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Introduction: Secondary stroke prevention depends on proper identification of the underlying etiology and initiation of optimal treatment after the index event. The aim of the NOR-FIB study was to detect and quantify underlying atrial fibrillation (AF) in patients with cryptogenic stroke (CS) or transient ischaemic attack (TIA) using insertable cardiac monitor (ICM), to optimise secondary prevention, and to test the feasibility of ICM usage for stroke physicians. Patients and methods: Prospective observational international multicenter real-life study of CS and TIA patients monitored for 12 months with ICM (Reveal LINQ) for AF detection. Results: ICM insertion was performed in 91.5% by stroke physicians, within median 9 days after index event. Paroxysmal AF was diagnosed in 74 out of 259 patients (28.6%), detected early after ICM insertion (mean 48 ± 52 days) in 86.5% of patients. AF patients were older (72.6 vs 62.2; p Discussion and conclusions: ICM was an effective tool for diagnosing underlying AF, capturing AF in 29% of the CS and TIA patients. AF was asymptomatic in most cases and would mainly have gone undiagnosed without ICM. The insertion and use of ICM was feasible for stroke physicians in stroke units.

Background Cryptogenic stroke is a heterogeneous condition, with a wide spectrum of possible underlying causes for which the optimal secondary prevention may differ substantially. Attempting a correct etiological diagnosis to reduce the stroke recurrence should be the fundamental goal of modern stroke management. Methods Prospective observational international multicenter study of cryptogenic stroke and cryptogenic transient ischemic attack (TIA) patients clinically monitored for 12 months to assign the underlying etiology. For atrial fibrillation (AF) detection continuous cardiac rhythm monitoring with insertable cardiac monitor (Reveal LINQ, Medtronic) was performed. The 12-month follow-up data for 250 of 259 initially included NOR-FIB patients were available for analysis. Results After 12 months follow-up probable stroke causes were revealed in 43% patients, while 57% still remained cryptogenic. AF and atrial flutter was most prevalent (29%). In 14% patients other possible causes were revealed (small vessel disease, large-artery atherosclerosis, hypercoagulable states, other cardioembolism). Patients remaining cryptogenic were younger (p 2DS2-VASc score (p p = 0.031) and mRS (p = 0.016) at discharge. Smoking was more prevalent in patients that were still cryptogenic (p = 0.014), while dyslipidaemia was less prevalent (p = 0.044). Stroke recurrence rate was higher in the cryptogenic group compared to the group where the etiology was revealed, 7.7% vs. 2.8%, (p = 0.091). Conclusion Cryptogenic stroke often indicates the inability to identify the cause in the acute phase and should be considered as a working diagnosis until efforts of diagnostic work up succeed in identifying a specific underlying etiology. Timeframe of 6-12-month follow-up may be considered as optimal. Trial registration ClinicalTrials.gov Identifier NCT02937077, EudraCT 2018-002298-23.

Stroke patients commonly suffer from post stroke fatigue (PSF). Despite a general consensus that brain perturbations constitute a precipitating event in the multifactorial etiology of PSF, the specific predictive value of conventional lesion characteristics such as size and localization remain unclear. The current study represents a novel approach to assess the neural correlates of PSF in chronic stroke patients. While previous research has focused primarily on lesion location or size, with mixed or inconclusive results, we targeted the extended structural network implicated by the lesion, and evaluated the added explanatory value of a disconnectivity approach with regards to the brain correlates of PSF. To this end, we estimated individual brain disconnectome maps in 84 stroke survivors in the chronic phase (≥ 3 months post stroke) using information about lesion location and normative white matter pathways obtained from 170 healthy individuals. PSF was measured by the Fatigue Severity Scale (FSS). Voxel wise analyses using non-parametric permutation-based inference were conducted on disconnectome maps to estimate regional effects of disconnectivity. Associations between PSF and global disconnectivity and clinical lesion characteristics were tested by linear models, and we estimated Bayes factor to quantify the evidence for the null and alternative hypotheses, respectively. The results revealed no significant associations between PSF and disconnectome measures or lesion characteristics, with moderate evidence in favor of the null hypothesis. These results suggest that symptoms of post-stroke fatigue are not simply explained by lesion characteristics or brain disconnectome measures in stroke patients in a chronic phase, and are discussed in light of methodological considerations.