Inconsistent centralised versus non-centralised ischaemic stroke aetiology

Abstract

Background and purposeThe Trial of Org 10 172 in Acute Stroke Treatment (TOAST) system is the most widely used aetiological categorisation system in clinical practice and research. Limited studies have validated the accuracy of routine aetiological diagnosis of patients with ischaemic stroke according to the TOAST criteria when the reported subtype is assumed to be correct. We investigated the agreement between centralised and non-centralised (site-reported, at discharge) stroke subtypes in the Third China National Stroke Registry (CNSR-III), and analysed the influence of classification consistency on evaluation during hospitalisation and for secondary prevention strategy.MethodsAll patients with ischaemic stroke from the CNSR-III study with complete diffusion-weighted imaging data were included. We used multivariable Cox proportional-hazard regression models to evaluate the factors associated with consistency between centralised and non-centralised stroke subtypes. Sensitivity analyses were conducted of the subgroup of patients with complete information.ResultsThis study included 12 180 patients (mean age, 62.3 years; and women, 31.4%). Agreement between centralised and non-centralised subtype was the highest for the large-artery atherosclerosis subtype stroke (77.4% of centralised patients), followed by the small-vessel occlusion subtype (40.6% of centralised patients). Agreements for cardioembolism and stroke of other determined aetiology subtypes were 38.7% and 12.2%, respectively. Patient-level and hospital-level factors were associated with the inconsistency between centralised/non-centralised aetiological subtyping. This inconsistency was related to differences in secondary prevention strategies. Only 15.3% of the newly diagnosed patients with cardioembolism underwent centralised subtyping with indications to receive oral anticoagulants at discharge. In comparison, 51.3% of the consistent cardioembolism group and 42.0% of the centrally reassigned cardioembolism group with anticoagulation indications were prescribed oral anticoagulants.ConclusionsSubstantial inconsistency exists between centralised and non-centralised subtyping in China. Inaccurate aetiological subtyping could lead to inadequate secondary prevention, especially in patients with cardioembolic stroke.

Distribution and prognosis of acute ischaemic stroke with negative diffusion-weighted imaging

“… 21 Neurologists and radiologists evaluated each centralised stroke subtype based on a previously established standardised element. 22 Two experts interpreted each case and recorded it on a standardised report form in the EDC system. They remained blind to one another’s input data during interpretation.…”

Interleukin-6 and YKL-40 predicted recurrent stroke after ischemic stroke or TIA: analysis of 6 inflammation biomarkers in a prospective cohort study

“…Trained research coordinators at each site collected information of age, prestroke modified Rankin Scale (mRS) score and National Institutes of Health Stroke Scale (NIHSS) score through a direct interview at admission, and baseline data from medical records, including sex, body mass index (BMI, the weight in kilograms divided by the square of the height in meters), cigarette smoking, medical history of hypertension, diabetes, hypercholesterolemia, ischemic stroke and coronary heart disease, and laboratory test of leukocyte counts. The TOAST (Trial of ORG 10,172 in Acute Stroke Treatment) criteria were used for centralized etiology classification of ischemic stroke [ 19 , 20 ].…”

Data-driven clustering approach to identify novel phenotypes using multiple biomarkers in acute ischaemic stroke: A retrospective, multicentre cohort study

“…14 The North American Symptomatic Carotid Endarterectomy Trial (NASCET) criteria was adopted to adjust the assessment of sECAS. 15,16 The brain tissue damage caused by the acute ischaemic stroke, measured as the volume of the ischaemic lesions, was calculated from the diffusion-weighted image (DWI) and apparent diffusion coefficient (ADC) scans using a deep learning segmentation model. 17…”