Prediction of underlying atrial fibrillation in patients with a cryptogenic stroke: results from the NOR-FIB Study.

BACKGROUND: Atrial fibrillation (AF) detection and treatment are key elements to reduce recurrence risk in cryptogenic stroke (CS) with underlying arrhythmia. The purpose of the present study was to assess the predictors of AF in CS and the utility of existing AF-predicting scores in The Nordic Atrial Fibrillation and Stroke (NOR-FIB) Study. METHOD: The NOR-FIB study was an international prospective observational multicenter study designed to detect and quantify AF in CS and cryptogenic transient ischaemic attack (TIA) patients monitored by the insertable cardiac monitor (ICM), and to identify AF-predicting biomarkers. The utility of the following AF-predicting scores was tested: AS5F, Brown ESUS-AF, CHA2DS2-VASc, CHASE-LESS, HATCH, HAVOC, STAF and SURF. RESULTS: In univariate analyses increasing age, hypertension, left ventricle hypertrophy, dyslipidaemia, antiarrhythmic drugs usage, valvular heart disease, and neuroimaging findings of stroke due to intracranial vessel occlusions and previous ischemic lesions were associated with a higher likelihood of detected AF. In multivariate analysis, age was the only independent predictor of AF. All the AF-predicting scores showed significantly higher score levels for AF than non-AF patients. The STAF and the SURF scores provided the highest sensitivity and negative predictive values, while the AS5F and SURF reached an area under the receiver operating curve (AUC) > 0.7. CONCLUSION: Clinical risk scores may guide a personalized evaluation approach in CS patients. Increasing awareness of the usage of available AF-predicting scores may optimize the arrhythmia detection pathway in stroke units.

Atrial fibrillation in cryptogenic stroke and TIA patients in The Nordic Atrial Fibrillation and Stroke (NOR-FIB) Study: Main results.

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 < 0.001), had higher pre-stroke CHA2DS2-VASc score (median 3 vs 2; p < 0.001) and admission NIHSS (median 2 vs 1; p = 0.001); and more often hypertension (p = 0.045) and dyslipidaemia (p = 0.005) than non-AF patients. The arrhythmia was recurrent in 91.9% and asymptomatic in 93.2%. At 12-month follow-up anticoagulants usage was 97.3%. 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.

Underlying causes of cryptogenic stroke and TIA in the nordic atrial fibrillation and stroke (NOR-FIB) study - the importance of comprehensive clinical evaluation.

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 < 0.001), had lower CHA2DS2-VASc score (p < 0.001) on admission, and lower NIHSS score (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.

Measuring the contribution of randomness, exposure, weather, and daylight to the variation in road accident counts.

Road accident counts are influenced by random variation as well as by various systematic, causal factors. To study these issues, a four-country, segmented data base has been compiled, each segment consisting of monthly accident counts, along with candidate explanatory factors, in the various counties (provinces) of Denmark, Finland, Norway, or Sweden. Using a generalized Poisson regression model, we are able to decompose the variation in accident counts into parts attributable to randomness, exposure, weather, daylight, or changing reporting routines and speed limits. To this purpose, a set of specialized goodness-of-fit measures have been developed, taking explicit account of the inevitable amount of random variation that would be present in any set of accident counts, no matter how well known the accident generating Poisson process. Pure randomness is seen to "explain" a major part of the variation in smaller accident counts (e.g. fatal accidents per county per month), while exposure is the dominant systematic determinant. The relationship between exposure and injury accidents appears to be almost proportional, while it is less than proportional in the case of fatal accidents or death victims. Together, randomness and exposure account for 80% to 90% of the observable variation in our data sets. A surprisingly large share of the variation in road casualty counts is thus explicable in terms of factors not ordinarily within the realm of traffic safety policy. In view of this observation, it may seem unlikely that very substantial reductions in the accident toll can be achieved without a decrease in the one most important systematic determinant: the traffic volume.

An aggregate accident model based on pooled, regional time-series data.

The determinants of personal injury road accidents and their severity are studied by means of generalized Poisson regression models estimated on the basis of combined cross-section/time-series data. Monthly data have been assembled for 18 Norwegian counties (every county but one), covering the period from January 1974 until December 1986. A rather wide range of potential explanatory factors are taken into account, including road use (exposure), weather, daylight, traffic density, road investment and maintenance expenditure, accident reporting routines, vehicle inspection, law enforcement, seat belt usage, proportion of inexperienced drivers, and alcohol sales. Separate probability models are estimated for the number of personal injury accidents, fatal accidents, injury victims, death victims, car occupants injured, and bicyclists and pedestrians injured. The fraction of personal injury accidents that are fatal is interpreted as an average severity measure and studied by means of a binomial logit model.