Rates, risks and routes to reduce vascular dementia (R4VaD), a UK-wide multicentre prospective observational cohort study of cognition after stroke: baseline data and statistical analysis plan (ISRCTN18274006)
Bath, P. M.; Backhouse, E. V.; Brown, R.; Woodhouse, L. J.; Doubal, F.; Quinn, T. J. V.; Markus, H. S.; McManus, R.; O'Brien, J. T.; Robinson, T.; Werring, D. J.; Sprigg, N.; Parry-Jones, A.; Touyz, R. M.; Williams, S.; Mah, Y.-H.; Emsley, H.; Wardlaw, J. M.; R4VAD Investigators,
Show abstract
BackgroundStroke is often followed by vascular cognitive impairment and vascular dementia; these are the most feared complication of stroke. However, there is limited understanding of post-stroke cognitive impairment. MethodsRates, Risks and Routes to Reduce Vascular Dementia (R4VaD) is an observational cohort study of post-stroke cognition. Patients with haemorrhagic or ischaemic stroke, or transient ischaemic attack, were recruited within six weeks of stroke from hospitals across the UK. Consent was obtained from patients with capacity or from relatives/friends in those without capacity. The primary outcome is cognition and its severity assessed using a 7-level ordinal outcome. Final cognition will be compared in those with mild stroke/TIA (worst NIHSS <=7) versus severe stroke (NIHSS >7). Secondary outcomes will include function, mood and quality of life. ResultsWe recruited 2441 patients from 50 hospitals. Of these, 2437 (99.8%) had a qualifying event of stroke or TIA. The mean age was 68.2 years (standard deviation 13.5), females 981 (40.3%), onset to recruitment 6 days [interquartile range 3-13] and diagnosis ICH 193 (7.9%), ischaemic stroke 2101 (86.2%), TIA 143 (5.9%). The distribution of cognition at baseline was: normal 1256 (51.6%), minor neurocognitive disorder-single domain 530 (21.8%), minor neurocognitive disorder-multi domain 320 (13.1%), major neurocognitive disorder-mild 237 (9.7%), major neurocognitive disorder-moderate 90 (3.7%) and major neurocognitive disorder-severe 3 (0.1%). We provide the statistical analysis plan in the appendix. ConclusionWe provide baseline data and the SAP. Final follow-up will be completed in quarter 2 2024. The data highlight the substantial under-appreciated cognitive burden of stroke, even in the first few days and weeks.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Effect of Time to Thrombolysis on Clinical Outcomes in Patients with Acute Ischemic Stroke Treated with Tenecteplase Compared to Alteplase: Analysis from the AcT Randomized Controlled Trial 95%
- Longitudinal trajectories of global and domain-specific cognition after stroke using the Oxford Cognitive Screen 94%
- Risk, clinical course and outcome of ischemic stroke in patients hospitalized with COVID-19: a multicenter cohort study 94%
Similar papers in this journal
- Circulating interleukin-6 levels and incident ischemic stroke: a systematic review and meta-analysis of population-based cohort studies 93%
- Adaptive trials in stroke: Current use & future directions 93%
- Lesions in putative language and attention regions are linked to more severe strokes in patients with higher white matter hyperintensity burden 92%
Similar papers in this journal
- Tenecteplase 0.4 mg/kg in moderate and severe acute ischemic stroke: A pooled analysis of NOR-TEST & NOR-TEST 2A 95%
- Influence of socioeconomic status on functional outcomes after stroke: a systematic review and meta-analysis 94%
- COVID-19 Infection Is Associated with Poor Outcomes in Patients with Intracerebral Hemorrhage 94%
Similar papers in this journal
- NOTCH3 variants are common in the general population and associated with stroke and vascular dementia: an analysis of 200,000 participants 95%
- Effect of Tranexamic acid for acute spontaneous intracerebral haemorrhage: A systematic review and individual patient data meta-analysis 92%
- The effect of GLP-1RA exenatide on Idiopathic Intracranial Hypertension: Randomised Clinical Trial 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.