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Background and purpose: 'Stroke chameleons' refer to a group of syndromes that initially are not diagnosed as cerebrovascular events but are then found to represent stroke. The objective of this study was to report on acute ischaemic stroke (AIS) patients with chest or epigastric pain of central origin, clinically resembling an acute coronary syndrome (ACS). Methods: A prospective list was kept of AIS patients admitted to our institution between 2002 and 2014 with stroke symptoms appearing as an ACS on first clinical evaluation. For each identified patient, clinical and radiological features, delay to correct diagnosis, stroke etiology and 3‐month functional outcome were reviewed. Data were mainly extracted from the Acute Stroke Registry and Analysis of Lausanne (ASTRAL). Results: Five AIS patients presenting mainly with chest or epigastric pain leading to a wrong diagnosis of ACS were identified. Cardiac evaluation showed minor electrocardiogram changes in two patients and isolated troponin elevation in one, subsequently shown to be of non‐coronary origin. The correct diagnosis of AIS was made only between 1 h and 72 h after hospital arrival. Four patients presented a vertebrobasilar stroke. None of the patients received acute stroke revascularization therapy despite two of them being in the time window for such treatment. Conclusion: Acute ischaemic stroke presentation can infrequently resemble an ACS. In cases of negative cardiac work‐up, a central origin of chest pain should be considered, especially in the presence of subtle other neurological symptoms or signs. Appropriate diagnosis of stroke could avoid treatment delays and improve outcomes. [ABSTRACT FROM AUTHOR]

Background and purpose: Hierarchical clustering, a common 'unsupervised' machine‐learning algorithm, is advantageous for exploring potential underlying aetiology in particularly heterogeneous diseases. We investigated potential embolic sources in embolic stroke of undetermined source (ESUS) using a data‐driven machine‐learning method, and explored variation in stroke recurrence between clusters. Methods: We used a hierarchical k‐means clustering algorithm on patients' baseline data, which assigned each individual into a unique clustering group, using a minimum‐variance method to calculate the similarity between ESUS patients based on all baseline features. Potential embolic sources were categorised into atrial cardiopathy, atrial fibrillation, arterial disease, left ventricular disease, cardiac valvulopathy, patent foramen ovale (PFO) and cancer. Results: Among 800 consecutive ESUS patients (43.3% women, median age 67 years), the optimal number of clusters was four. Left ventricular disease was most prevalent in cluster 1 (present in all patients) and perfectly associated with cluster 1. PFO was most prevalent in cluster 2 (38.9% of patients) and associated significantly with increased likelihood of cluster 2 [adjusted odds ratio: 2.69, 95% confidence interval (CI): 1.64–4.41]. Arterial disease was most prevalent in cluster 3 (57.7%) and associated with increased likelihood of cluster 3 (adjusted odds ratio: 2.21, 95% CI: 1.43–3.13). Atrial cardiopathy was most prevalent in cluster 4 (100%) and perfectly associated with cluster 4. Cluster 3 was the largest cluster involving 53.7% of patients. Atrial fibrillation was not significantly associated with any cluster. Conclusions: This data‐driven machine‐learning analysis identified four clusters of ESUS that were strongly associated with arterial disease, atrial cardiopathy, PFO and left ventricular disease, respectively. More than half of the patients were assigned to the cluster associated with arterial disease. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented which discusses bilateral symmetric thalamic infracts in the case of 51-year-old man with three-year history of idiopathic arterial hypertension and underwent brain computed tomography (CT) scan.