Stroke aetiological classification reliability and effect on trial sample size: systematic review, meta-analysis and statistical modelling

Background: Inter-observer variability in stroke aetiological classification may have an effect on trial power and estimation of treatment effect. We modelled the effect of misclassification on required sample size in a hypothetical cardioembolic (CE) stroke trial. Methods: We performed a systematic review to quantify the reliability (inter-observer variability) of various stroke aetiological classification systems. We then modelled the effect of this misclassification in a hypothetical trial of anticoagulant in CE stroke contaminated by patients with non-cardioembolic (non-CE) stroke aetiology. Rates of misclassification were based on the summary reliability estimates from our systematic review. We randomly sampled data from previous acute trials in CE and non-CE participants, using the Virtual International Stroke Trials Archive. We used bootstrapping to model the effect of varying misclassification rates on sample size required to detect a between-group treatment effect across 5000 permutations. We described outcomes in terms of survival and stroke recurrence censored at 90 days. Results: From 4655 titles, we found 14 articles describing three stroke classification systems. The inter-observer reliability of the classification systems varied from ‘fair’ to ‘very good’ and suggested misclassification rates of 5% and 20% for our modelling. The hypothetical trial, with 80% power and alpha 0.05, was able to show a difference in survival between anticoagulant and antiplatelet in CE with a sample size of 198 in both trial arms. Contamination of both arms with 5% misclassified participants inflated the required sample size to 237 and with 20% misclassification inflated the required sample size to 352, for equivalent trial power. For an outcome of stroke recurrence using the same data, base-case estimated sample size for 80% power and alpha 0.05 was n = 502 in each arm, increasing to 605 at 5% contamination and 973 at 20% contamination. Conclusions: Stroke aetiological classification systems suffer from inter-observer variability, and the resulting misclassification may limit trial power. Trial registration: Protocol available at reviewregistry540

Histiocytosis for the neurologist: a case of Erdheim-Chester disease

The histiocytoses are a rare but diverse group of disorders, ranging from localised, self-limiting lesions to disseminated, fulminant, multi-system disease. Some histiocytoses may cause or present with neurological disease and their recognition can be challenging. We illustrate this with a case, followed by a discussion of the clinical characteristics and management of the more common histiocytoses that may present to the neurologist

Classification of minor stroke: Intra- and inter-observer reliability

Background: The Oxfordshire Community Stroke Project (OCSP) and Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classifications are widely used for the assessment of major ischaemic stroke. We explored their intra- and inter-observer reliability in the classification of outpatient minor stroke. Methods: Four physicians of differing seniority and training backgrounds classified minor stroke using clinical data from 90 patients. Results: For both the OCSP and TOAST classifications, the intra-observer reliability varied from moderate to excellent (κ = 0.48–0.83). The inter-observer reliability was good (κ = 0.64) for the OCSP and moderate (κ = 0.42) for the TOAST. Thus, neither classification was consistently reliable. Conclusions: Our results may reflect the limited validity of these classifications in a typical minor stroke outpatient population and variable observer expertise

Brain inflammation is induced by co-morbidities and risk factors for stroke

► Risk factors for stroke include atherosclerosis, obesity, diabetes and hypertension. ► Stroke risk factors are associated with peripheral inflammation. ► Corpulent rats and atherogenic mice show increased inflammation in the brain. ► Pilot data show that patients at risk of stroke may also develop brain inflammation. ► Chronic peripheral inflammation can drive inflammatory changes in the brain