Cardiac monitoring in stroke units: importance of diagnosing atrial fibrillation in acute ischemic stroke

Fast-Track Long Term Continuous Heart Monitoring in a Stroke Clinic: A Feasibility Study

Background: Paroxysmal atrial fibrillation (PAF) or flutter is prevalent among patients with cryptogenic stroke. The goal of this study was to investigate the feasibility of incorporating a fast-track, long term continuous heart monitoring (LTCM) program within a stroke clinic. Method: We designed and implemented a fast-track LTCM program in our stroke clinics. The instrument that we used for the study was the ZioXT® device from IRhythm™ Technologies. To implement the program, all clinic support staff received training on the skin preparation and proper placement of the device. We prospectively followed every patient who had a request from one of our inpatient or outpatient stroke or neurology providers to receive LTCM. We recorded patients' demographics, the LTCM indication, as well as related quality measures including same-visit placement, wearing time, analyzable time, LTCM application to the preliminary finding time, as well as patients' out of pocket cost. Results: Out of 501 patients included in the study, 467 (93.2%) patients (mean age 65.9 ± 13; men: 48%) received LTCM; and 92.5% of the patients had the diagnosis of stroke or TIA. 93.7% of patients received their LTCM during the same outpatient visit in the stroke clinic. The mean wearing time for LTCM was 12.1 days (out of 14 days). The average analyzable time among our patients was 95.0%. Eighteen (3.9%, 95%CI: 2.4-6.0) patients had at least one episode of PAF that was sustained for more than 30 s. The rate of PAF was 5.9% (95% CI: 3.5-9.2) among patients with the diagnosis of stroke. Out of 467 patients, 392 (84%) had an out-of-pocket cost of < $100. Conclusion: It is feasible to implement a fast-track cardiac monitoring as part of a stroke clinic with proper training of stroke providers, clinic staff, and support from a cardiology team.

Long-Term Stroke Recurrence after Transient Ischemic Attack: Implications of Etiology

Background and Purpose

To analyze long-term stroke recurrence (SR) characteristics after transient ischemic attack (TIA) according to initial etiological classification.

Methods

A prospective cohort of 706 TIA patients was followed up in a single tertiary stroke center. Endpoint was SR. Etiologic subgroup was determined according to the evidence-based causative classification system. Location of TIA and SR was recorded as right, left, or posterior territory. Disability stroke recurrence (DSR) was defined as modified Rankin Scale (mRS) score >1 or a onepoint increase in those with previous mRS >1 at 3-month follow-up.

Results

During a follow-up of 3,493 patient-years (mean follow-up of 58.9±35.9 months), total SR was 125 (17.7%), corresponding to 3.6 recurrences per 100 patient-years. The etiology subgroups with a higher risk of SR were the unclassified (more than one cause) and large-artery atherosclerosis (LAA) categories. Of the SR cases, 88 (70.4%) had the same etiology as the index TIA; again, LAA etiology was the most frequent (83.9%). Notably, cardioaortic embolism was the most frequent cause (62.5%) of SR in the subgroup of 24 patients with undetermined TIA. Overall, SR occurred in the same territory in 74 of 125 patients (59.2%), with significant differences between etiological TIA subgroups (P=0.015). Eighty-two of 125 (65.6%) with SR had DSR, without differences between etiologies (P=0.453).

Conclusions

SR occurred mainly with the same etiology and location as initial TIA, although undetermined TIA was associated with a high proportion of cardioaortic embolism SR. More than half of the recurrences caused some disability, regardless of etiology.

Detection of Atrial Fibrillation Among Patients With Stroke Due to Large or Small Vessel Disease: A Meta-Analysis

BACKGROUND

Recent trials have demonstrated that extended cardiac monitoring increases the yield of paroxysmal atrial fibrillation (AF) detection in patients with cryptogenic stroke. The utility of extended cardiac monitoring is uncertain among patients with stroke caused by small and large vessel disease. We conducted a meta-analysis to estimate the yield of AF detection in this population.

METHODS AND RESULTS

We searched PubMed, Cochrane, and SCOPUS databases for studies on AF detection in stroke patients and excluded studies restricted to patients with cryptogenic stroke or transient ischemic attack. We abstracted AF detection rates for 3 populations grouped by stroke etiology: large vessel stroke, small vessel stroke, and stroke of undefined etiology (a mixture of cryptogenic, small vessel, large vessel, and other stroke etiologies). Our search yielded 30 studies (n=5687). AF detection rates were similar in patients with large vessel (2.2%, 95% CI 0.3-5.5; n=830) and small vessel stroke (2.4%, 95% CI 0.4-6.1; n=520). No studies had a monitoring duration longer than 7 days. The yield of AF detection in the undefined stroke population was higher (9.2%; 95% CI 7.1-11.5) compared to small vessel stroke (P=0.02) and large vessel stroke (P=0.02) populations.

CONCLUSIONS

AF detection rate is similar in patients with small and large vessel strokes (2.2-2.4%). Because no studies reported on extended monitoring (>7 days) in these stroke populations, we could not estimate the yield of AF detection with long-term cardiac monitoring. Randomized controlled trials are needed to examine the utility of AF detection with long-term cardiac monitoring (>7 days) in this patient population.

Long-term Arrhythmia Monitoring in Cryptogenic Stroke: Who, How, and for How Long?

Cryptogenic stroke and transient ischemic attack (TIA) account for approximately one-third of stroke patients [1]. Paroxys-mal atrial fibrillation (PAF) has been suggested as a major etiology of these cryptogenic strokes [2, 3]. PAF can be difficult to diagnose because it is intermittent, often brief, and asymptomatic. PAF might be more prevalent than persistent atrial fibrillation in stroke and TIA patients, especially in younger populations [4, 5]. In patients with atrial fibrillation, anticoagulation provides significant risk reduction [6]. A new generation of oral anticoagulants has been approved for non-valvular atrial fibrillation, providing a variety of therapeutic options for patients with atrial fibrillation and risk of stroke [7]. Prior practice included an admission electrocardiogram (ECG) and continuous telemetry monitoring while in hospital [8]. However, this approach can lead to under-detection of brief asymptomatic events, which can occur at variable intervals, often outside of the hospital setting. Technological advancements have led to devices that can monitor cardiac rhythms outside of the hospital for longer durations resulting in higher yield of detection of atrial fibrillation events. Moreover, recent studies show that the normal monitoring time for arrhythmias may be shorter than ideal in order to detect atrial fibrillation, and increasing this interval could significantly improve detection of atrial fibrillation in these patients [9, 10]. The aim of this study is to review the literature in order to define what subgroup of patients, with what methodologies, and for how long monitoring for atrial fibrillation should occur in patients presenting with cryptogenic stroke.

Optimal Duration of Monitoring for Atrial Fibrillation in Cryptogenic Stroke: A Nonsystematic Review

Atrial fibrillation (AF) is the most common form of cardiac arrhythmias and an independent risk factor for stroke. Despite major advances in monitoring strategies, clinicians tend to miss the diagnoses of AF and especially paroxysmal AF due mainly to its asymptomatic presentation and the rather limited duration dedicated for monitoring for AF after a stroke, which is 24 hours as per the current recommended guidelines. Hence, determining the optimal duration of monitoring for paroxysmal atrial fibrillation after acute ischemic stroke remains a matter of debate. Multiple trials were published in regard to this matter using both invasive and noninvasive monitoring strategies for different monitoring periods. The data provided by these trials showcase strong evidence suggesting a longer monitoring strategy beyond 24 hours is associated with higher detection rates of AF, with the higher percentage of patients detected consequently receiving proper secondary stroke prevention with anticoagulation and thus justifying the cost-effectiveness of such measures. Overall, we thus conclude that increasing the monitoring duration for AF after a cryptogenic stroke to at least 72 hours will indeed enhance the detection rates, but the cost-effectiveness of this monitoring strategy compared to longer monitoring durations is yet to be established.

Detection of occult atrial fibrillation in patients with embolic stroke of uncertain source: a work in progress

Atrial fibrillation accounts for a substantial proportion of ischemic strokes of known etiology and may be responsible for an additional subset of the 25–40% of strokes of unknown cause (so-called cryptogenic). Oral anticoagulation is significantly more effective than antiplatelet therapy in the secondary prevention of atrial fibrillation-related strokes, providing justification for developing more sensitive approaches to detecting occult paroxysms of atrial fibrillation. In this article, we summarize the current state of knowledge regarding the value of in-hospital and out-patient monitoring for detecting atrial fibrillation in the context of cryptogenic stroke. We review the evidence for and against screening with standard Holter monitors, external loop recorders, the newer real-time continuous attended cardiac monitoring systems, cardiac implantable electronic devices, and insertable loop recorders. We review key questions regarding prolonged cardiac arrhythmia monitoring, including the relationship between duration of the atrial fibrillation episode and risk of thromboembolism, frequency of monitoring and its impact on the diagnostic yield in detecting occult or subclinical atrial fibrillation, and the temporal proximity of device-detected atrial fibrillation to stroke events. We conclude by proposing avenues for further research.

A novel method for detection of the transition between atrial fibrillation and sinus rhythm

Automatic detection of atrial fibrillation (AF) for AF diagnosis, especially for AF monitoring, is necessarily desirable for clinical therapy. In this study, we proposed a novel method for detection of the transition between AF and sinus rhythm based on RR intervals. First, we obtained the delta RR interval distribution difference curve from the density histogram of delta RR intervals, and then detected its peaks, which represented the AF events. Once an AF event was detected, four successive steps were used to classify its type, and thus, determine the boundary of AF: 1) histogram analysis; 2) standard deviation analysis; 3) numbering aberrant rhythms recognition; and 4) Kolmogorov-Smirnov (K-S) test. A dataset of 24-h Holter ECG recordings (n = 433) and two MIT-BIH databases (MIT-BIH AF database and MIT-BIH normal sinus rhythm (NSR) database) were used for development and evaluation. Using the receiver operating characteristic curves for determining the threshold of the K-S test, we have achieved the highest performance of sensitivity and specificity (SP) (96.1% and 98.1%, respectively) for the MIT-BIH AF database, compared with other previously published algorithms. The SP was 97.9% for the MIT-BIH NSR database.