Publications by authors named "Alastair R Mobley"
Article Synopsis
- This study investigates how gender affects decision-making for oral anticoagulation in patients with atrial fibrillation (AF), using a large dataset of over 16 million patients from UK primary care between 2005-2020.
- It found that in patients aged 40-75 without prior strokes, women had a lower adjusted rate of primary outcomes (death, ischemic stroke, or thromboembolism) compared to men, primarily due to lower mortality rates in women.
- The study concludes that omitting gender from clinical risk scores could streamline the process of determining which AF patients should receive oral anticoagulation.
The prevention of thromboembolism in atrial fibrillation (AF) is typically restricted to patients with specific risk factors and ignores outcomes such as vascular dementia. This population-based cohort study used electronic healthcare records from 5,199,994 primary care patients (UK; 2005-2020). A total of 290,525 (5.
View Article and Find Full Text PDFIntroduction: The echocardiographic measurement of left ventricular ejection fraction (LVEF) is fundamental to the diagnosis and classification of patients with heart failure (HF).
Methods: This paper aimed to quantify LVEF automatically and accurately with the proposed pipeline method based on deep neural networks and ensemble learning. Within the pipeline, an Atrous Convolutional Neural Network (ACNN) was first trained to segment the left ventricle (LV), before employing the area-length formulation based on the ellipsoid single-plane model to calculate LVEF values.
Aims: Improving the efficiency of clinical trials is key to their continued importance in directing evidence-based patient care. Digital innovations, in particular the use of electronic healthcare records (EHRs), allow for large-scale screening and follow up of participants. However, it is critical these developments are accompanied by robust and transparent methods that can support high-quality and high clinical value research.
View Article and Find Full Text PDFBackground: Mortality remains unacceptably high in patients with heart failure and reduced left ventricular ejection fraction (LVEF) despite advances in therapeutics. We hypothesised that a novel artificial intelligence approach could better assess multiple and higher-dimension interactions of comorbidities, and define clusters of β-blocker efficacy in patients with sinus rhythm and atrial fibrillation.
Methods: Neural network-based variational autoencoders and hierarchical clustering were applied to pooled individual patient data from nine double-blind, randomised, placebo-controlled trials of β blockers.