguish prognostically-relevant phenotype clusters among patients with AF. We performed a hierarchical cluster analysis in a cohort of AF patients undergoing a first CA and evaluate associations between identified clusters and recurrences of arrhythmia following ablation.

Prediction of incident atrial fibrillation in post-stroke patients using machine learning: a French nationwide study.

Background: Targeting ischemic strokes patients at risk of incident atrial fibrillation (AF) for prolonged cardiac monitoring and oral anticoagulation remains a challenge. Clinical risk scores have been developed to predict post-stroke AF with suboptimal performances. Machine learning (ML) models are developing in the field of AF prediction and may be used to discriminate post-stroke patients at risk of new onset AF.

Pulsatile tympanic membrane displacement is associated with cognitive score in healthy subjects.

To test the hypothesis that pulsing of intracranial pressure has an association with cognition, we measured cognitive score and pulsing of the tympanic membrane in 290 healthy subjects. This hypothesis was formed on the assumptions that large intracranial pressure pulses impair cognitive performance and tympanic membrane pulses reflect intracranial pressure pulses. 290 healthy subjects, aged 20-80 years, completed the Montreal Cognitive Assessment Test.

Cardiovascular Health and Atrial Fibrillation or Flutter: A Cross-Sectional Study from ELSA-Brasil.

Background: The association between ideal cardiovascular health (ICVH) status and atrial fibrillation or flutter (AFF) diagnosis has been less studied compared to other cardiovascular diseases.

Objective: To analyze the association between AFF diagnosis and ICVH metrics and scores in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

Methods: This study analyzed data from 13,141 participants with complete data.

Regional Cerebral Blood Flow Changes in Healthy Ageing and Alzheimer's Disease: A Narrative Review.

Background: Cerebral blood flow is known to decline with increasing age and is a potential biomarker to distinguish between healthy and unhealthy ageing, where healthy ageing is defined as an absence of comorbidities in senescence. This review aims to synthesize evidence of cerebral blood flow changes over multiple brain regions, for use as a clinical reference or for in silico modelling.

Summary: The search identified 1,087 studies, of which 33 met the inclusion criteria to map the difference in cerebral blood flow reduction between healthy ageing and Alzheimer's disease.

Mathematical modelling of haemorrhagic transformation within a multiscale microvasculature network.

Haemorrhagic transformation (HT) is one of the most common complications after ischaemic stroke, caused by damage to the blood-brain barrier (BBB) that could be the result of stroke progression or a complication of stroke treatment with reperfusion therapy. The aim of this study is to develop further a previous simple HT mathematical model into an enlarged multiscale microvasculature model in order to investigate the effects of HT on the surrounding tissue and vasculature. In addition, this study investigates the relationship between tissue displacement and vascular geometry.

The Birmingham and Black Country cohort of Venous Thromboembolism (BBC-VTE) registry: Rationale, design and preliminary results.

The Birmingham Black Country Venous Thromboembolism registry (BBC-VTE) is a multi-ethnic cohort of patients who suffered a first episode of venous thromboembolism (VTE) and were admitted to various hospital sites across the West Midlands and Black Country regions in the United Kingdom. The BBC-VTE registry is a retrospective, observational cohort study which aims to collect data on outcomes including mortality, bleeding and VTE recurrence in this patient cohort. In addition, the comprehensive, structured data collected will allow us to conduct machine learning analyses for risk prediction in such patients and also to compare to previously derived mortality scores such as the PESI and the simplified PESI (sPESI).

Modelling the effects of cerebral microthrombi on tissue oxygenation and cell death.

Thrombectomy, the mechanical removal of a clot, is the most common way to treat ischaemic stroke with large vessel occlusions. However, perfusion cannot always be restored after such an intervention. It has been hypothesised that the absence of reperfusion is at least partially due to the clot fragments that block the downstream vessels.

The Ageing Brain: Investigating the Role of Age in Changes to the Human Cerebral Microvasculature With an Model.

Ageing causes extensive structural changes to the human cerebral microvasculature, which have a significant effect on capillary bed perfusion and oxygen transport. Current models of brain capillary networks in the literature focus on healthy adult brains and do not capture the effects of ageing, which is critical when studying neurodegenerative diseases. This study builds upon a statistically accurate model of the human cerebral microvasculature based on morphological data.

Modelling the impact of clot fragmentation on the microcirculation after thrombectomy.

Many ischaemic stroke patients who have a mechanical removal of their clot (thrombectomy) do not get reperfusion of tissue despite the thrombus being removed. One hypothesis for this 'no-reperfusion' phenomenon is micro-emboli fragmenting off the large clot during thrombectomy and occluding smaller blood vessels downstream of the clot location. This is impossible to observe in-vivo and so we here develop an in-silico model based on in-vitro experiments to model the effect of micro-emboli on brain tissue.

Coupling one-dimensional arterial blood flow to three-dimensional tissue perfusion models for trials of acute ischaemic stroke.

An acute ischaemic stroke is due to the sudden blockage of an intracranial blood vessel by an embolized thrombus. In the context of setting up trials for the treatment of acute ischaemic stroke, the effect of a stroke on perfusion and metabolism of brain tissue should be modelled to predict final infarcted brain tissue. This requires coupling of blood flow and tissue perfusion models.

Dynamic Changes in Microvascular Flow Conductivity and Perfusion After Myocardial Infarction Shown by Image-Based Modeling.

Background Microcirculation is a decisive factor in tissue reperfusion inadequacy following myocardial infarction ( MI ). Nonetheless, experimental assessment of blood flow in microcirculation remains a bottleneck. We sought to model blood flow properties in coronary microcirculation at different time points after MI and to compare them with healthy conditions to obtain insights into alterations in cardiac tissue perfusion.