Background And Purpose: Cognitive decline is one of the major outcomes after stroke. We have developed and evaluated a risk predictive tool of post-stroke cognitive decline and assessed its clinical utility.
Methods: In this population-based cohort, 4,783 patients with first-ever stroke from the South London Stroke Register (1995-2010) were included in developing the model.
Purpose: Understanding risk factors for an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is important for optimizing patient care. We re-analyzed data from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) study (NCT00292552) to identify factors predictive of re-exacerbations and associated with prolonged AECOPDs.
Methods: Patients with COPD from ECLIPSE with moderate/severe AECOPDs were included.
J Stroke Cerebrovasc Dis
October 2020
Background: This study developed and validated a dynamic prediction model for survival after ischaemic stroke up to 1 year.
Methods: Patients with stroke (n = 425) who participated in a sub-study (2002-2004) from the South London Stroke Register (SLSR) were selected for model derivation. The model was developed using the extended Cox model with time-dependent covariates.
Objective: We aim to identify and critically appraise clinical prediction models of mortality and function following ischaemic stroke.
Methods: Electronic databases, reference lists, citations were searched from inception to September 2015. Studies were selected for inclusion, according to pre-specified criteria and critically appraised by independent, blinded reviewers.
Introduction: Stroke is a leading cause of adult disability and death worldwide. The neurological impairments associated with stroke prevent patients from performing basic daily activities and have enormous impact on families and caregivers. Practical and accurate tools to assist in predicting outcome after stroke at patient level can provide significant aid for patient management.
View Article and Find Full Text PDFBackground: Medications targeting stroke risk factors have shown good efficacy, yet adherence is suboptimal. To improve adherence, its determinants must be understood. To date, no systematic review has mapped identified determinants into the Theoretical Domains Framework (TDF) in order to establish a more complete understanding of medication adherence.
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